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@article{Duggal2008,
abstract = {BACKGROUND: By assaying hundreds of thousands of single nucleotide polymorphisms, genome wide association studies (GWAS) allow for a powerful, unbiased review of the entire genome to localize common genetic variants that influence health and disease. Although it is widely recognized that some correction for multiple testing is necessary, in order to control the family-wide Type 1 Error in genetic association studies, it is not clear which method to utilize. One simple approach is to perform a Bonferroni correction using all n single nucleotide polymorphisms (SNPs) across the genome; however this approach is highly conservative and would "overcorrect" for SNPs that are not truly independent. Many SNPs fall within regions of strong linkage disequilibrium (LD) ("blocks") and should not be considered "independent".$\backslash$n$\backslash$nRESULTS: We proposed to approximate the number of "independent" SNPs by counting 1 SNP per LD block, plus all SNPs outside of blocks (interblock SNPs). We examined the effective number of independent SNPs for Genome Wide Association Study (GWAS) panels. In the CEPH Utah (CEU) population, by considering the interdependence of SNPs, we could reduce the total number of effective tests within the Affymetrix and Illumina SNP panels from 500,000 and 317,000 to 67,000 and 82,000 "independent" SNPs, respectively. For the Affymetrix 500 K and Illumina 317 K GWAS SNP panels we recommend using 10(-5), 10(-7) and 10(-8) and for the Phase II HapMap CEPH Utah and Yoruba populations we recommend using 10(-6), 10(-7) and 10(-9) as "suggestive", "significant" and "highly significant" p-value thresholds to properly control the family-wide Type 1 error.$\backslash$n$\backslash$nCONCLUSION: By approximating the effective number of independent SNPs across the genome we are able to 'correct' for a more accurate number of tests and therefore develop 'LD adjusted' Bonferroni corrected p-value thresholds that account for the interdepdendence of SNPs on well-utilized commercially available SNP "chips". These thresholds will serve as guides to researchers trying to decide which regions of the genome should be studied further.},
author = {Duggal, Priya and Gillanders, Elizabeth M and Holmes, Taura N and Bailey-Wilson, Joan E},
doi = {10.1186/1471-2164-9-516},
file = {:Users/rikutakei/Documents/Mendeley Desktop/BMC genomics/Duggal et al.{\_}2008{\_}Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies.pdf:pdf},
isbn = {1471-2164 (Electronic)$\backslash$r1471-2164 (Linking)},
issn = {1471-2164},
journal = {BMC Genomics},
keywords = {Algorithms,Genome,Genome-Wide Association Study,Genome-Wide Association Study: standards,Human,Humans,Linkage Disequilibrium,Polymorphism,Single Nucleotide},
pages = {516},
pmid = {18976480},
title = {{Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies.}},
volume = {9},
year = {2008}
}
@article{Coram2015,
abstract = {Elucidating the genetic basis of complex traits and diseases in non-European populations is particularly challenging because US minority populations have been under-represented in genetic association studies. We developed an empirical Bayes approach named XPEB (cross-population empirical Bayes), designed to improve the power for mapping complex-trait-associated loci in a minority population by exploiting information from genome-wide association studies (GWASs) from another ethnic population. Taking as input summary statistics from two GWASs - a target GWAS from an ethnic minority population of primary interest and an auxiliary base GWAS (such as a larger GWAS in Europeans) - our XPEB approach reprioritizes SNPs in the target population to compute local false-discovery rates. We demonstrated, through simulations, that whenever the base GWAS harbors relevant information, XPEB gains efficiency. Moreover, XPEB has the ability to discard irrelevant auxiliary information, providing a safeguard against inflated false-discovery rates due to genetic heterogeneity between populations. Applied to a blood-lipids study in African Americans, XPEB more than quadrupled the discoveries from the conventional approach, which used a target GWAS alone, bringing the number of significant loci from 14 to 65. Thus, XPEB offers a flexible framework for mapping complex traits in minority populations.},
author = {Coram, Marc A. and Candille, Sophie I. and Duan, Qing and Chan, Kei Hang K and Li, Yun and Kooperberg, Charles and Reiner, Alex P. and Tang, Hua},
doi = {10.1016/j.ajhg.2015.03.008},
file = {:Users/rikutakei/Documents/Mendeley Desktop/American Journal of Human Genetics/Coram et al.{\_}2015{\_}Leveraging multi-ethnic evidence for mapping complex traits in minority populations An empirical Bayes approach.pdf:pdf},
isbn = {0002-9297},
issn = {15376605},
journal = {Am. J. Hum. Genet.},
number = {5},
pages = {740--752},
pmid = {25892113},
publisher = {The American Society of Human Genetics},
title = {{Leveraging multi-ethnic evidence for mapping complex traits in minority populations: An empirical Bayes approach}},
volume = {96},
year = {2015}
}
@article{Risch1996,
abstract = {The identification of the genetic basis of complex human diseases such as schizophrenia and diabetes has proven difficult. In their Perspective, Risch and Merikangas propose that we can best accomplish this goal by combining the power of the human genome project with association studies, a method for determining the basis of a genetic disease.},
author = {Risch, N and Merikangas, K},
doi = {doi: 10.1126/science.273.5281.1516},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Science/Risch, Merikangas{\_}1996{\_}The future of genetic studies of complex human diseases.pdf:pdf},
isbn = {0036-8075 (Print)$\backslash$r0036-8075 (Linking)},
issn = {0036-8075},
journal = {Science (80-. ).},
keywords = {effective numbers of statistical tests},
mendeley-tags = {effective numbers of statistical tests},
number = {5281},
pages = {1516--1517},
pmid = {8801636},
title = {{The future of genetic studies of complex human diseases.}},
volume = {273},
year = {1996}
}
@article{Rosenberg2010,
abstract = {Genome-wide association (GWA) studies have identified a large number of SNPs associated with disease phenotypes. As most GWA studies have been performed in populations of European descent, this Review examines the issues involved in extending the consideration of GWA studies to diverse worldwide populations. Although challenges exist with issues such as imputation, admixture and replication, investigation of a greater diversity of populations could make substantial contributions to the goal of mapping the genetic determinants of complex diseases for the human population as a whole. {\textcopyright} 2010 Macmillan Publishers Limited. All rights reserved.},
author = {Rosenberg, N.A. and Huang, L. and Jewett, E.M. and Szpiech, Z.A. and Jankovic, I. and Boehnke, M.},
doi = {10.1038/nrg2760},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews Genetics/Rosenberg et al.{\_}2010{\_}Genome-wide association studies in diverse populations.pdf:pdf},
isbn = {1471-0056},
issn = {1471-0056},
journal = {Nat. Rev. Genet.},
keywords = {population substructure,relatedness},
mendeley-tags = {population substructure,relatedness},
number = {5},
pages = {356--366},
pmid = {20395969},
publisher = {Nature Publishing Group},
title = {{Genome-wide association studies in diverse populations}},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77951133654{\&}partnerID=40{\&}md5=1f5d9af5bf9942c9a4a6f8779ab37615{\%}5Cnhttp://www.scopus.com/record/display.url?eid=2-s2.0-77951133654{\&}origin=inward{\&}txGid=2wgE7zzEVrJlySCWJ3zsiHg{\%}3A31},
volume = {11},
year = {2010}
}
@article{Panagiotou2013,
abstract = {Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.},
author = {Panagiotou, Orestis A. and Willer, Cristen J. and Hirschhorn, Joel N. and Ioannidis, John P A},
doi = {10.1146/annurev-genom-091212-153520},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Annual review of genomics and human genetics/Panagiotou et al.{\_}2013{\_}The power of meta-analysis in genome-wide association studies.pdf:pdf},
isbn = {1527-8204},
issn = {1545-293X},
journal = {Annu. Rev. Genomics Hum. Genet.},
keywords = {common variants,consortium,gene discovery,meta analysis,missing heritability,rare,sample size,variance explained,variants},
mendeley-tags = {meta analysis},
number = {1},
pages = {441--65},
pmid = {23724904},
title = {{The power of meta-analysis in genome-wide association studies.}},
volume = {14},
year = {2013}
}
@article{Spielman1993,
abstract = {A population association has consistently been observed between insulin-dependent diabetes mellitus (IDDM) and the "class 1" alleles of the region of tandem-repeat DNA (5' flanking polymorphism [5'FP]) adjacent to the insulin gene on chromosome 11p. This finding suggests that the insulin gene region contains a gene or genes contributing to IDDM susceptibility. However, several studies that have sought to show linkage with IDDM by testing for cosegregation in affected sib pairs have failed to find evidence for linkage. As means for identifying genes for complex diseases, both the association and the affected-sib-pairs approaches have limitations. It is well known that population association between a disease and a genetic marker can arise as an artifact of population structure, even in the absence of linkage. On the other hand, linkage studies with modest numbers of affected sib pairs may fail to detect linkage, especially if there is linkage heterogeneity. We consider an alternative method to test for linkage with a genetic marker when population association has been found. Using data from families with at least one affected child, we evaluate the transmission of the associated marker allele from a heterozygous parent to an affected offspring. This approach has been used by several investigators, but the statistical properties of the method as a test for linkage have not been investigated. In the present paper we describe the statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test [TDT]). We then show the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs. The TDT provides strong evidence for linkage between the 5'FP and susceptibility to IDDM. The conclusions from this analysis apply in general to the study of disease associations, where genetic markers are usually closely linked to candidate genes. When a disease is found to be associated with such a marker, the TDT may detect linkage even when haplotype-sharing tests do not.},
author = {Spielman, R S and McGinnis, R E and Ewens, W J},
doi = {10.1126/SCIENCE.8091226},
file = {:Users/rikutakei/Documents/Mendeley Desktop/American journal of human genetics/Spielman, McGinnis, Ewens{\_}1993{\_}Transmission test for linkage disequilibrium the insulin gene region and insulin-dependent diabetes melli.pdf:pdf},
isbn = {0002-9297},
issn = {0002-9297},
journal = {Am. J. Hum. Genet.},
keywords = {Adult,Alleles,Analysis of Variance,Child,Diabetes Mellitus,Family,Female,Genetic Markers,Heterozygote Detection,Humans,Insulin,Insulin: genetics,Linkage Disequilibrium,Male,Mathematics,Nucleic Acid,Repetitive Sequences,Type 1,Type 1: genetics},
number = {3},
pages = {506--16},
pmid = {8447318},
title = {{Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).}},
volume = {52},
year = {1993}
}
@article{Price2006,
abstract = {Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.},
author = {Price, A L and N.j.patterson and R.m.plenge and M.e.weinblatt and N.a.shadick},
doi = {10.1038/ng1847},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nat. Genet/Price et al.{\_}2006{\_}Principal components analysis corrects for stratification in genome-wide association studies.pdf:pdf},
isbn = {1061-4036 (Print)$\backslash$r1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nat. Genet},
keywords = {Algorithms,Alleles,Case-Control Studies,Databases,Genetic Markers,Genome,Genomics,Genomics: statistics {\&} numerical data,Genotype,Human,Humans,Nucleic Acid,Phenotype,Polymorphism,Principal Component Analysis,Single Nucleotide,population substructure},
mendeley-tags = {population substructure},
number = {8},
pages = {904--909},
pmid = {16862161},
title = {{Principal components analysis corrects for stratification in genome-wide association studies}},
volume = {38},
year = {2006}
}
@article{Asimit2016,
abstract = {Studies that traverse ancestrally diverse populations may increase power to detect novel loci and improve fine-mapping resolution of causal variants by leveraging linkage disequilibrium differences between ethnic groups. The inclusion of African ancestry samples may yield further improvements because of low linkage disequilibrium and high genetic heterogeneity. We investigate the fine-mapping resolution of trans-ethnic fixed-effects meta-analysis for five type II diabetes loci, under various settings of ancestral composition (European, East Asian, African), allelic heterogeneity, and causal variant minor allele frequency. In particular, three settings of ancestral composition were compared: (1) single ancestry (European), (2) moderate ancestral diversity (European and East Asian), and (3) high ancestral diversity (European, East Asian, and African). Our simulations suggest that the European/Asian and European ancestry-only meta-analyses consistently attain similar fine-mapping resolution. The inclusion of African ancestry samples in the meta-analysis leads to a marked improvement in fine-mapping resolution. INTRODUCTION Numerous genome-wide association studies (GWASs) have been carried out, resulting in the identification of many susceptibility loci for a wide range of complex traits. 1 The detection of additional loci has resulted from GWAS meta-analyses (primarily in populations of European descent) and has been aided by imputation that allows the prediction of genotypes not typed on GWAS chips, but present in a higher density reference. Nonetheless, the joint effects of the loci identified to date have only accounted for a small proportion of the heritability of complex traits. Because of linkage disequilibrium (LD), many variants within identified loci have indistinguishable signals. This LD is beneficial to GWAS, as it increases the power to detect new associations, when the causal variant is not directly typed. However, the caveat to this is that it limits the potential of fine-mapping efforts to refine the location of causal variants. GWAS data from non-European populations are increasing in availability, and this provides the opportunity to meta-analyse GWAS across ancestrally diverse populations. Trans-ethnic meta-analysis may lead to an increase in power to detect novel loci and may improve fine-mapping resolution of causal variants by leveraging differences in the structure of LD between diverse populations. 2,3 The inclusion of African ancestry samples may yield substantial improvements in localisation of causal variants because of low LD and high genetic heterogeneity. 4 Several trans-ethnic analyses have shown empirical improvements in fine-mapping resolution. Examples include a refinement of signals at several type II diabetes loci in an analysis involving samples of European, East Asian, South Asian, and Mexican and Mexican-American ancestry, 5 as well as in previously identified adiposity loci in a trans-ethnic meta-analysis of samples from European and African ancestries. 6 Similarly, a refinement of signals was obtained at several lipid trait loci in a trans-ethnic fine-mapping study involving},
author = {Asimit, Jennifer L and Hatzikotoulas, Konstantinos and Mccarthy, Mark and Morris, Andrew P and Zeggini, Eleftheria},
doi = {10.1038/ejhg.2016.1},
file = {:Users/rikutakei/Documents/Mendeley Desktop/European Journal of Human Genetics/Asimit et al.{\_}2016{\_}Trans-ethnic study design approaches for fine-mapping.pdf:pdf},
isbn = {1476-5438; 1018-4813},
issn = {1018-4813},
journal = {Eur. J. Hum. Genet.},
number = {10},
pages = {1330--1336},
pmid = {26839038},
publisher = {Nature Publishing Group},
title = {{Trans-ethnic study design approaches for fine-mapping}},
volume = {24},
year = {2016}
}
@article{Johnson2010,
abstract = {BACKGROUND: As we enter an era when testing millions of SNPs in a single gene association study will become the standard, consideration of multiple comparisons is an essential part of determining statistical significance. Bonferroni adjustments can be made but are conservative due to the preponderance of linkage disequilibrium (LD) between genetic markers, and permutation testing is not always a viable option. Three major classes of corrections have been proposed to correct the dependent nature of genetic data in Bonferroni adjustments: permutation testing and related alternatives, principal components analysis (PCA), and analysis of blocks of LD across the genome. We consider seven implementations of these commonly used methods using data from 1514 European American participants genotyped for 700,078 SNPs in a GWAS for AIDS.$\backslash$n$\backslash$nRESULTS: A Bonferroni correction using the number of LD blocks found by the three algorithms implemented by Haploview resulted in an insufficiently conservative threshold, corresponding to a genome-wide significance level of $\alpha$ = 0.15 - 0.20. We observed a moderate increase in power when using PRESTO, SLIDE, and simpleℳ when compared with traditional Bonferroni methods for population data genotyped on the Affymetrix 6.0 platform in European Americans ($\alpha$ = 0.05 thresholds between 1 × 10(-7) and 7 × 10(-8)).$\backslash$n$\backslash$nCONCLUSIONS: Correcting for the number of LD blocks resulted in an anti-conservative Bonferroni adjustment. SLIDE and simpleℳ are particularly useful when using a statistical test not handled in optimized permutation testing packages, and genome-wide corrected p-values using SLIDE, are much easier to interpret for consumers of GWAS studies.},
author = {Johnson, Randall C and Nelson, George W and Troyer, Jennifer L and Lautenberger, James A and Kessing, Bailey D and Winkler, Cheryl A and O'Brien, Stephen J},
doi = {10.1186/1471-2164-11-724},
file = {:Users/rikutakei/Documents/Mendeley Desktop/BMC genomics/Johnson et al.{\_}2010{\_}Accounting for multiple comparisons in a genome-wide association study (GWAS).pdf:pdf},
isbn = {1471-2164},
issn = {1471-2164},
journal = {BMC Genomics},
keywords = {Case-Control Studies,Databases,Genetic,Genome-Wide Association Study,Genome-Wide Association Study: methods,Haplotypes,Haplotypes: genetics,Humans,Linkage Disequilibrium,Linkage Disequilibrium: genetics,Principal Component Analysis,Time Factors,effective numbers of statistical tests},
mendeley-tags = {effective numbers of statistical tests},
number = {1},
pages = {724},
pmid = {21176216},
title = {{Accounting for multiple comparisons in a genome-wide association study (GWAS).}},
volume = {11},
year = {2010}
}
@article{Sham2014,
abstract = {Significance testing was developed as an objective method for summarizing statistical evidence for a hypothesis. It has been widely adopted in genetic studies, including genome-wide association studies and, more recently, exome sequencing studies. However, significance testing in both genome-wide and exome-wide studies must adopt stringent significance thresholds to allow multiple testing, and it is useful only when studies have adequate statistical power, which depends on the characteristics of the phenotype and the putative genetic variant, as well as the study design. Here, we review the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.},
author = {Sham, Pak C and Purcell, Shaun M},
doi = {10.1038/nrg3706},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews Genetics/Sham, Purcell{\_}2014{\_}Statistical power and significance testing in large-scale genetic studies.pdf:pdf},
isbn = {1471-0056},
issn = {1471-0064},
journal = {Nat. Rev. Genet.},
keywords = {Genome-wide association studies,Statistical methods,effective numbers of statistical tests},
mendeley-tags = {effective numbers of statistical tests},
number = {5},
pages = {335--346},
pmid = {24739678},
publisher = {Nature Publishing Group},
title = {{Statistical power and significance testing in large-scale genetic studies}},
volume = {15},
year = {2014}
}
@article{Li2014,
abstract = {Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.},
author = {Li, Yun R and Keating, Brendan J},
doi = {10.1186/s13073-014-0091-5},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Genome medicine/Li, Keating{\_}2014{\_}Trans-ethnic genome-wide association studies advantages and challenges of mapping in diverse populations.pdf:pdf},
isbn = {1756-994X (Electronic)},
issn = {1756-994X},
journal = {Genome Med.},
number = {10},
pages = {91},
pmid = {25473427},
title = {{Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations.}},
volume = {6},
year = {2014}
}
@article{Roshyara2015,
abstract = {BACKGROUND: Genotype imputation is a common technique in genetic research. Genetic similarity between target population and reference dataset is crucial for high-quality results. Although several reference panels are available, it is often not clear which is the most optimal for a particular target dataset to be imputed. Maximizing genetic similarity between study sample and intended reference panels may be the straight forward method for selecting the genetically best-matched reference. However, the impact of genetic similarity on imputation accuracy has not yet been studied in detail.$\backslash$n$\backslash$nRESULTS: We performed a simulation study in 20 ethnic groups obtained from POPRES. High-quality SNPs were masked and re-imputed with MaCH, MaCH-minimac and IMPUTE2 using four different HapMap reference panels (CEU, CHB-JPT, MEX and YRI). Imputation accuracy was assessed by different statistics. Genetic similarity between ethnic groups and reference populations were measured by F -statistics (F ST ) originally proposed by Wright and G -statistics (G ST ) introduced by Nei and others. To assess the predictive power of these measures regarding imputation accuracy, we analysed relations between them and corresponding imputation accuracy scores. We found that population genetic distances between homogeneous reference and target populations were strongly linearly correlated with resulting imputation accuracies irrespective of considered distance measure, imputation accuracy measure, missingness and imputation software used. Possible exception was African population.$\backslash$n$\backslash$nCONCLUSION: Usage of G ST or F ST -related measures for predicting the optimal reference panel for imputation frameworks relying on a specific reference is highly recommended. A cut-off of G ST {\textless} 0.01 is recommended to achieve good imputation results for high-frequency variants and small data sets. The linear relationship is less pronounced for low-frequency variants for which we also observed a dependence of imputation accuracy on the number of polymorphic sites in the reference. We also show that the software specific measures MaCH-Rsq and IMPUTE-info must be interpreted with caution if the genetic distance of target and reference population is high.},
author = {Roshyara, Nab Raj and Scholz, Markus},
doi = {10.1186/s12863-015-0248-2},
file = {:Users/rikutakei/Documents/Mendeley Desktop/BMC genetics/Roshyara, Scholz{\_}2015{\_}Impact of genetic similarity on imputation accuracy.pdf:pdf},
issn = {1471-2156},
journal = {BMC Genet.},
keywords = {Genetic simila,Genotype imputation,Reference panel,f{\_}st,g{\_}st,genetic similarity,genotype imputation,imputation,imputation quality,reference panel,snp data},
mendeley-tags = {imputation},
number = {1},
pages = {90},
pmid = {26193934},
publisher = {BMC Genetics},
title = {{Impact of genetic similarity on imputation accuracy.}},
volume = {16},
year = {2015}
}
@article{Stephens2009,
abstract = {Bayesian statistical methods have recently made great inroads into many areas of science, and this advance is now extending to the assessment of association between genetic variants and disease or other phenotypes. We review these methods, focusing on single-SNP tests in genome-wide association studies. We discuss the advantages of the Bayesian approach over classical (frequentist) approaches in this setting and provide a tutorial on basic analysis steps, including practical guidelines for appropriate prior specification. We demonstrate the use of Bayesian methods for fine mapping in candidate regions, discuss meta-analyses and provide guidance for refereeing manuscripts that contain Bayesian analyses.},
author = {Stephens, Matthew and Balding, D J},
doi = {10.1038/nrg2615},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews Genetics/Stephens, Balding{\_}2009{\_}Bayesian statistical methods for genetic association studies.pdf:pdf},
isbn = {1471-0064 (Electronic)$\backslash$r1471-0056 (Linking)},
issn = {1471-0064},
journal = {Nat. Rev. Genet.},
keywords = {Bayes,Bayes Theorem,Genetics,Genome-Wide Association Study,Humans,Polymorphism,Single Nucleotide,Single Nucleotide: genetics},
number = {10},
pages = {681--690},
pmid = {19763151},
publisher = {Nature Publishing Group},
title = {{Bayesian statistical methods for genetic association studies}},
volume = {10},
year = {2009}
}
@article{Thornton2012,
abstract = {Genome-wide association studies (GWASs) are commonly used for the mapping of genetic loci that influence complex traits. A problem that is often encountered in both population-based and family-based GWASs is that of identifying cryptic relatedness and population stratification because it is well known that failure to appropriately account for both pedigree and population structure can lead to spurious association. A number of methods have been proposed for identifying relatives in samples from homogeneous populations. A strong assumption of population homogeneity, however, is often untenable, and many GWASs include samples from structured populations. Here, we consider the problem of estimating relatedness in structured populations with admixed ancestry. We propose a method, REAP (relatedness estimation in admixed populations), for robust estimation of identity by descent (IBD)-sharing probabilities and kinship coefficients in admixed populations. REAP appropriately accounts for population structure and ancestry-related assortative mating by using individual-specific allele frequencies at SNPs that are calculated on the basis of ancestry derived from whole-genome analysis. In simulation studies with related individuals and admixture from highly divergent populations, we demonstrate that REAP gives accurate IBD-sharing probabilities and kinship coefficients. We apply REAP to the Mexican Americans in Los Angeles, California (MXL) population sample of release 3 of phase III of the International Haplotype Map Project; in this sample, we identify third- and fourth-degree relatives who have not previously been reported. We also apply REAP to the African American and Hispanic samples from the Women's Health Initiative SNP Health Association Resource (WHI-SHARe) study, in which hundreds of pairs of cryptically related individuals have been identified. {\textcopyright} 2012 The American Society of Human Genetics.},
author = {Thornton, Timothy and Tang, Hua and Hoffmann, Thomas J. and Ochs-Balcom, Heather M. and Caan, Bette J. and Risch, Neil},
doi = {10.1016/j.ajhg.2012.05.024},
file = {:Users/rikutakei/Documents/Mendeley Desktop/American Journal of Human Genetics/Thornton et al.{\_}2012{\_}Estimating kinship in admixed populations.pdf:pdf},
isbn = {1537-6605 (Electronic)$\backslash$r0002-9297 (Linking)},
issn = {00029297},
journal = {Am. J. Hum. Genet.},
number = {1},
pages = {122--138},
pmid = {22748210},
title = {{Estimating kinship in admixed populations}},
volume = {91},
year = {2012}
}
@article{Parks2017,
author = {Parks, Tom and Mirabel, Mariana M and Kado, Joseph and Auckland, Kathryn and Nowak, Jaroslaw and Rautanen, Anna and Mentzer, Alexander J and Marijon, Eloi and Jouven, Xavier and Perman, Mai Ling and Cua, Tuliana and Kauwe, John K},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Unknown/Parks et al.{\_}2017{\_}Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania.pdf:pdf},
keywords = {imputation},
mendeley-tags = {imputation},
pages = {1--29},
title = {{Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania}},
year = {2017}
}
@article{Novembre2008,
author = {Novembre, John and Boyko, Adam R and Auton, Adam and Johnson, Toby and Bryc, Katarzyna and Indap, Amit and King, Karen S and Bergmann, Sven and Nelson, Matthew R and Stephens, Matthew and Bustamante, Carlos D},
doi = {10.1038/nature07331},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature/Novembre et al.{\_}2008{\_}Genes mirror geography within Europe.pdf:pdf},
journal = {Nature},
number = {6},
pages = {98--101},
title = {{Genes mirror geography within Europe}},
volume = {456},
year = {2008}
}
@article{Devlin1999,
abstract = {A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. To achieve this goal, we propose a statistical method that has several optimal properties: It can be used with case control data and yet, like family-based designs, controls for population heterogeneity; it is insensitive to the usual violations of model assumptions, such as cases failing to be strictly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better performance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.},
author = {Devlin, B and Roeder, K},
doi = {10.1111/j.0006-341X.1999.00997.x},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Biometrics/Devlin, Roeder{\_}1999{\_}Genomic control for association studies.pdf:pdf},
isbn = {0006-341X},
issn = {0006-341X},
journal = {Biometrics},
keywords = {bayesian inference,case-control,complex genetic disorder,genomic control,ity,outliers,population heterogene-,population substructure,relatedness,single nucleotide polymorphism genotypes},
mendeley-tags = {genomic control,population substructure,relatedness},
number = {4},
pages = {997--1004},
pmid = {11315092},
title = {{Genomic control for association studies.}},
volume = {55},
year = {1999}
}
@article{Turner2011,
author = {Turner, Stephen and Armstrong, Loren L and Bradford, Yuki and Carlson, Christopher S and Dana, C and Crenshaw, Andrew T and Andrade, Mariza De and Doheny, Kimberly F and Jonathan, L and Hayes, Geoffrey and Jarvik, Gail and Jiang, Lan and Kullo, Iftikhar J and Li, Rongling and Manolio, Teri a and Matsumoto, Martha and Mccarty, Catherine a and Andrew, N and Mirel, Daniel B and Paschall, Justin E and Pugh, Elizabeth W and Luke, V and Wilke, Russell a and Zuvich, Rebecca L and Ritchie, Marylyn D},
doi = {10.1002/0471142905.hg0119s68.Quality},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Current Proceedings in Human Genetics/Turner et al.{\_}2011{\_}Quality control procedures for genome wide association studies.pdf:pdf},
isbn = {0471142905},
issn = {1934-8266},
journal = {Curr. Proc. Hum. Genet.},
keywords = {public access,quality control,relatedness},
mendeley-tags = {quality control,relatedness},
number = {1},
pages = {1--24},
pmid = {21234875},
title = {{Quality control procedures for genome wide association studies}},
volume = {68},
year = {2011}
}
@article{Evangelou2013,
abstract = {Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.},
author = {Evangelou, Evangelos and Ioannidis, John P A},
doi = {10.1038/nrg3472},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews Genetics/Evangelou, Ioannidis{\_}2013{\_}Meta-analysis methods for genome-wide association studies and beyond.pdf:pdf},
isbn = {1471-0064 (Electronic)$\backslash$n1471-0056 (Linking)},
issn = {1471-0064},
journal = {Nat. Rev. Genet.},
keywords = {Bayes Theorem,Data Interpretation,Genetic Heterogeneity,Genome-Wide Association Study,Genome-Wide Association Study: methods,Humans,Meta-Analysis as Topic,Phenotype,Software,Statistical,meta analysis},
mendeley-tags = {meta analysis},
number = {6},
pages = {379--389},
pmid = {23657481},
publisher = {Nature Publishing Group},
title = {{Meta-analysis methods for genome-wide association studies and beyond.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23657481},
volume = {14},
year = {2013}
}
@misc{Hedges1998,
author = {Hedges, L. V and Vevea, J. L},
booktitle = {Psychol. Methods},
doi = {10.1037/1082-989X.3.4.486},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Psychological Methods/Hedges, Vevea{\_}1998{\_}Fixed- and Random-Effects Models in Meta-Analysis.pdf:pdf},
isbn = {1082-989X},
issn = {1082-989X},
number = {4},
pages = {486--504},
pmid = {96},
title = {{Fixed- and Random-Effects Models in Meta-Analysis}},
volume = {3},
year = {1998}
}
@article{Han2011,
abstract = {Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs. ?? 2011 The American Society of Human Genetics.},
author = {Han, Buhm and Eskin, Eleazar},
doi = {10.1016/j.ajhg.2011.04.014},
file = {:Users/rikutakei/Documents/Mendeley Desktop/American Journal of Human Genetics/Han, Eskin{\_}2011{\_}Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies.pdf:pdf},
isbn = {1537-6605 (Electronic)$\backslash$r0002-9297 (Linking)},
issn = {00029297},
journal = {Am. J. Hum. Genet.},
number = {5},
pages = {586--598},
pmid = {21565292},
publisher = {The American Society of Human Genetics},
title = {{Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies}},
volume = {88},
year = {2011}
}
@article{Wang2013,
abstract = {Genome-wide association studies (GWASs) have discovered thousands of variants that are associated with human health and disease. Whilst early GWASs have primarily focused on genetically homogeneous populations of European, East Asian and South Asian ancestries, the next-generation genome-wide surveys are starting to pool studies from ethnically diverse populations within a single meta-analysis. However, classical epidemiological strategies for meta-analyses that assume fixed- or random-effects may not be the most suitable approaches to combine GWAS findings as these either confer low statistical power or identify mostly loci where the variants carry homogeneous effect sizes that are present in most of the studies. In a trans-ethnic meta-analysis, it is likely that some genetic loci will exhibit heterogeneous effect sizes across the populations. This may be due to differences in study designs, differences arising from the interactions with other genetic variants, or genuine biological differences attributed to environmental, dietary or lifestyle factors that modulate the influence of the genes. Here we compare different strategies for meta-analyzing GWAS across genetically diverse populations, where we intentionally vary the effect sizes present across the different populations. We subsequently applied the methods that yielded the highest statistical power to a trans-ethnic meta-analysis of seven GWAS in type 2 diabetes, and showed that these methods identified bona fide associations that would otherwise have been missed by the classical strategies.},
author = {Wang, Xu and Chua, Hui Xiang and Chen, Peng and Ong, Rick Twee Hee and Sim, Xueling and Zhang, Weihua and Takeuchi, Fumihiko and Liu, Xuanyao and Khor, Chiea Chuen and Tay, Wan Ting and Cheng, Ching Yu and Suo, Chen and Liu, Jianjun and Aung, Tin and Chia, Kee Seng and Kooner, Jaspal S. and Chambers, John C. and Wong, Tien Yin and Tai, E. Shyong and Kato, Norihiro and Teo, Yik Ying},
doi = {10.1093/hmg/ddt064},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Human Molecular Genetics/Wang et al.{\_}2013{\_}Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies.pdf:pdf},
isbn = {1460-2083 (Electronic)$\backslash$n0964-6906 (Linking)},
issn = {09646906},
journal = {Hum. Mol. Genet.},
number = {11},
pages = {2303--2311},
pmid = {23406875},
title = {{Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies}},
volume = {22},
year = {2013}
}
@article{Marchini2010,
abstract = {In the past few years genome-wide association (GWA) studies have uncovered a large number of convincingly replicated associations for many complex human diseases. Genotype imputation has been used widely in the analysis of GWA studies to boost power, fine-map associations and facilitate the combination of results across studies using meta-analysis. This Review describes the details of several different statistical methods for imputing genotypes, illustrates and discusses the factors that influence imputation performance, and reviews methods that can be used to assess imputation performance and test association at imputed SNPs.;},
archivePrefix = {arXiv},
arxivId = {arXiv:1507.02142v2},
author = {Marchini, Jonathan and Howie, Bryan},
doi = {10.1038/nrg2796},
eprint = {arXiv:1507.02142v2},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews. Genetics/Marchini, Howie{\_}2010{\_}Genotype imputation for genome-wide association studies.pdf:pdf},
isbn = {1471-0064},
issn = {1471-0064},
journal = {Nat. Rev. Genet.},
keywords = {Biostatistics/*methods,Genetic,Genome-Wide Association Study*,Genotype,Models,Polymorphism,Single Nucleotide,imputation},
mendeley-tags = {imputation},
number = {7},
pages = {499--511},
pmid = {20517342},
publisher = {Nature Publishing Group},
title = {{Genotype imputation for genome-wide association studies}},
volume = {11},
year = {2010}
}
@article{Hirschhorn2005,
abstract = {Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.},
author = {Hirschhorn, J and Daly, M},
doi = {10.1038/nrg1521},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nat Rev Genet/Hirschhorn, Daly{\_}2005{\_}Genome-wide association studies for common diseases and complex traits.pdf:pdf},
isbn = {1471-0056},
issn = {1471-0056},
journal = {Nat Rev Genet},
number = {2},
pages = {95--108},
pmid = {15716906},
title = {{Genome-wide association studies for common diseases and complex traits}},
volume = {6},
year = {2005}
}
@article{Hedges1992,
author = {Hedges, Larry V},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Journal of Educational Statistics/Hedges{\_}1992{\_}Meta-Analysis.pdf:pdf},
journal = {J. Educ. Stat.},
keywords = {be divided into two,combined significance tests,has a long history,meta-analysis,meta-analytic work can,methods to combine the,research studies,research synthesis,results of independent empirical,significance of combined,tests of the statistical,the use of statistical,traditions},
number = {4},
pages = {279--296},
title = {{Meta-Analysis}},
volume = {17},
year = {1992}
}
@article{Han2012,
abstract = {Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect.},
author = {Han, Buhm and Eskin, Eleazar},
doi = {10.1371/journal.pgen.1002555},
editor = {Kerr, Kathleen},
file = {:Users/rikutakei/Documents/Mendeley Desktop/PLoS Genetics/Han, Eskin{\_}2012{\_}Interpreting Meta-Analyses of Genome-Wide Association Studies.PDF:PDF},
isbn = {1553-7404 (Electronic)$\backslash$r1553-7390 (Linking)},
issn = {1553-7404},
journal = {PLoS Genet.},
month = {mar},
number = {3},
pages = {e1002555},
pmid = {22396665},
title = {{Interpreting Meta-Analyses of Genome-Wide Association Studies}},
volume = {8},
year = {2012}
}
@article{Astle2009,
abstract = {We review the problem of confounding in genetic association studies, which arises principally because of population structure and cryptic relatedness. Many treatments of the problem consider only a simple "island" model of population structure. We take a broader approach, which views population structure and cryptic relatedness as different aspects of a single confounder: the unobserved pedigree defining the (often distant) relationships among the study subjects. Kinship is therefore a central concept, and we review methods of defining and estimating kinship coefficients, both pedigree-based and marker-based. In this unified framework we review solutions to the problem of population structure, including family-based study designs, genomic control, structured association, regression control, principal components adjustment and linear mixed models. The last solution makes the most explicit use of the kinships among the study subjects, and has an established role in the analysis of animal and plant breeding studies. Recent computational developments mean that analyses of human genetic association data are beginning to benefit from its powerful tests for association, which protect against population structure and cryptic kinship, as well as intermediate levels of confounding by the pedigree.},
archivePrefix = {arXiv},
arxivId = {1010.4681},
author = {Astle, W and Balding, Dj},
doi = {10.1214/09-STS307},
eprint = {1010.4681},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Statistical Science/Astle, Balding{\_}2009{\_}Population Structure and Cryptic Relatedness in Genetic Association Studies.pdf:pdf},
isbn = {0883-4237},
issn = {0883-4237},
journal = {Stat. Sci.},
keywords = {ADMIXED POPULATIONS,BIAS,COEFFICIENTS,Cryptic relatedness,DIABETES-MELLITUS,GENOME-WIDE ASSOCIATION,INFERENCE,LINKAGE DISEQUILIBRIUM,LOCI,MIXED-MODEL,STRATIFICATION,ascertainment,complex disease genetics,genomic control,kinship,mixed model,relatedness},
mendeley-tags = {relatedness},
number = {4},
pages = {451--471},
pmid = {26483834},
title = {{Population Structure and Cryptic Relatedness in Genetic Association Studies}},
volume = {24},
year = {2009}
}
@article{Bagos2013,
abstract = {In genetic association studies (GAS) as well as in genome-wide association studies (GWAS), the mode of inheritance (dominant, additive and recessive) is usually not known a priori. Assuming an incorrect mode of inheritance may lead to substantial loss of power, whereas on the other hand, testing all possible models may result in an increased type I error rate. The situation is even more complicated in the meta-analysis of GAS or GWAS, in which individual studies are synthesized to derive an overall estimate. Meta-analysis increases the power to detect weak genotype effects, but heterogeneity and incompatibility between the included studies complicate things further. In this review, we present a comprehensive summary of the statistical methods used for robust analysis and genetic model selection in GAS and GWAS. We then discuss the application of such methods in the context of meta-analysis. We describe the theoretical properties of the various methods and the foundations on which they are based. We also present the available software implementations of the described methods. Finally, since only few of the available robust methods have been applied in the meta-analysis setting, we present some simple extensions that allow robust meta-analysis of GAS and GWAS. Possible extensions and proposals for future work are also discussed.},
author = {Bagos, Pantelis G.},
doi = {10.1515/sagmb-2012-0016},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Statistical Applications in Genetics and Molecular Biology/Bagos{\_}2013{\_}Genetic model selection in genome-wide association studies Robust methods and the use of meta-analysis.pdf:pdf},
issn = {15446115},
journal = {Stat. Appl. Genet. Mol. Biol.},
keywords = {GWAS,Genetic association,Genetic model selection,Meta-analysis,Robust methods,meta analysis},
mendeley-tags = {meta analysis},
number = {3},
pages = {285--308},
pmid = {23629457},
title = {{Genetic model selection in genome-wide association studies: Robust methods and the use of meta-analysis}},
volume = {12},
year = {2013}
}
@article{Willer2010,
abstract = {Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats.Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/Contact: goncalo@umich.edu},
author = {Willer, Cristen J. and Li, Yun and Abecasis, Gon{\c{c}}alo R.},
doi = {10.1093/bioinformatics/btq340},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Bioinformatics/Willer, Li, Abecasis{\_}2010{\_}METAL Fast and efficient meta-analysis of genomewide association scans.pdf:pdf},
isbn = {1367-4803},
issn = {13674803},
journal = {Bioinformatics},
keywords = {meta analysis},
mendeley-tags = {meta analysis},
number = {17},
pages = {2190--2191},
pmid = {20616382},
title = {{METAL: Fast and efficient meta-analysis of genomewide association scans}},
volume = {26},
year = {2010}
}
@article{Yang2014,
abstract = {Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Yang, Jian and Zaitlen, Noah A and Goddard, Michael E and Visscher, Peter M and Price, Alkes L},
doi = {10.1038/ng.2876},
eprint = {NIHMS150003},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature genetics/Yang et al.{\_}2014{\_}Advantages and pitfalls in the application of mixed-model association methods.pdf:pdf},
isbn = {1546-1718 (Electronic)$\backslash$r1061-4036 (Linking)},
issn = {1546-1718},
journal = {Nat. Genet.},
keywords = {mixed linear model},
mendeley-tags = {mixed linear model},
number = {2},
pages = {100--6},
pmid = {24473328},
publisher = {Nature Publishing Group},
title = {{Advantages and pitfalls in the application of mixed-model association methods.}},
volume = {46},
year = {2014}
}
@article{Cook2016,
author = {Cook, James P and Mahajan, Anubha and Morris, Andrew P},
doi = {10.1038/ejhg.2016.150},
file = {:Users/rikutakei/Documents/Mendeley Desktop/European Journal of Human Genetics/Cook, Mahajan, Morris{\_}2016{\_}Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotype.pdf:pdf},
issn = {1018-4813},
journal = {Eur. J. Hum. Genet.},
keywords = {meta analysis},
mendeley-tags = {meta analysis},
number = {August},
pages = {1--6},
publisher = {Nature Publishing Group},
title = {{Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes}},
volume = {25},
year = {2016}
}
@article{Roshyara2016,
abstract = {A variety of modern software packages are available for genotype imputation relying on advanced concepts such as pre-phasing of the target dataset or utilization of admixed reference panels. In this study, we performed a comprehensive evaluation of the accuracy of modern imputation methods on the basis of the publicly available POPRES samples. Good quality genotypes were masked and re-imputed by different imputation frameworks: namely MaCH, IMPUTE2, MaCH-Minimac, SHAPEIT-IMPUTE2 and MaCH-Admix. Results were compared to evaluate the relative merit of pre-phasing and the usage of admixed references. We showed that the pre-phasing framework SHAPEIT-IMPUTE2 can overestimate the certainty of genotype distributions resulting in the lowest percentage of correctly imputed genotypes in our case. MaCH-Minimac performed better than SHAPEIT-IMPUTE2. Pre-phasing always reduced imputation accuracy. IMPUTE2 and MaCH-Admix, both relying on admixed-reference panels, showed comparable results. MaCH showed superior results if well-matched references were available (Nei's GST ≤ 0.010). For small to medium datasets, frameworks using genetically closest reference panel are recommended if the genetic distance between target and reference data set is small. Our results are valid for small to medium data sets. As shown on a larger data set of population based German samples, the disadvantage of pre-phasing decreases for larger sample sizes.},
author = {Roshyara, Nab Raj and Horn, Katrin and Kirsten, Holger and Ahnert, Peter and Scholz, Markus and An, P. and van Leeuwen, E. M. and Zeggini, E. and Lambert, J. C. and Olama, A. A. Al and Clark, A. G. and Li, J. and Marchini, J. and Howie, B. and Myers, S. and McVean, G. and Donnelly, P. and Peil, B. and Kabisch, M. and Fischer, C. and Hamann, U. and Bermejo, J. L. and Abecasis, G. R. and Altshuler, D. M. and Frazer, K. A. and Abecasis, G. R. and Burdick, J. T. and Chen, W.-M. and Abecasis, G. R. and Cheung, V. G. and Li, Y. and Willer, C. J. and Ding, J. and Scheet, P. and Abecasis, G. R. and Delaneau, O. and Marchini, J. and Howie, B. and Fuchsberger, C. and Stephens, M. and Marchini, J. and Abecasis, G. R. and Liu, E. Y. and Li, M. and Wang, W. and Li, Y. and Shriner, D. and Adeyemo, A. and Chen, G. and Rotimi, C. N. and Howie, B. and Marchini, J. and Stephens, M. and Li, Y. and Willer, C. and Sanna, S. and Abecasis, G. and Hao, K. and Chudin, E. and McElwee, J. and Schadt, E. E. and Huang, L. and Huang, L. and Jostins, L. and Morley, K. I. and Barrett, J. C. and Nelson, M. R. and Loeffler, M. and Roshyara, N. R. and Scholz, M. and Troendle, J. F. and Yu, K. F. and Roshyara, N. R. and Scholz, M. and Delaneau, O. and Zagury, J.-F. and Marchini, J. and Roshyara, N. R. and Kirsten, H. and Horn, K. and Ahnert, P. and Scholz, M.},
doi = {10.1038/srep34386},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Scientific Reports/Roshyara et al.{\_}2016{\_}Comparing performance of modern genotype imputation methods in different ethnicities.pdf:pdf},
issn = {2045-2322},
journal = {Sci. Rep.},
keywords = {imputation},
mendeley-tags = {imputation},
pages = {34386},
pmid = {27698363},
publisher = {Nature Publishing Group},
title = {{Comparing performance of modern genotype imputation methods in different ethnicities}},
volume = {6},
year = {2016}
}
@article{Joshi2013,
abstract = {The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1-10{\%}) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28-38{\%}, for SNPs with a minor allele frequency in the range 1-3{\%}.},
author = {Joshi, Peter K. and Prendergast, James and Fraser, Ross M. and Huffman, Jennifer E. and Vitart, Veronique and Hayward, Caroline and McQuillan, Ruth and Glodzik, Dominik and Pola??ek, Ozren and Hastie, Nicholas D. and Rudan, Igor and Campbell, Harry and Wright, Alan F. and Haley, Chris S. and Wilson, James F. and Navarro, Pau},
doi = {10.1371/journal.pone.0068604},
file = {:Users/rikutakei/Documents/Mendeley Desktop/PLoS ONE/Joshi et al.{\_}2013{\_}Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Stud.PDF:PDF},
isbn = {1932-6203 (Electronic)$\backslash$n1932-6203 (Linking)},
issn = {19326203},
journal = {PLoS One},
keywords = {imputation},
mendeley-tags = {imputation},
number = {7},
pmid = {23874685},
title = {{Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies}},
volume = {8},
year = {2013}
}
@article{Browning2008,
author = {Browning, Sharon R},
doi = {10.1007/s00439-008-0568-7},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Human Genetics/Browning{\_}2008{\_}Missing data imputation and haplotype phase inference for genome-wide association studies.pdf:pdf},
journal = {Hum. Genet.},
keywords = {imputation},
mendeley-tags = {imputation},
pages = {439--450},
title = {{Missing data imputation and haplotype phase inference for genome-wide association studies}},
volume = {124},
year = {2008}
}
@article{Mahajan2014,
abstract = {To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.},
archivePrefix = {arXiv},
arxivId = {NIHMS557904},
author = {Mahajan, Anubha and Go, Min Jin and Zhang, Weihua and Below, Jennifer E and Gaulton, Kyle J and Ferreira, Teresa and Horikoshi, Momoko and Johnson, Andrew D and Ng, Maggie C Y and Prokopenko, Inga and Saleheen, Danish and Wang, Xu and Zeggini, Eleftheria and Abecasis, Goncalo R and Adair, Linda S and Almgren, Peter and Atalay, Mustafa and Aung, Tin and Baldassarre, Damiano and Balkau, Beverley and Bao, Yuqian and Barnett, Anthony H and Barroso, Ines and Basit, Abdul and Been, Latonya F and Beilby, John and Bell, Graeme I and Benediktsson, Rafn and Bergman, Richard N and Boehm, Bernhard O and Boerwinkle, Eric and Bonnycastle, Lori L and Burtt, No{\"{e}}l and Cai, Qiuyin and Campbell, Harry and Carey, Jason and Cauchi, Stephane and Caulfield, Mark and Chan, Juliana C N and Chang, Li-Ching and Chang, Tien-Jyun and Chang, Yi-Cheng and Charpentier, Guillaume and Chen, Chien-Hsiun and Chen, Han and Chen, Yuan-Tsong and Chia, Kee-Seng and Chidambaram, Manickam and Chines, Peter S and Cho, Nam H and Cho, Young Min and Chuang, Lee-Ming and Collins, Francis S and Cornelis, Marylin C and Couper, David J and Crenshaw, Andrew T and van Dam, Rob M and Danesh, John and Das, Debashish and de Faire, Ulf and Dedoussis, George and Deloukas, Panos and Dimas, Antigone S and Dina, Christian and Doney, Alex S and Donnelly, Peter J and Dorkhan, Mozhgan and van Duijn, Cornelia and Dupuis, Jos{\'{e}}e and Edkins, Sarah and Elliott, Paul and Emilsson, Valur and Erbel, Raimund and Eriksson, Johan G and Escobedo, Jorge and Esko, Tonu and Eury, Elodie and Florez, Jose C and Fontanillas, Pierre and Forouhi, Nita G and Forsen, Tom and Fox, Caroline and Fraser, Ross M and Frayling, Timothy M and Froguel, Philippe and Frossard, Philippe and Gao, Yutang and Gertow, Karl and Gieger, Christian and Gigante, Bruna and Grallert, Harald and Grant, George B and Grrop, Leif C and Groves, Chrisropher J and Grundberg, Elin and Guiducci, Candace and Hamsten, Anders and Han, Bok-Ghee and Hara, Kazuo and Hassanali, Neelam and Hattersley, Andrew T and Hayward, Caroline and Hedman, Asa K and Herder, Christian and Hofman, Albert and Holmen, Oddgeir L and Hovingh, Kees and Hreidarsson, Astradur B and Hu, Cheng and Hu, Frank B and Hui, Jennie and Humphries, Steve E and Hunt, Sarah E and Hunter, David J and Hveem, Kristian and Hydrie, Zafar I and Ikegami, Hiroshi and Illig, Thomas and Ingelsson, Erik and Islam, Muhammed and Isomaa, Bo and Jackson, Anne U and Jafar, Tazeen and James, Alan and Jia, Weiping and J{\"{o}}ckel, Karl-Heinz and Jonsson, Anna and Jowett, Jeremy B M and Kadowaki, Takashi and Kang, Hyun Min and Kanoni, Stavroula and Kao, Wen Hong L and Kathiresan, Sekar and Kato, Norihiro and Katulanda, Prasad and Keinanen-Kiukaanniemi, Kirkka M and Kelly, Ann M and Khan, Hassan and Khaw, Kay-Tee and Khor, Chiea-Chuen and Kim, Hyung-Lae and Kim, Sangsoo and Kim, Young Jin and Kinnunen, Leena and Klopp, Norman and Kong, Augustine and Korpi-Hy{\"{o}}v{\"{a}}lti, Eeva and Kowlessur, Sudhir and Kraft, Peter and Kravic, Jasmina and Kristensen, Malene M and Krithika, S and Kumar, Ashish and Kumate, Jesus and Kuusisto, Johanna and Kwak, Soo Heon and Laakso, Markku and Lagou, Vasiliki and Lakka, Timo A and Langenberg, Claudia and Langford, Cordelia and Lawrence, Robert and Leander, Karin and Lee, Jen-Mai and Lee, Nanette R and Li, Man and Li, Xinzhong and Li, Yun and Liang, Junbin and Liju, Samuel and Lim, Wei-Yen and Lind, Lars and Lindgren, Cecilia M and Lindholm, Eero and Liu, Ching-Ti and Liu, Jian Jun and Lobbens, St{\'{e}}phane and Long, Jirong and Loos, Ruth J F and Lu, Wei and Luan, Jian'an and Lyssenko, Valeriya and Ma, Ronald C W and Maeda, Shiro and M{\"{a}}gi, Reedik and M{\"{a}}nnisto, Satu and Matthews, David R and Meigs, James B and Melander, Olle and Metspalu, Andres and Meyer, Julia and Mirza, Ghazala and Mihailov, Evelin and Moebus, Susanne and Mohan, Viswanathan and Mohlke, Karen L and Morris, Andrew D and M{\"{u}}hleisen, Thomas W and M{\"{u}}ller-Nurasyid, Martina and Musk, Bill and Nakamura, Jiro and Nakashima, Eitaro and Navarro, Pau and Ng, Peng-Keat and Nica, Alexandra C and Nilsson, Peter M and Nj{\o}lstad, Inger and N{\"{o}}then, Markus M and Ohnaka, Keizo and Ong, Twee Hee and Owen, Katharine R and Palmer, Colin N A and Pankow, James S and Park, Kyong Soo and Parkin, Melissa and Pechlivanis, Sonali and Pedersen, Nancy L and Peltonen, Leena and Perry, John R B and Peters, Annette and Pinidiyapathirage, Janini M and Platou, Carl G and Potter, Simon and Price, Jackie F and Qi, Lu and Radha, Venkatesan and Rallidis, Loukianos and Rasheed, Asif and Rathman, Wolfgang and Rauramaa, Rainer and Raychaudhuri, Soumya and Rayner, N William and Rees, Simon D and Rehnberg, Emil and Ripatti, Samuli and Robertson, Neil and Roden, Michael and Rossin, Elizabeth J and Rudan, Igor and Rybin, Denis and Saaristo, Timo E and Salomaa, Veikko and Saltevo, Juha and Samuel, Maria and Sanghera, Dharambir K and Saramies, Jouko and Scott, James and Scott, Laura J and Scott, Robert A and Segr{\`{e}}, Ayellet V and Sehmi, Joban and Sennblad, Bengt and Shah, Nabi and Shah, Sonia and Shera, A Samad and Shu, Xiao Ou and Shuldiner, Alan R and Sigurđsson, Gunnar and Sijbrands, Eric and Silveira, Angela and Sim, Xueling and Sivapalaratnam, Suthesh and Small, Kerrin S and So, Wing Yee and Stan{\v{c}}{\'{a}}kov{\'{a}}, Alena and Stefansson, Kari and Steinbach, Gerald and Steinthorsdottir, Valgerdur and Stirrups, Kathleen and Strawbridge, Rona J and Stringham, Heather M and Sun, Qi and Suo, Chen and Syv{\"{a}}nen, Ann-Christine and Takayanagi, Ryoichi and Takeuchi, Fumihiko and Tay, Wan Ting and Teslovich, Tanya M and Thorand, Barbara and Thorleifsson, Gudmar and Thorsteinsdottir, Unnur and Tikkanen, Emmi and Trakalo, Joseph and Tremoli, Elena and Trip, Mieke D and Tsai, Fuu Jen and Tuomi, Tiinamaija and Tuomilehto, Jaakko and Uitterlinden, Andre G and Valladares-Salgado, Adan and Vedantam, Sailaja and Veglia, Fabrizio and Voight, Benjamin F and Wang, Congrong and Wareham, Nicholas J and Wennauer, Roman and Wickremasinghe, Ananda R and Wilsgaard, Tom and Wilson, James F and Wiltshire, Steven and Winckler, Wendy and Wong, Tien Yin and Wood, Andrew R and Wu, Jer-Yuarn and Wu, Ying and Yamamoto, Ken and Yamauchi, Toshimasa and Yang, Mingyu and Yengo, Loic and Yokota, Mitsuhiro and Young, Robin and Zabaneh, Delilah and Zhang, Fan and Zhang, Rong and Zheng, Wei and Zimmet, Paul Z and Altshuler, David and Bowden, Donald W and Cho, Yoon Shin and Cox, Nancy J and Cruz, Miguel and Hanis, Craig L and Kooner, Jaspal and Lee, Jong-Young and Seielstad, Mark and Teo, Yik Ying and Boehnke, Michael and Parra, Esteban J and Chambers, Jonh C and Tai, E Shyong and McCarthy, Mark I and Morris, Andrew P},
doi = {10.1038/ng.2897},
eprint = {NIHMS557904},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature genetics/Mahajan et al.{\_}2014{\_}Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptib.pdf:pdf},
isbn = {1546-1718 (Electronic)$\backslash$r1061-4036 (Linking)},
issn = {1546-1718},
journal = {Nat. Genet.},
keywords = {Alleles,Asian Continental Ancestry Group,Asian Continental Ancestry Group: genetics,Case-Control Studies,Diabetes Mellitus,European Continental Ancestry Group,European Continental Ancestry Group: genetics,Genetic Predisposition to Disease,Genome-Wide Association Study,Hispanic Americans,Hispanic Americans: genetics,Humans,Polymorphism,Risk Factors,Single Nucleotide,Type 2,Type 2: genetics},
number = {3},
pages = {234--44},
pmid = {24509480},
title = {{Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.}},
volume = {46},
year = {2014}
}
@article{Tian2008,
abstract = {Accounting for the genetic substructure of human populations has become a major practical issue for studying complex genetic disorders. Allele frequency differences among ethnic groups and subgroups and admixture between different ethnic groups can result in frequent false-positive results or reduced power in genetic studies. Here, we review the problems and progress in defining population differences and the application of statistical methods to improve association studies. It is now possible to take into account the confounding effects of population stratification using thousands of unselected genome-wide single-nucleotide polymorphisms or, alternatively, selected panels of ancestry informative markers. These methods do not require any demographic information and therefore can be widely applied to genotypes available from multiple sources. We further suggest that it will be important to explore results in homogeneous population subsets as we seek to define the extent to which genomic variation influences complex phenotypes.},
author = {Tian, Chao and Gregersen, Peter K. and Seldin, Michael F.},
doi = {10.1093/hmg/ddn268},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Human Molecular Genetics/Tian, Gregersen, Seldin{\_}2008{\_}Accounting for ancestry Population substructure and genome-wide association studies.pdf:pdf},
isbn = {1460-2083 (Electronic)$\backslash$r0964-6906 (Linking)},
issn = {09646906},
journal = {Hum. Mol. Genet.},
keywords = {population substructure,relatedness},
mendeley-tags = {population substructure,relatedness},
number = {R2},
pages = {143--150},
pmid = {18852203},
title = {{Accounting for ancestry: Population substructure and genome-wide association studies}},
volume = {17},
year = {2008}
}
@article{Rosenberg2010,
author = {Rosenberg, Noah A and Huang, Lucy and Jewett, Ethan M and Szpiech, Zachary A},
doi = {10.1038/nrg2760},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews Genetics/Rosenberg et al.{\_}2010{\_}Genome-wide association studies in diverse populations(2).pdf:pdf},
issn = {1471-0056},
journal = {Nat. Rev. Genet.},
number = {5},
pages = {356--366},
publisher = {Nature Publishing Group},
title = {{Genome-wide association studies in diverse populations}},
volume = {11},
year = {2010}
}
@article{McCarthy2016,
abstract = {We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1{\%} and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.},
author = {McCarthy, Shane and Das, Sayantan and Kretzschmar, Warren and Delaneau, Olivier and Wood, Andrew R and Teumer, Alexander and Kang, Hyun Min and Fuchsberger, Christian and Danecek, Petr and Sharp, Kevin and Luo, Yang and Sidore, Carlo and Kwong, Alan and Timpson, Nicholas and Koskinen, Seppo and Vrieze, Scott and Scott, Laura J and Zhang, He and Mahajan, Anubha and Veldink, Jan and Peters, Ulrike and Pato, Carlos and van Duijn, Cornelia M and Gillies, Christopher E and Gandin, Ilaria and Mezzavilla, Massimo and Gilly, Arthur and Cocca, Massimiliano and Traglia, Michela and Angius, Andrea and Barrett, Jeffrey C and Boomsma, Dorrett and Branham, Kari and Breen, Gerome and Brummett, Chad M and Busonero, Fabio and Campbell, Harry and Chan, Andrew and Chen, Sai and Chew, Emily and Collins, Francis S and Corbin, Laura J and Smith, George Davey and Dedoussis, George and Dorr, Marcus and Farmaki, Aliki-Eleni and Ferrucci, Luigi and Forer, Lukas and Fraser, Ross M and Gabriel, Stacey and Levy, Shawn and Groop, Leif and Harrison, Tabitha and Hattersley, Andrew and Holmen, Oddgeir L and Hveem, Kristian and Kretzler, Matthias and Lee, James C and McGue, Matt and Meitinger, Thomas and Melzer, David and Min, Josine L and Mohlke, Karen L and Vincent, John B and Nauck, Matthias and Nickerson, Deborah and Palotie, Aarno and Pato, Michele and Pirastu, Nicola and McInnis, Melvin and Richards, J Brent and Sala, Cinzia and Salomaa, Veikko and Schlessinger, David and Schoenherr, Sebastian and Slagboom, P Eline and Small, Kerrin and Spector, Timothy and Stambolian, Dwight and Tuke, Marcus and Tuomilehto, Jaakko and {Van den Berg}, Leonard H and {Van Rheenen}, Wouter and Volker, Uwe and Wijmenga, Cisca and Toniolo, Daniela and Zeggini, Eleftheria and Gasparini, Paolo and Sampson, Matthew G and Wilson, James F and Frayling, Timothy and de Bakker, Paul I W and Swertz, Morris A and McCarroll, Steven and Kooperberg, Charles and Dekker, Annelot and Altshuler, David and Willer, Cristen and Iacono, William and Ripatti, Samuli and Soranzo, Nicole and Walter, Klaudia and Swaroop, Anand and Cucca, Francesco and Anderson, Carl A and Myers, Richard M and Boehnke, Michael and McCarthy, Mark I and Durbin, Richard and {Haplotype Reference Consortium}},
doi = {10.1038/ng.3643},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature genetics/McCarthy et al.{\_}2016{\_}A reference panel of 64,976 haplotypes for genotype imputation.pdf:pdf},
isbn = {1546-1718 (Electronic)$\backslash$r1061-4036 (Linking)},
issn = {1546-1718},
journal = {Nat. Genet.},
keywords = {imputation},
mendeley-tags = {imputation},
number = {10},
pmid = {27548312},
title = {{A reference panel of 64,976 haplotypes for genotype imputation.}},
volume = {48},
year = {2016}
}
@article{Magi2010,
abstract = {BACKGROUND Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies. RESULTS We have developed flexible, open-source software for the meta-analysis of genome-wide association studies. The software incorporates a variety of error trapping facilities, and provides a range of meta-analysis summary statistics. The software is distributed with scripts that allow simple formatting of files containing the results of each association study and generate graphical summaries of genome-wide meta-analysis results. CONCLUSIONS The GWAMA (Genome-Wide Association Meta-Analysis) software has been developed to perform meta-analysis of summary statistics generated from genome-wide association studies of dichotomous phenotypes or quantitative traits. Software with source files, documentation and example data files are freely available online at http://www.well.ox.ac.uk/GWAMA.},
author = {M{\"{a}}gi, Reedik and Morris, Andrew P},
doi = {10.1186/1471-2105-11-288},
file = {:Users/rikutakei/Documents/Mendeley Desktop/BMC bioinformatics/M{\"{a}}gi, Morris{\_}2010{\_}GWAMA software for genome-wide association meta-analysis.pdf:pdf},
isbn = {1471-2105 (Electronic)$\backslash$r1471-2105 (Linking)},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {meta analysis},
mendeley-tags = {meta analysis},
number = {ii},
pages = {288},
pmid = {20509871},
title = {{GWAMA: software for genome-wide association meta-analysis.}},
volume = {11},
year = {2010}
}
@article{Kim2015,
abstract = {BACKGROUND: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants.$\backslash$n$\backslash$nRESULTS: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 {\%}, and genomic coverage for rare variants up to 117.7 {\%} (MAF {\textless} 1 {\%}), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data.$\backslash$n$\backslash$nCONCLUSIONS: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants.},
author = {Kim, Young Jin and Lee, Juyoung and Kim, Bong-Jo and Park, Taesung},
doi = {10.1186/s12864-015-2192-y},
file = {:Users/rikutakei/Documents/Mendeley Desktop/BMC Genomics/Kim et al.{\_}2015{\_}A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP.pdf:pdf},
isbn = {10.1186/s12864-015-2192-y},
issn = {1471-2164},
journal = {BMC Genomics},
keywords = {Combined approach,Exome chip,Imputation,Rare varia,ac,combined approach,correspondence,exome chip,imputation,kr,rare variant,snu,stats,tspark},
mendeley-tags = {imputation},
number = {1},
pages = {1109},
pmid = {26715385},
publisher = {BMC Genomics},
title = {{A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data}},
volume = {16},
year = {2015}
}
@article{Smith2005,
author = {Smith, Michael W and Brien, Stephen J O},
doi = {10.1038/nrg1657},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews Genetics/Smith, Brien{\_}2005{\_}Mapping by admixture linkage disequilibrium advances, limitations and guidelines.pdf:pdf},
journal = {Nat. Rev. Genet.},
keywords = {admixture mapping},
mendeley-tags = {admixture mapping},
pages = {623--632},
title = {{Mapping by admixture linkage disequilibrium: advances, limitations and guidelines}},
volume = {6},
year = {2005}
}
@article{Hunter2000,
abstract = {Research conclusions in the social sciences are increasingly based on meta-analysis, making questions of the accuracy of meta-analysis critical to the integrity of the base of cumulative knowledge. Both fixed effects (FE) and random effects (RE) meta-analysis models have been used widely in published meta-analyses. This article shows that FE models typically manifest a substantial Type I bias in significance tests for mean effect sizes and for moderator variables (interactions), while RE models do not. Likewise, FE models, but not RE models, yield confidence intervals for mean effect sizes that are narrower than their nominal width, thereby overstating the degree of precision in meta-analysis findings. This article demonstrates analytically that these biases in FE procedures are large enough to create serious distortions in conclusions about cumulative knowledge in the research literature. We therefore recommend that RE methods routinely be employed in meta-analysis in preference to FE methods.},
author = {Hunter, John E and Schmidt, Frank L},
doi = {10.1111/1468-2389.00156},
file = {:Users/rikutakei/Documents/Mendeley Desktop/International Journal of Selection and Assessment/Hunter, Schmidt{\_}2000{\_}Fixed Effects vs. Random Effects Meta-Analysis Models Implications for Cumulative Research Knowledge.pdf:pdf},
isbn = {1468-2389},
issn = {0965-075X},
journal = {Int. J. Sel. Assess.},
number = {4},
pages = {275--292},
pmid = {141},
title = {{Fixed Effects vs. Random Effects Meta-Analysis Models: Implications for Cumulative Research Knowledge}},
url = {http://dx.doi.org/10.1111/1468-2389.00156},
volume = {8},
year = {2000}
}
@article{Morris2011,
abstract = {The detection of loci contributing effects to complex human traits, and their subsequent fine-mapping for the location of causal variants, remains a considerable challenge for the genetics research community. Meta-analyses of genomewide association studies, primarily ascertained from European-descent populations, have made considerable advances in our understanding of complex trait genetics, although much of their heritability is still unexplained. With the increasing availability of genomewide association data from diverse populations, transethnic meta-analysis may offer an exciting opportunity to increase the power to detect novel complex trait loci and to improve the resolution of fine-mapping of causal variants by leveraging differences in local linkage disequilibrium structure between ethnic groups. However, we might also expect there to be substantial genetic heterogeneity between diverse populations, both in terms of the spectrum of causal variants and their allelic effects, which cannot easily be accommodated through traditional approaches to meta-analysis. In order to address this challenge, I propose novel transethnic meta-analysis methodology that takes account of the expected similarity in allelic effects between the most closely related populations, while allowing for heterogeneity between more diverse ethnic groups. This approach yields substantial improvements in performance, compared to fixed-effects meta-analysis, both in terms of power to detect association, and localization of the causal variant, over a range of models of heterogeneity between ethnic groups. Furthermore, when the similarity in allelic effects between populations is well captured by their relatedness, this approach has increased power and mapping resolution over random-effects meta-analysis.},
author = {Morris, Andrew P.},
doi = {10.1002/gepi.20630},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Genetic Epidemiology/Morris{\_}2011{\_}Transethnic meta-analysis of genomewide association studies.pdf:pdf},
isbn = {1098-2272 (Electronic)$\backslash$r0741-0395 (Linking)},
issn = {07410395},
journal = {Genet. Epidemiol.},
keywords = {Bayesian partition model,Diverse populations,Fine-mapping,Genomewide association study,Meta-analysis,Transethnic,meta analysis,trans ancestry},
mendeley-tags = {trans ancestry,meta analysis},
number = {8},
pages = {809--822},
pmid = {22125221},
title = {{Transethnic meta-analysis of genomewide association studies}},
volume = {35},
year = {2011}
}
@article{McCarthy2008,
abstract = {The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.},
author = {McCarthy, M I and Abecasis, G and Cardon, L and Goldstein, D and Little, J and Ioannidis, J P and Hirschhorn, J},
doi = {10.1038/nrg2344},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature Reviews Genetics/McCarthy et al.{\_}2008{\_}Genome-wide association studies for complex traits consensus, uncertainty and challenges.pdf:pdf},
isbn = {1471-0056},
issn = {1471-0064},
journal = {Nat. Rev. Genet.},
keywords = {Alleles,Animals,Genetic Diseases: Inborn,Genetic Predisposition to Disease,Genetic Variation,Genome: Human,Humans,Quantitative Trait Loci,Quantitative Trait: Heritable},
number = {5},
pages = {356--369},
pmid = {18398418},
title = {{Genome-wide association studies for complex traits: consensus, uncertainty and challenges}},
volume = {9},
year = {2008}
}
@article{Wacholder2004,
abstract = {Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below .05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the ob- served P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or “noteworthy.” We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and read- ers of research articles to protect themselves from overinter- preting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for decid- ing whether to call a finding noteworthy formalizes the process already used informally by investigators—that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.},
author = {Wacholder, Sholom and Chanock, Stephen and Garcia-Closas, Montserrat and El ghormli, Laure and Rothman, Nathaniel},
doi = {10.1093/jnci/djh075},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Cancer Research/Wacholder et al.{\_}2004{\_}Assessing the Probability That a Positive Report is False An Approach for Molecular Epidemiolofy Studies.pdf:pdf},
isbn = {0027-8874, 1460-2105},
issn = {0027-8874},
journal = {Cancer Res.},
keywords = {FPRP,false positive report probability},
mendeley-tags = {false positive report probability,FPRP},
number = {6},
pages = {434--442},
pmid = {15026468},
title = {{Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiolofy Studies}},
volume = {96},
year = {2004}
}
@article{Minster,
author = {Minster, Ryan L and Hawley, Nicola L and Su, Chi-ting and Sun, Guangyun and Kershaw, Erin E and Cheng, Hong and Buhule, Olive D and Lin, Jerome and Reupena, Sefuiva and Tuitele, John and Naseri, Take and Urban, Zsolt and Deka, Ranjan and Weeks, Daniel E},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Unknown/Minster et al.{\_}Unknown{\_}A thrifty variant in.pdf:pdf},
keywords = {imputation},
mendeley-tags = {imputation},
pages = {1--42},
title = {{A thrifty variant in}}
}
@article{wtccc2012,
author = {{The Wellcome Trust Case Control Consortium}},
doi = {10.1038/ng.2435},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nat. Genet/The Wellcome Trust Case Control Consortium{\_}2012{\_}Bayesian refinement of association signals for 14 loci in 3 common diseases.pdf:pdf},
journal = {Nat. Genet},
keywords = {credible set,fine mapping},
mendeley-tags = {credible set,fine mapping},
number = {12},
pages = {1294--1301},
title = {{Bayesian refinement of association signals for 14 loci in 3 common diseases}},
volume = {44},
year = {2012}
}
@article{Spielman1998,
abstract = {Linkage analysis with genetic markers has been successful in the localization of genes for many monogenic human diseases. In studies of complex diseases, however, tests that rely on linkage disequilibrium (the simultaneous presence of linkage and association) are often more powerful than those that rely on linkage alone. This advantage is illustrated by the transmission/disequilibrium test (TDT). The TDT requires data (marker genotypes) for affected individuals and their parents; for some diseases, however, data from parents may be difficult or impossible to obtain. In this article, we describe a method, called the "sib TDT" (or "S-TDT"), that overcomes this problem by use of marker data from unaffected sibs instead of from parents, thus allowing application of the principle of the TDT to sibships without parental data. In a single collection of families, there might be some that can be analyzed only by the TDT and others that are suitable for analysis by the S-TDT. We show how all the data may be used jointly in one overall TDT-type procedure that tests for linkage in the presence of association. These extensions of the TDT will be valuable for the study of diseases of late onset, such as non-insulin-dependent diabetes, cardiovascular diseases, and other diseases associated with aging.},
author = {Spielman, Richard S. and Ewens, Warren J.},
doi = {10.1086/301714},
file = {:Users/rikutakei/Documents/Mendeley Desktop/American journal of human genetics/Spielman, Ewens{\_}1998{\_}A sibship test for linkage in the presence of association the sib transmissiondisequilibrium test.pdf:pdf},
isbn = {0002-9297 (Print)},
issn = {00029297},
journal = {Am. J. Hum. Genet.},
number = {2},
pages = {450--458},
pmid = {9463321},
title = {{A sibship test for linkage in the presence of association: the sib transmission/disequilibrium test.}},
volume = {62},
year = {1998}
}
@article{Hong2016,
author = {Hong, Jaeyoung and Lunetta, Kathryn L. and Cupples, L. Adrienne and Dupuis, Jos{\'{e}}e and Liu, Ching Ti},
doi = {10.1002/gepi.21963},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Genetic Epidemiology/Hong et al.{\_}2016{\_}Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies.pdf:pdf},
issn = {10982272},
journal = {Genet. Epidemiol.},
keywords = {Clustering strategy,GWAS,Heterogeneity,Meta-analysis,Simulation study,Trans-ethnic meta-analysis},
number = {4},
pages = {284--292},
title = {{Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies}},
volume = {40},
year = {2016}
}
@article{VanLeeuwen2015,
abstract = {Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks ({\~{}}35,000 samples) with the population-specific reference panel created by the Genome of The Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value {\textless}6.61 × 10(-4)), including a rare missense variant in ABCA6 (rs77542162, p.Cys1359Arg, frequency 0.034), which is predicted to be deleterious. The frequency of this ABCA6 variant is 3.65-fold increased in the Dutch and its effect ($\beta$LDL-C=0.135, $\beta$TC=0.140) is estimated to be very similar to those observed for single variants in well-known lipid genes, such as LDLR.},
author = {van Leeuwen, Elisabeth M and Karssen, Lennart C and Deelen, Joris and Isaacs, Aaron and Medina-Gomez, Carolina and Mbarek, Hamdi and Kanterakis, Alexandros and Trompet, Stella and Postmus, Iris and Verweij, Niek and van Enckevort, David J and Huffman, Jennifer E and White, Charles C and Feitosa, Mary F and Bartz, Traci M and Manichaikul, Ani and Joshi, Peter K and Peloso, Gina M and Deelen, Patrick and van Dijk, Freerk and Willemsen, Gonneke and de Geus, Eco J and Milaneschi, Yuri and Penninx, Brenda W J H and Francioli, Laurent C and Menelaou, Androniki and Pulit, Sara L and Rivadeneira, Fernando and Hofman, Albert and Oostra, Ben A and Franco, Oscar H and {Mateo Leach}, Irene and Beekman, Marian and de Craen, Anton J M and Uh, Hae-Won and Trochet, Holly and Hocking, Lynne J and Porteous, David J and Sattar, Naveed and Packard, Chris J and Buckley, Brendan M and Brody, Jennifer A and Bis, Joshua C and Rotter, Jerome I and Mychaleckyj, Josyf C and Campbell, Harry and Duan, Qing and Lange, Leslie A and Wilson, James F and Hayward, Caroline and Polasek, Ozren and Vitart, Veronique and Rudan, Igor and Wright, Alan F and Rich, Stephen S and Psaty, Bruce M and Borecki, Ingrid B and Kearney, Patricia M and Stott, David J and {Adrienne Cupples}, L and Jukema, J Wouter and van der Harst, Pim and Sijbrands, Eric J and Hottenga, Jouke-Jan and Uitterlinden, Andre G and Swertz, Morris A and van Ommen, Gert-Jan B and de Bakker, Paul I W and {Eline Slagboom}, P and Boomsma, Dorret I and Wijmenga, Cisca and van Duijn, Cornelia M},
doi = {10.1038/ncomms7065},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nature communications/van Leeuwen et al.{\_}2015{\_}Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol.pdf:pdf},
isbn = {doi:10.1038/ncomms7065},
issn = {2041-1723},
journal = {Nat. Commun.},
keywords = {imputation},
mendeley-tags = {imputation},
pages = {6065},
pmid = {25751400},
publisher = {Nature Publishing Group},
title = {{Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.}},
volume = {6},
year = {2015}
}
@article{Li2012,
abstract = {Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M(e)) for the adjustment of multiple testing, but current methods of calculation for M(e) are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M(e). Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M(e), and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of {\~{}}10(-7) as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds {\~{}}5 × 10(-8) for current or merged commercial genotyping arrays, {\~{}}10(-8) for all common SNPs in the 1000 Genomes Project dataset and {\~{}}5 × 10(-8) for the common SNPs only within genes.;},
author = {Li, Miao Xin and Yeung, Juilian M Y and Cherny, Stacey S. and Sham, Pak C.},
doi = {10.1007/s00439-011-1118-2},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Human Genetics/Li et al.{\_}2012{\_}Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays.pdf:pdf},
isbn = {1432-1203},
issn = {03406717},
journal = {Hum. Genet.},
keywords = {effective numbers of statistical tests},
mendeley-tags = {effective numbers of statistical tests},
number = {5},
pages = {747--756},
pmid = {22143225},
title = {{Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets}},
volume = {131},
year = {2012}
}
@article{Shi2016,
abstract = {Meta-analysis of trans-ethnic genome-wide association studies (GWAS) has proven to be a practical and profitable approach for identifying loci that contribute to the risk of complex diseases. However, the expected genetic effect heterogeneity cannot easily be accommodated through existing fixed-effects and random-effects methods. In response, we propose a novel random effect model for trans-ethnic meta-analysis with flexible modeling of the expected genetic effect heterogeneity across diverse populations. Specifically, we adopt a modified random effect model from the kernel regression framework, in which genetic effect coefficients are random variables whose correlation structure reflects the genetic distances across ancestry groups. In addition, we use the adaptive variance component test to achieve robust power regardless of the degree of genetic effect heterogeneity. Simulation studies show that our proposed method has well-calibrated type I error rates at very stringent significance levels and can improve power over the traditional meta-analysis methods. We reanalyzed the published type 2 diabetes GWAS meta-analysis (Consortium et al., 2014) and successfully identified one additional SNP that clearly exhibits genetic effect heterogeneity across different ancestry groups. Furthermore, our proposed method provides scalable computing time for genome-wide datasets, in which an analysis of one million SNPs would require less than 3 hours. {\textcopyright} 2016, The International Biometric Society},
archivePrefix = {arXiv},
arxivId = {15334406},
author = {Shi, Jingchunzi and Lee, Seunggeun},
doi = {10.1111/biom.12481},
eprint = {15334406},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Biometrics/Shi, Lee{\_}2016{\_}A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis.pdf:pdf},
isbn = {0000000000000},
issn = {15410420},
journal = {Biometrics},
keywords = {Effect-size heterogeneity,GWAS,Kernel regression,Meta-analysis,Random effect model,Trans-ethnic meta-analysis},
number = {3},
pages = {945--954},
pmid = {26916671},
title = {{A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis}},
volume = {72},
year = {2016}
}
@article{Horikoshi2016,
author = {Horikoshi, Momoko and Pasquali, Lorenzo and Wiltshire, Steven and JHuyghe, Jeroen R. and Mahajan, Anubha and Asimit, Jennifer L and Ferreira, Teresa and Locke, Adam E and Robertson, Neil R and Wang, Xu and Sim, Xueling and Fujita, Hayato and Hara, Kazuo and Young, Robin and Zhang, Weihua and Choi, Sungkyoung and Chen, Han and Kaur, Ismeet and Takeuchi, Fumihiko and Fontanillas, Pierre and Thuillier, Dorothee and Yengo, Loic and Below, Jennifer E and Tam, Claudia H. T. and Wu, Ying and {T2D-GENES Consortium} and Abecasis, Gon{\c{c}}alo and Altshuler, David and Bell, Graeme I. and Blangero, John and Burtt, Noel P. and Duggirala, Ravindranath and Florez, Jose C. and Hanis, Craig L. and Seielstad, Mark and Atzmon, Gil and Chan, Juliana C. N. and Ma, Ronald C.W. and Froguel, Philippe and Wilson, James G. and Bharadwaj, Dwaipayan and Dupius, Josee and Meigs, James B. and Cho, Yoon Shin and Park, Taesung and Kooner, Jaspal S. and Chambers, John C. and Saleheen, Danish and Kadowaki, Takashi and Tai, E. Shyong and Mohlke, Karen L. and Cox, Nancy J. and Ferrer, Jorge and Zeggini, Eleftheria and Kato, Norihiro and Teo, Yik Ying and Boehnke, Michael and McCarthy, Mark I. and Morris, Andrew P.},
doi = {10.1093/hmg/ddw048},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Human Molecular Genetics/Horikoshi et al.{\_}2016{\_}Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mec.pdf:pdf},
journal = {Hum. Mol. Genet.},
number = {10},
pages = {2070--2081},
title = {{Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms}},
volume = {25},
year = {2016}
}
@article{Mahajan2016,
author = {Mahajan, Anubha and Rodan, Aylin R and Le, Thu H and Gaulton, Kyle J and Haessler, Jeffrey and Stilp, Adrienne M and Kamatani, Yoichiro and Zhu, Gu and Sofer, Tamar and Puri, Sanjana and Schellinger, Jeffrey N and Chu, Pei-lun and Cechova, Sylvia and Zuydam, Natalie Van and Consortium, Summit and Arnlov, Johan and Flessner, Michael F and Giedraitis, Vilmantas and Heath, Andrew C and Kubo, Michiaki and Larsson, Anders and Lindgren, Cecilia M and Madden, Pamela A F and Montgomery, Grant W and Thornton, Timothy A and Lind, Lars and Papanicolaou, George J and Reiner, Alex P and Sundstro, Johan and Ingelsson, Erik and Cai, Jianwen and Martin, Nicholas G and Kooperberg, Charles and Matsuda, Koichi and Whitfield, John B and Okada, Yukinori and Laurie, Cathy C and Morris, Andrew P},
doi = {10.1016/j.ajhg.2016.07.012},
file = {:Users/rikutakei/Documents/Mendeley Desktop/The American Journal of Human Genetics/Mahajan et al.{\_}2016{\_}Trans-ethnic Fine Mapping Highlights Kidney-Function Genes Linked to Salt Sensitivity.pdf:pdf},
journal = {Am. J. Hum. Genet.},
keywords = {meta analysis,trans ancestry},
mendeley-tags = {trans ancestry,meta analysis},
pages = {636--646},
title = {{Trans-ethnic Fine Mapping Highlights Kidney-Function Genes Linked to Salt Sensitivity}},
volume = {99},
year = {2016}
}
@article{Kang2010,
abstract = {Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.},
author = {Kang, H M and Sul, J H and Service, S K and Zaitlen, N A and Kong, S Y and Freimer, N B and Sabatti, C and Eskin, E},
doi = {10.1038/ng.548},
file = {:Users/rikutakei/Documents/Mendeley Desktop/Nat Genet/Kang et al.{\_}2010{\_}Variance component model to account for sample structure in genome-wide association studies.pdf:pdf},
isbn = {1546-1718 (Electronic)$\backslash$r1061-4036 (Linking)},
issn = {1546-1718 (Electronic) 1061-4036 (Linking)},
journal = {Nat Genet},
keywords = {*Genome-Wide Association Study,*Models,Genetic,Humans,Models,Polymorphism,Population Groups/*genetics,Principal Component Analysis,Quantitative Trait Loci,Single Nucleotide,Software,Statistical,population substructure},
mendeley-tags = {population substructure},
number = {4},
pages = {348--354},
pmid = {20208533},
publisher = {Nature Publishing Group},
title = {{Variance component model to account for sample structure in genome-wide association studies}},
volume = {42},
year = {2010}
}