Expressive

rs11610143 - ACVR1B

Magnitude 2.2 · 3 studies on file

Reported associations

  • Multivariate genome-wide analyses of the well-being spectrum. - Nature genetics (2019) · Baselmans BML, Jansen R, Ip HF, van Dongen J, Abdellaoui A, van de Weijer MP, Bao Y, Smart M, Kumari M, Willemsen G, Hottenga JJ, Boomsma DI, de Geus EJC, Nivard MG, Bartels M · PubMed 30643256

    We introduce two novel methods for multivariate genome-wide-association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMAs and in an ~57% increase in the predictive power of polygenic risk scores. Supporting transcriptome- and methylome-wide analyses (TWAS and MWAS, respectively) uncovered an addition

  • Meta-analysis of genome-wide association studies identifies multiple lung cancer susceptibility loci in never-smoking Asian women. - Human molecular genetics (2016) · Wang Z, Seow WJ, Shiraishi K, Hsiung CA, Matsuo K, Liu J, Chen K, Yamji T, Yang Y, Chang IS, Wu C, Hong YC, Burdett L, Wyatt K, Chung CC, Li SA, Yeager M, Hutchinson A, Hu W, Caporaso N, Landi MT, Chatterjee N, Song M, Fraumeni JF, Kohno T, Yokota J, Kunitoh H, Ashikawa K, Momozawa Y, Daigo Y, Mitsudomi T, Yatabe Y, Hida T, Hu Z, Dai J, Ma H, Jin G, Song B, Wang Z, Cheng S, Yin Z, Li X, Ren Y, Guan P, Chang J, Tan W, Chen CJ, Chang GC, Tsai YH, Su WC, Chen KY, Huang MS, Chen YM, Zheng H, Li H, Cui P, Guo H, Xu P, Liu L, Iwasaki M, Shimazu T, Tsugane S, Zhu J, Jiang G, Fei K, Park JY, Kim YH, Sung JS, Park KH, Kim YT, Jung YJ, Kang CH, Park IK, Kim HN, Jeon HS, Choi JE, Choi YY, Kim JH, Oh IJ, Kim YC, Sung SW, Kim JS, Yoon HI, Kweon SS, Shin MH, Seow A, Chen Y, Lim WY, Liu J, Wong MP, Lee VH, Bassig BA, Tucker M, Berndt SI, Chow WH, Ji BT, Wang J, Xu J, Sihoe AD, Ho JC, Chan JK, Wang JC, Lu D, Zhao X, Zhao Z, Wu J, Chen H, Jin L, Wei F, Wu G, An SJ, Zhang XC, Su J, Wu YL, Gao YT, Xiang YB, He X, Li J, Zheng W, Shu XO, Cai Q, Klein R, Pao W, Lawrence C, Hosgood HD, Hsiao CF, Chien LH, Chen YH, Chen CH, Wang WC, Chen CY, Wang CL, Yu CJ, Chen HL, Su YC, Tsai FY, Chen YS, Li YJ, Yang TY, Lin CC, Yang PC, Wu T, Lin D, Zhou B, Yu J, Shen H, Kubo M, Chanock SJ, Rothman N, Lan Q · PubMed 26732429

    Genome-wide association studies (GWAS) of lung cancer in Asian never-smoking women have previously identified six susceptibility loci associated with lung cancer risk. To further discover new susceptibility loci, we imputed data from four GWAS of Asian non-smoking female lung cancer (6877 cases and 6277 controls) using the 1000 Genomes Project (Phase 1 Release 3) data as the reference and genotyped additional samples (5878 cases and 7046 controls) for possible replication. In our meta-analysis, three new loci achieved genome-wide significance, marked by single nucleotide polymorphism (SNP) rs7741164 at 6p21.1 (per-allele odds ratio (OR) = 1.17; P = 5.8 × 10(-13)), rs72658409 at 9p21.3 (per-allele OR = 0.77; P = 1.41 × 10(-10)) and rs11610143 at 12q13.13 (per-allele OR = 0.89; P = 4.96 ×

  • Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits - Unknown journal (n.d.) · Unknown authors · PubMed 30289880

    ABSTRACT: Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed m

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