Arrangement of gene pairs, retrotransposon insertions, and regulation of gene expression in plants

Plant genomes are extremely complex. Myriad factors contribute to their evolution and organization, as well as to the expression and regulation of individual genes. Here we present investigations into several such factors and their influence on genome structure and gene expression: the arrangement of pairs of physically adjacent genes, retrotransposons closely associated with genes, and the effect of retrotransposons on gene pair evolution. All sequenced plant genomes contain a significant fraction of retrotransposons, including that of rice. We investigated the effects of retrotransposons within rice genes and within a 1 kb putative promoter region upstream of each gene. We found that approximately one-sixth of all rice genes are closely associated with retrotransposons. Insertions within a gene’s promoter region tend to block gene expression, while retrotransposons within genes promote the existence of alternative splicing forms. We also identified several other trends in retrotransposon insertion and its effects on gene expression. Several studies have previously noted a connection among genes between physical proximity and correlated expression profiles. To determine the degree to which this correlation depends on an exact physical arrangement, we studied the expression and interspecies conservation of convergent and divergent gene pairs in rice, Arabidopsis, and Populus trichocarpa. Correlated expression among gene pairs was quite common in all three species, yet conserved arrangement was rare. However, conservation of gene pair arrangement was significantly more common among pairs with strongly correlated expression levels. In order to uncover additional properties of gene pair conservation and rearrangement, we performed a comparative analysis of convergent, divergent, and tandem gene pairs in rice, sorghum, maize, and Brachypodium. We noted considerable differences between gene pair types and species. We also constructed a putative evolutionary history for each pair, which led to several interesting discoveries. To further elucidate the causes of gene pair conservation and rearrangement, we identified retrotransposon insertions in and near rice gene pairs. Retrotransposon-associated pairs are less likely to be conserved, although there are significant differences in the possible effect of different types and locations of retrotransposon insertions. The three types of gene pair also varied in their susceptibility to retrotransposon-associated evolutionary changes.

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.