Expressive

rs11652146 - ZNF652

Magnitude 2.0 · 4 studies on file

Reported associations

  • Modeling the genomic architecture of adiposity and anthropometrics across the lifespan - Nature communications (2025) · Arehart CH, Lin M, Gibson RA, Raghavan S, Gignoux CR, Stanislawski MA, Grotzinger AD, Evans LM · PubMed 40796553

    ABSTRACT: Obesity-related conditions are among the leading causes of preventable death and are increasing in prevalence worldwide. Body size and composition are complex traits that are challenging to characterize due to environmental and genetic influences, longitudinal variation, heterogeneity between sexes, and differing health risks based on adipose distribution. Here, we construct a 4-factor genomic structural equation model using 18 measures, unveiling shared and distinct genetic architectures underlying birth size, abdominal size, adipose distribution, and adiposity. Multivariate genome-wide associations reveal the adiposity factor is enriched specifically in neural tissues and pathways, while adipose distribution is enriched more broadly across physiological systems. In addition, po

  • Genomics and phenomics of body mass index reveals a complex disease network - Nature communications (2023) · Huang J, Huffman JE, Huang Y, Do Valle Í, Assimes TL, Raghavan S, Voight BF, Liu C, Barabási AL, Huang RDL, Hui Q, Nguyen XT, Ho YL, Djousse L, Lynch JA, Vujkovic M, Tcheandjieu C, Tang H, Damrauer SM, Reaven PD, Miller D, Phillips LS, Ng MCY, Graff M, Haiman CA, Loos RJF, North KE, Yengo L, Smith GD, Saleheen D, Gaziano JM, Rader DJ, Tsao PS, Cho K, Chang KM, Wilson PWF, Sun YV, O'Donnell CJ · PubMed 36581621

    ABSTRACT: Elevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI

  • Pleiotropic genetic architecture and novel loci for C-reactive protein levels - Nature communications (2022) · Koskeridis F, Evangelou E, Said S, Boyle JJ, Elliott P, Dehghan A, Tzoulaki I · PubMed 36376304

    ABSTRACT: C-reactive protein is involved in a plethora of pathophysiological conditions. Many genetic loci associated with C-reactive protein are annotated to lipid and glucose metabolism genes supporting common biological pathways between inflammation and metabolic traits. To identify novel pleiotropic loci, we perform multi-trait analysis of genome-wide association studies on C-reactive protein levels along with cardiometabolic traits, followed by a series of in silico analyses including colocalization, phenome-wide association studies and Mendelian randomization. We find 41 novel loci and 19 gene sets associated with C-reactive protein with various pleiotropic effects. Additionally, 41 variants colocalize between C-reactive protein and cardiometabolic risk factors and 12 of them display

  • Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits - PLoS genetics (2019) · Qi G, Chatterjee N · 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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