Biomedical subjects

Kevin R Viel

Publications and source records attributed to Kevin R Viel.

3 recordsLinked to original sources

PubMed2007

A sequence variation scan of the coagulation factor VIII (FVIII) structural gene and associations with plasma FVIII activity levels.

Plasma factor VIII coagulant activity (FVIII:C) level is a highly heritable quantitative trait that is strongly correlated with thrombosis risk. Polymorphisms within only 1 gene, the ABO blood-group locus, have been unequivocally demonstrated to contribute to the broad population variability observed for this trait. Because less than 2.5% of the structural FVIII gene (F8) has been examined previously, we resequenced all known functional regions in 222 potentially distinct alleles from 137 unrelated nonhemophilic individuals representing 7 racial groups. Eighteen of the 47 variants identified, including 17 single-nucleotide polymorphisms (SNPs), were previously unknown. As the degree of linkage disequilibrium across F8 was weak overall, we used measured-genotype association analysis to evaluate the influence of each polymorphism on the FVIII:C levels in 398 subjects from 21 pedigrees known as the Genetic Analysis of Idiopathic Thrombophilia project (GAIT). Our results suggested that 92714C>G, a nonsynonymous SNP encoding the B-domain substitution D1241E, was significantly associated with FVIII:C level. After accounting for important covariates, including age and ABO genotype, the association persisted with each C-allele additively increasing the FVIII:C level by 14.3 IU dL(-1) (P = .016). Nevertheless, because the alleles of 56010G>A, a SNP within the 3' splice junction of intron 7, are strongly associated with 92714C>G in GAIT, additional studies are required to determine whether D1241E is itself a functional variant.

ABO Blood-Group System↗

PubMed2005

A comparison of discrete versus continuous environment in a variance components-based linkage analysis of the COGA data.

BACKGROUND: The information content of a continuous variable exceeds that of its categorical counterpart. The parameterization of a model may diminish the benefit of using a continuous variable. We explored the use of continuous versus discrete environment in variance components based analyses examining gene x environment interaction in the electrophysiological phenotypes from the Collaborative Study on the Genetics of Alcoholism. RESULTS: The parameterization using the continuous environment produced a greater number of significant gene x environment interactions and lower AICs (Akaike's information criterion). In these cases, the genetic variance increased with increasing cigarette pack-years, the continuous environment of interest. This did not, however, result in enhanced LOD scores when linkage analyses incorporated the gene x continuous environment interaction. CONCLUSION: Alternative parameterizations may better represent the functional relationship between the continuous environment and the genetic variance.

Alcoholism↗

PubMed2005

Dissection of heterogeneous phenotypes for quantitative trait mapping.

We discuss analyses of Genetic Analysis Workshop 14 data from the Collaborative Study on the Genetics of Alcoholism (COGA) as well as from a simulated complex disease, Kofendrerd personality disorder (KPD), with both genetic and phenotypic heterogeneity. Both data sets included numerous related phenotypes in addition to disease definitions. All analyses either chose from the given selection of phenotypes or defined new ones, including traits that may not have been related to alcoholism or KPD. Some contributors evaluated the genetic components of the trait. Many investigated genome-wide linkage and/or association, using microsatellites and/or single-nucleotide polymorphism (SNP) chip data. Here we will focus on methodological issues that the investigators faced. Their results depended on phenotype selection, whether continuous or discrete, the covariates included, and ethnicity of the study population. For SNP chip data, members of our group detected no difference in results for Affymetrix or Illumina chips, although higher marker density for association studies appeared to be advantageous. Overall, there were some observations that different chromosomal segments, i.e., physical locations on the p-arm, q-arm, or middle segment, might lead to possible differences in type I error rates. This finding and others highlight the importance of empirical determination of P-values to determine significance.

Alcoholism↗