Browsing by Subject "Candidate genes"

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    Ancestry May Confound Genetic Machine Learning: Candidate-Gene Prediction of Opioid Use Disorder as an Example
    (Elsevier, 2021) Hatoum, Alexander S.; Wendt, Frank R.; Galimberti, Marco; Polimanti, Renato; Neale, Benjamin; Kranzler, Henry R.; Gelernter, Joel; Edenberg, Howard J.; Agrawal, Arpana; Medical and Molecular Genetics, School of Medicine
    Background: Machine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding. Methods: We utilize five ML algorithms trained with 16 brain reward-derived "candidate" SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N = 1000, stratified into equal groups of n = 250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype. Results: None of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction. Conclusions: Researchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants.
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    Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry
    (Wiley, 2019-07) Hsu, Yi-Hsiang; Estrada, Karol; Evangelou, Evangelos; Ackert-Bicknell, Cheryl; Akesson, Kristina; Beck, Thomas; Brown, Suzanne J.; Capellini, Terence; Carbone, Laura; Cauley, Jane; Cheung, Ching-Lung; Cummings, Steven R.; Czerwinski, Stefan; Demissie, Serkalem; Econs, Michael; Evans, Daniel; Farber, Charles; Gautvik, Kaare; Harris, Tamara; Kammerer, Candace; Kemp, John; Koller, Daniel L.; Kung, Annie; Lawlor, Debbie; Lee, Miryoung; Lorentzon, Mattias; McGuigan, Fiona; Medina-Gomez, Carolina; Mitchell, Braxton; Newman, Anne; Nielson, Carrie; Ohlsson, Claes; Peacock, Munro; Reppe, Sjur; Richards, J. Brent; Robbins, John; Sigurdsson, Gunnar; Spector, Timothy D.; Stefansson, Kari; Streeten, Elizabeth; Styrkarsdottir, Unnur; Tobias, Jonathan; Trajanoska, Katerina; Uitterlinden, André; Vandenput, Liesbeth; Wilson, Scott G.; Yerges-Armstrong, Laura; Young, Mariel; Zillikens, Carola; Rivadeneira, Fernando; Kiel, Douglas P.; Karasik, David; Medicine, School of Medicine
    Hip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with ∼2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p ≤ 2.6 × 10-8 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 × 10-5 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12% to 22% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility.