Efficient Bayesian mixed-model analysis increases association power in large cohorts

Loh, Po−Ru; Tucker, George; Bulik-Sullivan, Brendan; Vilhjálmsson, Bjarni J.; Finucane, Hilary; Salem, Rany M.; Chasman, Daniel I.; Ridker, Paul M.; Neale, Benjamin M.; Berger, Bonnie; Patterson, Nick; Price, Alkes L.

doi:10.1038/ng.3190

Cited by 1,451 publications

(1,596 citation statements)

References 49 publications

(141 reference statements)

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“…Discovery GWAS analysis was performed on UK Biobank on V.3 imputed genotypes using BOLT-LMM V.2.34, which applies a linear mixed model to adjust for the effects of population structure and individual relatedness 16. This enabled the inclusion of all related individuals in our white European subset allowing a sample size of 451 099 individuals as detailed in online supplementary materials and methods.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide association analysis of diverticular disease points towards neuromuscular, connective tissue and epithelial pathomechanisms

Schafmayer¹,

Harrison²,

Buch³

et al. 2019

Gut

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ObjectiveDiverticular disease is a common complex disorder characterised by mucosal outpouchings of the colonic wall that manifests through complications such as diverticulitis, perforation and bleeding. We report the to date largest genome-wide association study (GWAS) to identify genetic risk factors for diverticular disease.DesignDiscovery GWAS analysis was performed on UK Biobank imputed genotypes using 31 964 cases and 419 135 controls of European descent. Associations were replicated in a European sample of 3893 cases and 2829 diverticula-free controls and evaluated for risk contribution to diverticulitis and uncomplicated diverticulosis. Transcripts at top 20 replicating loci were analysed by real-time quatitative PCR in preparations of the mucosal, submucosal and muscular layer of colon. The localisation of expressed protein at selected loci was investigated by immunohistochemistry.ResultsWe discovered 48 risk loci, of which 12 are novel, with genome-wide significance and consistent OR in the replication sample. Nominal replication (p<0.05) was observed for 27 loci, and additional 8 in meta-analysis with a population-based cohort. The most significant novel risk variant rs9960286 is located near CTAGE1 with a p value of 2.3×10−10 and 0.002 (ORallelic=1.14 (95% CI 1.05 to 1.24)) in the replication analysis. Four loci showed stronger effects for diverticulitis, PHGR1 (OR 1.32, 95% CI 1.12 to 1.56), FAM155A-2 (OR 1.21, 95% CI 1.04 to 1.42), CALCB (OR 1.17, 95% CI 1.03 to 1.33) and S100A10 (OR 1.17, 95% CI 1.03 to 1.33).ConclusionIn silico analyses point to diverticulosis primarily as a disorder of intestinal neuromuscular function and of impaired connective fibre support, while an additional diverticulitis risk might be conferred by epithelial dysfunction.

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Section: Methodsmentioning

confidence: 99%

Genome-wide association analysis of diverticular disease points towards neuromuscular, connective tissue and epithelial pathomechanisms

Schafmayer¹,

Harrison²,

Buch³

et al. 2019

Gut

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“…26 This approach corrects for all levels of inter-individual correlation of genotypes due to relatedness, from close relatives to cryptic relatedness caused by population stratification. We inverse normalised the socioeconomic status measures, then took the residuals using three covariates (age, sex, assessment centre location) and inverse normalised again.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank

Tyrrell

Jones

Beaumont

et al. 2016

BMJ

286

248

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ObjeCtiveTo determine whether height and body mass index (BMI) have a causal role in five measures of socioeconomic status. DesignMendelian randomisation study to test for causal effects of differences in stature and BMI on five measures of socioeconomic status. Mendelian randomisation exploits the fact that genotypes are randomly assigned at conception and thus not confounded by non-genetic factors.PartiCiPants 119 669 men and women of British ancestry, aged between 37 and 73 years. Main OutCOMe MeasuresAge completed full time education, degree level education, job class, annual household income, and Townsend deprivation index. resultsIn the UK Biobank study, shorter stature and higher BMI were observationally associated with several measures of lower socioeconomic status. The associations between shorter stature and lower socioeconomic status tended to be stronger in men, and the associations between higher BMI and lower socioeconomic status tended to be stronger in women. For example, a 1 standard deviation (SD) higher BMI was associated with a £210 (€276; $300; 95% confidence interval £84 to £420; P=6×10 −3 ) lower annual household income in men and a £1890 (£1680 to £2100; P=6×10 −15 ) lower annual household income in women. Genetic analysis provided evidence that these associations were partly causal. A genetically determined 1 SD (6.3 cm) taller stature caused a 0.06 (0.02 to 0.09) year older age of completing full time education (P=0.01), a 1.12 (1.07 to 1.18) times higher odds of working in a skilled profession (P=6×10 −7 ), and a £1130 (£680 to £1580) higher annual household income (P=4×10 −8 ). Associations were stronger in men. A genetically determined 1 SD higher BMI (4.6 kg/m 2 ) caused a £2940 (£1680 to £4200; P=1×10 −5 ) lower annual household income and a 0.10 (0.04 to 0.16) SD (P=0.001) higher level of deprivation in women only. COnClusiOnsThese data support evidence that height and BMI play an important partial role in determining several aspects of a person's socioeconomic status, especially women's BMI for income and deprivation and men's height for education, income, and job class. These findings have important social and health implications, supporting evidence that overweight people, especially women, are at a disadvantage and that taller people, especially men, are at an advantage.

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“…Other methods, including [17, 19, 18], can always raise the corresponding Kinship matrix to achieve a similar performance. In other words, the novelty of our paper can also be understood as introducing a general framework with the argument that raising the power of kinship matrix can effectively increase the power of confounding correction.…”

Section: Resultsmentioning

confidence: 99%

“…To address this challenge, Principle Component Analysis that extracts the major axes of population differentiation from genotype data has been explored [11]. Linear mixed model is another tool that provides a more dedicated control of modeling population structure with its random effect component and it has been shown to greatly reduce the impact of population structure [12, 13, 14, 15, 16, 17, 18, 19, 20]. …”

Section: Introductionmentioning

confidence: 99%

Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model

Wang

Liu

Xiao

et al. 2018

Methods

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Genome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.

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Efficient Bayesian mixed-model analysis increases association power in large cohorts

Cited by 1,451 publications

References 49 publications

Genome-wide association analysis of diverticular disease points towards neuromuscular, connective tissue and epithelial pathomechanisms

Genome-wide association analysis of diverticular disease points towards neuromuscular, connective tissue and epithelial pathomechanisms

Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank

Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model

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