Heritability and Covariate Traits in Sardinian and Other

点击翻译按钮获取中文摘要

With the goal of determining genetic and epidemiological factors in age-related traits, we are applying new statistical approaches to look for covariates and hidden correlations in large population data sets. A particular focus is the NIA-sponsored study of the founder Sardinian population, where inter-relatedness and stable environment of the population over many generations can simplify the analysis. The study has been scoring >200 dichotomized traits (smoking, etc.) and 98 quantitative traits (?endophenotypes? or ?quantitative risk-related genetic or environmental factors?) that can be scored on a continuous scale. The use of quantitative traits permits the study of the entire range of allelic variation in a population. Traits of special interest include a range of cardiovascular risk factors, anthropometric measurements, blood test values, and facets of personality. In a current 5-year contract, a team of Sardinian scientists has recruited over 6,100 subjects from a selected group of four towns in east-central Sardinia, and has measured all traits for each subject. The sample cohort numbers over half of the population of the region aged 14-102; they are native-born, and at least 96 percent are known to have all grandparents born in the same province. The group include 4933 phenotyped sib pairs, 4266 phenotyped parent-child pairs, >4069 phenotyped cousin pairs, and more than 6459 phenotyped avuncular pairs. This sample is large enough and well enough phenotyped to show that even in this founder population, the variance for individual traits is comparable to that in outbred populations; and it is large enough and interrelated enough to infer highly significant estimates of genetic heritability for traits. In the first publications from the study,we report heritability analyses for 98 quantitative traits, focusing on facets of personality and cardiovascular function. We also summarize results of bivariate analyses for all pairs of traits and of heterogeneity analyses for each trait. We found a significant genetic component for every trait. On average genetic effects explained 40% of the variance for 38 blood tests, 51% for 5 anthropometric measures, 25% for 20 measures of cardiovascular function, and 19% for 35 personality traits. Four traits showed significant evidence for an X-linked component. Bivariate analyses suggested overlapping genetic determinants for many traits, including multiple personality facets and several traits related to the metabolic syndrome; but we found no evidence for shared genetic determinant that might underlie the reported association of some personality traits and cardiovascular risk factors. Models allowing for heterogeneity suggested that, in this cohort, the genetic variance was typically larger in females and in younger individuals, but interesting exceptions were observed. For example, narrow heritability of blood pressure was ~26% in individuals >42 years old, but only ~8% in younger individuals. Despite the heterogeneity in effect sizes, the same loci appear to contribute to variance in young and old and in males and females. In summary, we find significant evidence for heritability of many medically important traits, including cardiovascular function and personality. Evidence for heterogeneity by age and sex suggest that models allowing for these differences will be important in mapping quantitative traits. Genome-wide scans (genotyping) should have the power to detect loci that contribute the order of 10 percent of variance for a trait. With this cohort, full-genome scans with batteries of up to 500,000 single-nucleotide markers are almost complete, and are expected to point to genes/variants that determine a significant portion of the genetic contribution to variance for each trait studied. In addition, second visits have been initiated for the study cohort to permit the assessment of longitudinal trends and outcomes, as well as the assessment of additional phenotypes related to bone density and frailty as a function of age. While the genome scans of the population begin to search for genes involved in the determination of particular traits, targeted data analysis is being initiated to look for correlated/overlapping genetic and epidemiological factors, including unexpected correlations, and to compare possible correlations with other large population cohort studies, including the Baltimore Longitudinal Study of Aging.