We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.The summary statistics are available at https://www.thessgac.org/data.
The Social Science Genetic Association Consortium
We, the Social Science Genetic Association Consortium (SSGAC), aim to bring together the expertise of medical geneticists and social scientists to study how a range of health-relevant outcomes are influenced by specific genetic variants, the environment (including lifestyle), and their interaction. In accessing the U.K. Biobank data, we are specifically interested in the following health-relevant outcomes: cognitive function, dementia, depression, smoking, and alcohol drinking. Our research will contribute to quantifying how several risk factors (e.g. lifestyle, environment, genes), both separately and in combination, influence public health and well-being. Incorporating insights from the social sciences and investigating social scientific outcomes helps to achieve this objective. For example, a GWAS on subjective well-being in a very large sample could identify genetic factors associated with (absence of) depression that would not be possible to identify by studying depression directly in a much smaller sample. Furthermore, accurate polygenic risk scores can be used to study how lifestyle and environmental factors mediate genetic effects on health. We will use several methods, e.g.:
? Genomewide association studies (GWAS) that aim to identify individual genetic variants associated with a particular outcome.
? GWAS of a ?proxy phenotype??a biologically-distal phenotype available in larger samples?to identify candidate genetic variants for association with a health-relevant outcome available in smaller samples.
? Estimation of economic and statistical models of health-relevant outcomes as a function of genetic variants, environmental factors, and their interaction. We will typically use all available observations in the UKB that (i) are of European decent, (ii) have been successfully genotyped, and (iii) have measures of the phenotype(s) under investigation.
|Lead investigator:||Professor Daniel Benjamin|
|Lead institution:||National Bureau of Economic Research|
6 related Returns
|Return ID||App ID||Description||Archive Date|
|3065||11425||Are Bigger Brains Smarter? Evidence From a Large-Scale Preregistered Study||14 Dec 2020|
|717||11425||Gemome-wide association study identifies 74 loci associated with educational attainment||17 Oct 2017|
|718||11425||Genetic variants associated with subjective well-being, depressive symptoms,and neuroticism identified through genome-wide analysis||17 Oct 2017|
|2159||11425||Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences||7 Apr 2020|
|1782||11425||Pleiotropy-robust Mendelian Randomization||30 Sep 2019|
|3586||11425||Resource Profile and User Guide of the Polygenic Index Repository||24 Jun 2021|
|2888||Multi-trait analysis of genome-wide association summary statistics using MTAG||Turley et al||2018||Nature Genetics (2018)|