Dementia is a clinical state caused by neurodegeneration and characterized by a loss of function in cognitive domains and behavior. Alzheimer’s disease (AD) is the most common form of dementia. Although the amyloid β (Aβ) protein and hyperphosphorylated tau aggregates in the brain are considered to be the key pathological hallmarks of AD, the exact cause of AD is yet to be identified. In addition, clinical diagnoses of AD can be error prone. Many previous studies have compared the clinical diagnosis of AD against the gold standard of autopsy confirmation and shown substantial AD misdiagnosis Hippocampal sclerosis of aging (HS-Aging) is one type of dementia that is often clinically misdiagnosed as AD. AD and HS-Aging are controlled by different genetic architectures. Familial AD, which often occurs early in life, is linked to mainly mutations in three genes: APP, PSEN1, and PSEN2. Late-onset AD (LOAD) is strongly associated with the ε4 allele of apolipoprotein E (APOE) gene. In addition to the APOE gene, genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) in or close to some genes associated with LOAD. On the other hand, GRN, TMEM106B, ABCC9, and KCNMB2 have been reported to harbor risk alleles associated with HS-Aging pathology. Although GWAS have succeeded in revealing numerous susceptibility variants for dementias, it is an ongoing challenge to identify functional loci and to understand how they contribute to dementia pathogenesis. Until recently, rare variants were not investigated comprehensively. GWAS rely on genotype imputation which is not reliable for rare variants. Therefore, imputed rare variants are typically removed from GWAS analysis. Recent advances in sequencing technologies enable accurate genotyping of rare variants, thus potentially improving our understanding the role of rare variants on disease. There are significant computational and statistical challenges for these sequencing studies. Traditional single variant-based association tests are underpowered to detect rare variant associations. Instead, more powerful and computationally efficient approaches for aggregating the effects of rare variants have become a standard approach for association testing. The sequence-kernel association test (SKAT) is one of the most powerful rare variant analysis methods. A recently-proposed scan-statistic-based test is another approach to detect the location of rare variant clusters influencing disease. In the first study, we examined the gene-based associations of the four putative risk genes, GRN, TMEM106B, ABCC9, and KCNMB2 with HS-aging pathology. We analyzed haplotype associations of a targeted ABCC9 region with HS-Aging pathology and with ABCC9 gene expression. In the second study, we elucidated the role of the non-coding SNPs identified in the International Genomics of Alzheimer’s Project (IGAP) consortium GWAS within a systems genetics framework to understand the flow of biological information underlying AD. In the last study, we identified genetic regions which contain rare variants associated with AD using a scan-statistic-based approach.

Genome-wide association studies (GWAS) have greatly improved our understanding of the genetic basis of complex traits. However, there are two major limitations with GWAS. First, most common variants identified by GWAS individually or in combination explain only a small proportion of heritability. This raises the possibility that additional forms of genetic variation, such as rare variants, could contribute to the missing heritability. The second limitation is that GWAS typically cannot identify which genes are being affected by the associated variants. Examination of rare variants, especially those in coding regions of the genome, can help address these issues. Moreover, several studies have recently identified low-frequency variants at both known and novel loci associated with complex traits, suggesting that functionally significant rare variants exist in the human population.

Investigators have discovered thousands of genetic variants associated with various traits using genome-wide association studies (GWAS). These discoveries have substantially improved our understanding of the genetic architecture of many complex traits. Despite the striking success, these trait-associated loci collectively explain relatively little of disease risk. Many reasons for this unexplained heritability have been suggested and two understudied components are hypothesized to have an impact in complex disease etiology: rare variants and gene-environment (GE) interactions. Advances in next generation sequencing have offered the opportunity to comprehensively investigate the genetic contribution of rare variants on complex traits. Such diseases are multifactorial, suggesting an interplay of both genetics and environmental factors, but most GWAS have focused on the main effects of genetic variants and disregarded GE interactions. In this dissertation, we develop statistical methods to detect GE interactions for rare variant analysis for various types of outcomes in both independent and related samples. We leverage the joint information across a set of rare variants and implement variance component score tests to reduce the computational burden. First, we develop a GE interaction test for rare variants for binary and continuous traits in related individuals, which avoids having to restrict to unrelated individuals and thereby retaining more samples. Next, we propose a method to test GE interactions in rare variants for time-to-event outcomes. Rare variant tests for survival outcomes have been underdeveloped, despite their importance in medical studies. We use a shrinkage method to impose a ridge penalty on the genetic main effects to deal with potential multicollinearity. Finally, we compare different types of penalties, such as least absolute shrinkage selection operator and elastic net regularization, to examine the performance of our second method under various simulation scenarios. We illustrate applications of the proposed methods to detect gene x smoking interaction influencing body mass index and time-to-fracture in the Framingham Heart Study. Our proposed methods can be readily applied to a wide range of phenotypes and various genetic epidemiologic studies, thereby providing insight into biological mechanisms of complex diseases, identifying high-penetrance subgroups, and eventually leading to the development of better diagnostics and therapeutic interventions.

Genome-wide Association Studies (GWAS) is the study used to associate common variants and phenotypes and has uncovered thousands of disease-associated variants. However, there is limited research on the contribution of a rare variant. The UK Biobank (UKB) contains detailed medical records and genetic information for nearly 500,000 individuals and offers a great opportunity for genetic association studies on rare variants. Here we focused on the role of rare protein-coding variants on UKB phenotypes. We selected three diseases for analysis: breast cancer, hypothyroidism and type II diabetes. We defined criteria for qualifying variants and pruned the control group to reduce interference signals from similar phenotypes. We identified the most known biomarkers for those diseases, such as BRCA1 and BRCA2 gene for breast cancer, TG and TSHR gene for hypothyroidism and GCK for type II diabetes. This result supports the model validity and clarifies the contribution of rare variants to diseases. Moreover, we also tried the geneset based collapsing method to aggregate information across genes to strengthen the signal from rare variants and build a diagnosis model that only relies on the genetic information. Our model could achieve great performance with an AUC of more than 20% improvement for type II diabetes and breast cancer and more than 90% accuracy for hypothyroidism.

OBJECTIVES: We have previously reported a top-ranked risk gene [i.e., serine incorporator 2 gene (SERINC2)] for alcohol dependence in individuals of European descent by analyzing the common variants in a genome-wide association study. In the present study, we comprehensively examined the rare variants [minor allele frequency (MAF)<0.05] in the NKAIN1-SERINC2 region to confirm our previous finding. MATERIALS AND METHODS: A discovery sample (1409 European-American patients with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family participants with 1645 alcohol-dependent probands) were subjected to an association analysis. A total of 39 903 individuals from 19 other cohorts with 11 different neuropsychiatric and neurological disorders served as contrast groups. The entire NKAIN1-SERINC2 region was imputed in all cohorts using the same reference panels of genotypes that included rare variants from the whole-genome sequencing data. We stringently cleaned the phenotype and genotype data, and obtained a total of about 220 single-nucleotide polymorphisms in individuals of European descent and about 450 single-nucleotide polymorphisms in the individuals of African descent with 0

OBJECTIVES: We have previously reported a top-ranked risk gene [i.e., serine incorporator 2 gene (SERINC2)] for alcohol dependence in individuals of European descent by analyzing the common variants in a genome-wide association study. In the present study, we comprehensively examined the rare variants [minor allele frequency (MAF)<0.05] in the NKAIN1-SERINC2 region to confirm our previous finding. MATERIALS AND METHODS: A discovery sample (1409 European-American patients with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family participants with 1645 alcohol-dependent probands) were subjected to an association analysis. A total of 39 903 individuals from 19 other cohorts with 11 different neuropsychiatric and neurological disorders served as contrast groups. The entire NKAIN1-SERINC2 region was imputed in all cohorts using the same reference panels of genotypes that included rare variants from the whole-genome sequencing data. We stringently cleaned the phenotype and genotype data, and obtained a total of about 220 single-nucleotide polymorphisms in individuals of European descent and about 450 single-nucleotide polymorphisms in the individuals of African descent with 0

No description available.

My thesis aims at identifying both genetic and environmental causes of major depressive disorder (MDD), using a large case-control study: 6,000 Chinese women with recurrent MDD and 6,000 controls. One of the major challenges for conducting genetic research on MDD is disease heterogeneity. The first question addressed is how different MDD is from highly comorbid anxiety disorders. I examine how anxiety disorders predict clinical features of depression and the degree of heterogeneity in their predictive pattern. The second question addressed is whether clinically defined MDD is a single disorder, or whether it consists of multiple subtypes. Results are then compared with and interpreted in the context of Western studies. Furthermore, latent class analysis and factor analysis results are also used in association analysis to explore more genetically homogeneous subtypes. Genetic data were derived using a novel strategy, low pass whole genome sequence analysis. Using genotypes imputed from the sequence data, I show that a cluster of single nucleotide polymorphisms (SNPs) is significantly associated with a binary disease phenotype including only cases with = 4 episodes of MDD, suggesting that recurrence might be an indication of genetic predisposition. The third issue examined is the contribution of rare variants to disease susceptibility. Again using sparse sequence data, I identified exonic sequence variants and performed gene-based analysis by comparing the number of variants between cases and controls in every gene. Furthermore I performed gene enrichment test by combining P values of SNP association tests at different minor allele frequency ranges. Overall, I did not find convincing evidence that rare variants aggregately contribute to disease susceptibility. However, the gene-based analysis resulted in an unexpected finding: cases have an excess of variants in all thirteen-protein coding mitochondrial genes, which was due to copy number differences in the mitochondrial genome. Both human phenotypic data as well as mice experimental data show that the increase in the mitochondrial copy number in cases is due to chronic stress.

No description available.

Background: Coronary Artery Disease (CAD) represents the leading cause of mortality and morbidity worldwide despite declines in the prevalence of environmental risk factors. This trend has drawn attention to the risk conferred by genetic variation. Twin and linkage studies demonstrate a profound hereditary risk for CAD, especially in young individuals. Rare genetic variants conferring high risk for extreme disease phenotypes can provide invaluable insight into novel mechanisms underlying CAD development. Methods: Whole exome sequencing was performed to characterize rare protein-altering variants in 52 early-onset CAD (EOCAD) patients encompassing the DECODE study. The enrichment of Mendelian dyslipidemias in EOCAD was assessed through interrogation of pathogenic mutations among known lipid genes. The identification of novel genetic CAD associations was conducted through case-only and case-control approaches across all protein-coding genes using rare variant burden and variance component tests. Lastly, beta coefficients for significant risk genes from the European population in the Early-onset Myocardial Infarction (EOMI) cohort (N=552) were used to construct calibrated, single-sample rare variant gene scores (RVGS) in DECODE Europeans (N=39) and a local European CAD-free cohort (N=77). Results: A 20-fold enrichment of Familial hypercholesterolemia mutation carriers was detected in EOCAD cases compared to CAD-free controls (P=0.005). Association analysis using EOMI Europeans revealed exome-wide and nominal significance for two known CAD/MI genes: CELSR2 (P=1.1x10-17) and APOA5 (P=0.001). DECODE association revealed exome-wide and nominal significance for genes involved in endothelial integrity and immune cell activity. RVGS based upon beta coefficients of significant CAD/MI risk genes were significantly increased in DECODE (z-score=1.84; p=0.03) and insignificantly decreased among CAD-free individuals (z-score=-1.61; p=0.053). Conclusion: Rare variants play a pivotal role in the development early CAD through Mendelian and polygenic mechanisms. Construction of RVGS that are calibrated against population and technical biases can facilitate discovery of single-sample and cohort-based associations beyond what is detectable using standard methods. / Thesis / Master of Science (MSc)