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 ◽  
2010 ◽  
Vol 53 (11) ◽  
pp. 876-883 ◽  
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.

scholarly journals Genome-wide association studies for yield component traits in a macadamia breeding population

2019 ◽  
Author(s):  
Katie O'Connor ◽  
Ben Hayes ◽  
Craig Hardner ◽  
Catherine Nock ◽  
Abdul Baten ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Complex Traits ◽  
Association Studies ◽  
Kernel Weight ◽  
Genome Wide Association ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome

Abstract Background: Breeding for new macadamia cultivars with high nut yield is expensive in terms of time, labour and cost. Most trees set nuts after four to five years, and candidate varieties for breeding are evaluated for at least eight years for various traits. Genome-wide association studies (GWAS) are promising methods to reduce evaluation and selection cycles by identifying genetic markers linked with key traits, potentially enabling early selection through marker-assisted selection. This study used 295 progeny from 32 full-sib families and 29 parents (18 phenotyped) which were planted across four sites, with each tree genotyped for 4,113 SNPs. ASReml-R was used to perform association analyses with linear mixed models including a genomic relationship matrix to account for population structure. Traits investigated were: nut weight (NW), kernel weight (KW), kernel recovery (KR), percentage of whole kernels (WK), tree trunk circumference (TC), percentage of racemes that survived from flowering through to nut set, and number of nuts per raceme. Results: Seven SNPs were significantly associated with NW (at a genome-wide false discovery rate of <0.05), and four with WK. Multiple regression, as well as mapping of markers to genome assembly scaffolds suggested that some SNPs were detecting the same QTL. There were 44 significant SNPs identified for TC although multiple regression suggested detection of 16 separate QTLs. Conclusions: These findings have important implications for macadamia breeding, and highlight the difficulties of heterozygous populations with rapid LD decay. By coupling validated marker-trait associations detected through GWAS with MAS, genetic gain could be increased by reducing the selection time for economically important nut characteristics. Genomic selection may be a more appropriate method to predict complex traits like tree size and yield.

Evaluating Strategies for Marker Ranking in Genome-wide Association Studies of Complex Traits

2010 ◽  
Vol 49 (06) ◽  
pp. 632-640 ◽  
Author(s):  
J. Hebebrand ◽  
H.-E. Wichmann ◽  
K.-H. Jöckel ◽  
A. Scherag
Keyword(s):  
False Positive ◽  
Complex Traits ◽  
Association Studies ◽  
A Priori ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
P Values ◽  
Genome Wide ◽  
A Genome ◽  
Mean Square Errors

Summary Background: Genome-wide association studies (GWAS) were highly successful in identifying new susceptibility loci of complex traits. Such studies usually start with genotyping fixed arrays of genetic markers in an initial sample. Out of these markers, some are selected which will be further genotyped in independentsamples. Due tothevery low a priori probability of a true positive association, the vast majority of all marker signals will turn out to be false positive. Thus, several methods to sort marker data have been proposed which will be evaluated here. Objectives: We compared statistical properties of ranking by p-values, q-values, the False Positive Report Probability (FPRP) and the Bayesian False-Discovery Probability (BFDP). Methods: We performed simulation studies for a genomic region derived from GWAS data sets and calculated descriptive statistics as well as mean square errors with regard to the true marker ranking. Additionally, we applied all measures to a GWAS for early onset extreme obesity superimposing a priori information on candidate genes. Results: Despite the known, more extreme probability results for traditional p-values, we observed that both p-values and the BFDP were more precise in reconstructing the “true” order of the markers in a region. In addition, the BFDP was useful to attenuate unexpected effects at a genome-wide scale. Conclusions: For the purpose of selecting markers from an initial GWAS and within the limits of this study, we recommend either ranking by p-values or the application of a full Bayesian approach for which the BFDP is a first approximation.

scholarly journals Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use

2019 ◽  
Author(s):  
Weimiao Wu ◽  
Zhong Wang ◽  
Ke Xu ◽  
Xinyu Zhang ◽  
Amei Amei ◽  
...  
Keyword(s):  
Association Analysis ◽  
Complex Traits ◽  
Binary Data ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cocaine Use ◽  
Genome Wide ◽  
A Genome ◽  
Time Varying Covariates

SUMMARYLongitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for the phenotype distribution due to ascertainment. Here, we propose L-BRAT, a retrospective, generalized estimating equations-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.

scholarly journals Integrating genome-wide association mapping of additive and dominance genetic effects to improve genomic prediction accuracy in Eucalyptus

2019 ◽  
Author(s):  
Biyue Tan ◽  
Pär K. Ingvarsson
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Association Studies ◽  
Wood Property ◽  
Genomic Variation ◽  
Added Value ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Prediction Ability ◽  
Genome Wide

SummaryGenome-wide association studies (GWAS) is a powerful and widely used approach to decipher the genetic control of complex traits. A major challenge for dissecting quantitative traits in forest trees is statistical power. In this study, we use a population consisting of 1123 samples from two successive generations that have been phenotyped for growth and wood property traits and genotyped using the EuChip60K chip, yielding 37,832 informative SNPs. We use multi-locus GWAS models to assess both additive and dominance effects to identify markers associated with growth and wood property traits in the eucalypt hybrids. Additive and dominance association models identified 78 and 82 significant SNPs across all traits, respectively, which captured between 39 and 86% of the genomic-based heritability. We also used SNPs identified from the GWAS and SNPs using less stringent significance thresholds to evaluate predictive abilities in a genomic selection framework. Genomic selection models based on the top 1% SNPs captured a substantially greater proportion of the genetic variance of traits compared to when all SNPs were used for model training. The prediction ability of estimated breeding values was significantly improved for all traits using either the top 1% SNPs or SNPs identified using a relaxed p-value threshold (p<10-3). This study highlights the added value of also considering dominance effects for identifying genomic regions controlling growth traits in trees. Moreover, integrating GWAS results into genomic selection method provides enhanced power relative to discrete associations for identifying genomic variation potentially useful in tree breeding.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Dinesh K. Saini ◽  
Yuvraj Chopra ◽  
Jagmohan Singh ◽  
Karansher S. Sandhu ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Comprehensive Evaluation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mapping Complex Traits

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals Prioritization of genes associated with the pathogenesis of leukosis in cattle

2019 ◽  
Vol 22 (8) ◽  
pp. 1063-1069 ◽  
Author(s):  
N. S. Yudin ◽  
N. L. Podkolodnyy ◽  
T. A. Agarkova ◽  
E. V. Ignatieva
Keyword(s):  
Protein Interactions ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Mammalian Species ◽  
Genome Wide Association ◽  
Farm Animals ◽  
Genome Wide Association Studies ◽  
Protein Protein Interactions ◽  
Genome Wide ◽  
A Genome

Selection by means of genetic markers is a promising approach to the eradication of infectious diseases in farm animals, especially in the absence of effective methods of treatment and prevention. Bovine leukemia virus (BLV) is spread throughout the world and represents one of the biggest problems for the livestock production and food security in Russia. However, recent genome-wide association studies have shown that sensitivity/resistance to BLV is polygenic. The aim of this study was to create a catalog of cattle genes and genes of other mammalian species involved in the pathogenesis of BLV-induced infection and to perform gene prioritization using bioinformatics methods. Based on manually collected information from a range of open sources, a total of 446 genes were included in the catalog of cattle genes and genes of other mammals involved in the pathogenesis of BLV-induced infection. The following criteria were used to prioritize 446 genes from the catalog: (1) the gene is associated with leukemia according to a genome-wide association study; (2) the gene is associated with leukemia according to a case-control study; (3) the role of the gene in leukemia development has been studied using knockout mice; (4) protein-protein interactions exist between the gene-encoded protein and either viral particles or individual viral proteins; (5) the gene is annotated with Gene Ontology terms that are overrepresented for a given list of genes; (6) the gene participates in biological pathways from the KEGG or REACTOME databases, which are over-represented for a given list of genes; (7) the protein encoded by the gene has a high number of protein-protein interactions with proteins encoded by other genes from the catalog. Based on each criterion, a rank was assigned to each gene. Then the ranks were summarized and an overall rank was determined. Prioritization of 446 candidate genes allowed us to identify 5 genes of interest (TNF,LTB,BOLA-DQA1,BOLA-DRB3,ATF2), which can affect the sensitivity/resistance of cattle to leukemia.

scholarly journals GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

2019 ◽  
Author(s):  
Jan A. Freudenthal ◽  
Markus J. Ankenbrand ◽  
Dominik G. Grimm ◽  
Arthur Korte
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Large Datasets ◽  
Genome Wide Association ◽  
Small Data ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Non Gaussian

AbstractMotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naïve Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.ResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.AvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

Sign in / Sign up

Export Citation Format

Share Document