Improved power of sib-pair linkage analysis using measures of complex trait dynamics

A computational method to detect epistatic effects contributing to a quantitative trait

We develop a new computational method to detect epistatic effects that contribute to a complex quantitative trait. Rather than looking for epistatic effects that show statistical significance when considered in isolation, we search for a close approximation to the quantitative trait by a sum of epistatic effects. Our search algorithm consists of a sequence of random walks around the space of sums of epistatic effects. An important feature of our approach is that there is learning between random walks, i.e. the control mechanism that chooses steps in our random walks adapts to the experiences of earlier random walks. We test the effectiveness of our algorithms by applying them to synthetic datasets where the phenotype is a sum of epistatic effects plus normally distributed noise. Our test statistic is the rate of success that our methods achieve in identifying the underlying epistatic effects. We report on the effectiveness of our methods as we vary parameters that are intrinsic to the computation (length of random walks and degree of learning) as well as parameters that are extrinsic to the computation (number of markers, number of individuals, noise level, architecture of the epistatic effects).

The relationship between plasma t-PA and PAI-1 levels is dependent on epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms

Thrombus formation and degradation is partly due to a complex interplay between tissue-type plasminogen activator (t-PA) and plasminogen activator inhibitor 1 (PAI-1). There is accumulating evidence that plasma levels of t-PA and PAI-1 may be influenced by an interaction between the fibrinolytic and renin-angiotensin systems. The goal of this study was to conduct an exploratory data analysis to determine whether there is evidence that the relationship (i.e. correlation) between plasma t-PA and PAI-1 is influenced by interactive effects of the angiotensin converting enzyme (ACE) insertion/deletion (I/D) and plasminogen activator inhibitor 1 (PAI-1) 4G/5G polymorphisms in a sample of 50 unrelated African Americans and 117 unrelated Caucasians. In a single-locus analysis, no evidence for heterogeneity of plasma t-PA and PAI-1 correlations among either ACE I/D or PAI-1 4G/5G genotypes was detected. However, using the combinatorial partitioning method for exploratory data analysis, we identified evidence that is suggestive of heterogeneity of plasma t-PA and PAI-1 correlations among multilocus ACE I/D and PAI-1 4G/5G genotypes in African American females, Caucasian females, Caucasian males, but not African American males. From these results, we propose as a working hypothesis that the correlation between plasma t-PA and PAI-1 may be dependent on epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms. This study supports the idea that interactions between the fibrinolytic and renin-angiotensin systems play an important role in the genetic architecture of plasma t-PA and PAI-1.