scholarly journals Dissecting genetic trends to understand breeding practices in livestock: a maternal pig line example

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Rostam Abdollahi-Arpanahi ◽  
Daniela Lourenco ◽  
Andres Legarra ◽  
Ignacy Misztal
Keyword(s):  
Genomic Selection ◽  
Average Daily Gain ◽  
Single Step ◽  
Nucleotide Polymorphisms ◽  
Genetic Trend ◽  
Multiple Trait ◽  
Phenotypic Data ◽  
Maternal Line ◽  
Essential Questions ◽  
Genetic Trends

Abstract Background Understanding whether genomic selection has been effective in livestock and when the results of genomic selection became visible are essential questions which we have addressed in this paper. Three criteria were used to identify practices of breeding programs over time: (1) the point of divergence of estimated genetic trends based on pedigree-based best linear unbiased prediction (BLUP) versus single-step genomic BLUP (ssGBLUP), (2) the point of divergence of realized Mendelian sampling (RMS) trends based on BLUP and ssGBLUP, and (3) the partition of genetic trends into that contributed by genotyped and non-genotyped individuals and by males and females. Methods We used data on 282,035 animals from a commercial maternal line of pigs, of which 32,856 were genotyped for 36,612 single nucleotide polymorphisms (SNPs) after quality control. Phenotypic data included 228,427, 101,225, and 11,444 records for birth weight, average daily gain in the nursery, and feed intake, respectively. Breeding values were predicted in a multiple-trait framework using BLUP and ssGBLUP. Results The points of divergence of the genetic and RMS trends estimated by BLUP and ssGBLUP indicated that genomic selection effectively started in 2019. Partitioning the overall genetic trends into that for genotyped and non-genotyped individuals revealed that the contribution of genotyped animals to the overall genetic trend increased rapidly from ~ 74% in 2016 to 90% in 2019. The contribution of the female pathway to the genetic trend also increased since genomic selection was implemented in this pig population, which reflects the changes in the genotyping strategy in recent years. Conclusions Our results show that an assessment of breeding program practices can be done based on the point of divergence of genetic and RMS trends between BLUP and ssGBLUP and based on the partitioning of the genetic trend into contributions from different selection pathways. However, it should be noted that genetic trends can diverge before the onset of genomic selection if superior animals are genotyped retroactively. For the pig population example, the results showed that genomic selection was effective in this population.

scholarly journals Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

2020 ◽  
Author(s):  
Matthew McGowan ◽  
Zhiwu Zhang ◽  
Jiabo Wang ◽  
Haixiao Dong ◽  
Xiaolei Liu ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Markers ◽  
Random Effects ◽  
Genome Wide Association Study ◽  
Single Step ◽  
Hybrid Breeding ◽  
Multiple Trait ◽  
Underlying Distribution ◽  
Genome Wide ◽  
Before And After

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

scholarly journals Ideas in Genomic Selection with the Potential to Transform Plant Molecular Breeding: A Review

2020 ◽  
Author(s):  
Matthew McGowan ◽  
Jiabo Wang ◽  
Haixiao Dong ◽  
Xiaolei Liu ◽  
Yi Jia ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Markers ◽  
Random Effects ◽  
Genome Wide Association Study ◽  
Single Step ◽  
Hybrid Breeding ◽  
Multiple Trait ◽  
Underlying Distribution ◽  
Genome Wide ◽  
Before And After

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

35 Detecting Effective Starting Point of Genomic Selection by Divergent Trends from BLUP and ssGBLUP

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 19-19
Author(s):  
Rostam Abdollahi-Arpanahi ◽  
Daniela Lourenco ◽  
Ignacy Misztal
Keyword(s):  
Birth Weight ◽  
Beef Cattle ◽  
Genomic Selection ◽  
Single Step ◽  
Weaning Weight ◽  
Start Date ◽  
Angus Cattle ◽  
Starting Point ◽  
Essential Question ◽  
Genetic Trends

Abstract Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the point of divergence of genetic trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP) may indicate the start date of genomic selection. This study aimed to find the start date of genomic selection for a set of economically important traits in pigs and Angus cattle by comparing genetic and Realized Mendelian sampling (RMS) trends obtained using BLUP and ssGBLUP. In pigs, the genetic trends from ssGBLUP and BLUP diverged for all traits with different intensity. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little divergence for birth weight. In pigs, the RMS trends from ssGBLUP and BLUP diverged strongly for one trait and weakly for another trait. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little trend for birth weight. Divergence of the genetic trends from ssGBLUP and BLUP indicate onset of the genomic selection. Presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the two species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

scholarly journals 9 A comparison of different genomic prediction methods in the Canadian Landrace swine population

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 10-10
Author(s):  
Siavash Salek Ardestani ◽  
Mohsen Jafarikia ◽  
Brian Sullivan ◽  
Mehdi Sargolzaei ◽  
Younes Miar
Keyword(s):  
Genomic Prediction ◽  
Performance Test ◽  
Average Daily Gain ◽  
Single Step ◽  
Breeding Value ◽  
Prediction Methods ◽  
Nucleotide Polymorphisms ◽  
Economic Traits ◽  
Genetic Progress ◽  
Breeding Values

Abstract Increasing the accuracy of breeding value prediction can lead to more profitability through accelerating genetic progress for economic traits. The objective of this study was to assess the predictive abilities and unbiasedness of best linear unbiased prediction (BLUP) and popular genomic prediction methods of BayesC, BayesC(π = 0.99), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP). Genotypic information (50K and 60K) of 4,890 performance tested Landrace pigs before February 2019 and 471 validation Landrace pigs that both had phenotypic information on backfat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) from two Canadian pig breeding companies (AlphaGene and Alliance Genetics Canada) were used. The de-regressed breeding values (DEBV) were employed in GBLUP and Bayesian methods. A total number of 48,580 single nucleotide polymorphisms remained after quality control and imputation steps. The prediction accuracies were calculated using the correlation between predicted breeding values before performance test and DEBVs after performance test. All employed genomic prediction methods showed higher prediction accuracies for BFT (50.80–52.68%), ADG (26.61–34.47%), and LMD (18.25–25.08%) compared to BLUP method (BFT = 28.54%, ADG = 16.41%, LMD = 17.15%). The highest prediction accuracies for BFT and ADG were obtained using ssGBLUP method, and for LMD it was obtained using BayesC(π = 0.99). The BayesC(π = 0.99) showed also the lowest prediction biases across the studied traits (+0.05 for BFT, 0.00 for AGD, and -0.10 for LMD). In conclusion, our results revealed the superiority of ssGBLUP (for BFT and ADG) and BayesC(π = 0.99) (for LMD) over other tested methods in this study. However, the prediction accuracies from the tested genomic prediction methods were not significantly different from each other. Thus, employing these methods can be helpful for accelerating the genetic improvement of BFT, ADG, and LMD in the moderate population size of Canadian Landrace.

scholarly journals Detecting effective starting point of genomic selection by divergent trends from BLUP and ssGBLUP in pigs, beef cattle, and broilers

2021 ◽  
Author(s):  
Rostam Abdollahi Arpanahi ◽  
Daniela Lourenco ◽  
Ignacy Misztal
Keyword(s):  
Birth Weight ◽  
Beef Cattle ◽  
Genomic Selection ◽  
Broiler Chickens ◽  
Average Daily Gain ◽  
Breeding Cycle ◽  
Production Traits ◽  
Weaning Weight ◽  
Daily Gain ◽  
Genetic Trends

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the effective start point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. For this purpose, three datasets comprised a pig dataset with 117k genotypes and 1.3M animals in pedigree, Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain and backfat. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little diverging for birth weight. In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for average daily gain than for backfat. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with a trivial trend for birth weight. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates onset of the genomic selection. Presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the 3 species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

scholarly journals Detecting effective starting point of genomic selection by divergent trends from BLUP and ssGBLUP in pigs, beef cattle, and broilers

2021 ◽  
Author(s):  
Rostam Abdollahi-Arpanahi ◽  
Daniela Lourenco ◽  
Ignacy Misztal
Keyword(s):  
Birth Weight ◽  
Beef Cattle ◽  
Genomic Selection ◽  
Broiler Chickens ◽  
Average Daily Gain ◽  
Breeding Cycle ◽  
Production Traits ◽  
Starting Point ◽  
Daily Gain ◽  
Genetic Trends

Abstract Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: a pig dataset with 117k genotypes and 1.3M animals in pedigree, an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain and backfat. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little divergence for birth weight. In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for average daily gain than for backfat. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with a trivial trend for birth weight. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

ESTIMATION OF GENETIC TRENDS FOR CANADIAN STATION-TESTED BEEF BULLS

1988 ◽  
Vol 68 (1) ◽  
pp. 49-56 ◽  
Author(s):  
E. P. de ROSE ◽  
J. W. WILTON
Keyword(s):  
Key Words ◽  
Average Daily Gain ◽  
Genetic Trend ◽  
Genetic Progress ◽  
Testing Program ◽  
Scrotal Circumference ◽  
Beef Bulls ◽  
Estimated Breeding Values ◽  
Daily Gain ◽  
Genetic Trends

Data collected from Canadian central test stations were used to obtain estimates of genetic progress for test station average daily gain (ADG-T), height at hooks (HH), ultrasonically measured backfat thickness midway between the 11th and 12th ribs (FAT) and scrotal circumference (SC). Where possible, records for preweaning average daily gain (ADG-W) from the Federal-Provincial Herd Record of Performance program were obtained for tested bulls. Estimated breeding values were regressed on year of birth to obtain estimates of genetic trend for each trait, for the six principal breeds enrolled in the testing program: Aberdeen Angus (AA), Charolais (CH), Hereford (HE), Limousin (LM), Shorthorn (SH) and Simmental (SM). Estimated trends were generally significant with the exception of those for the LM breed, and the trait of SC. For FAT, trends were generally positive for the exotic breeds, and negative for the British breeds. Significant positive trends were observed for HH, ADG-W and ADG-T for most breeds. Examples of estimated genetic trends, for the HE breed, were 0.0022 kg d−1 yr−1, 0.0023 kg d−1 yr−1, 0.090 cm yr−1 (NS), − 0.029 mm yr−1, and 0.009 cm yr−1 (NS), respectively, for ADG-W, ADG-T, HH, FAT and SC (NS = not significant). Most estimated trends were low, in the order of 0.1 to 0.5% of population mean per year. Key words: Genetic trend, central test stations, beef bulls

scholarly journals Emerging issues in genomic selection

2021 ◽  
Author(s):  
I Misztal ◽  
I Aguilar ◽  
D Lourenco ◽  
L Ma ◽  
J Steibel ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Sparse Matrices ◽  
Large Datasets ◽  
Single Step ◽  
Genomic Information ◽  
Long Term Effects ◽  
Additive Variance ◽  
Recent Developments ◽  
Emerging Issues ◽  
Using Data

Abstract Genomic selection is now practiced successfully across many species. However, many questions remain such as long-term effects, estimations of genomic parameters, robustness of GWAS with small and large datasets, and stability of genomic predictions. This study summarizes presentations from at the 2020 ASAS symposium. The focus of many studies until now is on linkage disequilibrium (LD) between two loci. Ignoring higher level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWAS studies using small genomic datasets frequently find many marker-trait associations whereas studies using much bigger datasets find only a few. Most current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit computation of p-values from GBLUP, where models can be arbitrarily complex but restricted to genotyped animals only, and to single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as one SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. While many issues in genomic selection have been solved, many new issues that require additional research continue to surface.

scholarly journals Time-Related Mapping of Quantitative Trait Loci Underlying Tiller Number in Rice

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 297-303 ◽  
Author(s):  
Wei-Ren Wu ◽  
Wei-Ming Li ◽  
Ding-Zhong Tang ◽  
Hao-Ran Lu ◽  
A J Worland
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Tiller Number ◽  
Multiple Trait ◽  
Phenotypic Data ◽  
Recombinant Inbred Population ◽  
Trait Loci ◽  
Final Observation

Abstract Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.

scholarly journals Comparison of models for missing pedigree in single-step genomic prediction

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yutaka Masuda ◽  
Shogo Tsuruta ◽  
Matias Bermann ◽  
Heather L Bradford ◽  
Ignacy Misztal
Keyword(s):  
Density Function ◽  
Single Step ◽  
Joint Density ◽  
Pedigree Information ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Breeding Values ◽  
Joint Density Function ◽  
Information Updates ◽  
Genetic Trends

Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas–Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g′). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g′)=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.

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