FaST linear mixed models for genome-wide association studies

Lippert, Christoph; Listgarten, Jennifer; Liu, Ying; Kadie, Carl; Davidson, Roger; Heckerman, David

doi:10.1038/nmeth.1681

Cited by 1,119 publications

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“…New Analysis Methodology Underpinning New Discovery GWAS data have led to new analysis methods that fall into a number of categories depending on their purpose: (1) methods of better modeling population structure and relatedness between individuals in a sample during association analyses, [28][29][30][31][32][33][34] (2) methods of detecting novel variants and gene loci on the basis of GWAS summary statistics, [35][36][37] (3) methods of estimating and partitioning genetic (co)variance, 38,39 and (4) methods of inferring causality. [40][41][42] In addition, GWAS discoveries and interpretation have benefited substantially from improved algorithms in statistical imputation of unobserved genotypes and statistical imputation of human leukocyte antigen (HLA) genes and amino acid polymorphisms.…”

Section: Pleiotropy Is Pervasivementioning

confidence: 99%

10 Years of GWAS Discovery: Biology, Function, and Translation

Visscher

Wray

Zhang

et al. 2017

The American Journal of Human Genetics

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2,536

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Section: Pleiotropy Is Pervasivementioning

confidence: 99%

10 Years of GWAS Discovery: Biology, Function, and Translation

Visscher

Wray

Zhang

et al. 2017

The American Journal of Human Genetics

3,132

2,536

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“…Namely, given the eigendecomposition , the matrix is given by , where is the matrix of the componentwise square roots of the entries of . In GWAS, the eigendecomposition of is computed both when using an LMM 24 and when performing regression using principal component covariates 12 , and is thus available for use in LEAP at no further computational cost.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…The most important parameter that is fitted in LMMs is the variances ratio . Given this parameter, all other parameters can be evaluated via closed form formulas 24 . There is a close connection between this parameter and the narrow-sense heritability, , expressed via .…”

Section: Use In Gwasmentioning

confidence: 99%

“…This is because the true heritability is the proportion to which causal variants in the kinship matrix influence the phenotypic variance, whereas there are no causal variants in the oracle LMM kinship matrix. We therefore fit the value of for oracle LMMs by maximizing the restricted maximum likelihood of the null model, as is common in LMM usage 24 (the estimated heritability in such cases is different from 0, because causal and non-causal variants are confounded due to the presence of population structure and family relatedness).…”

Section: Investigating the Causes Of Power Loss In Ascertained Case Cmentioning

confidence: 99%

“…When using liability estimates as phenotypes, was determined according to the liability heritability estimator , which was estimated using the method of ref 2 , with the formula . When testing synthetic data sets, tested SNPs were excluded from the genetic similarity matrix to prevent proximal contamination 7,24,25 . In the analysis of real data sets, when testing SNPs on a certain chromosome, this chromosome was excluded from the LMM genetic similarity matrix to prevent proximal contamination 24,25 .…”

Section: Lmm Analysesmentioning

confidence: 99%

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Accurate liability estimation improves power in ascertained case-control studies

Weissbrod¹,

Lippert²,

Geiger³

et al. 2015

Nat Methods

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Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in nonrandomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (Liability Estimator As a Phenotype; https://github.com/omerwe/LEAP) that tests for association with estimated latent values corresponding to severity of phenotype, and demonstrate that this can lead to a substantial power increase. Main TextIn recent years, genome-wide association studies (GWAS) have uncovered thousands of risk variants for genetic traits 1 . Only a small fraction of disease variance is explained by discovered variants, possibly because contemporary sample sizes are relatively small and that causal variants tend to have small effect sizes 2 . To identify such variants, future studies will need to include hundreds of thousands of individuals.Population structure and family relatedness 3 lead to spurious results and increased type I error rate. As sample sizes continue to increase, this difficulty becomes even 2 more severe, because larger samples are more likely to include individuals with a different genetic ancestry, or related individuals.Recently, LMMs have emerged as the method of choice for GWAS, due to their robustness to diverse sources of confounding 3 . LMMs gain resilience to confounding by testing for association conditioned on pairwise kinship coefficients between study subjects. Although designed for continuous phenotypes, LMMs have been successfully used in several large case-control GWAS 4-6 , because alternative methods cannot capture diverse sources of confounding 3 .However, LMMs in ascertained case-control studies, wherein cases are oversampled relative to the disease prevalence, lose power with increasing sample size compared to alternative methods 7 . This loss is due to several model violations: Dependence between tested causal variants and variants used to estimate kinship, dependence between genetic and environmental effects, and use of a non-continuous trait (Supplementary Note). Thus, the use of LMMs resolves the difficulty of sensitivity to confounding, but leads to a different difficulty instead.A possible remedy is to test for associations with a model that directly represents the case-control phenotype and takes the ascertainment scheme into account (Supplementary Note). Such models assume that observed case-control phenotypes are generated by an unobserved stochastic process with a well-defined distribution. One prominent example is the liability threshold model 8 , which associates individuals with a latent normally distributed variable called the liability, such that cases are individuals whose liability exceeds a given cutoff. Despite their elegance, such models are extremely computationally expensive, rendering whole genome association tests infeasible in most circumstances.As an alternative, we propose approximating such models by first estimating latent liability values and model param...

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The Genetic Architecture of Depression in Individuals of East Asian Ancestry

et al. 2021

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for the 23andMe Research Team, China Kadoorie Biobank Collaborative Group, and Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium IMPORTANCE Most previous genome-wide association studies (GWAS) of depression have used data from individuals of European descent. This limits the understanding of the underlying biology of depression and raises questions about the transferability of findings between populations. OBJECTIVE To investigate the genetics of depression among individuals of East Asian and European descent living in different geographic locations, and with different outcome definitions for depression.DESIGN, SETTING, AND PARTICIPANTS Genome-wide association analyses followed by meta-analysis, which included data from 9 cohort and case-control data sets comprising individuals with depression and control individuals of East Asian descent. This study was conducted between January 2019 and May 2021.EXPOSURES Associations of genetic variants with depression risk were assessed using generalized linear mixed models and logistic regression. The results were combined across studies using fixed-effects meta-analyses. These were subsequently also meta-analyzed with the largest published GWAS for depression among individuals of European descent. Additional meta-analyses were carried out separately by outcome definition (clinical depression vs symptom-based depression) and region (East Asian countries vs Western countries) for East Asian ancestry cohorts. MAIN OUTCOMES AND MEASURES Depression status was defined based on health records and self-report questionnaires.RESULTS There were a total of 194 548 study participants (approximate mean age, 51.3 years; 62.8% women). Participants included 15 771 individuals with depression and 178 777 control individuals of East Asian descent. Five novel associations were identified, including 1 in the meta-analysis for broad depression among those of East Asian descent: rs4656484 (β = −0.018, SE = 0.003, P = 4.43x10 −8 ) at 1q24.1. Another locus at 7p21.2 was associated in a meta-analysis restricted to geographically East Asian studies (β = 0.028, SE = 0.005, P = 6.48x10 −9 for rs10240457). The lead variants of these 2 novel loci were not associated with depression risk in European ancestry cohorts (β = −0.003, SE = 0.005, P = .53 for rs4656484 and β = −0.005, SE = 0.004, P = .28 for rs10240457). Only 11% of depression loci previously identified in individuals of European descent reached nominal significance levels in the individuals of East Asian descent. The transancestry genetic correlation between cohorts of East Asian and European descent for clinical depression was r = 0.413 (SE = 0.159). Clinical depression risk was negatively genetically correlated with body mass index in individuals of East Asian descent (r = −0.212, SE = 0.084), contrary to findings for individuals of European descent. CONCLUSIONS AND RELEVANCEThese results support caution against generalizing findings about depression risk factors across populations and highlight the need to incre...

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FaST linear mixed models for genome-wide association studies

Cited by 1,119 publications

References 17 publications

10 Years of GWAS Discovery: Biology, Function, and Translation

10 Years of GWAS Discovery: Biology, Function, and Translation

Accurate liability estimation improves power in ascertained case-control studies

The Genetic Architecture of Depression in Individuals of East Asian Ancestry

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