Evolutionary evidence of the effect of rare variants on disease etiology.

The common disease/common variant hypothesis has been popular for describing the genetic architecture of common human diseases for several years. According to the originally stated hypothesis, one or a few common genetic variants with a large effect size control the risk of common diseases. A growing body of evidence, however, suggests that rare single-nucleotide polymorphisms (SNPs), i.e. those with a minor allele frequency of less than 5%, are also an important component of the genetic architecture of common human diseases. In this study, we analyzed the relevance of rare SNPs to the risk of common diseases from an evolutionary perspective and found that rare SNPs are more likely than common SNPs to be functional and tend to have a stronger effect size than do common SNPs. This observation, and the fact that most of the SNPs in the human genome are rare, suggests that rare SNPs are a crucial element of the genetic architecture of common human diseases. We propose that the next generation of genomic studies should focus on analyzing rare SNPs. Further, targeting patients with a family history of the disease, an extreme phenotype, or early disease onset may facilitate the detection of risk-associated rare SNPs.

Power of a simplified multivariate test for genetic linkage.

In this paper we compare the power of the multivariate Haseman-Elston (MHE) test proposed earlier by Amos et al. (1990) and a computationally rapid new version of the multivariate Haseman-Elston test (NMHE) (Elston et al. 2000). We show that the power of NMHE was, for different simulation setups, identical or higher than that of MHE. In the bivariate case, the power of the NMHE method was somewhat less than that of the computationally intensive maximum likelihood variance components method (Amos et al. 2001). We present comparisons of the empirical distributions of the NMHE test to its limiting distributions for a range of numbers of traits. The distribution of the NMHE test appeared to conform satisfactorily to its limiting asymptotic distribution in large samples. Otherwise, empirical critical values for NMHE are somewhat higher than predicted, i.e. the test proposed by Elston et al. (2000) is non-conservative. The use of empirical critical values is therefore recommended for limited sample sizes (less than several hundred families). We also present the results of a linkage analysis performed by the NMHE method on a set of 4 body size-related traits. The method identified meaningful combinations of traits that showed significant linkage on chromosome 2 and suggestive linkage to regions on chromosomes 16 and 17.

Modeling of long-term screening for lung carcinoma.

BACKGROUND: Results from the Mayo Lung Project (MLP), a randomized clinical trial for the early detection of lung carcinoma, were interpreted as proof that the early detection of lung carcinoma by chest X-ray does not reduce the mortality from this disease. Recent analysis of extended follow-up data from the MLP subjects found that after approximately 20 years there still was no apparent difference in lung carcinoma mortality between a study group and a control group. METHODS: To view this result within context, the authors utilized a previously published simulation model of the MLP, with parametric values that were estimated at the time of the original publication based on the data collected by the MLP. RESULTS: The model produced predictions of the extended follow-up statistics that were found to be consistent with the data published in the prior study. The authors believe this provides long-term validation for the model. Conversely, the same model demonstrated that had the study subjects been screened annually for the extended follow-up period, the difference in mortality would be noticeable, even with the low sensitivity of chest X-ray detection. CONCLUSIONS: The results of current study strongly suggest that long-term screening with chest X-ray results in a reduction in lung carcinoma mortality. The limited extent of this benefit is the result of the low sensitivity of chest X-ray as a screening tool.

Cost-benefit analysis of recombination and its application for understanding of chiasma interference.

A cost-benefit analysis of recombination was undertaken. The beneficial effects of crossing-over are proportional to the frequency of recombinant offspring, while its harmful effects (errors of crossing-over leading to mutations) are proportional to the number of crossover exchanges. An equilibrium point should exist where the beneficial effects of crossing-over are balanced by its harmful effects. It is suggested that natural selection sustains a number of crossover exchanges per meiosis at the level that provides highest benefit-cost difference. Chiasma interference prevents the arising of closely located exchanges which are less effective in the production of recombinants than exchanges separated by some "interference distance". Computer simulation shows that chiasma interference increases the recombination effectiveness of the multiple crossover exchanges as compared to the case without interference.