Variance components models in statistical genetics: extensions and applications

Variance components linkage analysis is a powerful method to detect quantitative trait loci (QTLs) for complex diseases. It has the advantages of easy applicability to large extended pedigrees and provides a good flexible framework to accommodate more complicated models like gene-gene, gene-environmental interactions. This dissertation consists of two major parts. In the first part, I propose two approaches for deriving relative-to-relative covariances that are indispensable for expanding the applications of standard variance components linkage approach to more complicated genetic models such as those involving genomic imprinting. In the first approach, I extend 'Li and Sacks' ITO method to model ordered genotypes and derive some generalized linear functions of the extended transition matrices. I demonstrate the wide applicability of this extension by applying it to calculate the covariance in unilineal and bilineal relatives under genomic imprinting. In the second approach, I derive a general formula for calculating the genetic covariance using ordered genotypes for any type of relative pair, which does not have the limitation of extended ITO method to biallelic loci and to unilineal and bilineal relatives. I also propose a recursive algorithm to calculate necessary coefficients in the formula, which opens up the possibility of calculating even inbred relative-to-relative covariance.In the second part of my dissertation, I discuss linkage evidence for susceptibility loci for adiposity-related phenotypes in the Samoan population, an extensive summary of our multicenter study "Genome-scan for Obesity Susceptibility Loci in Samoans". Obesity, BMI greater than or equal to 30 kg/m^2, in the U.S. has become a major and serious public health problem, affecting 33% of adults in 2002. Obesity increases risks for serious diet-related diseases, such as cardiovascular disease, type-2 diabetes, and certain forms of cancers. Obesity is a typical multi-factorial disease with overwhelming evidence of genetic effects, yet their roles in obesity are largely unknown. Our current research findings will help further understand the whole picture of the genetics of obesity, which may have great influence on early prevention and later interventions of human obesity, making it a fundamentally important contribution to public health

Advancing Our Understanding of the Inheritance and Transmission of Pectus Excavatum

Pectus excavatum is the most common congenital chest wall abnormality expressed in children, yet its inheritance is poorly understood. Here we present the first comprehensive assessment of the inheritance of this disorder. After evaluating 48 pedigrees and 56 clinical traits of probands and family members, we find strong evidence of autosomal recessive, genetic control for this disorder. Additionally there is likely more than one pectus disease-associated allele, as well as a relatively large number of disease allele carriers in the human population. Some clinical traits appear important and may serve as reliable indicators for predicting the likelihood of pectus excavatum in children before severe symptoms present. Quantifying sex-ratio bias in probands demonstrates a highly significant male bias associated with pectus excavatum. When combined with pedigree data, sex-bias is indicative of sex-linked, sex-limited, and/or epigenetic control such as X-inactivation, reiterating a point made with pedigrees alone, which is that more than one mutation is likely responsible for this disorder

Use of theoretical and estimated identity-by-descent (IBD) allele sharing measures in genome-wide linkage and association studies, with application to large pedigrees

PhD ThesisTraditionally, identity-by-descent (IBD) sharing among related individuals is estimated on the basis of the assumed pedigree structure, possibly combined with genotyping information for some or all subjects at a series of genetic markers. Recently, there has been interest in using dense SNP genotype data to estimate both average (across the genome) and local (at particular locations) IBD sharing by pairs of individuals. Although originally intended for inference of pedigree relatedness, these genetically estimated IBDs can potentially replace the traditional IBD estimates used in various genetic data analysis methods. I compared IBD estimates from various software packages (PLINK, KING and linear mixed model (LMM) packages including EMMAX, FaST-LMM, GenABEL, GEMMA and MMM) with the theoretical estimates, and examined their utility in application to LMM association analysis of real and simulated qualitative and quantitative phenotypes from a Brazilian family-based study of visceral leishmaniasis (VL) and from the 18th Genetic Analysis Workshop (GAW) data. Generally, the results from the different software packages were highly concordant. When used to model correlations between individuals in LMM analysis, these approaches achieved good control of type 1 error (well beyond that attainable using theoretical IBD estimates), while also achieving superior power to comparable non- LMM methods. Furthermore, although technically misspecified, LMM methods were also successfully applied to simulated longitudinal data. In addition, a new nonparametric linkage analysis method, Regional IBD Analysis (RIA), is proposed, where theoretical IBD estimates are replaced with the average and local genetic IBD estimates. This method was compared with traditional methods for non-parametric linkage analysis (either exact methods using small pedigrees from a study of vesicoureteral reflux disorder (VUR) or simulation-based methods using large pedigrees from the VL study) and was found to perform at least equally well while taking less time.Faculty of Medicine Ramathibodi Hospital, Mahidol Universit

QTL mapping technology using variance components in general pedigrees applied to the poultry industry

The subject area for this thesis is detection of chromosomal regions or QTL causing complex variation at the phenotypic level. In particular, the differentiation of sources of additive and non additive variation. Unlike QTL mapping using divergent or inbred lines, this study aims to explore methods within populations, facilitating direct application of techniques such as marker assisted selection. Specifically, objectives were to evaluate a linear model or variance components (VC) approach to explore the existence and magnitude of variation caused by additive, dominant and imprinted QTL segregating in general pedigrees. This has been achieved by combining extensive simulation and analysis of real commercial poultry data. Linear models were constructed to simultaneously estimate fixed, polygenic and QTL effects. Different genetic models were compared by hierarchical extension to incorporate more variance components, and likelihood ratio test statistics derived from the comparison of full with reduced or null models. A range of additive, dominant and imprinted QTL effects were simulated within two-generation poultry, pig and human type pedigrees. Effects of family size and structure on power, accuracy of variance component estimation, and distribution of the test statistic, were evaluated. Empirical thresholds were derived by simulating populations under the null hypotheses for each type of simulated pedigree and permutation analysis in real data. In the commercial poultry data, dominant and imprinted QTL effects were found for bodyweight and conformation score. Under simulation, although power to detect QTL effects was high in two-generation livestock pedigrees, considerable variation was found in power and behaviour of test statistics. Power to detect dominance was greater in pig and poultry than human type pedigrees with theoretical thresholds increasingly conservative as the number of dams per sire decreased, highlighting the need for empirical derivation of the critical test statistic. The detection of variance caused by imprinted genes and in particular estimates of variance components were also heavily dependent upon the number of sire and dam families used to estimate them. Results showed that VC analysis can be used to routinely detect genetic effects including imprinting and dominance in complex pedigrees. The work presented is the most extensive evaluation of the detection of non additive QTL using VC methods to date. Results challenge standard assumptions made about power and null distributions and show that optimal use of methodology is dependent on pedigree structure

Genetic linkage studies in the pseudoautosomal region of the human sex chromosomes

The two pseudoautosomal regions (PARs) of the human sex chromosomes have drawn considerable interest from researchers in cytogenetics, cytology, evolutionary biology and developmental genetics. However, theses two regions have been widely ignored by the two genetic mapping approaches, using linkage and association analysis methods. At least 29 genes are known to be located in the PARs, most of them of unknown function. Accurate and comprehensive linkage maps are crucial for the success of gene mapping projects. The difference between male and female genetic maps, chromosomal position and population under study, are a challenge to genetic map construction in diploid organisms in which sex is determined by a pair of different sex chromosomes. A high-resolution genetic map that is based on the largest set of polymorphic markers in the PARs so far has been estimated. Based on this map it is determined how genetically different in size is the female X chromosomes from the male X chromosome

Molecular epidemiology of complex heritable disease : applications in genomics and metabolomics

Modern high-throughput molecular technologies (collectively referred to as “omic” platforms) are generating unprecedented amounts of data on human variation. The four papers in this thesis each investigate and characterize associations between common, complex, heritable disease, and genetic or metabolomic markers from omic platforms. In paper I, we searched bipolar affective disorder (BPAD) pedigrees for genomic copy-number variants (CNVs, segmental deletions or duplications) segregating with disease. In one pedigree, a deletion in the gene MAGI1 was observed in six out of six affected members. Upon further inspection, another pedigree was found with two out of three affected members carrying a duplication in the same gene. A pooled association analysis was subsequently carried out using in-house and public data sets on CNVs in control subjects and cases of BPAD, schizophrenia (SZ), or schizoaffective disorder (SA). MAGI1 CNVs greater than 100 kb were found to be rare, nonsignificantly more common in BPAD cases than in controls, and significantly more common in the pooled case sample of BPAD, SZ, and SA than in controls. In paper II, we studied a rare single nucleotide polymorphism (SNP) in the gene HOXB13, which had been recently reported to be strongly associated with prostate cancer (PC) risk. We genotyped and analyzed the variant G84E (rs138213197) in the two large Swedish PC case-control samples CAPS and Stockholm-1 (in total 4,903 cases and 4,589 controls). G84E was less rare in the Swedish samples than in the United States population previously studied, with a carrier rate over 1% in Swedish population controls. The variant was associated with a more than threefold increased relative risk of PC in both Swedish samples. G84E carriers’ absolute lifetime risk to age 80 of PC was estimated to 33%. For G84E carriers in the uppermost quartile of a genetic risk score based on common risk SNPs, the same lifetime risk was estimated to 48%. In paper III, a replication study of previously reported genetic associations with testicular germ cell tumor (TGCT) risk was performed. SNPs in six genes (ATF7IP, BAK1, DMRT1, KITLG, SPRY4, and TERT) were genotyped and analyzed in a combined case-parent, case-control sample from Sweden and Norway. In total, 831 case-parent triads, 474 dyads, 712 singleton cases, and 3,919 control subjects were analyzed. Our results supported the previously reported association with TGCT risk for SNPs in all six genes. Tests of interaction effects revealed no allelic effect differences for the two major TGCT histological subtypes seminoma and non-seminoma. However, a variant in the gene SPRY4 was found to differ significantly in effect depending on the sex of the parent from which it was inherited. Only maternally inherited alleles were associated with TGCT risk. In paper IV, a large range of small molecules in human serum, collectively called the metabolome, were studied for association with PC risk and aggressiveness. Samples from 188 controls, 188 PC patients with indolent disease, and 99 PC patients with aggressive disease were analyzed by ultra-performance liquid chromatography coupled with mass spectrometry, generating 6,138 quantitative molecular features. All features were tested for association with PC status, adjusted for patient age and sample storage time. Two features were significantly associated after correction for multiple testing, but none of them could be identified as specific molecules. Testing the PC-associated features for association with 1.4 million SNPs genome-wide produced the strongest associations in variants in annotated genes, which may aid future molecular identification efforts. In conclusion, we have used omics platforms and modern computational tools to increase our knowledge about specific genetic risk factors and metabolomic markers for complex heritable disease. Our results may come of use in future etiological research as well as in genetic and molecular risk assessment

Design and Association Methods for Next-generation Sequencing Studies for Quantitative Traits.

Advances in exome sequencing and the development of exome genotyping arrays are enabling explorations of association between rare coding variants and complex traits using sequencing-based GWAS. However, the cost of sequencing remains high, optimal study design for sequencing-based association studies is an open question, powerful association methods and software to detect trait-associated rare and low-frequency variants are in great need. Containing 5% of information in human genome, chromosome X analysis has been largely neglected in routine GWAS analysis. In this dissertation, I focus on three topics: First, I describe a computationally efficient approach to re-construct gene-level association test statistics from single-variant summary statistics and their covariance matrices for single studies and meta-analyses. By simulation and real data examples, I evaluate our methods under the null, investigate scenarios when family samples have larger power than population samples, compare power of different types of gene-level tests under various trait-generating models, and demonstrate the usage of our methods and the C++ software, RAREMETAL, by meta-analyzing SardiNIA and HUNT data on lipids levels. Second, I describe a variance component approach and a series of gene-level tests for X-linked rare variants analysis. By simulations, I demonstrate that our methods are well controlled under the null. I evaluate power to detect an autosomal or X-linked gene of same effect size, and investigate the effect of sex ratio in a sample to power of detecting an X-linked gene. Finally I demonstrate usage of our method and the C++ software by analyzing various quantitative traits measured in the SardiNIA study and report detected X-linked variants and genes. Third, I describe a novel likelihood-based approach and the C++ software, RAREFY, to prioritize samples that are more likely to be carriers of trait-associated variants in a sample, with limited budget. I first describe the statistical method for small pedigrees and then describe an MCMC approach to make our method computationally feasible for large pedigrees. By simulations and real data analysis, I compare our approach with other methods in both trait-associated allele discovery power and association power, and demonstrate the usage of our method on pedigrees from the SardiNIA study.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113521/1/sfengsph_1.pd