Expressive

rs1027407 - GPM6A, GPM6A-DT

Magnitude 2.0 · 2 studies on file

Reported associations

  • Gene-level analysis reveals the genetic aetiology and therapeutic targets of schizophrenia. - Nature human behaviour (2025) · Dang X, Teng Z, Yang Y, Li W, Liu J, Hui L, Zhou D, Gong D, Dai SS, Li Y, Li X, Lv L, Zeng Y, Yuan Y, Ma X, Liu Z, Li T, Luo XJ · PubMed 39753749

    Genome-wide association studies (GWASs) have reported multiple risk loci for schizophrenia (SCZ). However, the majority of the associations were from populations of European ancestry. Here we conducted a large-scale GWAS in Eastern Asian populations (29,519 cases and 44,392 controls) and identified ten Eastern Asian-specific risk loci, two of which have not been previously reported. A further cross-ancestry GWAS meta-analysis (96,806 cases and 492,818 controls) including populations from diverse ancestries identified 61 previously unreported risk loci. Systematic variant-level analysis, including fine mapping, functional genomics and expression quantitative trait loci, prioritized potential causal variants. Gene-level analyses, including transcriptome-wide association study, proteome-wide

  • 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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