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A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence.

ABSTRACT: Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (r_g?=?0.70). We used these findings as foundations for our use of a novel approach-multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)-to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination-as well as genes expressed in the synapse, and those involved in the regulation of the nervous system-may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.

SUBMITTER: Hill WD

PROVIDER: S-EPMC6344370 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence.

Hill W D WD Marioni R E RE Maghzian O O Ritchie S J SJ Hagenaars S P SP McIntosh A M AM Gale C R CR Davies G G Deary I J IJ

Molecular psychiatry 20180111 2

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (r<sub>g</sub> = 0.70). We used these findings as foundations for our use of a novel approach-multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)-to combine two large genome-wide association studies (GWASs) of education and intellige ...[more]

PMID: 29326435

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Project description:BackgroundFor years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a "combined" SS (CSS) evaluation that extends the "combined" BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits (somatic cell score, clinical mastitis, conception rate for heifers and cows, and udder depth) is added.ResultsGEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival.ConclusionsA CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a "combined" evaluation of functional traits is more modest for these males.

Dataset Information

A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence.

Publications

A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence.

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