rs10498671 - BMP6

Magnitude 2.2 · 2 studies on file

Reported associations

  • Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction. - Nature genetics (2026) · He Y, Lu W, Jee YH, Shih MY, Wang Y, Tsuo K, Qian DC, Diao JA, Huang H, Patel CJ, Byun J, Pasaniuc B, Atkinson EG, Amos CI, Feng YA, Moll M, Cho MH, Martin AR · PubMed 41565855

    While respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma share many risk factors, most studies investigate them in isolation and in predominantly European-ancestry populations. Here, we conducted the most powerful multi-trait and multi-ancestry genetic analysis of respiratory diseases and auxiliary traits to date, identifying 25 new loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross-trait and cross-ancestry), a multi-trait and multi-ancestry polygenic risk score (PRS) approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD and lung cancer compared to trait- and ancestry-matched PRSs in a multi-an

  • A saturated map of common genetic variants associated with human height - Unknown journal (n.d.) · Unknown authors · PubMed 36224396

    ABSTRACT: Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation


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