Assessing the response to genomic selection by simulation.
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ABSTRACT: Key message
We propose a simulation approach to compute response to genomic selection on a multi-environment framework to provide breeders the number of entries that need to be selected from the population to have a defined probability of selecting the truly best entry from the population and the probability of obtaining the truly best entries when some top-ranked entries are selected. The goal of any plant breeding program is to maximize genetic gain for traits of interest. In classical quantitative genetics, the genetic gain can be obtained from what is known as "Breeder's equation". In the past, only phenotypic data were used to compute the genetic gain. The advent of genomic prediction (GP) has opened the door to the utilization of dense markers for estimating genomic breeding values or GBV. The salient feature of GP is the possibility to carry out genomic selection with the assistance of the kinship matrix, hence improving the prediction accuracy and accelerating the breeding cycle. However, estimates of GBV as such do not provide the full information on the number of entries to be selected as in the classical response to selection. In this paper, we use simulation, based on a fitted mixed model for GP in a multi-environmental framework, to answer two typical questions of a plant breeder: (1) How many entries need to be selected to have a defined probability of selecting the truly best entry from the population; (2) what is the probability of obtaining the truly best entries when some top-ranked entries are selected.
SUBMITTER: Buntaran H
PROVIDER: S-EPMC9325815 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWith the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees.MethodsStochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ.ResultsIncluding the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10-20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required.ConclusionsThis study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.
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Project description:BackgroundTranscribed pseudogenes are copies of protein-coding genes that have accumulated indicators of coding-sequence decay (such as frameshifts and premature stop codons), but nonetheless remain transcribed. Recent experimental evidence indicates that transcribed pseudogenes may regulate the expression of homologous genes, through antisense interference, or generation of small interfering RNAs (siRNAs). Here, we assessed the genomic evidence for such transcribed pseudogenes of potential functional importance, in the human genome. The most obvious indicators of such functional importance are significant evidence of conservation and selection pressure.ResultsA variety of pseudogene annotations from multiple sources were pooled and filtered to obtain a subset of sequences that have significant mid-sequence disablements (frameshifts and premature stop codons), and that have clear evidence of full-length mRNA transcription. We found 1750 such transcribed pseudogene annotations (TPAs) in the human genome (corresponding to approximately 11.5% of human pseudogene annotations). We checked for syntenic conservation of TPAs in other mammals (rhesus monkey, mouse, rat, dog and cow). About half of the human TPAs are conserved in rhesus monkey, but strikingly, very few in mouse (approximately 3%). The TPAs conserved in rhesus monkey show evidence of selection pressure (relative to surrounding intergenic DNA) on: (i) their GC content, and (ii) their rate of nucleotide substitution. This is in spite of distributions of Ka/Ks (ratios of non-synonymous to synonymous substitution rates), congruent with a lack of protein-coding ability. Furthermore, we have identified 68 human TPAs that are syntenically conserved in at least two other mammals. Interestingly, we observe three TPA sequences conserved in dog that have intermediate character (i.e., evidence of both protein-coding ability and pseudogenicity), and discuss the implications of this.ConclusionThrough evolutionary analysis, we have identified candidate sequences for functional human transcribed pseudogenes, and have pinpointed 68 strong candidates for further investigation as potentially functional transcribed pseudogenes across multiple mammal species.
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Project description:We report a genomic selection (GS) study of growth and wood quality traits in an outbred F2 hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40-0.57 for individual selection and 0.56-0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25?kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.
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Project description:Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs' unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year) vs. PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12-88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26-165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33-39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.
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Project description:Limited lifespan and senescence are quantitative traits, controlled by many interacting genes with individually small and environmentally plastic effects, complicating genetic analysis. We performed genome wide analysis of gene expression for two Drosophila melanogaster lines selected for postponed senescence and one control, unselected line to identify candidate genes affecting lifespan as well as variation in lifespan. We obtained gene expression profiles for young flies of all lines, all lines at the time only 10% of the control lines survived, and the time at which 10% of the selected lines survived. Transcriptional responses to aging involved 19% of the genome. The transcriptional signature of aging involved the down-regulation of genes affecting proteolysis, metabolism, oxidative phosphorylation, and mitochrondrial function; and the up-regulation of genes affecting protein synthesis, immunity, defense responses, and the detoxification of xenobiotic substances. The transcriptional signature of postponed senescence involved the up-regulation of proteases and phosphatases and genes affecting detoxification of xenobiotics; and the down-regulation of genes affecting immunity, defense responses, metabolism and muscle function. Functional tests of 17 mutations confirmed 12 novel genes affecting Drosophila lifespan. Identification of genes affecting longevity by analysis of gene expression changes in lines selected for postponed senescence thus complements alternative genetic approaches.
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Project description:This study assessed the efficiency of Genomic selection (GS) or genome-wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.
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Project description:We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.
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Project description:BackgroundCrossbreeding is an important way to improve production beef cattle performance. Pinan cattle is a new hybrid cattle obtained from crossing Piedmontese bulls with Nanyang cows. After more than 30 years of cross-breeding, Pinan cattle show a variety of excellent characteristics, including fast growth, early onset of puberty, and good meat quality. In this study, we analyzed the genetic diversity, population structure, and genomic region under the selection of Pinan cattle based on whole-genome sequencing data of 30 Pinan cattle and 169 published cattle genomic data worldwide. RESULTS: Estimating ancestry composition analysis showed that the composition proportions for our Pinan cattle were mainly Piedmontese and a small amount of Nanyang cattle. The analyses of nucleotide diversity and linkage disequilibrium decay indicated that the genomic diversity of Pinan cattle was higher than that of European cattle and lower than that of Chinese indigenous cattle. De-correlated composite of multiple selection signals, which combines four different statistics including θπ, CLR, FST, and XP-EHH, was computed to detect the signatures of selection in the Pinan cattle genome. A total of 83 genes were identified, affecting many economically important traits. Functional annotation revealed that these selected genes were related to immune (BOLA-DQA2, BOLA-DQB, LSM14A, SEC13, and NAALADL2), growth traits (CYP4A11, RPL26, and MYH10), embryo development (REV3L, NT5E, CDX2, KDM6B, and ADAMTS9), hornless traits (C1H21orf62), and climate adaptation (ANTXR2).ConclusionIn this paper, we elucidated the genomic characteristics, ancestry composition, and selective signals related to important economic traits in Pinan cattle. These results will provide the basis for further genetic improvement of Pinan cattle and reference for other hybrid cattle related studies.
| S-EPMC9215082 | biostudies-literature
Project description:Gir is one of the main cattle breeds raised in tropical South American countries. Strong artificial selection through its domestication resulted in increased genetic differentiation among the countries in recent years. Over the years, genomic studies in Gir have become more common. However, studies of population structure and signatures of selection in divergent Gir populations are scarce and need more attention to better understand genetic differentiation, gene flow, and genetic distance. Genotypes of 173 animals selected for growth traits and 273 animals selected for milk production were used in this study. Clear genetic differentiation between beef and dairy populations was observed. Different criteria led to genetic divergence and genetic differences in allele frequencies between the two populations. Gene segregation in each population was forced by artificial selection, promoting isolation, and increasing genetic variation between them. Results showed evidence of selective forces in different regions of the genome. A total of 282 genes were detected under selection in the test population based on the fixation index (Fst), integrated haplotype score (iHS), and cross-population extend haplotype homozygosity (XP-EHH) approaches. The QTL mapping identified 35 genes associated with reproduction, milk composition, growth, meat and carcass, health, or body conformation traits. The investigation of genes and pathways showed that quantitative traits associated to fertility, milk production, beef quality, and growth were involved in the process of differentiation of these populations. These results would support further investigations of population structure and differentiation in the Gir breed.
| S-EPMC6071998 | biostudies-literature
Project description:BACKGROUND:Autochthonous cattle breeds are an important source of genetic variation because they might carry alleles that enable them to adapt to local environment and food conditions. Original Braunvieh (OB) is a local cattle breed of Switzerland used for beef and milk production in alpine areas. Using whole-genome sequencing (WGS) data of 49 key ancestors, we characterize genomic diversity, genomic inbreeding, and signatures of selection in Swiss OB cattle at nucleotide resolution. RESULTS:We annotated 15,722,811 SNPs and 1,580,878 Indels including 10,738 and 2763 missense deleterious and high impact variants, respectively, that were discovered in 49 OB key ancestors. Six Mendelian trait-associated variants that were previously detected in breeds other than OB, segregated in the sequenced key ancestors including variants causal for recessive xanthinuria and albinism. The average nucleotide diversity (1.6 × 10- 3) was higher in OB than many mainstream European cattle breeds. Accordingly, the average genomic inbreeding derived from runs of homozygosity (ROH) was relatively low (FROH = 0.14) in the 49 OB key ancestor animals. However, genomic inbreeding was higher in OB cattle of more recent generations (FROH = 0.16) due to a higher number of long (> 1 Mb) runs of homozygosity. Using two complementary approaches, composite likelihood ratio test and integrated haplotype score, we identified 95 and 162 genomic regions encompassing 136 and 157 protein-coding genes, respectively, that showed evidence (P < 0.005) of past and ongoing selection. These selection signals were enriched for quantitative trait loci related to beef traits including meat quality, feed efficiency and body weight and pathways related to blood coagulation, nervous and sensory stimulus. CONCLUSIONS:We provide a comprehensive overview of sequence variation in Swiss OB cattle genomes. With WGS data, we observe higher genomic diversity and less inbreeding in OB than many European mainstream cattle breeds. Footprints of selection were detected in genomic regions that are possibly relevant for meat quality and adaptation to local environmental conditions. Considering that the population size is low and genomic inbreeding increased in the past generations, the implementation of optimal mating strategies seems warranted to maintain genetic diversity in the Swiss OB cattle population.
| S-EPMC6950892 | biostudies-literature