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Background and Objective: Standard population genetic theory says that deleterious genetic variants are likely rare and fairly recently introduced. However, can this expectation lead to more powerful tests of association between diseases and rare genetic variation? The gene genealogy describes the relationships between haplotypes sampled from the general population. Although ancestral tree-based methods, inspired by the gene genealogy concept, have been developed for finding associations with common genetic variants, here we ask whether gene genealogies can help in identifying genomic regions containing multiple rare causal variants. Methods: With data simulated under several demographic models and using known gene genealogies, we developed and compared several tree-based statistics to determine which, if any, could detect the type of clustering expected with rare causal variants and whether the genealogic tree provides additional information about disease associations. Results and Conclusions: We found that a novel statistic based on the scaled distance between the tips of a tree performed better than other tree-based statistics. When data were simulated with mild population growth, this statistic outperformed two standard non-tree-based methods, showing that an ancestral tree-based approach has potential for rare variant discovery. © 2014 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

In the case-parent trio design, unrelated children affected with a disease are genotyped along with their parents. Information may also be collected on environmental factors in the children. The design permits estimation and testing of genetic effects and gene-by-environment interaction. Recently, it has been demonstrated that when genotypes are measured at a non-causal test locus, population stratification can create spurious interaction. That is, the environmental factor can appear to modify the disease risk associated with genotypes at the test locus without modifying the disease risk of genotypes at the causal locus. One design-based approach that is robust to spurious interaction requires the environmental factor to also be available on an unaffected sibling of the affected child. We explore the source of spurious interaction and suggest an alternate approach that mitigates its effects using case-parent triads. Our approach is based on adjusting the risk model using ancestry informative markers or random markers measured on the affected child and does not require data on unaffected siblings. We apply an approach to generating case-parent data, implemented in a freely-available R package soon to be released on the Comprehensive R Archive Network (CRAN). [ABSTRACT FROM AUTHOR]

Allelic transmission rates from parents to cases are frequently stratified by an environmental risk factor E and compared, with heterogeneity interpreted as gene-environment interaction or . Though generally invalid, such analyses continue to appear. We revisit why heterogeneity is not equivalent to in a range of settings not considered previously. The objective is a fuller understanding of the bias in transmission rates and what is driving it. Extending previously published findings, we derive parental mating-type probabilities in cases and use them to obtain transmission rates, which we then compare to . Through simulation, we investigate the practical implications of the bias for a transmission-based test of . We find that general population characteristics distort the picture of obtained from transmission rates: the stratum-specific mating-type probabilities under dependence and the allele frequency under independence. Furthermore, the transmission-based test has inflated error rates relative to a likelihood-based test. Our investigation provides further insight into how and why transmission-based tests and descriptive summaries can mislead about . For exploring , we suggest graphical displays of the transmission rates within parental mating types, as they are robust to population stratification and the penetrance model. [ABSTRACT FROM AUTHOR]

Background/aims: Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically., Methods: We investigate the properties of such a "contribution plot" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse., Results: The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI)., Conclusions: The contribution plot with shrinkage estimation can reveal truly associated explanatory variables., (© 2019 S. Karger AG, Basel.)