Expressive

rs10143492 - FCF1 - YLPM1

Magnitude 2.2 · 2 studies on file

Reported associations

  • Multi-trait GWAS for diverse ancestries: mapping the knowledge gap - Unknown journal (n.d.) · Unknown authors · PubMed 38627641

    ABSTRACT: Background Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. Methods Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits acro

  • Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder - Unknown journal (n.d.) · Unknown authors · PubMed 37985818

    ABSTRACT: Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its g

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