Browsing by Author "Crosslin, David R"

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    An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease.
    (Cell host & microbe, 2018-08) Wang, Liuyang; Pittman, Kelly J; Barker, Jeffrey R; Salinas, Raul E; Stanaway, Ian B; Williams, Graham D; Carroll, Robert J; Balmat, Tom; Ingham, Andy; Gopalakrishnan, Anusha M; Gibbs, Kyle D; Antonia, Alejandro L; eMERGE Network; Heitman, Joseph; Lee, Soo Chan; Jarvik, Gail P; Denny, Joshua C; Horner, Stacy M; DeLong, Mark R; Valdivia, Raphael H; Crosslin, David R; Ko, Dennis C
    Pathogens have been a strong driving force for natural selection. Therefore, understanding how human genetic differences impact infection-related cellular traits can mechanistically link genetic variation to disease susceptibility. Here we report the Hi-HOST Phenome Project (H2P2): a catalog of cellular genome-wide association studies (GWAS) comprising 79 infection-related phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance for infection-associated phenotypes ranging from pathogen replication to cytokine production. We combined H2P2 with clinical association data from patients to identify a SNP near CXCL10 as a risk factor for inflammatory bowel disease. A SNP in the transcriptional repressor ZBTB20 demonstrated pleiotropy, likely through suppression of multiple target genes, and was associated with viral hepatitis. These data are available on a web portal to facilitate interpreting human genome variation through the lens of cell biology and should serve as a rich resource for the research community.
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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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    Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events.
    (Circ Cardiovasc Genet, 2010-04) Shah, Svati H; Bain, James R; Muehlbauer, Michael J; Stevens, Robert D; Crosslin, David R; Haynes, Carol; Dungan, Jennifer; Newby, L Kristin; Hauser, Elizabeth R; Ginsburg, Geoffrey S; Newgard, Christopher B; Kraus, William E
    BACKGROUND: Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events. METHODS AND RESULTS: We performed mass-spectrometry-based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls ("initial"), and 140 CAD cases and 140 controls ("replication"). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined ("event" group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled ("event-replication" group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis-derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P=0.002, replication P=0.01), and 1 comprising urea cycle metabolites (factor 9, initial P=0.0004, replication P=0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P=0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P=0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P=0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P=0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P=0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P=0.01). CONCLUSIONS: Metabolite profiles are associated with CAD and subsequent cardiovascular events.
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    Detectable clonal mosaicism from birth to old age and its relationship to cancer.
    (Nature genetics, 2012-05-06) Laurie, Cathy C; Laurie, Cecelia A; Rice, Kenneth; Doheny, Kimberly F; Zelnick, Leila R; McHugh, Caitlin P; Ling, Hua; Hetrick, Kurt N; Pugh, Elizabeth W; Amos, Chris; Wei, Qingyi; Wang, Li-e; Lee, Jeffrey E; Barnes, Kathleen C; Hansel, Nadia N; Mathias, Rasika; Daley, Denise; Beaty, Terri H; Scott, Alan F; Ruczinski, Ingo; Scharpf, Rob B; Bierut, Laura J; Hartz, Sarah M; Landi, Maria Teresa; Freedman, Neal D; Goldin, Lynn R; Ginsburg, David; Li, Jun; Desch, Karl C; Strom, Sara S; Blot, William J; Signorello, Lisa B; Ingles, Sue A; Chanock, Stephen J; Berndt, Sonja I; Le Marchand, Loic; Henderson, Brian E; Monroe, Kristine R; Heit, John A; de Andrade, Mariza; Armasu, Sebastian M; Regnier, Cynthia; Lowe, William L; Hayes, M Geoffrey; Marazita, Mary L; Feingold, Eleanor; Murray, Jeffrey C; Melbye, Mads; Feenstra, Bjarke; Kang, Jae H; Wiggs, Janey L; Jarvik, Gail P; McDavid, Andrew N; Seshan, Venkatraman E; Mirel, Daniel B; Crenshaw, Andrew; Sharopova, Nataliya; Wise, Anastasia; Shen, Jess; Crosslin, David R; Levine, David M; Zheng, Xiuwen; Udren, Jenna I; Bennett, Siiri; Nelson, Sarah C; Gogarten, Stephanie M; Conomos, Matthew P; Heagerty, Patrick; Manolio, Teri; Pasquale, Louis R; Haiman, Christopher A; Caporaso, Neil; Weir, Bruce S
    We detected clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells with the same abnormal karyotype (>5-10%; presumably of clonal origin) in the presence of normal cells. The frequency of detectable clonal mosaicism in peripheral blood is low (<0.5%) from birth until 50 years of age, after which it rapidly rises to 2-3% in the elderly. Many of the mosaic anomalies are characteristic of those found in hematological cancers and identify common deleted regions with genes previously associated with these cancers. Although only 3% of subjects with detectable clonal mosaicism had any record of hematological cancer before DNA sampling, those without a previous diagnosis have an estimated tenfold higher risk of a subsequent hematological cancer (95% confidence interval = 6-18).