Browsing by Author "Kos, Mark Z."

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    Common biological networks underlie genetic risk for alcoholism in African- and European-American populations
    (Wiley, 2013) Kos, Mark Z.; Yan, Jia; Dick, Danielle M.; Agrawal, Arpana; Bucholz, Kathleen K.; Rice, John P.; Johnson, Eric O.; Schuckit, Marc; Kuperman, Sam; Kramer, John; Goate, Alison M.; Tischfield, Jay A.; Foroud, Tatiana; Nurnberger, John, Jr.; Hesselbrock, Victor; Porjesz, Bernice; Bierut, Laura J.; Edenberg, Howard J.; Almasy, Laura; Medical and Molecular Genetics, School of Medicine
    Alcohol dependence (AD) is a heritable substance addiction with adverse physical and psychological consequences, representing a major health and economic burden on societies worldwide. Genes thus far implicated via linkage, candidate gene and genome-wide association studies (GWAS) account for only a small fraction of its overall risk, with effects varying across ethnic groups. Here we investigate the genetic architecture of alcoholism and report on the extent to which common, genome-wide SNPs collectively account for risk of AD in two US populations, African-Americans (AAs) and European-Americans (EAs). Analyzing GWAS data for two independent case-control sample sets, we compute polymarker scores that are significantly associated with alcoholism (P = 1.64 × 10(-3) and 2.08 × 10(-4) for EAs and AAs, respectively), reflecting the small individual effects of thousands of variants derived from patterns of allelic architecture that are population specific. Simulations show that disease models based on rare and uncommon causal variants (MAF < 0.05) best fit the observed distribution of polymarker signals. When scoring bins were annotated for gene location and examined for constituent biological networks, gene enrichment is observed for several cellular processes and functions in both EA and AA populations, transcending their underlying allelic differences. Our results reveal key insights into the complex etiology of AD, raising the possibility of an important role for rare and uncommon variants, and identify polygenic mechanisms that encompass a spectrum of disease liability, with some, such as chloride transporters and glycine metabolism genes, displaying subtle, modifying effects that are likely to escape detection in most GWAS designs.
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    Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence
    (Wiley Blackwell (Blackwell Publishing), 2014-07) Yan, Jia; Aliev, Fazil; Webb, Bradley T.; Kendler, Kenneth S.; Williamson, Vernell S.; Edenberg, Howard J.; Agrawal, Arpana; Kos, Mark Z.; Almasy, Laura; Nurnberger, John I.; Schuckit, Marc A.; Kramer, John R.; Rice, John P.; Kuperman, Samuel; Goate, Alison M.; Tischfield, Jay A.; Porjesz, Bernice; Dick, Danielle M.; Department of Biochemistry and Molecular Biology, IU School of Medicine
    Family-based and genome-wide association studies (GWAS) of alcohol dependence (AD) have reported numerous associated variants. The clinical validity of these variants for predicting AD compared with family history information has not been reported. Using the Collaborative Study on the Genetics of Alcoholism (COGA) and the Study of Addiction: Genes and Environment (SAGE) GWAS samples, we examined the aggregate impact of multiple single nucleotide polymorphisms (SNPs) on risk prediction. We created genetic sum scores by adding risk alleles associated in discovery samples, and then tested the scores for their ability to discriminate between cases and controls in validation samples. Genetic sum scores were assessed separately for SNPs associated with AD in candidate gene studies and SNPs from GWAS analyses that met varying P-value thresholds. Candidate gene sum scores did not exhibit significant predictive accuracy. Family history was a better classifier of case-control status, with a significant area under the receiver operating characteristic curve (AUC) of 0.686 in COGA and 0.614 in SAGE. SNPs that met less stringent P-value thresholds of 0.01-0.50 in GWAS analyses yielded significant AUC estimates, ranging from mean estimates of 0.549 for SNPs with P < 0.01 to 0.565 for SNPs with P < 0.50. This study suggests that SNPs currently have limited clinical utility, but there is potential for enhanced predictive ability with better understanding of the large number of variants that might contribute to risk.