scholarly journals Human Genomic Loci Important in Common Infectious Diseases: Role of High-Throughput Sequencing and Genome-Wide Association Studies

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Gerald Mboowa ◽  
Ivan Sserwadda ◽  
Marion Amujal ◽  
Norah Namatovu
Keyword(s):  
Infectious Diseases ◽  
Positive Selection ◽  
High Throughput ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Host Pathogen

HIV/AIDS, tuberculosis (TB), and malaria are 3 major global public health threats that undermine development in many resource-poor settings. Recently, the notion that positive selection during epidemics or longer periods of exposure to common infectious diseases may have had a major effect in modifying the constitution of the human genome is being interrogated at a large scale in many populations around the world. This positive selection from infectious diseases increases power to detect associations in genome-wide association studies (GWASs). High-throughput sequencing (HTS) has transformed both the management of infectious diseases and continues to enable large-scale functional characterization of host resistance/susceptibility alleles and loci; a paradigm shift from single candidate gene studies. Application of genome sequencing technologies and genomics has enabled us to interrogate the host-pathogen interface for improving human health. Human populations are constantly locked in evolutionary arms races with pathogens; therefore, identification of common infectious disease-associated genomic variants/markers is important in therapeutic, vaccine development, and screening susceptible individuals in a population. This review describes a range of host-pathogen genomic loci that have been associated with disease susceptibility and resistant patterns in the era of HTS. We further highlight potential opportunities for these genetic markers.

scholarly journals Prostate cancer genomics by high-throughput technologies: genome-wide association study and sequencing analysis

2013 ◽  
Vol 20 (4) ◽  
pp. R171-R181 ◽  
Author(s):  
Hidewaki Nakagawa
Keyword(s):  
Prostate Cancer ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Genome Wide Association Study ◽  
Cancer Genomics ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Sequencing Analysis ◽  
Genome Wide

Prostate cancer (PC) is the most common malignancy in males. It is evident that genetic factors at both germline and somatic levels play critical roles in prostate carcinogenesis. Recently, genome-wide association studies (GWAS) by high-throughput genotyping technology have identified more than 70 germline variants of various genes or chromosome loci that are significantly associated with PC susceptibility. They include multiple 8q24 loci, prostate-specific genes, and metabolism-related genes. Somatic alterations in PC genomes have been explored by high-throughput sequencing technologies such as whole-genome sequencing and RNA sequencing, which have identified a variety of androgen-responsive events and fusion transcripts represented by E26 transformation-specific (ETS) gene fusions. Recent innovations in high-throughput genomic technologies have enabled us to analyze PC genomics more comprehensively, more precisely, and on a larger scale in multiple ethnic groups to increase our understanding of PC genomics and biology in germline and somatic studies, which can ultimately lead to personalized medicine for PC diagnosis, prevention, and therapy. However, these data indicate that the PC genome is more complex and heterogeneous than we expected from GWAS and sequencing analyses.

scholarly journals GWASpro: a high-performance genome-wide association analysis server

2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Learning Curves ◽  
Experimental Designs ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Secure large-scale genome-wide association studies using homomorphic encryption

2020 ◽  
Vol 117 (21) ◽  
pp. 11608-11613 ◽  
Author(s):  
Marcelo Blatt ◽  
Alexander Gusev ◽  
Yuriy Polyakov ◽  
Shafi Goldwasser
Keyword(s):  
Large Scale ◽  
Homomorphic Encryption ◽  
Association Studies ◽  
Genome Wide Association ◽  
Single Server ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
User Interactions ◽  
Individual Level ◽  
Genome Wide

Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.

scholarly journals A critical evaluation of results from genome-wide association studies of micronutrient status and their utility in the practice of precision nutrition

2019 ◽  
Vol 122 (2) ◽  
pp. 121-130 ◽  
Author(s):  
Marie-Joe Dib ◽  
Ruan Elliott ◽  
Kourosh R. Ahmadi
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Critical Evaluation ◽  
Water Soluble ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Micronutrient Deficiencies ◽  
Micronutrient Status ◽  
Genome Wide ◽  
Fat Soluble Vitamins

AbstractRapid advances in ‘omics’ technologies have paved the way forward to an era where more ‘precise’ approaches – ‘precision’ nutrition – which leverage data on genetic variability alongside the traditional indices, have been put forth as the state-of-the-art solution to redress the effects of malnutrition across the life course. We purport that this inference is premature and that it is imperative to first review and critique the existing evidence from large-scale epidemiological findings. We set out to provide a critical evaluation of findings from genome-wide association studies (GWAS) in the roadmap to precision nutrition, focusing on GWAS of micronutrient disposition. We found that a large number of loci associated with biomarkers of micronutrient status have been identified. Mean estimates of heritability of micronutrient status ranged between 20 and 35 % for minerals, 56–59 % for water-soluble and 30–70 % for fat-soluble vitamins. With some exceptions, the majority of the identified genetic variants explained little of the overall variance in status for each micronutrient, ranging between 1·3 and 8 % (minerals), <0·1–12 % (water-soluble) and 1·7–2·3 % for (fat-soluble) vitamins. However, GWAS have provided some novel insight into mechanisms that underpin variability in micronutrient status. Our findings highlight obvious gaps that need to be addressed if the full scope of precision nutrition is ever to be realised, including research aimed at (i) dissecting the genetic basis of micronutrient deficiencies or ‘response’ to intake/supplementation (ii) identifying trans-ethnic and ethnic-specific effects (iii) identifying gene–nutrient interactions for the purpose of unravelling molecular ‘behaviour’ in a range of environmental contexts.

Progress in the genetics of common obesity and type 2 diabetes

2010 ◽  
Vol 12 ◽  
Author(s):  
Karani S. Vimaleswaran ◽  
Ruth J.F. Loos
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Epidemiological Methods ◽  
Genome Wide Association Studies ◽  
Obesity And Diabetes ◽  
Genome Wide ◽  
Genomics And Proteomics

The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

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