Functional Mapping of Dynamic Traits with Robust t-Distribution

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate -distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis

Multivariate methods and software for association mapping in dose¿response genome¿wide association studies

Abstract Background The large sample sizes, freedom of ethical restrictions and ease of repeated measurements make cytotoxicity assays of immortalized lymphoblastoid cell lines a powerful new in vitro method in pharmacogenomics research. However, previous studies may have over‐simplified the complex differences in dose‐response profiles between genotypes, resulting in a loss of power. Methods The current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome‐wide association studies. This manuscript builds upon a previous study, where differences in dose‐response curves between genotypes were constructed using the hill slope equation. Conclusion Overall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user‐friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats

Haplotype-Based Pharmacogenetic Analysis for Longitudinal Quantitative Traits in the Presence of Dropout

We propose a variety of methods based on the generalized estimation equations to address the issues encountered in haplotype-based pharmacogenetic analysis, including analysis of longitudinal data with outcome-dependent dropouts, and evaluation of the high-dimensional haplotype and haplotype-drug interaction effects in an overall manner. We use the inverse probability weights to handle the outcome-dependent dropouts under the missing-at-random assumption, and incorporate the weighted L1-penalty to select important main and interaction effects with high dimensionality. The proposed methods are easy to implement, computationally efficient, and provide an optimal balance between false positives and false negatives in detecting genetic effects

The maintenance of genetic variation by balancing selection

Adaptive evolution occurs when selection acts on genetic variation for phenotypic traits. In doing so, selection is expected to remove fitness variation in the population. Contrary to this expectation, DNA sequencing has shown that populations harbour high levels of standing genetic variation for fitness. This paradox results in a long-standing question: what maintains genetic variation? One possible mechanism is ‘balancing selection’, where selection actively maintains polymorphism. Once considered unlikely, studies using genomic and phenotypic approaches have recently given new support for balancing selection and have provided evidence of balancing selection in several species. However, it is often difficult to connect genetic and phenotypic evidence for balancing selection with evidence of the action of selection in real time. This limits our understanding of how balancing selection occurs and its contribution to maintaining genetic variation. To address these knowledge gaps, I first assayed the fitness effects of a polymorphism in the Drosophila melanogaster gene fruitless, which shows a signature of balancing selection in wild populations. I show that this polymorphism displays antagonistic pleiotropy, a possible mechanism for balancing selection at this locus (Chapter 2). I next used experimental evolution and pool-sequencing to track the frequency of the fruitless polymorphism over time in laboratory populations (Chapter 3). I was able to demonstrate that the fruitless polymorphism is probably evolving under balancing selection in these populations, although this result is complicated by 44% of putatively neutral SNPs also being diagnosed as under balancing selection. I next expanded this approach to diagnose selection at 397 candidate sexually antagonistic SNPs. 60% appeared to be under balancing selection (Chapter 4). The equilibrium allele frequency of these SNPs was positively related to that in two wild populations, illustrating that the short-term evolution in the cages is correlated to long-term evolution in wild populations. That shows that selection is consistent and supports the inference of balancing selection. Overall, this thesis describes the action of balancing selection in maintaining fitness influencing polymorphisms in D. melanogaster and develops methods to diagnose active balancing selection at the population level

Genetic Determinants of Human Longevity

In the last two decades, due to the continuous increase of lifespans in Westernsocieties, and the consequent growing of the elderly population, have witnessedan increase in the number of studies on biological and molecular factors able topromote healthy aging and reach longevity. The study of the genetic componentof human longevity demonstrated that it accounts for 25% of intra populationphenotype variance. The efforts made to characterize the genetic determinantssuggested that the maintenance of cellular integrity, inflammation, oxidativestress response, DNA repair, as well as the use of nutrients, represent the mostimportant pathways correlated with a longer lifespan. However, although aplethora of variants were indicated to be associated with human longevity, onlyvery few were successfully replicated in different populations, probably becauseof population specificity, missing heritability as well as a complex interactionamong genetic factors with lifestyle and cultural factors, which modulate theindividual chance of living longer. Thus, many challenges remain to be addressedin the search for the genetic components of human longevity. This Special Issue isaimed to unify the progress in the analysis of the genetic determinants of humanlongevity, to take stock of the situation and point to future directions of the field.We invite submissions for reviews, research articles, short-communicationsdealing with genetic association studies in human longevity, including all types ofgenetic variation, as well as the characterization of longevity-related genes

Dissecting genetic interactions in complex traits

Of central importance in the dissection of the components that govern complex traits is understanding the architecture of natural genetic variation. Genetic interaction, or epistasis, constitutes one aspect of this, but epistatic analysis has been largely avoided in genome wide association studies because of statistical and computational difficulties. This thesis explores both issues in the context of two-locus interactions. Initially, through simulation and deterministic calculations it was demonstrated that not only can epistasis maintain deleterious mutations at intermediate frequencies when under selection, but that it may also have a role in the maintenance of additive variance. Based on the epistatic patterns that are evolutionarily persistent, and the frequencies at which they are maintained, it was shown that exhaustive two dimensional search strategies are the most powerful approaches for uncovering both additive variance and the other genetic variance components that are co-precipitated. However, while these simulations demonstrate encouraging statistical benefits, two dimensional searches are often computationally prohibitive, particularly with the marker densities and sample sizes that are typical of genome wide association studies. To address this issue different software implementations were developed to parallelise the two dimensional triangular search grid across various types of high performance computing hardware. Of these, particularly effective was using the massively-multi-core architecture of consumer level graphics cards. While the performance will continue to improve as hardware improves, at the time of testing the speed was 2-3 orders of magnitude faster than CPU based software solutions that are in current use. Not only does this software enable epistatic scans to be performed routinely at minimal cost, but it is now feasible to empirically explore the false discovery rates introduced by the high dimensionality of multiple testing. Through permutation analysis it was shown that the significance threshold for epistatic searches is a function of both marker density and population sample size, and that because of the correlation structure that exists between tests the threshold estimates currently used are overly stringent. Although the relaxed threshold estimates constitute an improvement in the power of two dimensional searches, detection is still most likely limited to relatively large genetic effects. Through direct calculation it was shown that, in contrast to the additive case where the decay of estimated genetic variance was proportional to falling linkage disequilibrium between causal variants and observed markers, for epistasis this decay was exponential. One way to rescue poorly captured causal variants is to parameterise association tests using haplotypes rather than single markers. A novel statistical method that uses a regularised parameter selection procedure on two locus haplotypes was developed, and through extensive simulations it can be shown that it delivers a substantial gain in power over single marker based tests. Ultimately, this thesis seeks to demonstrate that many of the obstacles in epistatic analysis can be ameliorated, and with the current abundance of genomic data gathered by the scientific community direct search may be a viable method to qualify the importance of epistasis

Abstracts of Papers, 86th Annual Meeting of the Virginia Academy of Science

Abstracts for the 86th Annual Meeting of the Virginia Academy of Science, May 20-23, 2008, Hampton University, Hampton, VA

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio