Expressive

rs11934532 - LINC00290 - LINC02500

Magnitude 2.2 · 2 studies on file

Reported associations

  • Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction - Unknown journal (n.d.) · Unknown authors · PubMed 34446935

    ABSTRACT: Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior, and ADHD, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scor

  • Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals - Unknown journal (n.d.) · Unknown authors · PubMed 35361970

    ABSTRACT: We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significan

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