Methods to estimate genetic components of variance for quantitative traits in family studies.

The aim of this paper was to compare several methods of estimating the genetic components of a quantitative trait in familial data. The Expectation and Maximization (E-M) algorithm, the Newton-Raphson method, and the scoring method were compared for estimating polygenic and environmental effects on nuclear families. We also compared scoring and quasilikelihood (QL) methods when a linked genetic marker was available to estimate effects from a major gene. Generally, all procedures performed similarly in estimating polygenic and environmental variance components. The E-M algorithm yielded more precise estimators when heritability was low. The scoring method was much faster than the other methods and yielded slightly more precise estimates of mean effects but slightly less precise estimates of the variance components. Estimates of major gene effects were not affected by the number of alleles at the trait locus. For these relatively large sample sizes, QL and scoring had similar precision, but QL took 32 times longer than scoring. Finally, we compared the results of applying these methods to data from the Bogalusa Heart Study. Results showed larger imprecision when the QL method was applied, consistent with earlier studies that showed decreased precision of quasilikelihood compared with maximum likelihood in moderately small sample sizes.

Fine mapping of a genetic locus for Peutz-Jeghers syndrome on chromosome 19p.

Peutz-Jeghers syndrome (PJS) was recently mapped in a single report to the telomeric region of chromosome 19p (A. Hemminki et al., Nat. Genet., 15: 87-90, 1997). Our studies confirm this location and provide further localization of the PJS locus. In the five families examined, there were no recombinants with the marker D19S886. The multipoint log odds score at D19S886 is 7.52, and we found no evidence for genetic heterogeneity. We also found that all carriers expressed the PJS phenotype and no noncarriers displayed PJS sequellae, indicating complete penetrance with no sporadic cases.

Comparison of model-free linkage mapping strategies for the study of a complex trait.

We compared several strategies for identifying and estimating effects from a genetic locus in the etiology of a complex trait. For our analyses we used data from simulated trait 1 and chromosome 5. Results from analysis of the first 20 replicates showed that a components of variance test provided considerably better power for identifying linkage than tests that consider pair differences. We also compared the power from constructing tests with a single marker, an approximate method using five markers jointly, or a multipoint analysis using all 25 markers on chromosome 5 jointly. Results from this analysis showed substantially better power when all markers were jointly used in the analysis. Results from considering all replicates showed that all methods of estimation provided maximal test statistics at the correct marker position, but the components of variance procedure provided more power to detect the correct position than other methods.

Assessing linkage on chromosome 5 using components of variance approach: univariate versus multivariate.

We report results when one disease related quantitative trait (Q1) is analyzed versus multiple related quantitative traits using a components of variance approach [Amos, 1994; de Andrade and Amos, 1996]. In both cases, we used age, sex and environmental factor (EF) as covariates. Analysis with ascertainment correction of samples selected through probands with extremely high trait values was also performed and is presented. Testing procedures to detect linkage using the components of variance approach are discussed. The univariate and multivariate analyses showed effects of locus 15 on chromosome 5 only for Q1. This result was confirmed when using ascertainment correction.