Measuring and Using Genetic Ancestry Information in Genome-wide Admixture Mapping and Association Mapping of Complex Diseases PDF Download
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Author: Chao Tian
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Chao Tian
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Eleftheria Zeggini
Publisher: Academic Press
ISBN: 0123751438
Category : Medical
Languages : en
Pages : 353
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Book Description
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
Author: Lisa Anne Brown
Publisher:
ISBN:
Category :
Languages : en
Pages : 94
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Book Description
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.
Author: Andrew R. Collins
Publisher: Springer Science & Business Media
ISBN: 1597453897
Category : Medical
Languages : en
Pages : 529
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Book Description
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.
Author: Kelsey Grinde
Publisher:
ISBN:
Category :
Languages : en
Pages : 170
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Book Description
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.
Author: Riyan Cheng
Publisher: Frontiers Media SA
ISBN: 2889712125
Category : Science
Languages : en
Pages : 148
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Book Description
Author: David Siegmund
Publisher: Springer Science & Business Media
ISBN: 0387496866
Category : Medical
Languages : en
Pages : 337
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Book Description
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.
Author: Katarzyna Bryc
Publisher:
ISBN:
Category :
Languages : en
Pages : 358
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Book Description
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.
Author: Xiangqing Sun
Publisher: Frontiers Media SA
ISBN: 2889199320
Category : Genetics
Languages : en
Pages : 86
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Book Description
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.
Author: Tatsuhiko Tsunoda
Publisher: Springer Nature
ISBN: 9811381771
Category : Medical
Languages : en
Pages : 209
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Book Description
This book examines the utility of genome-wide association studies (GWAS) in the era of next-generation sequencing and big data, identifies limitations and potential means of overcoming them, and looks to the future of GWAS and what may lay beyond. GWAS are among the most powerful tools for elucidating the genetic aspects of human and disease diversity. In Genome-Wide Association Studies, experts in the field explore in depth the impacts of GWAS on genomic research into a variety of common diseases, including cardiovascular, autoimmune, diabetic, cancer, and infectious diseases. The book will equip readers with a sound understanding both of the types of disease and phenotypes that are suited for GWAS and of the ways in which a road map resulting from GWAS can lead to the realization of personalized/precision medicine: functional analysis, drug seeds, pathway analysis, disease mechanism, risk prediction, and diagnosis.
