Detecting the signature of natural selection with microsatellites

Natural selection is one of the major factors in the evolution of all organisms. Detecting the signature of natural selection has been a central theme in evolutionary genetics. With the availability of microsatellite data, it is of interest to study how natural selection can be detected with microsatellites. ^ The overall aim of this research is to detect signatures of natural selection with data on genetic variation at microsatellite loci. The null hypothesis to be tested is the neutral mutation theory of molecular evolution, which states that different alleles at a locus have equivalent effects on fitness. Currently used tests of this hypothesis based on data on genetic polymorphism in natural populations presume that mutations at the loci follow the infinite allele/site models (IAM, ISM), in the sense that at each site at most only one mutation event is recorded, and each mutation leads to an allele not seen before in the population. Microsatellite loci, which are abundant in the genome, do not obey these mutation models, since the new alleles at such loci can be created either by contraction or expansion of tandem repeat sizes of core motifs. Since the current genome map is mainly composed of microsatellite loci and this class of loci is still most commonly studied in the context of human genome diversity, this research explores how the current test procedures for testing the neutral mutation hypothesis should be modified to take into account a generalized model of forward-backward stepwise mutations. In addition, recent literature also suggested that past demographic history of populations, presence of population substructure, and varying rates of mutations across loci all have confounding effects for detecting signatures of natural selection. ^ The effects of the stepwise mutation model and other confounding factors on detecting signature of natural selection are the main results of the research. ^

Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^

Theoretical and experimental studies of linkage disequilibrium in human populations

Linkage disequilibrium (LD) is defined as the nonrandom association of alleles at two or more loci in a population and may be a useful tool in a diverse array of applications including disease gene mapping, elucidating the demographic history of populations, and testing hypotheses of human evolution. However, the successful application of LD-based approaches to pertinent genetic questions is hampered by a lack of understanding about the forces that mediate the genome-wide distribution of LD within and between human populations. Delineating the genomic patterns of LD is a complex task that will require interdisciplinary research that transcends traditional scientific boundaries. The research presented in this dissertation is predicated upon the need for interdisciplinary studies and both theoretical and experimental projects were pursued. In the theoretical studies, I have investigated the effect of genotyping errors and SNP identification strategies on estimates of LD. The primary importance of these two chapters is that they provide important insights and guidance for the design of future empirical LD studies. Furthermore, I analyzed the allele frequency distribution of 26,530 single nucleotide polymorphisms (SNPs) in three populations and generated the first-generation natural selection map of the human genome, which will be an important resource for explaining and understanding genomic patterns of LD. Finally, in the experimental study, I describe a novel and simple, low-cost, and high-throughput SNP genotyping method. The theoretical analyses and experimental tools developed in this dissertation will facilitate a more complete understanding of patterns of LD in human populations. ^