Journal - BMC proceedings (England )

Abstract :

ABSTRACT : We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We conducted linkage only, linkage and association, and association only tests under this framework. We also compared these results with variance-component linkage analysis and regression analyses. The results indicate that the method shows some promise, but finding genes that have very small (<0.1%) contributions to trait variance may require additional sources of information. All methods examined fared poorly for the smallest in the simulated "polygene" range (h2 of 0.0015 to 0.0002).

Journal - Annals of human genetics (England )

Abstract :

In many genetic disorders in which a primary disease-causing locus has been identified, evidence exists for additional trait variation due to genetic factors. These findings have led to studies seeking secondary 'modifier' loci. Identification of modifier loci provides insight into disease mechanisms and may provide additional screening and treatment targets. We believe that modifier loci can be identified by re-analysis of genome screen data while controlling for primary locus effects. To test this hypothesis, we simulated multiple replicates of typical genome screening data on to two real family structures from a study of hypertrophic cardiomyopathy. With this marker data, we simulated two trait models with characteristics similar to one measure of hypertrophic cardiomyopathy. Both trait models included 3 genes. In the first, the trait was influenced by a primary gene, a secondary 'modifier' gene, and a third very small effect gene. In the second, we modeled an interaction between the first two genes. We examined power and false positive rates to map the secondary locus while controlling for the effect of the primary locus with two types of analyses. First, we examined Monte Carlo Markov chain (MCMC) simultaneous segregation and linkage analysis as implemented in Loki, for which we calculated two scoring statistics. Second, we calculated LOD scores using an individual-specific liability class based on the quantitative trait value. We found that both methods produced scores that are significant on a genome-wide level in some replicates. We conclude that mapping of modifier loci in existing samples is possible with these methods.

Journal - Annals of epidemiology (UNITED STATES )

Abstract :

PURPOSE: The primary goal of the HERITAGE Family Study was to document the role of the genotype in the response to aerobic exercise training. Toward this end, nuclear families were enrolled in a 20-week exercise training program, with a large variety of tests performed before and after the training. Since study drift has the potential to adversely affect the results, reproducibility and potential bias over six consecutive 4-month periods were examined for selected test. METHODS: Intraclass correlations (ICC), technical errors (TE), coefficients of variation within subject (CV), and means were calculated with use of the pretraining test results for each of the six time periods. To check for homogeneity, hypothesis tests were performed on the intraclass correlations and means. If homogeneity was not found across all six periods, further tests were performed to assess differences between pairs of time periods. RESULTS: There was little evidence for real drifts in reproducibility, with most tests having ICCs of 0.8 or better. Only a few tests showed any change over time, and in no case was there evidence of a systematic drift in mean values. CONCLUSIONS: Overall, the reproducibility of the HERITAGE Family Study tests and assays considered in this paper was found to be very good, with no evidence of any systematic drift over time.

Journal - BMC Proceedings

Abstract :

DESCRIPTION (provided by applicant): Bayesian Monte Carlo Markov chain (MCMC) techniques have shown promise in dissecting complex genetic traits such as cancer. Cancer is complex both in that failures of more than a single gene are thought to be required to lead to disease and in that both inherited genetic factors and environmental factors play a role. The methods introduced by Heath (1997) and implemented in the program Loki have been able to localize genes contributing to complex traits in both real and simulated data sets. Loki carries out a simultaneous segregation and linkage analysis, estimating not only the location of quantitative trait loci (QTL), but also many other parameters, including the number of QTL, the effects at each QTL, covariate effects (such as environmental exposure), and the segregation patterns of those QTL. These methods can produce posterior probability, distributions for all estimated parameters, and in the past we have focused on the posterior probability distribution of QTL linkage over the genome or simply a particular chromosome to identify regions in which QTL are located. Interpretation of the results of these methods and assessment of their significance has been difficult, meaning that many have found these methods difficult to use and full use of all the information estimated has not been made. We propose to examine a scoring method to produce an easy to interpret score for initial QTL linkage. This score, the Log Of the Posterior placement probability ratio (LOP), designed specifically for complex oligogenic trait linkage detection. LOP contrasts with a lod score in that while a lod score is calculated under a single linkage model, LOP is calculated with Monte Carlo integration over a large number of models. We have done some very promising proof-of-concept work on LOP, but further study is required to completely explore the properties of LOP. We plan to explore how our current implementations of this score perform with different family structures and different trait models. This exploration will result in guidelines for study design and rules for the interpretation of the results, as well as ideas for further improvement of the statistical methods. These guidelines will be used in future studies, which we believe will lead to the identification of additional disease-related genes.

Abstract :

DESCRIPTION (provided by applicant): Bayesian Monte Carlo Markov chain (MCMC) techniques have shown promise in dissecting complex genetic traits such as cancer. Cancer is complex both in that failures of more than a single gene are thought to be required to lead to disease and in that both inherited genetic factors and environmental factors play a role. The methods introduced by Heath (1997) and implemented in the program Loki have been able to localize genes contributing to complex traits in both real and simulated data sets. Loki carries out a simultaneous segregation and linkage analysis, estimating not only the location of quantitative trait loci (QTL), but also many other parameters, including the number of QTL, the effects at each QTL, covariate effects (such as environmental exposure), and the segregation patterns of those QTL. These methods can produce posterior probability, distributions for all estimated parameters, and in the past we have focused on the posterior probability distribution of QTL linkage over the genome or simply a particular chromosome to identify regions in which QTL are located. Interpretation of the results of these methods and assessment of their significance has been difficult, meaning that many have found these methods difficult to use and full use of all the information estimated has not been made. We propose to examine a scoring method to produce an easy to interpret score for initial QTL linkage. This score, the Log Of the Posterior placement probability ratio (LOP), designed specifically for complex oligogenic trait linkage detection. LOP contrasts with a lod score in that while a lod score is calculated under a single linkage model, LOP is calculated with Monte Carlo integration over a large number of models. We have done some very promising proof-of-concept work on LOP, but further study is required to completely explore the properties of LOP. We plan to explore how our current implementations of this score perform with different family structures and different trait models. This exploration will result in guidelines for study design and rules for the interpretation of the results, as well as ideas for further improvement of the statistical methods. These guidelines will be used in future studies, which we believe will lead to the identification of additional disease-related genes.