An improved tree-based statistical method for genome-wide association study

10.7287/peerj.preprints.27118v1 ◽

2018 ◽

Author(s):

Dwueng-Chwuan Jhwueng

Keyword(s):

Association Study ◽

Evolutionary History ◽

Genome Wide Association Study ◽

Comparative Method ◽

Genome Wide Association ◽

Data Set ◽

Phylogenetic Comparative Method ◽

Snp Data ◽

Genome Wide ◽

Evolutionary Relatedness

In genetic studies, quantitative traits are found possibly associated with genetic data. Due to advanced sequencing technology, many methods have been proposed in genome wide association study (GWAS) to search the single nucleotide polymorphism (SNP) associated with the traits. Currently several methods that account for the evolutionary relatedness among individuals were developed. When comparing with conventional methods without evolutionary relatedness among individuals, tree based methods are found to have better performance when the population structure increases. In this work, we extend a couple of methods in previous studies by varying the magnitude of relatedness. The magnitude of relatedness of the evolutionary history is controlled by an Ornstein-Uhlenbeck (OU) process through its parameters. Our method combines a pertinent process and phylogenetic comparative method where the incorporated evolutionary history is built by SNP data. We perform simulation as well as analyze drosophila longevity data set.

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An improved tree-based statistical method for genome-wide association study

10.7287/peerj.preprints.27118 ◽

2018 ◽

Author(s):

Dwueng-Chwuan Jhwueng

Keyword(s):

Association Study ◽

Evolutionary History ◽

Genome Wide Association Study ◽

Comparative Method ◽

Genome Wide Association ◽

Data Set ◽

Phylogenetic Comparative Method ◽

Snp Data ◽

Genome Wide ◽

Evolutionary Relatedness

In genetic studies, quantitative traits are found possibly associated with genetic data. Due to advanced sequencing technology, many methods have been proposed in genome wide association study (GWAS) to search the single nucleotide polymorphism (SNP) associated with the traits. Currently several methods that account for the evolutionary relatedness among individuals were developed. When comparing with conventional methods without evolutionary relatedness among individuals, tree based methods are found to have better performance when the population structure increases. In this work, we extend a couple of methods in previous studies by varying the magnitude of relatedness. The magnitude of relatedness of the evolutionary history is controlled by an Ornstein-Uhlenbeck (OU) process through its parameters. Our method combines a pertinent process and phylogenetic comparative method where the incorporated evolutionary history is built by SNP data. We perform simulation as well as analyze drosophila longevity data set.

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LASSO model selection with post-processing for a genome-wide association study data set

BMC Proceedings ◽

10.1186/1753-6561-5-s9-s24 ◽

2011 ◽

Vol 5 (S9) ◽

Cited By ~ 4

Author(s):

Allan J Motyer ◽

Chris McKendry ◽

Sally Galbraith ◽

Susan R Wilson

Keyword(s):

Model Selection ◽

Association Study ◽

Genome Wide Association Study ◽

Study Data ◽

Genome Wide Association ◽

Post Processing ◽

Data Set ◽

Genome Wide ◽

A Genome

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Functional Partitioning of Genomic Variance and Genome-Wide Association Study for Carcass Traits in Korean Hanwoo Cattle Using Imputed Sequence Level SNP Data

Frontiers in Genetics ◽

10.3389/fgene.2018.00217 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 10

Author(s):

Mohammad S. A. Bhuiyan ◽

Dajeong Lim ◽

Mina Park ◽

Soohyun Lee ◽

Yeongkuk Kim ◽

...

Keyword(s):

Association Study ◽

Genome Wide Association Study ◽

Carcass Traits ◽

Genome Wide Association ◽

Snp Data ◽

Genome Wide ◽

Genomic Variance ◽

Hanwoo Cattle ◽

Functional Partitioning

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A Bayesian Gene-Based Genome-Wide Association Study Analysis of Osteosarcoma Trio Data Using a Hierarchically Structured Prior

Cancer Informatics ◽

10.1177/1176935118775103 ◽

2018 ◽

Vol 17 ◽

pp. 117693511877510 ◽

Cited By ~ 6

Author(s):

Yi Yang ◽

Saonli Basu ◽

Lisa Mirabello ◽

Logan Spector ◽

Lin Zhang

Keyword(s):

Risk Factors ◽

Association Study ◽

Genome Wide Association Study ◽

Bone Cancer ◽

Genome Wide Association ◽

Ratio Test ◽

Data Set ◽

Estrogen Receptor Signaling ◽

Genome Wide ◽

A Genome

Osteosarcoma is considered to be the most common primary malignant bone cancer among children and young adults. Previous studies suggest growth spurts and height to be risk factors for osteosarcoma. However, studies on the genetic cause are still limited given the rare occurrence of the disease. In this study, we investigated in a family trio data set that is composed of 209 patients and their unaffected parents and conducted a genome-wide association study (GWAS) to identify genetic risk factors for osteosarcoma. We performed a Bayesian gene-based GWAS based on the single-nucleotide polymorphism (SNP)-level summary statistics obtained from a likelihood ratio test of the trio data, which uses a hierarchically structured prior that incorporates the SNP-gene hierarchical structure. The Bayesian approach has higher power than SNP-level GWAS analysis due to the reduced number of tests and is robust by accounting for the correlations between SNPs so that it borrows information across SNPs within a gene. We identified 217 genes that achieved genome-wide significance. Ingenuity pathway analysis of the gene set indicated that osteosarcoma is potentially related to TP53, estrogen receptor signaling, xenobiotic metabolism signaling, and RANK signaling in osteoclasts.

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ModStore:Genotype Imputationasa Service Powered by SG10K Reference Panel

Current Bioinformatics ◽

10.2174/1574893615999200831112522 ◽

2020 ◽

Vol 15 ◽

Author(s):

Weiwen Zhang ◽

Long Wang ◽

Theint Theint Aye

Keyword(s):

Association Study ◽

High Performance ◽

Genome Wide Association Study ◽

Imputation Accuracy ◽

Genotype Imputation ◽

Reference Panel ◽

Genome Wide Association ◽

Specific Reference ◽

Data Set ◽

Genome Wide

Background: Asia is the largest continent in the world with a large group of populations. However, we are still in lack of an imputation server with an Asian-specific reference panel to estimate genotypes for genome wide association study in Asia. Currently, two well-known imputation servers are available, i.e., Michigan imputation server in the US and Sanger in the UK. However, the quality of imputation for Southeast Asia's populations is not satisfying by using their genotype imputation services and reference panels. Objective: In this paper, we develop ModStore imputation server with a specially designed reference panel to offer genotype imputation as a service, aiming to increase the power of genome wide association study of Singapore in the context of National Precision Medicine. Method: We present the implementation and customization of ModStore imputation server on high performance computing infrastructure. Meanwhile, we construct a reference panel based on whole-genome sequencing of Singaporeans, referred to as the SG10K reference panel, for improving the imputation accuracy of Southeast Asia's populations. Results: Experiment results show that by using the SG10K reference panel, over 79% improvement of mean Rsq can be achieved for the imputation of three Singapore ethnic populations data set, i.e., Malay, Chinese, and Indian, under MAF<0.005 compared to the 1000 Genome reference panel. Conclusion: With ModStore imputation server, genotype imputation can be performed more accurately for data derived from array-based pharmacogenomics and pre-existing Southeast Asia's population-scale genetic.

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