Speciation with gene-flow in eastern North American red oaks (Quercus section Lobatae)

As foundational species, oaks (Quercus : Fagaceae) support the activities of both humans and wildlife. However, many oaks in North America are declining, a crisis exacerbated by the previous disappearance of other hard mast-producing trees. In addition, the economic demands placed on this drought-tolerant group may intensify if climate change extirpates other, relatively mesophytic species. Genetic tools can help address these management challenges. To this end, we developed a suite of 27 microsatellite markers, of which 22 are derived from expressed sequence tags (ESTs). Many of these markers bear significant homology to known genes and may be able to directly assay functional genetic variation. Markers obtained from enriched microsatellite libraries, on the other hand, are typically located in heterochromatic regions and should reflect demographic processes. Considered jointly, genic and genomic microsatellites can elucidate patterns of gene-flow and natural selection, which are fundamental to both an organism's evolutionary ecology and conservation biology. To this end, we employed the developed markers in an FST-based genome scan to detect the signature of divergent selection among the red oaks (Quercus section Lobatae). Three candidate genes with putative roles in stress responses demonstrated patterns of diversity consistent with adaptation to heterogeneous selective pressures. These genes may be important in both local genetic adaptation within species and divergence among them. Next, we used an isolation-with-migration model to quantify levels of gene-flow among four red oaks species during speciation. Both speciation in allopatry and speciation with gene-flow were found to be major drivers of red oak biodiversity. Loci playing a key role in speciation are also likely to be ecologically important within species

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.