scholarly journals Large-scale multiple testing in genome-wide association studies via region-specific hidden Markov models

2013 ◽  
Vol 14 (1) ◽  
pp. 282 ◽  
Author(s):  
Jian Xiao ◽  
Wensheng Zhu ◽  
Jianhua Guo
Keyword(s):  
Hidden Markov Models ◽  
Multiple Testing ◽  
Large Scale ◽  
Markov Models ◽  
Hidden Markov ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

2016 ◽  
Author(s):  
Hong Gao ◽  
Hua Tang ◽  
Carlos Bustamante
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Association Studies ◽  
Genome Wide Association ◽  
Trend Test ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem of detecting causal SNPs with low to moderate susceptibility, whose effects are often obscured by substantial background noises. Here we present a novel method that serves as an optimal technique for reducing background noises and improving detection power in genome-wide association studies. The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state. We conducted extensive simulations based on the human whole genome genotype data from the GlaxoSmithKline-POPRES project to calibrate the sensitivity and specificity of our method and compared with many popular approaches for detecting positive signals including the χ^2 test for association and the Cochran-Armitage trend test. Our simulation results suggested that at very low false positive rates (<10^-6), our method reaches the power of 0.9, and is more powerful than any other approaches, when the allelic effect of the causal variant is non-additive or unknown. Application of our method to the data set generated by Welcome Trust Case Control Consortium using 14,000 cases and 3,000 controls confirmed its powerfulness and efficiency under the context of the large-scale genome-wide association studies.

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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