Expressive

rs12346772 - RNY4P18 - MIR7702

Magnitude 2.2 · 2 studies on file

Reported associations

  • Trans-ethnic meta-analysis of white blood cell phenotypes. - Human molecular genetics (2015) · Keller MF, Reiner AP, Okada Y, van Rooij FJ, Johnson AD, Chen MH, Smith AV, Morris AP, Tanaka T, Ferrucci L, Zonderman AB, Lettre G, Harris T, Garcia M, Bandinelli S, Qayyum R, Yanek LR, Becker DM, Becker LC, Kooperberg C, Keating B, Reis J, Tang H, Boerwinkle E, Kamatani Y, Matsuda K, Kamatani N, Nakamura Y, Kubo M, Liu S, Dehghan A, Felix JF, Hofman A, Uitterlinden AG, van Duijn CM, Franco OH, Longo DL, Singleton AB, Psaty BM, Evans MK, Cupples LA, Rotter JI, O'Donnell CJ, Takahashi A, Wilson JG, Ganesh SK, Nalls MA · PubMed 25096241

    White blood cell (WBC) count is a common clinical measure used as a predictor of certain aspects of human health, including immunity and infection status. WBC count is also a complex trait that varies among individuals and ancestry groups. Differences in linkage disequilibrium structure and heterogeneity in allelic effects are expected to play a role in the associations observed between populations. Prior genome-wide association study (GWAS) meta-analyses have identified genomic loci associated with WBC and its subtypes, but much of the heritability of these phenotypes remains unexplained. Using GWAS summary statistics for over 50 000 individuals from three diverse populations (Japanese, African-American and European ancestry), a Bayesian model methodology was employed to account for heter

  • Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits - Unknown journal (n.d.) · Unknown authors · PubMed 40220762

    ABSTRACT: Summary Genome-wide association studies (GWASs) of high-dimensional traits, such as blood cell or metabolic traits, often use univariate approaches, ignoring trait relationships. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through a GWAS of latent factors. Here, we introduce flashfmZero, a zero-correlation latent-factor-based multi-trait fine-mapping approach. In an application to 25 latent factors derived from 99 blood cell traits in the INTERVAL cohort, we show that latent factor GWASs enable the detection of signals generating sub-threshold associations with several blood cell traits. The 99% credible sets (CS99) from flashfmZero were equal to or smaller in size than those from univariate fine-mapping of blood cell trait

Auto-generated from study metadata. AI-synthesised commentary is added when this entry is regenerated through content-service's LLM mode.