PHYLOGENETIC ANALYSES, GENE DOSAGE EFFECT AND SELECTION IN MEMBERS OF THE LATERAL BOUNDARY DOMAIN (LBD) GENES TRANSCRIPTION FACTOR FAMILY

Wood formation is an economically and environmentally important process and has played a significant role in the evolution of terrestrial plants. Despite its significance, the molecular underpinnings of the process are still poorly understood. We have previously shown that four Lateral Boundary Domain (LBD) transcription factors have important roles in the regulation of wood formation with two (LBD1 and LBD4) involved in secondary phloem and ray cell development and two (LBD15 and LBD18) in secondary xylem formation. Here, we used comparative phylogenetic analyses to test potential roles of the four LBD genes in the evolution of woodiness. We studied the copy number and variation in DNA and amino acid sequences of the four LBDs in a wide range of woody and herbaceous plant taxa with fully sequenced and annotated genomes. LBD1 showed the highest gene copy number across the studied species, and LBD1 gene copy number was strongly and significantly correlated with the level of ray seriation. The lianas, cucumber and grape, with multiseriate ray cells showed the highest gene copy number (12 and 11, respectively). Because lianas’ growth habit requires significant twisting and bending, the less lignified ray parenchyma cells likely facilitate stem flexibility and maintenance of xylem conductivity. We further demonstrate conservation of amino acids in the LBD18 protein sequences that are specific to woody taxa. Neutrality tests showed evidence for strong purifying selection on these gene regions across various orders, indicating adaptive convergent evolution of LBD18. Structural modeling demonstrates that the conserved amino acids have a significant impact on the tertiary protein structure and thus are likely of significant functional importance.

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.