The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architectureSpringer Science and Business Media LLC - Tập 54 Số 1
Yvonne C. J. Wientjes, P. Bijma, M.P.L. Calus, Bas J. Zwaan, Zulma Vitezica, Joost van den Heuvel
AbstractBackgroundGenomic selection has revolutionized genetic improvement in animals and plants, but little is known about its long-term effects. Here, we investigated the long-term effects of genomic selection on response to selection, genetic variance, and the genetic architecture of traits using stochastic simulations. We defined the genetic architecture as the set of causal loci underlying each trait, their allele frequencies, and their statistical additive effects. We simulated a livestock population under 50 generations of phenotypic, pedigree, or genomic selection for a single trait, controlled by either only additive, additive and dominance, or additive, dominance, and epistatic effects. The simulated epistasis was based on yeast data.
ResultsShort-term response was always greatest with genomic selection, while response after 50 generations was greater with phenotypic selection than with genomic selection when epistasis was present, and was always greater than with pedigree selection. This was mainly because loss of genetic variance and of segregating loci was much greater with genomic and pedigree selection than with phenotypic selection. Compared to pedigree selection, selection response was always greater with genomic selection. Pedigree and genomic selection lost a similar amount of genetic variance after 50 generations of selection, but genomic selection maintained more segregating loci, which on average had lower minor allele frequencies than with pedigree selection. Based on this result, genomic selection is expected to better maintain genetic gain after 50 generations than pedigree selection. The amount of change in the genetic architecture of traits was considerable across generations and was similar for genomic and pedigree selection, but slightly less for phenotypic selection. Presence of epistasis resulted in smaller changes in allele frequencies and less fixation of causal loci, but resulted in substantial changes in statistical additive effects across generations.
ConclusionsOur results show that genomic selection outperforms pedigree selection in terms of long-term genetic gain, but results in a similar reduction of genetic variance. The genetic architecture of traits changed considerably across generations, especially under selection and when non-additive effects were present. In conclusion, non-additive effects had a substantial impact on the accuracy of selection and long-term response to selection, especially when selection was accurate.
Genetic parameters for growth and faecal worm egg count following Haemonchus contortus experimental infestations using pedigree and molecular informationSpringer Science and Business Media LLC - Tập 46 - Trang 1-9 - 2014
Fabrizio Assenza, Jean-Michel Elsen, Andrés Legarra, Clément Carré, Guillaume Sallé, Christèle Robert-Granié, Carole R Moreno
Haemonchosis is a parasitic disease that causes severe economic losses in sheep industry. In recent years, the increasing resistance of the parasite to anthelmintics has raised the need for alternative control strategies. Genetic selection is a promising alternative but its efficacy depends on the availability of genetic variation and on the occurrence of favourable genetic correlations between the traits included in the breeding goal. The objective of this study was twofold. First, to estimate both the heritability of and the genetic correlations between growth traits and parasite resistance traits, using bivariate linear mixed animal models, from the phenotypes and genotypes of 1004 backcross lambs (considered as a single population), which underwent two subsequent experimental infestations protocols with Haemonchus contortus. Second, to compare the precision of the estimates when using two different relationship matrices: including pedigree information only or including also SNP (single nucleotide polymorphism) information. Heritabilities were low for average daily gain before infestation (0.10 to 0.15) and average daily gain during the first infestation (0.11 to 0.16), moderate for faecal egg counts during the first infestation (0.21 to 0.38) and faecal egg counts during the second infestation (0.48 to 0.55). Genetic correlations between both growth traits and faecal egg count during the naïve infestation were equal to zero but the genetic correlation between faecal egg count during the second infestation and growth was positive in a Haemonchus contortus free environment and negative in a contaminated environment. The standard errors of the estimates obtained by including SNP information were smaller than those obtained by including pedigree information only. The genetic parameters estimates suggest that growth performance can be selected for independently of selection on resistance to naïve infestation. Selection for increased growth in a non-contaminated environment could lead to more susceptible animals with long-term exposure to the infestation but it could be possible to select for increased growth in a contaminated environment while also increasing resistance to the long-term exposure to the parasite. The use of molecular information increases the precision of the estimates.
A cautionary tale of low-pass sequencing and imputation with respect to haplotype accuracySpringer Science and Business Media LLC - Tập 56 - Trang 1-19 - 2024
David Wragg, Wengang Zhang, Sarah Peterson, Murthy Yerramilli, Richard Mellanby, Jeffrey J. Schoenebeck, Dylan N. Clements
Low-pass whole-genome sequencing and imputation offer significant cost savings, enabling substantial increases in sample size and statistical power. This approach is particularly promising in livestock breeding, providing an affordable means of screening individuals for deleterious alleles or calculating genomic breeding values. Consequently, it may also be of value in companion animal genomics to support pedigree breeding. We sought to evaluate in dogs the impact of low coverage sequencing and reference-guided imputation on genotype concordance and association analyses. DNA isolated from saliva of 30 Labrador retrievers was sequenced at low (0.9X and 3.8X) and high (43.5X) coverage, and down-sampled from 43.5X to 9.6X and 17.4X. Genotype imputation was performed using a diverse reference panel (1021 dogs), and two subsets of the former panel (256 dogs each) where one had an excess of Labrador retrievers relative to other breeds. We observed little difference in imputed genotype concordance between reference panels. Association analyses for a locus acting as a disease proxy were performed using single-marker (GEMMA) and haplotype-based (XP-EHH) tests. GEMMA results were highly correlated (r ≥ 0.97) between 43.5X and ≥ 3.8X depths of coverage, while for 0.9X the correlation was lower (r ≤ 0.8). XP-EHH results were less well correlated, with r ranging from 0.58 (0.9X) to 0.88 (17.4X). Across a random sample of 10,000 genomic regions averaging 17 kb in size, we observed a median of three haplotypes per dog across the sequencing depths, with 5% of the regions returning more than eight haplotypes. Inspection of one such region revealed genotype and phasing inconsistencies across sequencing depths. We demonstrate that saliva-derived canine DNA is suitable for whole-genome sequencing, highlighting the feasibility of client-based sampling. Low-pass sequencing and imputation require caution as incorrect allele assignments result when the subject possesses alleles that are absent in the reference panel. Larger panels have the capacity for greater allelic diversity, which should reduce the potential for imputation error. Although low-pass sequencing can accurately impute allele dosage, we highlight issues with phasing accuracy that impact haplotype-based analyses. Consequently, if accurately phased genotypes are required for analyses, we advocate sequencing at high depth (> 20X).
Mapping quantitative trait loci (QTL) in sheep. I. A new male framework linkage map and QTL for growth rate and body weightSpringer Science and Business Media LLC - Tập 41 - Trang 1-17 - 2009
Herman W Raadsma, Peter C Thomson, Kyall R Zenger, Colin Cavanagh, Mary K Lam, Elisabeth Jonas, Marilyn Jones, Gina Attard, David Palmer, Frank W Nicholas
A male sheep linkage map comprising 191 microsatellites was generated from a single family of 510 Awassi-Merino backcross progeny. Except for ovine chromosomes 1, 2, 10 and 17, all other chromosomes yielded a LOD score difference greater than 3.0 between the best and second-best map order. The map is on average 11% longer than the Sheep Linkage Map v4.7 male-specific map. This map was employed in quantitative trait loci (QTL) analyses on body-weight and growth-rate traits between birth and 98 weeks of age. A custom maximum likelihood program was developed to map QTL in half-sib families for non-inbred strains (QTL-MLE) and is freely available on request. The new analysis package offers the advantage of enabling QTL × fixed effect interactions to be included in the model. Fifty-four putative QTL were identified on nine chromosomes. Significant QTL with sex-specific effects (i.e. QTL × sex interaction) in the range of 0.4 to 0.7 SD were found on ovine chromosomes 1, 3, 6, 11, 21, 23, 24 and 26.
Độ chính xác của giá trị chọn giống gen trong bò thịt American Angus sử dụng phân cụm K-means cho xác thực chéo Dịch bởi AI Springer Science and Business Media LLC - - 2011
Mahdi Saatchi, M. C. McClure, Stephanie McKay, Megan M Rolf, Jae-Woo Kim, Jared E. Decker, Tasia M. Taxis, Richard H. Chapple, Holly R. Ramey, S. L. Northcutt, Stewart Bauck, B. W. Woodward, Jack C. M. Dekkers, Rohan L. Fernando, Robert D. Schnabel, Dorian J. Garrick, Jeremy F. Taylor
Tóm tắt
Đặt vấn đề
Chọn giống gen là một công nghệ mới phát triển đang bắt đầu cách mạng hóa việc chăn nuôi động vật. Mục tiêu của nghiên cứu này là ước tính ảnh hưởng của dấu hiệu gen để xây dựng các phương trình dự đoán cho giá trị gen trực tiếp cho 16 tính trạng được ghi nhận thường xuyên ở bò thịt American Angus và định lượng độ chính xác tương ứng của dự đoán.
Phương pháp
Các giá trị chọn giống ước tính không hồi quy được sử dụng làm quan sát trong một phân tích có trọng số để xây dựng giá trị gen trực tiếp cho 3570 bò đực đã được genotyping bằng Illumina BovineSNP50 BeadChip. Những con bò này được phân nhóm thành năm nhóm sử dụng phân cụm K-means trên các ước lượng gia phả của mối quan hệ di truyền bổ sung giữa các động vật, với mục tiêu tăng cường quan hệ trong nhóm và giảm quan hệ giữa các nhóm. Tất cả năm tổ hợp của bốn nhóm được sử dụng cho việc huấn luyện mô hình, với xác thực chéo được thực hiện trong nhóm không được sử dụng trong huấn luyện. Các mô hình động vật hai biến được sử dụng cho mỗi tính trạng để ước tính tương quan di truyền giữa các giá trị chọn giống ước tính không hồi quy và các giá trị gen trực tiếp.
#genomic selection #beef cattle #genetic correlation #K-means clustering #direct genomic values
The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemesSpringer Science and Business Media LLC - Tập 44 - Trang 1-9 - 2012
Samuel A Clark, John M Hickey, Hans D Daetwyler, Julius HJ van der Werf
The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values. Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated. The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy. An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.
Phân tích xác suất và Bayes tiết lộ gene chính ảnh hưởng đến cấu trúc cơ thể, carcass, chất lượng thịt và số lượng tiểu núm giả ở dòng lợn Trung Quốc - Châu Âu Tiameslan Dịch bởi AI Springer Science and Business Media LLC - Tập 35 - Trang 1-18 - 2003
Sanchez Marie-Pierre, Jean-Pierre Bidanel, Siqing Zhang, Jean Naveau, Thierry Burlot, Pascale Le Roy
Các phân tích tách biệt đã được thực hiện bằng cách sử dụng cả phương pháp xác suất tối đa – thông qua thuật toán Quasi Newton – (ML-QN) và phương pháp Bayes – thông qua lấy mẫu Gibbs – (Bayesian-GS) trên dòng lợn Trung Quốc - Châu Âu Tiameslan. Các gene chính đã được tìm kiếm cho độ dày mỡ lưng siêu âm trung bình (ABT), mỡ carcass (X2 và X4) và độ sâu nạc (X5), số ngày từ 20 đến 100 kg (D20100), hiệu suất công nghệ Napole (NTY), số lượng tiểu núm giả (FTN) và tiểu núm chính (GTN), cũng như tổng số tiểu núm (TTN). Tính chất rời rạc của FTN cũng được xem xét thêm bằng cách sử dụng mô hình ngưỡng dưới phương pháp ML. Các kết quả thu được với cả hai phương pháp nhất quán cho thấy sự hiện diện của các gene chính ảnh hưởng đến ABT, X2, NTY, GTN và FTN. Các gene chính cũng được gợi ý cho X4 và X5 bằng cách sử dụng ML-QN, nhưng không phải theo phương pháp Bayesian-GS. Gene chính ảnh hưởng đến FTN đã được xác nhận bằng mô hình ngưỡng. Các tương quan di truyền cũng như ước lượng hiệu ứng gene và tần suất kiểu gen cho thấy sự hiện diện của bốn gene chính khác nhau. Gene đầu tiên sẽ ảnh hưởng đến các đặc tính béo (ABT, X2 và X4), gene thứ hai ảnh hưởng đến đặc tính nạc (X5), gene thứ ba là NTY và gene cuối cùng là GTN và FTN. Tần suất kiểu gen của các động vật giống và sự thay đổi của chúng theo thời gian đã phù hợp với sự chọn lọc được thực hiện trong dòng Tiameslan.
#gene chính #cấu trúc cơ thể #chất lượng thịt #tiểu núm giả #phân tích xác suất #phân tích Bayes
Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variantsSpringer Science and Business Media LLC - Tập 50 - Trang 1-13 - 2018
Chunyan Zhang, Robert Alan Kemp, Paul Stothard, Zhiquan Wang, Nicholas Boddicker, Kirill Krivushin, Jack Dekkers, Graham Plastow
Increasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs. Phenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG. Use of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.
