Browsing by Author "Qin, Xuejun"

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    Assessment of LD matrix measures for the analysis of biological pathway association.
    (Stat Appl Genet Mol Biol, 2010) Crosslin, David R; Qin, Xuejun; Hauser, Elizabeth R
    Complex diseases will have multiple functional sites, and it will be invaluable to understand the cross-locus interaction in terms of linkage disequilibrium (LD) between those sites (epistasis) in addition to the haplotype-LD effects. We investigated the statistical properties of a class of matrix-based statistics to assess this epistasis. These statistical methods include two LD contrast tests (Zaykin et al., 2006) and partial least squares regression (Wang et al., 2008). To estimate Type 1 error rates and power, we simulated multiple two-variant disease models using the SIMLA software package. SIMLA allows for the joint action of up to two disease genes in the simulated data with all possible multiplicative interaction effects between them. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequilibrium (LD) patterns with other markers. We measured the effects of marginal disease effect size, haplotype LD, disease prevalence and minor allele frequency have on cross-locus interaction (epistasis). In the setting of strong allele effects and strong interaction, the correlation between the two disease genes was weak (r=0.2). In a complex system with multiple correlations (both marginal and interaction), it was difficult to determine the source of a significant result. Despite these complications, the partial least squares and modified LD contrast methods maintained adequate power to detect the epistatic effects; however, for many of the analyses we often could not separate interaction from a strong marginal effect. While we did not exhaust the entire parameter space of possible models, we do provide guidance on the effects that population parameters have on cross-locus interaction.
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    Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.
    (Scientific reports, 2021-04-14) Hong, Julian C; Hauser, Elizabeth R; Redding, Thomas S; Sims, Kellie J; Gellad, Ziad F; O'Leary, Meghan C; Hyslop, Terry; Madison, Ashton N; Qin, Xuejun; Weiss, David; Bullard, A Jasmine; Williams, Christina D; Sullivan, Brian A; Lieberman, David; Provenzale, Dawn
    Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.
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    GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.
    (The American journal of psychiatry, 2023-10) Docherty, Anna R; Mullins, Niamh; Ashley-Koch, Allison E; Qin, Xuejun; Coleman, Jonathan RI; Shabalin, Andrey; Kang, JooEun; Murnyak, Balasz; Wendt, Frank; Adams, Mark; Campos, Adrian I; DiBlasi, Emily; Fullerton, Janice M; Kranzler, Henry R; Bakian, Amanda V; Monson, Eric T; Rentería, Miguel E; Walss-Bass, Consuelo; Andreassen, Ole A; Behera, Chittaranjan; Bulik, Cynthia M; Edenberg, Howard J; Kessler, Ronald C; Mann, J John; Nurnberger, John I; Pistis, Giorgio; Streit, Fabian; Ursano, Robert J; Polimanti, Renato; Dennis, Michelle; Garrett, Melanie; Hair, Lauren; Harvey, Philip; Hauser, Elizabeth R; Hauser, Michael A; Huffman, Jennifer; Jacobson, Daniel; Madduri, Ravi; McMahon, Benjamin; Oslin, David W; Trafton, Jodie; Awasthi, Swapnil; Berrettini, Wade H; Bohus, Martin; Chang, Xiao; Chen, Hsi-Chung; Chen, Wei J; Christensen, Erik D; Crow, Scott; Duriez, Philibert; Edwards, Alexis C; Fernández-Aranda, Fernando; Galfalvy, Hanga; Gandal, Michael; Gorwood, Philip; Guo, Yiran; Hafferty, Jonathan D; Hakonarson, Hakon; Halmi, Katherine A; Hishimoto, Akitoyo; Jain, Sonia; Jamain, Stéphane; Jiménez-Murcia, Susana; Johnson, Craig; Kaplan, Allan S; Kaye, Walter H; Keel, Pamela K; Kennedy, James L; Kim, Minsoo; Klump, Kelly L; Levey, Daniel F; Li, Dong; Liao, Shih-Cheng; Lieb, Klaus; Lilenfeld, Lisa; Marshall, Christian R; Mitchell, James E; Okazaki, Satoshi; Otsuka, Ikuo; Pinto, Dalila; Powers, Abigail; Ramoz, Nicolas; Ripke, Stephan; Roepke, Stefan; Rozanov, Vsevolod; Scherer, Stephen W; Schmahl, Christian; Sokolowski, Marcus; Starnawska, Anna; Strober, Michael; Su, Mei-Hsin; Thornton, Laura M; Treasure, Janet; Ware, Erin B; Watson, Hunna J; Witt, Stephanie H; Woodside, D Blake; Yilmaz, Zeynep; Zillich, Lea; Adolfsson, Rolf; Agartz, Ingrid; Alda, Martin; Alfredsson, Lars; Appadurai, Vivek; Artigas, María Soler; Van der Auwera, Sandra; Azevedo, M Helena; Bass, Nicholas; Bau, Claiton HD; Baune, Bernhard T; Bellivier, Frank; Berger, Klaus; Biernacka, Joanna M; Bigdeli, Tim B; Binder, Elisabeth B; Boehnke, Michael; Boks, Marco P; Braff, David L; Bryant, Richard; Budde, Monika; Byrne, Enda M; Cahn, Wiepke; Castelao, Enrique; Cervilla, Jorge A; Chaumette, Boris; Corvin, Aiden; Craddock, Nicholas; Djurovic, Srdjan; Foo, Jerome C; Forstner, Andreas J; Frye, Mark; Gatt, Justine M; Giegling, Ina; Grabe, Hans J; Green, Melissa J; Grevet, Eugenio H; Grigoroiu-Serbanescu, Maria; Gutierrez, Blanca; Guzman-Parra, Jose; Hamshere, Marian L; Hartmann, Annette M; Hauser, Joanna; Heilmann-Heimbach, Stefanie; Hoffmann, Per; Ising, Marcus; Jones, Ian; Jones, Lisa A; Jonsson, Lina; Kahn, René S; Kelsoe, John R; Kendler, Kenneth S; Kloiber, Stefan; Koenen, Karestan C; Kogevinas, Manolis; Krebs, Marie-Odile; Landén, Mikael; Leboyer, Marion; Lee, Phil H; Levinson, Douglas F; Liao, Calwing; Lissowska, Jolanta; Mayoral, Fermin; McElroy, Susan L; McGrath, Patrick; McGuffin, Peter; McQuillin, Andrew; Mehta, Divya; Melle, Ingrid; Mitchell, Philip B; Molina, Esther; Morken, Gunnar; Nievergelt, Caroline; Nöthen, Markus M; O'Donovan, Michael C; Ophoff, Roel A; Owen, Michael J; Pato, Carlos; Pato, Michele T; Penninx, Brenda WJH; Potash, James B; Power, Robert A; Preisig, Martin; Quested, Digby; Ramos-Quiroga, Josep Antoni; Reif, Andreas; Ribasés, Marta; Richarte, Vanesa; Rietschel, Marcella; Rivera, Margarita; Roberts, Andrea; Roberts, Gloria; Rouleau, Guy A; Rovaris, Diego L; Sanders, Alan R; Schofield, Peter R; Schulze, Thomas G; Scott, Laura J; Serretti, Alessandro; Shi, Jianxin; Sirignano, Lea; Sklar, Pamela; Smeland, Olav B; Smoller, Jordan W; Sonuga-Barke, Edmund JS; Trzaskowski, Maciej; Tsuang, Ming T; Turecki, Gustavo; Vilar-Ribó, Laura; Vincent, John B; Völzke, Henry; Walters, James TR; Weickert, Cynthia Shannon; Weickert, Thomas W; Weissman, Myrna M; Williams, Leanne M; Wray, Naomi R; Zai, Clement C; Agerbo, Esben; Børglum, Anders D; Breen, Gerome; Demontis, Ditte; Erlangsen, Annette; Gelernter, Joel; Glatt, Stephen J; Hougaard, David M; Hwu, Hai-Gwo; Kuo, Po-Hsiu; Lewis, Cathryn M; Li, Qingqin S; Liu, Chih-Min; Martin, Nicholas G; McIntosh, Andrew M; Medland, Sarah E; Mors, Ole; Nordentoft, Merete; Olsen, Catherine M; Porteous, David; Smith, Daniel J; Stahl, Eli A; Stein, Murray B; Wasserman, Danuta; Werge, Thomas; Whiteman, David C; Willour, Virginia; VA Million Veteran Program (MVP); MVP Suicide Exemplar Workgroup; Suicide Working Group of the Psychiatric Genomics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Eating Disorder Working Group of the Psychiatric Genomics Consortium; German Borderline Genomics Consortium; Coon, Hilary; Beckham, Jean C; Kimbrel, Nathan A; Ruderfer, Douglas M

    Objective

    Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures.

    Methods

    This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses.

    Results

    Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors.

    Conclusions

    This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.
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    Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis.
    (PLoS Genet, 2015-11) Kraus, William E; Muoio, Deborah M; Stevens, Robert; Craig, Damian; Bain, James R; Grass, Elizabeth; Haynes, Carol; Kwee, Lydia; Qin, Xuejun; Slentz, Dorothy H; Krupp, Deidre; Muehlbauer, Michael; Hauser, Elizabeth R; Gregory, Simon G; Newgard, Christopher B; Shah, Svati H
    Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10-6-2.3x10-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk.