Simulated Data for Genomic Selection and Genome-Wide Association Studies Using a Combination of Coalescent and Gene Drop Methods

Detection of epistatic interactions in genome-wide association studies is a computationally hard problem. Many detection algorithms have been proposed and will continue to be. Most of those algorithms measure their predictive power by running on simulated data many times under various disease models. However, we find that there have been subtle differences in interpreting the meaning of existing disease models among the previous studies on detection of epistatic interactions. We elucidate those differences and suggest that future studies on epistatic interactions in GWAS state explicitly which versions/interpretations are employed. We also provide a way to facilitate setting parameters of disease models.

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Genome-wide association and genomic selection in animal breedingThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽

10.1139/g10-076 ◽

2010 ◽

Vol 53 (11) ◽

pp. 876-883 ◽

Cited By ~ 135

Author(s):

Ben Hayes ◽

Mike Goddard

Keyword(s):

Genomic Selection ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association ◽

Relationship Matrix ◽

Genome Wide Association Studies ◽

Simple Method ◽

Breeding Values ◽

Genome Wide ◽

A Genome

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.

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Genome-wide association study and genomic predictions for resistance against Piscirickettsia salmonis in coho salmon (Oncorhynchus kisutch) using ddRAD sequencing

10.1101/124099 ◽

2017 ◽

Cited By ~ 3

Author(s):

Agustín Barría ◽

Kris A. Christensen ◽

Katharina Correa ◽

Ana Jedlicki ◽

Jean P. Lhorente ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Improvement ◽

Coho Salmon ◽

Association Studies ◽

Oncorhynchus Kisutch ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Selection Models ◽

Genome Wide ◽

Piscirickettsia Salmonis

ABSTRACTPiscirickettsia salmonis is one of the main infectious diseases affecting coho salmon (Oncorhynchus kisutch) farming. Current treatments have been ineffective for the control of the disease. Genetic improvement for P. salmonis resistance has been proposed as a feasible alternative for the control of this infectious disease in farmed fish. Genotyping by sequencing (GBS) strategies allow genotyping hundreds of individuals with thousands of single nucleotide polymorphisms (SNPs), which can be used to perform genome wide association studies (GWAS) and predict genetic values using genome-wide information. We used double-digest restriction-site associated DNA (ddRAD) sequencing to dissect the genetic architecture of resistance against P. salmonis in a farmed coho salmon population and identify molecular markers associated with the trait. We also evaluated genomic selection (GS) models in order to determine the potential to accelerate the genetic improvement of this trait by means of using genome-wide molecular information. 764 individuals from 33 full-sib families (17 highly resistant and 16 highly susceptible) which were experimentally challenged against P. salmonis were sequenced using ddRAD sequencing. A total of 4,174 SNP markers were identified in the population. These markers were used to perform a GWAS and testing genomic selection models. One SNP related with iron availability was genome-wide significantly associated with resistance to P. salmonis defined as day of death. Genomic selection models showed similar accuracies and predictive abilities than traditional pedigree-based best linear unbiased prediction (PBLUP) method.

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Sparse Bayesian learning for predicting phenotypes and ranking influential markers in yeast

10.1101/489245 ◽

2018 ◽

Author(s):

Maryam Ayat ◽

Michael Domaratzki

Keyword(s):

Genomic Selection ◽

Bayesian Learning ◽

Association Studies ◽

Kernel Functions ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Sparse Bayesian Learning ◽

Negative Effects ◽

String Kernels ◽

Genome Wide

Genomic selection and genome-wide association studies are two related problems that can be applied to the plant breeding industry. Genomic selection is a method to predict phenotypes (i.e., traits) such as yield and drought resistance in crops from high-density markers positioned throughout the genome of the varieties. In this paper, we employ employ sparse Bayesian learning as a technique for genomic selection and ranking markers based on their relevance to a trait, which can aid in genome-wide association studies. We define and explore two different forms of the sparse Bayesian learning for predicting phenotypes and identifying the most influential markers of a trait, respectively. In particular, we introduce a new framework based on sparse Bayesian and ensemble learning for ranking influential markers of a trait. Then, we apply our methods on a real-world \textit{Saccharomyces cerevisiae} dataset, and analyse our results with respect to existing related works, trait heritability, as well as the accuracies obtained from the use of different kernel functions including linear, Gaussian, and string kernels. We find that sparse Bayesian methods are not only as good as other machine learning methods in predicting yeast growth in different environments, but are also capable of identifying the most important markers, including both positive and negative effects on the growth, from which biologists can get insight. This attribute can make our proposed ensemble of sparse Bayesian learners favourable in ranking markers based on their relevance to a trait.

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Genome-Wide Association Studies and Genomic Selection in Pearl Millet: Advances and Prospects

Frontiers in Genetics ◽

10.3389/fgene.2019.01389 ◽

2020 ◽

Vol 10 ◽

Cited By ~ 5

Author(s):

Rakesh K. Srivastava ◽

Ram B. Singh ◽

Vijaya Lakshmi Pujarula ◽

Srikanth Bollam ◽

Madhu Pusuluri ◽

...

Keyword(s):

Genomic Selection ◽

Pearl Millet ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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EpiGEN: an epistasis simulation pipeline

Bioinformatics ◽

10.1093/bioinformatics/btaa245 ◽

2020 ◽

Vol 36 (19) ◽

pp. 4957-4959

Author(s):

David B Blumenthal ◽

Lorenzo Viola ◽

Markus List ◽

Jan Baumbach ◽

Paolo Tieri ◽

...

Keyword(s):

Arbitrary Order ◽

Association Studies ◽

Simulated Data ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Supplementary Data ◽

Single Nucleotide ◽

Genome Wide

Abstract Summary Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes. Availability and implementation EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary information Supplementary data are available at Bioinformatics online.

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Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies

Frontiers in Genetics ◽

10.3389/fgene.2014.00179 ◽

2014 ◽

Vol 5 ◽

Cited By ~ 10

Author(s):

Suman Kundu ◽

Raluca Mihaescu ◽

Catherina M. C. Meijer ◽

Rachel Bakker ◽

A. Cecile J. W. Janssens

Keyword(s):

Genetic Risk ◽

Association Studies ◽

Simulated Data ◽

Predictive Ability ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Risk Models ◽

Genome Wide

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Detecting Epistasis by LASSO-Penalized-Model Search Algorithm in Human Genome-Wide Association Studies

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.2426 ◽

2014 ◽

Vol 989-994 ◽

pp. 2426-2430

Author(s):

Zhi Hui Zhou ◽

Gui Xia Liu ◽

Ling Tao Su ◽

Liang Han ◽

Lun Yan

Keyword(s):

Human Genome ◽

Search Algorithm ◽

Association Studies ◽

Simulated Data ◽

Penalized Regression ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Model Search ◽

Number Of Factors ◽

Genome Wide

Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L1-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number of factors far exceeds the number of samples. We compare the performance of LPMA with its competitors. Through simulated data experiments, LPMA performs better regarding to the identification of epistasis and prediction accuracy.

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Genome-Wide Association Studies for Methane Production in Dairy Cattle

Genes ◽

10.3390/genes10120995 ◽

2019 ◽

Vol 10 (12) ◽

pp. 995

Author(s):

R. Calderón-Chagoya ◽

J.H. Hernandez-Medrano ◽

F.J. Ruiz-López ◽

A. Garcia-Ruiz ◽

V.E. Vega-Murillo ◽

...

Keyword(s):

Genomic Selection ◽

Genome Wide Association Study ◽

Association Studies ◽

Principal Component ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Milk Fatty Acids ◽

Ch4 Emissions ◽

Ch4 Production ◽

Genome Wide

Genomic selection has been proposed for the mitigation of methane (CH4) emissions by cattle because there is considerable variability in CH4 emissions between individuals fed on the same diet. The genome-wide association study (GWAS) represents an important tool for the detection of candidate genes, haplotypes or single nucleotide polymorphisms (SNP) markers related to characteristics of economic interest. The present study included information for 280 cows in three dairy production systems in Mexico: 1) Dual Purpose (n = 100), 2) Specialized Tropical Dairy (n = 76), 3) Familiar Production System (n = 104). Concentrations of CH4 in a breath of individual cows at the time of milking (MEIm) were estimated through a system of infrared sensors. After quality control analyses, 21,958 SNPs were included. Associations of markers were made using a linear regression model, corrected with principal component analyses. In total, 46 SNPs were identified as significant for CH4 production. Several SNPs associated with CH4 production were found at regions previously described for quantitative trait loci of composition characteristics of meat, milk fatty acids and characteristics related to feed intake. It was concluded that the SNPs identified could be used in genomic selection programs in developing countries and combined with other datasets for global selection.

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Genome-wide association studies for growth traits in broilers

BMC Genomic Data ◽

10.1186/s12863-021-01017-7 ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Dachang Dou ◽

Linyong Shen ◽

Jiamei Zhou ◽

Zhiping Cao ◽

Peng Luan ◽

...

Keyword(s):

Genomic Selection ◽

Candidate Genes ◽

Rapid Growth ◽

Genetic Parameters ◽

Growth Traits ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Methods ◽

Genome Wide

Abstract Background The identification of markers and genes for growth traits may not only benefit for marker assist selection /genomic selection but also provide important information for understanding the genetic foundation of growth traits in broilers. Results In the current study, we estimated the genetic parameters of eight growth traits in broilers and carried out the genome-wide association studies for these growth traits. A total of 113 QTNs discovered by multiple methods together, and some genes, including ACTA1, IGF2BP1, TAPT1, LDB2, PRKCA, TGFBR2, GLI3, SLC16A7, INHBA, BAMBI, APCDD1, GPR39, and GATA4, were identified as important candidate genes for rapid growth in broilers. Conclusions The results of this study will provide important information for understanding the genetic foundation of growth traits in broilers.

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