Genetic Architecture and Evolution of Complex Traits and Diseases in Diverse Human Populations PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Genetic Architecture and Evolution of Complex Traits and Diseases in Diverse Human Populations PDF full book. Access full book title Genetic Architecture and Evolution of Complex Traits and Diseases in Diverse Human Populations by Mashaal Sohail. Download full books in PDF and EPUB format.
Author: Mashaal Sohail
Publisher: Frontiers Media SA
ISBN: 2889748715
Category : Science
Languages : en
Pages : 108
Get Book Here
Book Description
Author: Mashaal Sohail
Publisher: Frontiers Media SA
ISBN: 2889748715
Category : Science
Languages : en
Pages : 108
Get Book Here
Book Description
Author: Kodi Taraszka
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Exploring how genetic architecture shapes complex traits and diseases is a central premise of human genetics. Over the years, genome-wide association studies (GWAS) have enabled the discovery of numerous genetic variants associated with a variety of complex traits. In addition to the large array of traits analyzed, GWAS in diverse ancestral populations have also seen a significant increase in sample sizes. These efforts led to tens of thousands of publicly available GWAS summary statistics whose known correlation structure could be leveraged for further discovery. In this dissertation, I present two novel methods for the meta-analysis of GWAS summary statistics as well as conduct a pan-cancer meta-analysis of somatic variant burden. For one method, I present a likelihood ratio test for the joint analysis of genetically correlated traits and provide a per trait interpretation framework of the omnibus association. For the other method, I present a Bayesian framework that improves fine mapping of significant associations for one trait by leveraging the complementary information from distinct ancestral backgrounds. In addition to these methods, I analyzed how clinical and polygenic germline features influence somatic variant burden within and across cancer types.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Farhad Hormozdiari
Publisher:
ISBN:
Category :
Languages : en
Pages : 239
Get Book Here
Book Description
Genome-wide association studies (GWAS) have successfully identified thousands of risk loci for complex traits. Identifying these variants requires annotating all possible variations between any two individuals, followed by detecting the variants that affect the disease status or traits. High-throughput sequencing (HTS) advancements have made it possible to sequence cohort of individuals in an efficient manner both in term of cost and time. However, HTS technologies have raised many computational challenges. I first propose an efficient method to recover dense genotype data by leveraging low sequencing and imputation techniques. Then, I introduce a novel statistical method (CNVeM) to identify Copy-number variations (CNVs) loci using HTS data. CNVeM was the first method that incorporates multi-mapped reads, which are discarded by all existing methods. Unfortunately, among all GWAS variants only a handful of them have been successfully validated to be biologically causal variants. Identifying causal variants can aid us to understand the biological mechanism of traits or diseases. However, detecting the causal variants is challenging due to linkage disequilibrium (LD) and the fact that some loci contain more than one causal variant. In my thesis, I will introduce CAVIAR (CAusal Variants Identification in Associated Regions) that is a new statistical method for fine mapping. The main advantage of CAVIAR is that we predict a set of variants for each locus that will contain all of the true causal variants with a high confidence level (e.g. 95%) even when the locus contains multiple causal variants. Next, I aim to understand the underlying mechanism of GWAS risk loci. A standard approach to uncover the mechanism of GWAS risk loci is to integrate results of GWAS and expression quantitative trait loci (eQTL) studies; we attempt to identify whether or not a significant GWAS variant also influences expression at a nearby gene in a specific tissue. However, detecting the same variant being causal in both GWAS and eQTL is challenging due to complex LD structure. I will introduce eCAVIAR (eQTL and GWAS CAusal Variants Identification in Associated Regions), a statistical method to compute the probability that the same variant is responsible for both the GWAS and eQTL signal, while accounting for complex LD structure. We integrate Glucose and Insulin-related traits meta-analysis with GTEx to detect the target genes and the most relevant tissues. Interestingly, we observe that most loci do not colocalize between GWAS and eQTL. Lastly, I propose an approach called phenotype imputation that allows one to perform GWAS on a phenotype that is difficult to collect. In our approach, we leverage the correlation structure between multiple phenotypes to impute the uncollected phenotype. I demonstrate that we can analytically calculate the statistical power of association test using imputed phenotype, which can be helpful for study design purposes
Author: Nicholas Alexander Sinnott-Armstrong
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This thesis focuses on using genetics to better connect complex trait associations to the molecular and cellular processes they affect. Rather than performing top-down post-genome wide association studies (post-GWAS), which has become increasingly popular in recent years as a way to dissect the factors contributing to individual risk loci, we focus here on bottom-up post-GWAS: can we use traits for which the molecular processes governing their concentrations are already well understood, and identify variants affecting them which then might lead to disease or other downstream effects? I hope that you believe this is possible, and useful, in reading these selected works which compose my dissertation.
Author: Rathi Suresh
Publisher:
ISBN:
Category :
Languages : en
Pages : 312
Get Book Here
Book Description
Author: Anthony Francis Herzig
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
My thesis project is concerned with tapping the potential of population isolates for the dissection of complex trait architecture. Specifically, isolates can aid the identification of variants that are usually rare in other populations. This thesis principally contains in depth investigations into genetic imputation and heritability analysis in isolates. We approached both of these studies from two main angles; first from a methodological standpoint where we created extensive simulation datasets in order to investigate how the specificities of an isolate should determine strategies for analyses. Secondly, we demonstrated such concepts through analysis of genetic data in the known isolate of Cilento. Imputation is a crucial step to performing association analyses in an isolate and represents a cost-efficient method for gaining dense genetic data for the population. The effectiveness of imputation is of course dependent on its accuracy. Hence, we investigated the wide range of possible strategies to gain maximal imputation accuracy in an isolate. We showed that software using algorithms which specifically evoke known characteristics of isolates were, unexpectedly, not as successful as those designed for general populations. We also demonstrated a very small study specific imputation reference panel performing very strongly in an isolate; particularly for rare variants. For many complex traits, there exist discordances between estimates of heritabilities from studies in closely related individuals and from studies on unrelated individuals. In particular, we noted that most researchers consider dominant (non-additive) genetic effects as unlikely to play a significant role despite contrasting results from previous studies on isolates. Our second analysis revealed possible mechanisms to explain such disparate published heritability estimates between isolated populations and general populations. This allowed us to make interesting deductions from our own heritability analyses of the Cilento dataset, including an indication of a non-null dominance component involved in the distribution of low-density lipoprotein level measurements (LDL). This led us to perform genome-wide association analyses of additive and non-additive components for LDL in Cilento and we were able to identify genes that had been previously linked to the trait in other studies. In the contexts of both of our studies, we observed the importance of retaining genotype uncertainty (genotype dosage following imputation or genotype likelihoods from sequencing data). As a prospective of this thesis, we have proposed ways to incorporate this uncertainty into certain methods used in this project. Our findings for imputation strategies and heritability analysis will be highly valuable for the continued study of the isolate of Cilento but will also be instructive to researchers working on other isolated populations and also applicable to the study of complex diseases in general.
Author: D.C. Rao
Publisher: Academic Press
ISBN: 0080569110
Category : Medical
Languages : en
Pages : 788
Get Book Here
Book Description
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
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Brielin C. Brown
Publisher:
ISBN:
Category :
Languages : en
Pages : 90
Get Book Here
Book Description
Advances in DNA sequencing technology have resulted in the ability to generate genetic data at costs unimaginable even ten years ago. This has resulted in a tremendous amount of data, with large studies providing genotypes of hundreds of thousands of individuals at millions of genetic locations. This rapid increase in the scale of genetic data necessitates the development of computational methods that can analyze this data rapidly without sacrificing statistical rigor. The low cost of DNA sequencing also provides an opportunity to tailor medical care to an individuals unique genetic signature. However, this type of precision medicine is limited by our understanding of how genetic variation shapes disease. Our understanding of so- called complex diseases is particularly poor, and most identified variants explain only a tiny fraction of the variance in the disease that is expected to be due to genetics. This is further complicated by the fact that most studies of complex disease go directly from genotype to phenotype, ignoring the complex biological processes that take place in between. Herein, we discuss several advances in the field of complex trait genetics. We begin with a review of computational and statistical methods for working with genotype and phenotype data, as well as a discussion of methods for analyzing RNA-seq data in effort to bridge the gap between genotype and phenotype. We then describe our methods for 1) improving power to detect common variants associated with disease, 2) determining the extent to which different world populations share similar disease genetics and 3) identifying genes which show differential expression between the two haplotypes of a single individual. Finally, we discuss opportunities for future investigation in this field.
