16 results on '"Goddard, Michael"'
Search Results
2. Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency.
- Author
-
Bolormaa, Sunduimijid, MacLeod, Iona M., Khansefid, Majid, Marett, Leah C., Wales, William J., Miglior, Filippo, Baes, Christine F., Schenkel, Flavio S., Connor, Erin E., Manzanilla-Pech, Coralia I. V., Stothard, Paul, Herman, Emily, Nieuwhof, Gert J., Goddard, Michael E., and Pryce, Jennie E.
- Subjects
GENETIC variation ,SINGLE nucleotide polymorphisms ,PHENOTYPES ,GENOME-wide association studies ,HERITABILITY ,GENETIC correlations ,HEIFERS - Abstract
Background: Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. Results: GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (r
g ) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. Conclusions: The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
3. Mutant alleles differentially shape fitness and other complex traits in cattle.
- Author
-
Xiang, Ruidong, Breen, Ed J., Bolormaa, Sunduimijid, Jagt, Christy J. Vander, Chamberlain, Amanda J., Macleod, Iona M., and Goddard, Michael E.
- Subjects
BIOLOGICAL fitness ,ALLELES ,GENETIC mutation ,PHENOTYPES ,MILK proteins ,CATTLE fertility ,PREGNANCY in animals - Abstract
Mutant alleles (MAs) that have been classically recognised have large effects on phenotype and tend to be deleterious to traits and fitness. Is this the case for mutations with small effects? We infer MAs for 8 million sequence variants in 113k cattle and quantify the effects of MA on 37 complex traits. Heterozygosity for variants at genomic sites conserved across 100 vertebrate species increase fertility, stature, and milk production, positively associating these traits with fitness. MAs decrease stature and fat and protein concentration in milk, but increase gestation length and somatic cell count in milk (the latter indicative of mastitis). However, the frequency of MAs decreasing stature and fat and protein concentration, increasing gestation length and somatic cell count were lower than the frequency of MAs with the opposite effect. These results suggest bias in the mutations direction of effect (e.g. towards reduced protein in milk), but selection operating to reduce the frequency of these MAs. Taken together, our results imply two classes of genomic sites subject to long-term selection: sites conserved across vertebrates show hybrid vigour while sites subject to less long-term selection show a bias in mutation towards undesirable alleles. Xiang and colleagues infer mutant alleles in 113,000 cattle to quantify the effect of these mutations on complex traits including body size, fertility and milk production, and compare these mutation sites across 100 species of vertebrates. Some sites show long term selective pressure, are heavily conserved and demonstrate hybrid vigour, whereas sites selected over shorter time periods are biased towards undesirable mutation. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
4. Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues.
- Author
-
Prowse-Wilkins, Claire P., Wang, Jianghui, Xiang, Ruidong, Garner, Josie B., Goddard, Michael E., and Chamberlain, Amanda J.
- Subjects
MAMMARY glands ,GENETIC variation ,IMMUNOPRECIPITATION ,POST-translational modification ,BOS ,GENE expression ,PHENOTYPES - Abstract
Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
5. Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations.
- Author
-
Xiang, Ruidong, MacLeod, Iona M., Daetwyler, Hans D., de Jong, Gerben, O'Connor, Erin, Schrooten, Chris, Chamberlain, Amanda J., and Goddard, Michael E.
- Subjects
CATTLE ,PHENOTYPES ,FORECASTING ,DAIRY cattle - Abstract
The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand. Genomic prediction of phenotype may be improved by using DNA mutations with functional, evolutionary, and pleiotropic consequences. Here the authors describe a method for genome-wide fine-mapping of QTLs and develop a genotyping array for improved prediction of genetic values for cattle traits. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
6. Effect direction meta-analysis of GWAS identifies extreme, prevalent and shared pleiotropy in a large mammal.
- Author
-
Xiang, Ruidong, van den Berg, Irene, MacLeod, Iona M., Daetwyler, Hans D., and Goddard, Michael E.
- Subjects
META-analysis ,GENETIC pleiotropy ,GENOMES ,PHENOTYPES ,CATTLE - Abstract
In genome-wide association studies (GWAS), variants showing consistent effect directions across populations are considered as true discoveries. We model this information in an Effect Direction MEta-analysis (EDME) to quantify pleiotropy using GWAS of 34 Cholesky-decorrelated traits in 44,000+ cattle with sequence variants. The effect-direction agreement between independent bull and cow datasets was used to quantify the false discovery rate by effect direction (FDRed) and the number of affected traits for prioritised variants. Variants with multi-trait p < 1e–6 affected 1∼22 traits with an average of 10 traits. EDME assigns pleiotropic variants to each trait which informs the biology behind complex traits. New pleiotropic loci are identified, including signals from the cattle FTO locus mirroring its bystander effects on human obesity. When validated in the 1000-Bull Genome database, the prioritized pleiotropic variants consistently predicted expected phenotypic differences between dairy and beef cattle. EDME provides robust approaches to control GWAS FDR and quantify pleiotropy. Xiang et al. developed an Effect Direction Meta-analysis (EDME) approach to identify true pleiotropy. They used Cholesky-transformation to decorrelate the traits and identified many pleiotropic variants that consistently predicted phenotypic differences in cattle. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
7. Detailed phenotyping identifies genes with pleiotropic effects on body composition.
- Author
-
Bolormaa, Sunduimijid, Hayes, Ben J., van der Werf, Julius H. J., Pethick, David, Goddard, Michael E., and Daetwyler, Hans D.
- Subjects
BODY composition ,HUMAN genetic variation ,GENETIC pleiotropy ,GLYCOGEN synthases ,PHENOTYPES - Abstract
Background: Genetic variation in both the composition and distribution of fat and muscle in the body is important to human health as well as the healthiness and value of meat from cattle and sheep. Here we use detailed phenotyping and a multi-trait approach to identify genes explaining variation in body composition traits. Results: A multi-trait genome wide association analysis of 56 carcass composition traits measured on 10,613 sheep with imputed and real genotypes on 510,174 SNPs was performed. We clustered 71 significant SNPs into five groups based on their pleiotropic effects across the 56 traits. Among these 71 significant SNPs, one group of 11 SNPs affected the fatty acid profile of themuscle and were close to 8 genes involved in fatty acid or triglyceride synthesis. Another group of 23 SNPs had an effect on mature size, based on their pattern of effects across traits, but the genes near this group of SNPs did not share any obvious function. Many of the likely candidate genes near SNPs with significant pleiotropic effects on the 56 traits are involved in intra-cellular signalling pathways. Among the significant SNPs were some with a convincing candidate gene due to the function of the gene (e.g. glycogen synthase affecting glycogen concentration) or because the same gene was associated with similar traits in other species. Conclusions: Using a multi-trait analysis increased the power to detect associations between SNP and body composition traits compared with the single trait analyses. Detailed phenotypic information helped to identify a convincing candidate in some cases as did information from other species. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
8. Estimation and Partition of Heritability in Human Populations Using Whole-Genome Analysis Methods.
- Author
-
Vinkhuyzen, Anna A.E., Wray, Naomi R., Yang, Jian, Goddard, Michael E., and Visscher, Peter M.
- Subjects
HERITABILITY ,PHENOTYPES ,SINGLE nucleotide polymorphisms ,BIOINFORMATICS ,LINEAR statistical models ,PARAMETER estimation - Abstract
Understanding genetic variation of complex traits in human populations has moved from the quantification of the resemblance between close relatives to the dissection of genetic variation into the contributions of individual genomic loci. However, major questions remain unanswered: How much phenotypic variation is genetic; how much of the genetic variation is additive and can be explained by fitting all genetic variants simultaneously in one model, and what is the joint distribution of effect size and allele frequency at causal variants? We review and compare three whole-genome analysis methods that use mixed linear models (MLMs) to estimate genetic variation. In all methods, genetic variation is estimated from the relationship between close or distant relatives on the basis of pedigree information and/or single nucleotide polymorphisms (SNPs). We discuss theory, estimation procedures, bias, and precision of each method and review recent advances in the dissection of genetic variation of complex traits in human populations. By using genome-wide data, it is now established that SNPs in total account for far more of the genetic variation than the statistically highly significant SNPs that have been detected in genome-wide association studies. All SNPs together, however, do not account for all of the genetic variance estimated by pedigree-based methods. We explain possible reasons for this remaining 'missing heritability.' [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
9. Pitfalls of predicting complex traits from SNPs.
- Author
-
Wray, Naomi R., Yang, Jian, Hayes, Ben J., Price, Alkes L., Goddard, Michael E., and Visscher, Peter M.
- Subjects
SINGLE nucleotide polymorphisms ,GENOMES ,PHENOTYPES ,GENOTYPE-environment interaction ,GENETIC polymorphisms - Abstract
The success of genome-wide association studies (GWASs) has led to increasing interest in making predictions of complex trait phenotypes, including disease, from genotype data. Rigorous assessment of the value of predictors is crucial before implementation. Here we discuss some of the limitations and pitfalls of prediction analysis and show how naive implementations can lead to severe bias and misinterpretation of results. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
10. A commentary on 'common SNPs explain a large proportion of the heritability for human height' by Yang et al. (2010).
- Author
-
Visscher, Peter M, Yang, Jian, and Goddard, Michael E
- Subjects
HERITABILITY ,GENETIC polymorphisms ,LINKAGE disequilibrium ,STATISTICS ,PHENOTYPES ,HUMAN genetic variation ,RELATEDNESS (Psychology) ,PERSONALITY ,COMPARATIVE studies ,DISEASE susceptibility ,GENES ,GENETICS ,HUMAN genome ,RESEARCH methodology ,MEDICAL cooperation ,RESEARCH ,STATURE ,EVALUATION research ,SEQUENCE analysis - Abstract
Recently a paper authored by ourselves and a number of co-authors about the proportion of phenotypic variation in height that is explained by common SNPs was published in Nature Genetics (Yang et al., 2010). Common SNPs explain a large proportion of the heritability for human height (Yang et al.). During the refereeing process (the paper was rejected by two other journals before publication in Nature Genetics) and following the publication of Yang et al. (2010) it became clear to us that the methodology we applied, the interpretation of the results and the consequences of the findings on the genetic architecture of human height and that for other traits such as complex disease are not well understood or appreciated. Here we explain some of these issues in a style that is different from the primary publication, that is, in the form of a number of comments and questions and answers. We also report a number of additional results that show that the estimates of additive genetic variation are not driven by population structure. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
11. Sensitivity of genomic selection to using different prior distributions.
- Author
-
Verbyla, Klara L., Bowman, Philip J., Hayes, Ben J., and Goddard, Michael E.
- Subjects
GENOMICS ,GENETIC polymorphisms ,NUCLEOTIDES ,BAYESIAN analysis ,PHENOTYPES ,GENETIC markers - Abstract
Genomic selection describes a selection strategy based on genomic estimated breeding values (GEBV) predicted from dense genetic markers such as single nucleotide polymorphism (SNP) data. Different Bayesian models have been suggested to derive the prediction equation, with the main difference centred around the specification of the prior distributions. Methods: The simulated dataset of the 13th QTL-MAS workshop was analysed using four Bayesian approaches to predict GEBV for animals without phenotypic information. Different prior distributions were assumed to assess their affect on the accuracy of the predicted GEBV. Conclusion: All methods produced GEBV that were highly correlated with the true breeding values. The models appear relatively insensitive to the choice of prior distributions for QTL-MAS data set and this is consistent with uniformity of performance of different methods found in real data. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
12. Systems genetics: the added value of gene expression.
- Author
-
Visscher, Peter M. and Goddard, Michael E.
- Subjects
- *
GENETIC research , *GENE expression , *GENOTYPE-environment interaction , *PHENOTYPES , *TISSUES , *DNA , *ARRAY processors , *MESSENGER RNA , *GENE frequency , *YEAST , *DRUG resistance - Abstract
Understanding causal relationships between genotypes and phenotypes is a long-standing aim in genetics. In addition to high-throughput technologies that allow the measurement of many DNA variants it is possible to measure gene expression in specific tissues using array technology. "Systems genetics" is an emerging discipline that combines dense data on genotypes, gene expression, and outcome phenotypes to answer fundamental questions about causal pathways from genotype to phenotype. A recent paper by Chen et al. [Mol. Syst. Biol. 5, 310 2009] addressed the question of whether relative levels of mRNA expression help to elucidate causal paths from genotype to phenotype, using drug resistance in yeast as a model. The authors show that data on genetic markers and on gene expression, measured in a drug-free environment, can be combined to predict the growth of a yeast strain in the presence of a drug. They argue that their prediction can be used to identify causal pathways and for a subset of the genes used in prediction, the authors demonstrate that these genes cause an effect on drug sensitivity by deleting the gene or overexpressing it or swapping alleles between strains of yeast. This approach can also be applied to other species, including humans, and may become a tool in the study of personalized medicine. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
13. Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data.
- Author
-
Sang Hong Lee, van der Werf, Julius H. J., Hayes, Ben J., Goddard, Michael E., and Visscher, Peter M.
- Subjects
GENOMES ,LOCUS (Genetics) ,GENETIC polymorphisms ,PHENOTYPES ,HEMOGLOBINS ,POPULATION genetics - Abstract
Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
14. Two-Variance-Component Model Improves Genetic Prediction in Family Datasets.
- Author
-
Tucker, George, Loh, Po-Ru, MacLeod, Iona M., Hayes, Ben J., Goddard, Michael E., Berger, Bonnie, and Price, Alkes L.
- Subjects
- *
VARIANCES , *GENETIC markers , *ANIMAL breeding , *PHENOTYPES , *GENOTYPES - Abstract
Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively with best linear unbiased prediction (BLUP) methods. Such methods were pioneered in plant and animal-breeding literature and have since been applied to predict human traits, with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two-variance-component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated with genetic markers. In simulations using real genotypes from the Candidate-gene Association Resource (CARe) and Framingham Heart Study (FHS) family cohorts, we demonstrate that the two-variance-component model achieves gains in prediction r 2 over standard BLUP at current sample sizes, and we project, based on simulations, that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two-variance-component model significantly improves prediction r 2 in each case, with up to a 20% relative improvement. We also find that standard mixed-model association tests can produce inflated test statistics in datasets with related individuals, whereas the two-variance-component model corrects for inflation. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
15. Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model
- Author
-
Ben J. Hayes, Peter M. Visscher, Michael E. Goddard, Naomi R. Wray, Gerhard Moser, Sang Hong Lee, Moser, Gerhard, Lee, Sang Hong, Hayes, Ben J, Goddard, Michael E, Wray, Naomi R, and Visscher, Peter M
- Subjects
Mixed model ,Cancer Research ,lcsh:QH426-470 ,Genome-wide association study ,Single-nucleotide polymorphism ,heritability ,Quantitative trait locus ,Biology ,Bayesian method ,Polymorphism, Single Nucleotide ,variant discovery ,Bayes' theorem ,Quantitative Trait, Heritable ,Statistics ,Genetics ,Animals ,Humans ,Molecular Biology ,Genetics (clinical) ,Ecology, Evolution, Behavior and Systematics ,Genetics & Heredity ,SNP arrays ,Models, Genetic ,phenotypes ,Genetic Diseases, Inborn ,Bayes Theorem ,gene discovery ,Heritability ,Explained variation ,Genetic architecture ,3. Good health ,lcsh:Genetics ,Research Article - Abstract
Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches., Author Summary Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model. We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of seven common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits. These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.
- Published
- 2015
16. Predicting unobserved phenotypes for complex traits from whole-genome SNP data
- Author
-
Julius H. J. van der Werf, Ben J. Hayes, Sang Hong Lee, Michael E. Goddard, Peter M. Visscher, Lee, Sang Hong, van der Werf, Julius HJ, Hayes, Ben J, Goddard, Michael E, and Visscher, Peter M
- Subjects
0106 biological sciences ,Cancer Research ,lcsh:QH426-470 ,Genetics and Genomics/Animal Genetics ,Population ,Quantitative Trait Loci ,Single-nucleotide polymorphism ,Genome-wide association study ,Quantitative trait locus ,Biology ,Genetics and Genomics/Complex Traits ,medicine.disease_cause ,Bayesian method ,01 natural sciences ,Polymorphism, Single Nucleotide ,03 medical and health sciences ,Bayes' theorem ,Mice ,single nucleotide polymorphism ,Heredity ,Genetics ,medicine ,GWAS ,SNP ,Animals ,education ,Molecular Biology ,Genetics (clinical) ,Ecology, Evolution, Behavior and Systematics ,030304 developmental biology ,Genetic association ,Genetics & Heredity ,0303 health sciences ,education.field_of_study ,Genome ,Models, Statistical ,Models, Genetic ,phenotypes ,Bayes Theorem ,3. Good health ,lcsh:Genetics ,Phenotype ,genome-wide association studies ,010606 plant biology & botany ,Research Article - Abstract
Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs., Author Summary Results from recent genome-wide association studies indicate that for most complex traits, there are many loci that contribute to variation in observed phenotype and that the effect of a single variant (single nucleotide polymorphism, SNP) on a phenotype is small. Here, we propose a method that combines the effects of multiple SNPs to make a prediction of a phenotype that has not been observed. We apply the method to data on mice, using phenotypic and genomic data from some individuals to predict phenotypes in other, either related or unrelated, individuals. We find that correlations between predicted and actual phenotypes are in the range of 0.4 to 0.9. The method also shows that the SNPs used in the prediction appear in regions that are known to contain genes associated with the traits studied. The prediction of unobserved phenotypes from high-density SNP data and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial breeding programs.
- Published
- 2008
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.