Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Measuring And Using Genetic Ancestry Information In Genome Wide Admixture Mapping And Association Mapping Of Complex Diseases
Download Measuring And Using Genetic Ancestry Information In Genome Wide Admixture Mapping And Association Mapping Of Complex Diseases full books in PDF, epub, and Kindle. Read online Measuring And Using Genetic Ancestry Information In Genome Wide Admixture Mapping And Association Mapping Of Complex Diseases ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Measuring and Using Genetic Ancestry Information in Genome-wide Admixture Mapping and Association Mapping of Complex Diseases by : Chao Tian
Download or read book Measuring and Using Genetic Ancestry Information in Genome-wide Admixture Mapping and Association Mapping of Complex Diseases written by Chao Tian and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: There exists much variation in genetic ancestry within and between ethnic groups, which causes substantial population stratification to be present not only in recently admixed populations like African Americans but also in generally assumed homogeneous populations like European Americans. In Chapter One I reviewed the recent studies of measuring and using genetic ancestry in human complex disease studies. Genetics variations constitute an important basis for Admixture Mapping. Many complex diseases show population specific prevalence that could be due to the differences of particular disease-susceptible genes among founding populations of different ancestry. Statistical methods can be applied to infer the locus ancestry along the chromosome in admixed individuals and tests for the association of the locus ancestry with the disease in admixed population, so called admixture mapping. Admixture mapping requires a genome-wide panel of relatively evenly spaced markers that can distinguish the locus ancestral origins in admixed individuals. In Chapter Two and Chapter Three I introduced our defined genome-wide Single-Nucleotide-Polymorphism panels that can extract ancestry information mostly with the least markers for African American and Mexican American admixed populations. On the other hand, a consequence of population stratification is the potential for false allelic associations and thus the inconsistent reports across genome-wide association studies. Statistical methods can be applied to discern and correct for the individual ancestry differences using Genome-wide association panel. In Chapter Four I introduced our findings of the European substructures, which have significant genetic variation along the north to south and west to east geographic axis. One of our recent report showed that after accounting for genetic ancestry difference, some locus are no long associated to Rheumatoid Arthritis but they appeared as very strong candidates without accounting for the substructure. In Chapter Five I introduced our findings of the East Asian substructures. Our analysis showed that there exist genetic variations both between different East Asian groups and within the Han Chinese population. In Chapter Six I reviewed the current available methods and importance of accounting for ancestry in genome-wide association studies. In Chapter Seven, I discussed some implications and future research directions.
Book Synopsis Analysis of Complex Disease Association Studies by : Eleftheria Zeggini
Download or read book Analysis of Complex Disease Association Studies written by Eleftheria Zeggini and published by Academic Press. This book was released on 2010-11-17 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Statistical Methods in Admixture Mapping by : Lisa Anne Brown
Download or read book Statistical Methods in Admixture Mapping written by Lisa Anne Brown and published by . This book was released on 2016 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic admixture occurs when two or more previously isolated populations combine to form an admixed population. The study of admixed populations can provide valuable insights into the complex relationship between environmental exposures, genetic background and complex traits. Gene mapping by linkage admixture disequilibrium, or admixture mapping, is a powerful approach for the identification of genetic loci influencing complex traits in ancestrally diverse populations. Admixture mapping leverages genomic heterogeneity among sampled individuals for improved gene discovery, where genetic loci with unusual deviations in local ancestry and that are significantly associated with a trait are identified. Admixture mapping can serve both as a primary method for discovery of novel genetic variants and as a complement to association mapping. In this dissertation, we thoroughly investigate the performance of existing statistical methods used for admixture mapping and we develop new methods that improve upon existing approaches. We also characterize the correlation structure of genetic loci in admixed populations and develop new genome-wide significance thresholds for admixture mapping under a range of models that should be useful for the future studies. Using real genotyping data in a large sample of African Americans, we find evidence of assortative mating, and in simulation studies with simulated phenotypes, we demonstrate that ancestry-related assortative can induce genome-wide inflation of admixture mapping test statistics and false positive associations. We also show how to appropriately adjust for this inflation and protect against spurious admixture associations. Finally, new linear and logistic mixed model methodology is developed for admixture mapping of quantitative and binary traits, respectively, in the presence of relatedness and population structure. We evaluate the performance of these methods through extensive simulation studies. The methods are applied to large-scale genetic studies of African American and Hispanic/Latino populations for genome-wide admixture mapping analyses where novel candidate loci for a variety of biomedical traits are identified.
Book Synopsis Linkage Disequilibrium and Association Mapping by : Andrew R. Collins
Download or read book Linkage Disequilibrium and Association Mapping written by Andrew R. Collins and published by Springer Science & Business Media. This book was released on 2008-02-05 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: As researchers continue to make enormous progress in mapping disease genes, exciting, novel, and complex analyses have emerged. In this book, scientists from around the world, who are leaders in this field, contribute their vast experience and expertise to produce a comprehensive and fascinating text for researchers and clinicians alike. They provide cutting-edge analysis of the most up-to-date and preeminent information available.
Book Synopsis Statistical Inference in Admixed Populations by : Kelsey Grinde
Download or read book Statistical Inference in Admixed Populations written by Kelsey Grinde and published by . This book was released on 2019 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the genetic causes of human diseases and traits has long been of interest in the scientific community. However, the large majority of research in this area has been conducted in European populations. This dissertation focuses on developing statistical methods for genetic studies in admixed populations, such as African Americans and Hispanics/Latinos, that have been historically underrepresented in genetics research. The diverse, mixed ancestry of admixed populations presents unique opportunities for statistical inference, many of which are explored in this work. Here, we focus in particular on two important tasks: inferring genetic ancestry from genotype and sequence data, and identifying genetic variants associated with complex traits and diseases. We propose and evaluate methods for inferring local ancestry on chromosome X, correcting for multiple testing in genome-wide admixture mapping studies, and controlling for confounding by global ancestry in admixture mapping and genome-wide association studies in admixed populations. We motivate our proposed methods with theoretical results, simulation studies, and applications to genotype and whole genome sequence data from large studies of African American and Hispanic/Latino individuals. Our work provides solutions to a number of the statistical challenges posed by genetic studies in admixed populations, and we hope that our results will help guide future studies in these populations.
Book Synopsis Statistical Methods, Computing, and Resources for Genome-Wide Association Studies by : Riyan Cheng
Download or read book Statistical Methods, Computing, and Resources for Genome-Wide Association Studies written by Riyan Cheng and published by Frontiers Media SA. This book was released on 2021-08-24 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Genome-wide Patterns of Population Structure and Ancestry Among Continental and Admixed Populations by : Katarzyna Bryc
Download or read book Genome-wide Patterns of Population Structure and Ancestry Among Continental and Admixed Populations written by Katarzyna Bryc and published by . This book was released on 2011 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Population genetics seeks to use genetic data to illuminate patterns of human diversity, investigate how populations are related, and to provide insights into population history, such as migrations events and population sizes. Furthermore, an understanding of population genetics is necessary to disentangle population structure from genetic associations with traits, to learn how genes affect phenotype or to perform disease association mapping. I use high-density single nucleotide polyphorphism (SNP) data to examine population structure in humans among several world-wide populations. I show that principal components analysis (PCA) and STRUCTURE, a bayesian clustering method, are able to resolve structure both among continents as well as illuminate substructure within Europe, South Asia, and East Asia. In an analysis of 12 West African populations, I demonstrate that population structure within the West African samples reflects linguistic relationships and geographical distances, and also shows signals of the Bantu expansion. I proceed to focus on several questions involving populations of mixed ancestry, or admixed populations. First, I introduce a new method for inferring individual ancestry along the genome, or "local ancestry". This method leverages principal component analysis to allow computationally efficient ancestry estimation using high-density SNP data. I apply this method to a sample of African Americans and witness a large range of ancestry proportions across in- dividuals in this panel. I find that the African Americans have a greater propotion of African ancestry on the X chromosome versus the autosomes, consistent with a greater female African and male European ancestry contribution. Since previous studies have suggested a West African ancestral population of African Americans, I use estimates of African and European segments of the genome to examine which of 12 West African populations is closest to the African ancestral population. I find that, consistent with the West African results of previous studies and historical records, the African regions of African American genomes show the lowest genetic divergence to West African populations Igbo, Brong, and Yoruba, which are non-Bantu Niger-Kordofanian speaking populations. Hispanic/Latino (HL) populations possess a complex genetic structure reflecting recent admixture among Native American, European, and West African populations. I estimate ancestry among five Hispanic/Latino populations (Mexico, Ecuador, Colombia, Puerto Rico, and Dominican Republic) and illuminate patterns of ancestry among populations. These differences among HL populations reflect geographic proximity to slave trade routes and ports, European colonizations, and historical migrations. I show a consistent sex bias in ancestry proportions across all five HL populations with higher Native American and lower European ancestry on the X chromosome compared to the autosomes. The ancestry difference on the X versus the autosomes suggests a greater Native American female and European male ancestry contribution bias in all five HL populations, and is further supported by Y chromosome and mitochondrial DNA haplotyping. Lastly, I discuss challenges in identifying the closest Native American ancestral population to the HL populations, such as poor Native American population sampling or substructure within the Americas. However, I am able to show that the Nahua (for Meso-American populations) and the Quechua (for South American populations) are the two populations least differentiated from the Native American segments of the HL individuals.
Book Synopsis Novel Approaches to the Analysis of Family Data in Genetic Epidemiology by : Xiangqing Sun
Download or read book Novel Approaches to the Analysis of Family Data in Genetic Epidemiology written by Xiangqing Sun and published by Frontiers Media SA. This book was released on 2016-08-17 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) for complex disorders with large case-control populations have been performed on hundreds of traits in more than 1200 published studies (http://www.genome.gov/gwastudies/) but the variants detected by GWAS account for little of the heritability of these traits, leading to an increasing interest in using family based designs. While GWAS studies are designed to find common variants with low to moderate attributable risks, family based studies are expected to find rare variants with high attributable risk. Because family-based designs can better control both genetic and environmental background, this study design is robust to heterogeneity and population stratification. Moreover, in family-based analysis, the background genetic variation can be modeled to control the residual variance which could increase the power to identify disease associated rare variants. Analysis of families can also help us gain knowledge about disease transmission and inheritance patterns. Although a family-based design has the advantage of being robust to false positives, novel and powerful methods to analyze families in genetic epidemiology continue to be needed, especially for the interaction between genetic and environmental factors associated with disease. Moreover, with the rapid development of sequencing technology, advances in approaches to the design and analysis of sequencing data in families are also greatly needed. The 11 articles in this book all introduce new methodology and, using family data, substantial new findings are presented in the areas of infectious diseases, diabetes, eye traits, autism spectrum disorder and prostate cancer.
Book Synopsis The Statistics of Gene Mapping by : David Siegmund
Download or read book The Statistics of Gene Mapping written by David Siegmund and published by Springer Science & Business Media. This book was released on 2007-05-27 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book details the statistical concepts used in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.
Book Synopsis Statistical Methods for Gene Mapping in Complex Diseases by : Ingileif Bryndís Hallgrímsdóttir
Download or read book Statistical Methods for Gene Mapping in Complex Diseases written by Ingileif Bryndís Hallgrímsdóttir and published by . This book was released on 2005 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Integration and Development of Machine Learning Methodologies to Improve the Power of Genome-wide Association Studies by : Jing Li
Download or read book Integration and Development of Machine Learning Methodologies to Improve the Power of Genome-wide Association Studies written by Jing Li and published by . This book was released on 2016 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have led to a great number of new findings in human genetics and genetic epidemiology. GWAS identifies DNA sequence variations using human genome data and identifies the genetic risk factors for common diseases. There are many challenges that remain when mapping the complex underlying relationships between genotypes and phenotypes in GWAS. Here, we attempt to improve the power to detect correct mapping in GWAS for disease prevention and treatment. We examine a number of assumptions in GWAS that have been made over the past decade, which need to be updated and discussed in light of recent GWAS algorithm development. To achieve this goal, we discuss some of the current assumptions of GWAS and all possible factors that could affect predictive power. Using simulation studies, we show statistical evidence of how different factors, including sample size, heritability, model misspecification, and measurement error, affect the power to detect correct genetic associations. These data have the potential to improve the design of GWAS. As epistasis is the key to studying GWAS, we specifically studied epistasis, which is believed to account for part of the missing heritability. To detect interactions, we developed permuted Random Forest (pRF), a scale-free method, which is based on the traditional machine learning method Random Forest (RF). This method accurately detects single nucleotide polymorphism (SNP)-SNP interactions and top interacting SNP pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions. We systematically tested this approach on a simulation study with datasets possessing various genetic constraints including heritability, number of SNPs, and sample size. Our methodology shows high success rates for detecting interacting SNP pairs. We also applied our approach to two bladder cancer datasets, which shows results consistent with well-studied methodologies and we built permuted Random Forest networks (PRFN), in which we used nodes to represent SNPs and edges to indicate interactions. Data suggest the pRF method could improve detection of pure gene-gene interactions. Classic methods used to detect genetic association in GWAS involved separating biological knowledge from genetic information, thus wasting useful biological information when modeling associations between genotypes and phenotypes. We therefore further developed a biological information guided machine learning methodology, based on Encyclopedia of DNA Elements (ENCODE), called ENCODE information guided synthetic feature Random Forest (E-SFRF). Instead of studying biological associations at the SNP level, we separated SNPs based on ENCODE information and grouped them into a particular gene or enhancer to calculate the synthetic feature (SF) on a higher level. In our study, we focused on genes or enhancers from the AHR pathway, which is involved in cancer development. This work showed that the E-SFRF method could identify consistent main effect models based on SFs from two independent bladder cancer studies. We further studied the SNP-SNP interactions inside the top main effect SFs and discovered interesting SNP-SNP interactions that may lead to strong main effects. We believe our method could increase the possibility of replicating results across different GWAS datasets by increasing both the consistency and accuracy in genetic studies. Overall, we have found that studying interactions among SNPs is essential to increasing the power to uncover genetic architectures. By developing different machine learning methods, pRF, and further incorporating biological information to develop E-SFRF, we were able to detect pure gene-gene interactions in a scale-free and non-parametric way, helping to increase repeatability and reliability of GWAS using biological knowledge.
Book Synopsis Ancestry Estimation and Application to the Genetic of Complex Diseases in Human by : Rami Mohamed Nassir
Download or read book Ancestry Estimation and Application to the Genetic of Complex Diseases in Human written by Rami Mohamed Nassir and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the relationship between genotypes and phenotypes is one of the major aims in biology and medicine. An important foundation to understand this relationship is the clear understanding of the pattern of genetic diversity in different human populations and how it correlates with complex genetic diseases. This would facilitate determining why there are differences in susceptibility to common disease among individuals and populations from different continental ancestry groups. In chapter one, I review the critical background for studying the genetics of complex disease. This includes recent studies for ascertaining the genetic structure of human populations using genetic markers, the importance of genetic variations and how it affects the development of specific phenotypes and specific methods to incorporate genetic substructure in association tests. In chapter two, I introduce our ancestry informative markers (AIMs) set for assessing the continental ancestry and admixture proportions in common populations in America. This set of 128 SNPs can correct for populations stratification in admixed population sample sets. In chapter three, I present additional studies validating these AIMs in multiple population groups from Oceana, South Asia, East Asia, Sub-Saharan African, North and South America and Europe. In addition, a subset of this AIM's, which consists of 93 AIM's are effective in identifying the continental subject groups from the Human Genome Diversity Panels. In chapter Four, I present the application of these AIMs to evaluate the association of genetic admixture in African American and Hispanic populations in Women Health Initiative study (WHI) with different measurements of obesity including Body Mass Index (BMI) and Waist-Hip Ratio (WHR). In chapter five, I further present results of the association of genetic admixture in the same populations and different measurements of hypertension. In chapter six, I discuss some perspective and the direction for the future of this research.
Book Synopsis In the Light of Evolution by : National Academy of Sciences
Download or read book In the Light of Evolution written by National Academy of Sciences and published by . This book was released on 2007 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Arthur M. Sackler Colloquia of the National Academy of Sciences address scientific topics of broad and current interest, cutting across the boundaries of traditional disciplines. Each year, four or five such colloquia are scheduled, typically two days in length and international in scope. Colloquia are organized by a member of the Academy, often with the assistance of an organizing committee, and feature presentations by leading scientists in the field and discussions with a hundred or more researchers with an interest in the topic. Colloquia presentations are recorded and posted on the National Academy of Sciences Sackler colloquia website and published on CD-ROM. These Colloquia are made possible by a generous gift from Mrs. Jill Sackler, in memory of her husband, Arthur M. Sackler.
Book Synopsis The Science of Health Disparities Research by : Irene Dankwa-Mullan
Download or read book The Science of Health Disparities Research written by Irene Dankwa-Mullan and published by John Wiley & Sons. This book was released on 2021-03-16 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrates the various disciplines of the science of health disparities in one comprehensive volume The Science of Health Disparities Research is an indispensable source of up-to-date information on clinical and translational health disparities science. Building upon the advances in health disparities research over the past decade, this authoritative volume informs policies and practices addressing the diseases, disorders, and gaps in health outcomes that are more prevalent in minority populations and socially disadvantaged communities. Contributions by recognized scholars and leaders in the field—featuring contemporary research, conceptual models, and a broad range of scientific perspectives—provide an interdisciplinary approach to reducing inequalities in population health, encouraging community engagement in the research process, and promoting social justice. In-depth chapters help readers better understand the specifics of minority health and health disparities while demonstrating the importance of advancing theory, refining measurement, improving investigative methods, and diversifying scientific research. In 26 chapters, the book examines topics including the etiology of health disparities research, the determinants of population health, research ethics, and research in African American, Asians, Latino, American Indian, and other vulnerable populations. Providing a unified framework on the principles and applications of the science of health disparities research, this important volume: Defines the field of health disparities science and suggests new directions in scholarship and research Explains basic definitions, principles, and concepts for identifying, understanding and addressing health disparities Provides guidance on both conducting health disparities research and translating the results Examines how social, historical and contemporary injustices may influence the health of racial and ethnic minorities Illustrates the increasing national and global importance of addressing health disparities Discusses population health training, capacity-building, and the transdisciplinary tools needed to advance health equity A significant contribution to the field, The Science of Health Disparities Research is an essential resource for students and basic and clinical researchers in genetics, population genetics, and public health, health care policymakers, and epidemiologists, medical students, and clinicians, particularly those working with minority, vulnerable, or underserved populations.
Book Synopsis Genetic Dissection of Complex Traits by : D.C. Rao
Download or read book Genetic Dissection of Complex Traits written by D.C. Rao and published by Academic Press. This book was released on 2008-04-23 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more
Book Synopsis Statistical Methods in Genetic Association by : Ge Zhang
Download or read book Statistical Methods in Genetic Association written by Ge Zhang and published by . This book was released on 2007 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Association studies offer great promise in dissecting the genetic basic of human complex diseases. The rapid expansion of genomic information and the cost-effective genotyping technologies have enabled us to systematically interrogate the role of human genetic variation in common diseases by genome-wide association (GWA) mapping. However, the scale and complexity of such studies will raise significant challenges in study design and data analysis. In this dissertation, we investigated several statistical problems that relevant to population-based association studies and the fine-scale mapping of genetic variants that influence susceptibility to complex diseases. First, we developed a variance-based effect size estimator for the locus-specific genetic effect. Comparing to the traditional measures, the proposed estimator is less sensitive to the risk allele frequency and the population prevalence of the disease. We demonstrated the sample size requirement would be considerable large to obtain an accurate estimate on moderate genetic effect and the sample size will increase exponentially with increased demand for precision. We next compared the power of different association test statistics. We observed that the genotype based single-locus tests is generally more powerful than the multi-locus or haplotype based statistics, especially for risk alleles far from additive; and the power of genotype based tests can be uniformly improved by applying the ordered restriction on genotypic risks. Finally, we tested different GWA strategies and explored the factors that may influence the power of GWA studies by extensive simulations using empirical genotype data from the HapMap ENCODE Project. Our results indicate that current commercial genome-wide typing products are capable of capturing most of the common risk variants; however, their power in detecting rare risk variants or variants within recombination hot spots is not satisfactory. We also showed that the properties of the risk variants (e.g. allele frequency, local recombination rate, and functional category) have significant impacts on the power of GWA. The results generated from this comprehensive exercise would be helpful for developing efficient GWA studies.
Book Synopsis Gene Association Mapping in the Era of Next-generation Sequencing and Systems Biology by : Tianxiao Zhang
Download or read book Gene Association Mapping in the Era of Next-generation Sequencing and Systems Biology written by Tianxiao Zhang and published by . This book was released on 2016 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, advancement of genotyping technology, first microarray then "next-generation" sequencing, has enabled scientists to examine the susceptible genes that contribute to the risk of complex disorders using a genome-wide, "hypothesis free" strategy. However, despite this "hypothesis free" label, these genome-wide approaches (including genome-wide association and whole genome sequencing studies) depend on two implicit assumptions. The first assumption is that the genetic risk of complex traits is contributed by independent genes/variants (assumption of independence).The second assumption is that different genes have equal potentiality to confer to the genetic predisposition of the complex traits (assumption of equality). Despite the huge success in susceptible gene association mapping in the last decade, more and more evidence has indicated that these two underlying assumptions of these genome-wide approaches may not be sound. Other than just studying one locus at a time, alternative methods which can carry out global analyses of biological molecules in populations have been developed to understand the influence of the whole biological system on complex traits. Network based approaches, in particular, have proven informative.This dissertation will cover a few important issues concerning sequencing based study design and its applications in chapter II, III and IV. Human protein-protein interaction network will be constructed and a few of human gene network related issues will be studied and discussed in chapter V and VI. Abstracts for each chapter were summarized as followed.Chapter 2: In this chapter, we proposed a two-stage, gene-based method for association mapping of rare variants by applying four different non-collapsing algorithms. Using the Genome Analysis Workshop 18 whole genome sequencing dataset of simulated blood pressure phenotypes, we studied and contrasted the false positive rate of each algorithm using receiver operating characteristic curves. The statistical power of these methods was also evaluated and compared through the analysis of 200 simulated replications in a smaller genotype data set. We showed that the Fisher's method was superior to the other three 3 non-collapsing methods, but was no better than the standard method implemented with famSKAT.Chapter 3: In this chapter, we aimed to identify potential susceptibility variants for bipolar disorder via the combination of exome sequencing and linkage analysis on 6 related subjects from a four-generation family. Our study identified a list of five potential candidate genes for bipolar disorder. Among these five genes, GRID1 (Glutamate Receptor Delta-1 Subunit), which was previously reported to be associated with several psychiatric disorders and brain related traits, is of particular interest. Our findings suggest a potential role for these genes and the related rare variants in the onset and development of bipolar disorder in this one family.Chapter 4: In this chapter, we investigated the potential of FMO genes to confer risk of nicotine dependence via deep targeted sequencing in 2,820 study subjects comprising of nicotine 1,583 dependents and 1,237 controls from European and African Americans. Specifically, we focused on the two genomic segments including FMO1, FMO3 and the pseudo gene FMO6P, and aimed to investigate the potential association between FMO genes and nicotine dependence. We identified different clusters of significant common variants in European (with most significant SNP rs6674596, P=0.0004, OR=0.67, MAF_EA=0.14) and African Americans (with the most significant SNP rs6608453, P=0.001, OR=0.64, MAF_AA=0.1). Most of the significant variants identified were SNPs located within intronic regions or with unknown functional significance.Chapter 5: In this chapter, we aimed to investigate the followed three scientific questions: 1) Can centrality reflect the biological significance of genes in a general human gene network? 2) Among these four commonly used centrality measures, does any of them outperform others? 3) Will they do better if we combine several centrality measures together using machine learning algorithms? To answer these scientific questions, we constructed a comprehensive human gene-gene network using protein-protein interaction data. Four essential gene sets were extracted from a variety of data sources serving as true answers in the evaluation and optimization process. Our analytic results indicated that there is a connection between the essentiality and centrality of human genes. A pattern of strong correlations was identified among the four commonly used centrality measures for a general human PPI network and the performance of each centrality measure was similar to others serving as predictors of the essentiality of genes. The improvement of the prediction models was limited when we combined several different centrality measures.Chapter 6: In this chapter, we aimed to investigate the potential enrichment pattern in centrality of susceptible genes for certain complex disorders in a functional specific sub-network. Gene expression data of human brain tissue recorded in the Human Protein Atlas were extracted and utilized to construct a series of brain function specific sub-networks. Susceptible genes from three categories of complex disorders, including neurodegenerative disorder, psychiatric disorder and non-brain related disorder, were extracted from the GWAS catalogue. We identified a significant enrichment pattern of high centrality of susceptibility genes contributing to neurodegenerative and psychiatric disorders in these sub-networks. Our findings indicate that susceptibility genes of complex disorder might have higher centralities in functional specific sub-networks.
