Genetic factors have been shown to play an important role in determining interindividual variation in plasma HDL-C levels, but the specific genetic determinants of HDL cholesterol (HDL-C) levels have not been elucidated. In this study, the effects of variation in the genomic regions encoding hepatic lipase, apolipoprotein AI/CIII/AIV, and the cholesteryl ester transfer protein on plasma HDL-C levels were examined in 73 normotriglyceridemic, Caucasian nuclear families. Genetic factors accounted for 56.5±13% of the interindividual variation in plasma HDL-C levels. For each candidate gene, adjusted plasma HDL-C levels of sibling pairs who shared zero, one, or two parental alleles identicalby-descent were compared using sibling-pair linkage analysis. Allelic variation in the genes encoding hepatic lipase and apolipoprotein AI/CIII/AIV accounted for 25 and 22%, respectively, of the total interindividual variation in plasma HDL-C levels. In contrast, none of the variation in plasma HDL-C levels could be accounted for by allelic variation in the cholesteryl ester transfer protein. These findings indicate that a major fraction of the genetically determined variation in plasma HDL-C levels is conferred by allelic variation at the hepatic lipase and the apolipoprotein AI/CIIH/AIV gene loci. (J. Clin.
The Haseman & Elston (1972) sibling-pair regression method has been used to detect and estimate the variance contribution to observed values of a quantitative trait by allelic variation in specific candidate genes. The procedure was developed under a model with a single biallelic trait locus. This assumption does not hold for several known systems. In this paper we prove that for candidate gene analysis the Haseman-Elston procedure extends to the case of multiple trait loci, each possibly having more than two alleles. Simulation experiments comparing single-locus to two-locus models show that fitting the extended regression equations maintains nominal significance levels, but the power to detect linkage to trait variation is not improved by including additional loci. These results indicate that the original proposal is statistically robust to violations of the underlying genetic model. Practical issues associated with quantifying the relative variance contribution by individual loci are also discussed. Applications of the extended regression equations to lipoprotein(a) and high density lipoprotein cholesterol are given for illustration. Interindividual variability in observed values of most quantitative traits arises from both genetic and environmental sources. The proportion of trait variation attributable to genetic factors can be estimated from phenotypic correlations among family members, but identification of the specific genetic polymorphisms that confer heritable variation in trait levels has proved considerably more difficult. The contribution to trait variation by a specific locus can be assessed by comparing the trait levels of sibling pairs sharing different numbers of alleles at the locus. If allelic variation in a candidate gene is associated with variation in a trait, then trait levels should be more similar in siblings sharing both alleles of the candidate gene identical by descent (ibd) than in siblings sharing neither allele ibd. Conversely, if variation in a candidate gene does not influence a trait, then concordance for trait levels among siblings should be independent of the number of alleles of the candidate gene they share. Haseman & Elston (1972) showed that the slope of the regression of squared differences in trait values between two siblings on the proportion of alleles shared ibd at a locus provides a test statistic for linkage between the trait locus and a marker locus.The original Haseman and Elston (H-E) procedure and most subsequent extensions and related work have assumed a genetic model with a single trait locus with two alleles, and incomplete information on ibd sharing leading to the use of estimated ibd proportions. Kruglyak & Lander
The Haseman & Elston (1972) sibling-pair regression method has been used to detect and estimate the variance contribution to observed values of a quantitative trait by allelic variation in specific candidate genes. The procedure was developed under a model with a single biallelic trait locus. This assumption does not hold for several known systems. In this paper we prove that for candidate gene analysis the Haseman-Elston procedure extends to the case of multiple trait loci, each possibly having more than two alleles. Simulation experiments comparing single-locus to two-locus models show that fitting the extended regression equations maintains nominal significance levels, but the power to detect linkage to trait variation is not improved by including additional loci. These results indicate that the original proposal is statistically robust to violations of the underlying genetic model. Practical issues associated with quantifying the relative variance contribution by individual loci are also discussed. Applications of the extended regression equations to lipoprotein(a) and high density lipoprotein cholesterol are given for illustration.
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