Genetic Variation and Population Substructure in Outbred CD-1 Mice: Implications for Genome-Wide Association Studies

AbstractGenome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is de-spite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.1Author SummaryGenome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

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No genetic evidence for involvement of alcohol dehydrogenase genes in risk for Parkinson’s disease

10.1101/784405 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jonggeol Jeffrey Kim ◽

Sara Bandres-Ciga ◽

Cornelis Blauwendraat ◽

Ziv Gan-Or ◽

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Genetic Variation ◽

Alcohol Dehydrogenase ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Causal Gene ◽

Risk Variants ◽

Genome Wide

AbstractMultiple genes have been implicated in Parkinson’s disease (PD), including causal gene variants and risk variants typically identified using genome-wide association studies (GWAS). Variants in the alcohol dehydrogenase genes ADH1C and ADH1B are among the genes that have been associated with PD, suggesting that this family of genes may be important in PD. As part of the International Parkinson’s Disease Genomics Consortium’s (IPDGC) efforts to scrutinize previously reported risk factors for PD, we explored genetic variation in the alcohol dehydrogenase genes ADH1A, ADH1B, ADH1C, ADH4, ADH5, ADH6, and ADH7 using imputed GWAS data from 15,097 cases and 17,337 healthy controls. Rare-variant association tests and single-variant score tests did not show any statistically significant association of alcohol dehydrogenase genetic variation with the risk for PD.

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Abstract 56: Genome-wide Association Studies of Incident Stroke: The Charge Consortium

Stroke ◽

10.1161/str.45.suppl_1.56 ◽

2014 ◽

Vol 45 (suppl_1) ◽

Author(s):

Stephanie Debette ◽

Ganesh Chauhan ◽

Audrey Chu ◽

Myriam Fornage ◽

Josh C Bis ◽

...

Keyword(s):

Association Studies ◽

Meta Analysis ◽

Acute Stage ◽

Genome Wide Association ◽

Population Substructure ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Large Artery ◽

Aging Research ◽

Genome Wide

Background: Despite a high heritability, only few stroke risk genes are known. Genetic association studies performed in a hospital-based setting may fail to detect genes modulating both stroke susceptibility and severity, given early deaths at the acute stage. This selection bias is avoided when studying incident stroke in a population-based setting. Methods: We conducted a meta-analysis of genome-wide association studies of incident stroke in 11 community-based longitudinal studies from the Cohorts of Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Genome-wide Cox regressions were performed adjusting for age, gender and population substructure, using 1000GpIv3 imputed genotypes. Results were combined using inverse variance weighted meta-analysis. Results: The study sample comprised 65,204 participants (71.5% women) of European ancestry, aged 66.2±8.0 years at DNA draw, followed up for 10.8±3.8 years. In 11 studies, 3,389 participants developed incident stroke, and in 8 studies, 2,223 developed incident ischemic stroke (IS): 531 cardioembolic [CE] and 1,576 atherothrombotic [AT]. The most significant association with incident stroke was for a novel variant on chr9p23 (MAF=0.35), HR=1.15 [95%CI:1.09[[Unable to Display Character: ‒]]1.21], p=8.5х10-8: p=2.54x10-5 for IS; 1.19x10-4, AT-IS; and 0.019, CE-IS. Associations were in the same direction for all participating studies, and 5 additional SNPs in this locus reached p<10-6. The most significant association with incident IS was for rs11833579 [NINJ2], HR=1.21[1.13[[Unable to Display Character: ‒]]1.30], p=2.1х10-7, but p-random-effects=9.54x10-3 (p-heterogeneity=0.02, I2=57.9%). We replicated published associations for CE-IS (rs6843082-G [PITX2], HR=1.30[1.13-1.49], p=1.95x10-4) and for large artery stroke with AT-IS (rs2107595-A [HDAC9], HR=1.13[1.03[[Unable to Display Character: ‒]]1.24], p=0.012) Conclusion: In the largest GWAS of incident stroke, we detected one novel association with all stroke, requiring confirmation in independent samples. Expansion of the discovery sample and replication of findings are planned in the coming months. Detecting genetic variants associated with incident stroke may provide important clues for understanding pathways involved in stroke susceptibility and tolerance to acute vascular brain injury.

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