Browsing by Author "Carey, Caitlin E."

Now showing 1 - 4 of 4
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    Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations
    (Elsevier, 2019-10-10) Peterson, Roseann E.; Kuchenbaecker, Karoline; Walters, Raymond K.; Chen, Chia-Yen; Popejoy, Alice B.; Periyasamy, Sathish; Lam, Max; Iyegbe, Conrad; Strawbridge, Rona J.; Brick, Leslie; Carey, Caitlin E.; Martin, Alicia R.; Meyers, Jacquelyn L.; Su, Jinni; Chen, Junfang; Edwards, Alexis C.; Kalungi, Allan; Koen, Nastassja; Majara, Lerato; Schwarz, Emanuel; Smoller, Jordan W.; Stahl, Eli A.; Sullivan, Patrick F.; Vassos, Evangelos; Mowry, Bryan; Prieto, Miguel L.; Cuellar-Barboza, Alfredo; Bigdeli, Tim B.; Edenberg, Howard J.; Huang, Hailiang; Duncan, Laramie E.; Biochemistry and Molecular Biology, School of Medicine
    Genome-wide association studies (GWAS) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.
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    Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria
    (Wiley, 2019-06-04) Lai, Dongbing; Wetherill, Leah; Bertelsen, Sarah; Carey, Caitlin E.; Kamarajan, Chella; Kapoor, Manav; Meyers, Jacquelyn L.; Anokhin, Andrey P.; Bennett, David A.; Bucholz, Kathleen K.; Chang, Katharine K.; Jager, Philip L. De; Dick, Danielle M.; Hesselbrock, Victor; Kramer, John; Kuperman, Samuel; Nurnberger, John I.; Raj, Towfique; Schuckit, Marc; Scott, Denise M.; Taylor, Robert E.; Tischfield, Jay; Hariri, Ahmad R.; Edenberg, Howard J.; Agrawal, Arpana; Bogdan, Ryan; Porjesz, Bernice; Goate, Alison M.; Foroud, Tatiana; Medical and Molecular Genetics, School of Medicine
    Genome-wide association studies (GWAS) of alcohol dependence (AD) have reliably identified variation within alcohol metabolizing genes (e.g., ADH1B) but have inconsistently located other signals, which may be partially attributable to symptom heterogeneity underlying the disorder. We conducted GWASs of DSM-IV AD (primary analysis), DSM-IV AD criterion count (secondary analysis), and individual dependence criteria (tertiary analysis) among 7,418 (1,121 families) European American (EA) individuals from the Collaborative Study on the Genetics of Alcoholism (COGA). Trans-ancestral meta-analyses combined these results with data from 3,175 (585 families) African American (AA) individuals from COGA. In the EA GWAS, three loci were genome-wide significant: rs1229984 in ADH1B for AD criterion count (p=4.16E-11) and Desire to cut drinking (p=1.21E-11); rs188227250 (chromosome 8, Drinking more than intended, p=6.72E-09); rs1912461 (chromosome 15, Time spent drinking, p=1.77E-08). In the trans-ancestral meta-analysis, rs1229984 was associated with multiple phenotypes and two additional loci were genome-wide significant: rs61826952 (chromosome 1, DSM-IV AD, p=8.42E-11); rs7597960 (chromosome 2, Time spent drinking, p=1.22E-08). Associations with rs1229984 and rs18822750 were replicated in independent datasets. Polygenic risk scores derived from the EA GWAS of AD predicted AD in two EA datasets (p<0.01; 0.61-1.82% of variance). Identified novel variants (i.e., rs1912461, rs61826952) were associated with differential central evoked theta power (loss minus gain; p=0.0037) and reward-related ventral striatum reactivity (p=0.008), respectively. This study suggests that studying individual criteria may unveil new insights into the genetic etiology of AD liability.
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    Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci
    (Springer Nature, 2023) Mignogna, Gianmarco; Carey, Caitlin E.; Wedow, Robbee; Baya, Nikolas; Cordioli, Mattia; Pirastu, Nicola; Bellocco, Rino; Fiuza Malerbi, Kathryn; Nivard, Michel G.; Neale, Benjamin M.; Walters, Raymond K.; Ganna, Andrea; Medical and Molecular Genetics, School of Medicine
    Response to survey questionnaires is vital for social and behavioural research, and most analyses assume full and accurate response by participants. However, nonresponse is common and impedes proper interpretation and generalizability of results. We examined item nonresponse behaviour across 109 questionnaire items in the UK Biobank (N = 360,628). Phenotypic factor scores for two participant-selected nonresponse answers, 'Prefer not to answer' (PNA) and 'I don't know' (IDK), each predicted participant nonresponse in follow-up surveys (incremental pseudo-R2 = 0.056), even when controlling for education and self-reported health (incremental pseudo-R2 = 0.046). After performing genome-wide association studies of our factors, PNA and IDK were highly genetically correlated with one another (rg = 0.73 (s.e. = 0.03)) and with education (rg,PNA = -0.51 (s.e. = 0.03); rg,IDK = -0.38 (s.e. = 0.02)), health (rg,PNA = 0.51 (s.e. = 0.03); rg,IDK = 0.49 (s.e. = 0.02)) and income (rg,PNA = -0.57 (s.e. = 0.04); rg,IDK = -0.46 (s.e. = 0.02)), with additional unique genetic associations observed for both PNA and IDK (P < 5 × 10-8). We discuss how these associations may bias studies of traits correlated with item nonresponse and demonstrate how this bias may substantially affect genome-wide association studies. While the UK Biobank data are deidentified, we further protected participant privacy by avoiding exploring non-response behaviour to single questions, assuring that no information can be used to associate results with any particular respondents.
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    Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation
    (Springer Nature, 2024) Carey, Caitlin E.; Shafee, Rebecca; Wedow, Robbee; Elliott, Amanda; Palmer, Duncan S.; Compitello, John; Kanai, Masahiro; Abbott, Liam; Schultz, Patrick; Karczewski, Konrad J.; Bryant, Samuel C.; Cusick, Caroline M.; Churchhouse, Claire; Howrigan, Daniel P.; King, Daniel; Smith, George Davey; Neale, Benjamin M.; Walters, Raymond K.; Robinson, Elise B.; Medical and Molecular Genetics, School of Medicine
    Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.