This book provides an introduction to the latest gene mapping techniques and their applications in biomedical research and evolutionary biology. It especially highlights the advances made in large-scale genomic sequencing. Results of studies that illustrate how the new approaches have improved our understanding of the genetic basis of complex phenotypes including multifactorial diseases (e.g., cardiovascular disease, type 2 diabetes, and obesity), anatomic characteristics (e.g., the craniofacial complex), and neurological and behavioral phenotypes (e.g., human brain structure and nonhuman primate behavior) are presented. Topics covered include linkage and association methods, gene expression, copy number variation, next-generation sequencing, comparative genomics, population structure, and a discussion of the Human Genome Project. Further included are discussions of the use of statistical genetic and genetic epidemiologic techniques to decipher the genetic architecture of normal and disease-related complex phenotypes using data from both humans and non-human primates.

The study of transcriptomics is key to understanding complex diseases. This new edition will build on the foundation of the first edition while incorporating the progress that has been made in the field of transcriptomics in the past six years, including bioinformatics for data analysis. Written by leading experts, chapters address new subjects such as methodological advances in large-scale sequencing, the sequencing of single-cells, and spatial transcriptomics. The new edition will address how transcriptomics may be used in combination with genetic strategies to identify causative genes in monogenic and complex genetic diseases. Coverage will also explore transcriptomics in challenging groups of diseases, such as cancer, inflammation, bacterial infection, and autoimmune diseases. The updated volume will be useful for geneticists, genome biologists, biomedical researchers, molecular biologists, bioinformaticians, and students, among others.

Biological traits and diseases tend to be very complex. Time is an aspect that deserves particular attention to study and decipher biological traits and disease mechanisms: many processes including biological rhythms, neurodevelopmental and neurodegenerative mechanisms, and aging have a time-dependent trajectory. Biological rhythms, such as circadian rhythms are a reflection of biological processes over 24 hours. In the case of developmental and aging processes, they reflect biological activities over a much longer time scale, typically across years or even decades. In recent years these research fields have been cross-fertilizing each other. Examples include apparent alterations of circadian regulation in adult and aging individuals and a potential link between circadian disruption and Autism Spectrum Disorders, Alzheimer’s Disease, and Major Depressive Disorder. Recent research aimed at decoding these time-related complexities has led to the implementation and utilization of various -omics methods. Transcriptomics and proteomics have matured into “standard” methods for profiling expression changes on a large scale across different time points. Single-cell sequencing technology will gain popularity for decoding cell-type diversity. With regard to data analysis, the identification of differentially expressed genes and proteins across time is of great interest. Granted, there are also topic-specific methods too. For circadian rhythm research, molecules that show rhythmic activity signals are of prime interest, whereas for life span studies the major focus is the identification of genes whose expression changes over long time periods. These topic-specific research methods can greatly benefit from each other’s expertise.

A central goal in genomics is to identify genetic variations and their impact on underlying molecular changes that lead to disease. With the advances in whole genome sequencing, many studies have been able to identify thousands of genetic loci associated with human traits. These studies mainly focus on single-nucleotide variants (SNVs) and novel insertion and deletions in the genome, while ignoring more complex variants. Here, I consider the problem of genotyping Variable Number Tandem Repeats (VNTRs), composed of inexact tandem duplications of short (6-100 bp) repeating units that span 3% of the human genome. While some VNTRs are known to play a role in complex disorders (e.g. Alzheimer's, Myoclonus epilepsy, and Diabetes), the majority of them have not been studied well due to computational difficulty in genotyping VNTRs on a large scale. Here, I will present our progress on developing efficient computational algorithms to profile VNTRs from high throughput sequencing data and identify possible variations within them. I applied our method to generate the largest catalog of VNTR genotypes to this date, which provides insights into the landscape of VNTR variations in different populations. I show the contribution of tandem repeats in mediating expression levels of key genes with known associations to neurological disorders and familial cancers, and argue the causality of this relation. Finally, I will describe our efforts to directly understand the impact of these variations on human phenotypes, which improves our understanding of genetic architecture of complex diseases.

According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. - Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests - Extensive list of references including links to tutorial websites - Case studies and Tips and Tricks

Translation of knowledge from basic science to medicine is essential to improving both clinical research and practice. In this translation, high-throughput genomic approaches can greatly accelerate our understanding of molecular mechanisms of diseases. A successful high-throughput genomic study of disease requires, first, comprehensive and efficient platforms to collect genomic data from clinical samples, and second, computational analysis methods that utilize databases of prior biological knowledge together with experimental data to derive clinically meaningful results. In this thesis, we discuss the development of a new microarray platform as well as computational methods for knowledge-based analysis along with their applications in clinical research. First, we and other colleagues have developed a new high-density oligonucleo-tide array of the human transcriptome for high-throughput and cost-efficient analysis of patient samples in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing, and also pro-vides assays for coding SNP detection and non-coding transcripts. Compared with high-throughput mRNA sequencing technology, we show that this array is highly re-producible in estimating gene and exon expression, and sensitive in detecting expres-sion changes. In addition, the exon-exon junction feature of this array is shown to im-prove detection efficiency for mRNA alternative splicing when combined with an ap-propriate computational method. We implemented the use of this array in a multi-center clinical program and have obtained comparable levels of high quality and re-producible data. With low costs and high throughputs for sample processing, we antic-ipate that this array platform will have a wide range of applications in high-throughput clinical studies. Second, we investigated knowledge-based methods that utilize prior know-ledge from biology and medicine to improve analysis and interpretation of high-throughput genomic data. We have developed knowledge-based methods to enrich our prior knowledge, illustrate dynamic response to external stimulus, and identify distur-bances in cellular pathways by chemical exposure, as well as discover hidden biological signatures for the prediction of patient outcomes. Finally, we applied a knowledge-based approach in a large scale genomic study of trauma patients. Cooperating with clinical information, prior knowledge improved the interpretation of common and dif-ferential genomic response to injury, and provided efficient risk assessment for patient outcomes. The clinical and genomic data as well as analysis results in this trauma study were systematically organized and provided to research communities as new knowledge of traumatic injury. The microarray platform and knowledge-based methods presented in this thesis provide appropriate research tools for high-throughput translational medicine in a large clinical setting. This thesis is expected to advance understanding and treatment for dis-eases, and finally, improve public health.

Genetic Analysis of Complex Diseases An up-to-date and complete treatment of the strategies, designs and analysis methods for studying complex genetic disease in human beings In the newly revised Third Edition of Genetic Analysis of Complex Diseases, a team of distinguished geneticists delivers a comprehensive introduction to the most relevant strategies, designs and methods of analysis for the study of complex genetic disease in humans. The book focuses on concepts and designs, thereby offering readers a broad understanding of common problems and solutions in the field based on successful applications in the design and execution of genetic studies. This edited volume contains contributions from some of the leading voices in the area and presents new chapters on high-throughput genomic sequencing, copy-number variant analysis and epigenetic studies. Providing clear and easily referenced overviews of the considerations involved in genetic analysis of complex human genetic disease, including sampling, design, data collection, linkage and association studies and social, legal and ethical issues. Genetic Analysis of Complex Diseases also provides: A thorough introduction to study design for the identification of genes in complex traits Comprehensive explorations of basic concepts in genetics, disease phenotype definition and the determination of the genetic components of disease Practical discussions of modern bioinformatics tools for analysis of genetic data Reflecting on responsible conduct of research in genetic studies, as well as linkage analysis and data management New expanded chapter on complex genetic interactions This latest edition of Genetic Analysis of Complex Diseases is a must-read resource for molecular biologists, human geneticists, genetic epidemiologists and pharmaceutical researchers. It is also invaluable for graduate students taking courses in statistical genetics or genetic epidemiology.

With more efficient genotyping technologies and lower sequencing cost, genome-wide association studies (GWAS) have been broadly applied to many complex human traits. However, people of European descent remain the most prominent subjects in genetic research and other ethnic groups might not fully benefit from the effort of GWAS. In addition to expanding GWAS to include more diverse populations, new approaches that enable trans-ethnic or multi-ethnic analyses in GWAS will also be a crucial stepping stone for future genetic studies. To address this disparity and knowledge gap, we developed and applied a new approach, cross-population allele screen (CPAS) prior to GWAS, to identify population-specific variants that are associated with complex traits or diseases (Chapter 2). In our study, we identified novel genetic variants that are associated with serum triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and body mass index (BMI), exhibiting differential allele frequencies between Finns and Mexicans. Notably, one of the novel TGs-associated genes, SIK family kinase 3 (SIK3), harbors an Amerindian-specific common risk variant (allele frequency=18% in Mexicans), which is not observed in other continental populations, and the risk allele carriers also exhibit higher serum TG levels after a high-fat meal. In addition, this locus displays a signal of positive selection in Mexicans, suggesting that a delayed serum lipid clearance might have been evolutionally advantageous for ancient Amerindian people. While GWAS have uncovered many trait-associated loci, translating GWAS results to actionable medical information remains nontrivial due to the difficulty of pinpointing the true causal variants and genes. To understand the molecular mechanism of GWAS variants, many functional genomic approaches have been developed. In this dissertation, I will present two computational methods to integrate genetic and transcriptomic data to infer functional variants and possible underlying genes. First, we developed Functional Summary-based Imputation (FUSION) that can leverage GWAS summary statistics and a relatively small reference panel of transcriptomes to infer the association between gene expression and traits (Chapter 3). Using FUSION as well as subcutaneous adipose and whole blood RNA-sequence (RNA-seq) data, we performed transcript-wide associated studies (TWAS) and identified 69 novel genes associated with BMI, serum lipids, and height. With the constantly growing GWAS summary statistics and transcriptomic data, we can further utilize FUSION to apply TWAS to many different traits and tissues. To account for the increasing presence of large-scale RNA-seq cohorts, we created a new computational tool, ASElux, which can efficiently perform allele-specific expression (ASE) estimation that was previously prohibited due to excessive computing time (Chapter 4). We implemented a hybrid index system in ASElux to first build an individualized reference genome with available genotype data, and ASElux will then only align variant-carrying reads that are informative for ASE calculation. Thus, ASElux can correct for the reference allele bias during alignment with much shorter computing time. In our comparison test, ASElux is 4-33 times faster than other commonly used software or pipelines for ASE and obtain a similar or better accuracy. We applied ASElux to 273 lung RNA-seq samples, and uncovered a splice variant, rs11078928, which could explain the molecular mechanism of an asthma GWAS hit, rs11078927. We envision that the speed and efficiency of ASElux can facilitate ASE analysis in many RNA-seq datasets to uncover functional variants in the future. In Chapters 5 and 6, I will present our studies utilizing epigenomic and transcriptomic data to gain insight into the causal mechanisms of obesity and non-alcoholic fatty liver disease (NAFLD). To elucidate molecular mechanisms underlying obesity-related GWAS variants, we integrated promoter-enhancer interactions in human primary adipocytes with adipose cis expression quantitative trait locus (eQTL) variants (Chapter 5). Using promoter capture Hi-C, we first assayed chromosomal interactions in human primary adipocytes. In combination with human subcutaneous adipose transcriptomes, we then identified four genes associated with BMI or obesity-related traits that are also under cis regulation via chromosomal looping. We further performed electrophoretic mobility shift assays (EMSAs) to validate the allelic effect of a cis eQTL, rs4776984, regulating mitogen-activated protein kinase 5 (MAP2K5). The reference allele displayed a lower protein binding affinity than the alternative allele, in line with the computationally predicted disruptive effect. Finally, we also reported 38 additional BMI candidate genes under the regulation of chromosomal interactions for future studies of obesity. In our NAFLD study (Chapter 6), we tested the hypothesis that obesity may impair the function of adipose tissue, which can lead to ectopic fat accumulation in the liver, resulting in NAFLD. To understand the molecular pathogenesis of NAFLD driven by obesity, we examined the liver histology and subcutaneous adipose transcriptomes from 259 morbidly obese Finnish individuals that underwent a bariatric surgery. One year after the surgery, we re-profiled their adipose transcriptomes to assess the effect of the weight loss on adipose gene expression. At baseline, we identified adipose expression of 43 genes downregulated in non-alcoholic steatohepatitis (NASH) patients. Of these, the adipose expression of 17 genes was negatively correlated with liver steatosis and serum TGs. In a large panel of mouse strains, expression of five of the 17 genes was also correlated with a diet-induced liver steatosis. Specifically, the adipose expression of one of the five genes, death associated protein kinase 2 (DAPK2), recovered after the weight-loss at the one-year follow-up. Combining phenotype and longitudinal transcriptome data, we performed mediation analyses to demonstrate the causal effect of DAPK2 adipose expression on NAFLD. When DAPK2 expression was knocked down in human primary preadipocytes, five key genes involved in autophagy, of which two also function in adipocyte differentiation, were also downregulated. Our findings suggest an obesity-induced reduction of DAPK2 expression as a new pathogenic mechanism of NAFLD through impairment of autophagy pathway and adipocyte differentiation. In summary, our work presented in Chapters 5 and 6, employing functional genomic approaches and computational methods to decipher disease mechanisms of obesity and NAFLD, highlights strategies to understand the molecular pathogenesis of human disease beyond GWAS.