36 Effect of Blending and Tuning Relationship Matrices in Single-step Genomic BLUP

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 19-20
Author(s):  
Taylor M McWhorter ◽  
Andre Garcia ◽  
Matias Bermann ◽  
Andres Legarra ◽  
Ignacio Aguilar ◽  
...  
Keyword(s):  
Single Step ◽  
Fixation Index ◽  
Genomic Information ◽  
Software Suite ◽  
Genetic Base ◽  
Actual Difference ◽  
The Impact ◽  
Genomic Predictions

Abstract Single-step GBLUP (ssGBLUP) relies on the combination of genomic (G) and pedigree relationships for all (A) and genotyped animals (A22). The procedure implemented in the BLUPF90 software suite first involves combining a small percentage of A22 into G (blending) to avoid singularity problems, then an adjustment to account for the fact the genetic base in G and A22 is different (tuning). However, blending before tuning may not reflect the actual difference between pedigree and genomic base because the blended matrix already contains a portion of A22. The objective of this study was to evaluate the impact of tuning before blending on predictivity, bias, and inflation of GEBV, indirect predictions (IP), and SNP effects from ssGBLUP using American Angus and US Holstein data. We used four different scenarios to obtain genomic predictions: BlendFirst_TunedG2, TuneFirst_TunedG2, BlendFirst_TunedG4, and TuneFirst_TunedG4. TunedG2 adjusts mean diagonals and off-diagonals of G to be similar to the ones in A22, whereas TunedG4 adjusts based on the fixation index. Over 6 million growth records were available for Angus and 5.9 million udder depth records for Holsteins. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Predictivity and reliability were obtained for 19,056 and 1,711 validation Angus and Holsteins, respectively. We observed the same predictivity and reliability for GEBV or IP in all four scenarios, ranging from 0.47 to 0.60 for Angus and was 0.67 for Holsteins. Slightly less bias was observed when tuning was done before blending. Correlation of SNP effects between scenarios was > 0.99. Refined tuning before blending had no impact on GEBV and marginally reduced the bias. This option will be implemented in the BLUPF90 software suite.

scholarly journals Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population

2020 ◽  
Vol 11 ◽  
Author(s):  
Vinícius Silva Junqueira ◽  
Paulo Sávio Lopes ◽  
Daniela Lourenco ◽  
Fabyano Fonseca e Silva ◽  
Fernando Flores Cardoso
Keyword(s):  
Beef Cattle ◽  
Variance Components ◽  
Genetic Relationships ◽  
A Priori ◽  
Predictive Ability ◽  
Single Step ◽  
Pedigree Information ◽  
Phenotypic Variance ◽  
Genomic Information ◽  
The Impact

Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

scholarly journals Multi-Trait Single-Step GBLUP Improves Accuracy of Genomic Prediction for Carcass Traits Using Yearling Weight and Ultrasound Traits in Hanwoo

2021 ◽  
Vol 12 ◽  
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deukhwan Lee ◽  
Noelia Ibáñez-Escriche
Keyword(s):  
Beef Cattle ◽  
Carcass Traits ◽  
Single Step ◽  
Birth Date ◽  
Linear Regression Method ◽  
Genomic Information ◽  
Muscle Area ◽  
Eye Muscle ◽  
Correlated Traits ◽  
The Impact

There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score), resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure.

scholarly journals Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction

2020 ◽  
Vol 98 (12) ◽  
Author(s):  
Ignacy Misztal ◽  
Shogo Tsuruta ◽  
Ivan Pocrnic ◽  
Daniela Lourenco
Keyword(s):  
Prediction Accuracy ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Relationship Matrix ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Best Linear Unbiased ◽  
The Impact ◽  
Genomic Predictions ◽  
Unbiased Prediction

Abstract Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.

scholarly journals Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1815
Author(s):  
Enrico Mancin ◽  
Beniamino Tuliozi ◽  
Cristina Sartori ◽  
Nadia Guzzo ◽  
Roberto Mantovani
Keyword(s):  
Average Daily Gain ◽  
Cattle Breed ◽  
Single Step ◽  
Genomic Information ◽  
Dispersion Parameters ◽  
Indigenous Cattle ◽  
North East ◽  
Estimated Breeding Values ◽  
The Impact

The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed.

scholarly journals Grid search approach to discriminate between old and recent inbreeding using phenotypic, pedigree and genomic information

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Pattarapol Sumreddee ◽  
El Hamidi Hay ◽  
Sajjad Toghiani ◽  
Andrew Roberts ◽  
Samuel E. Aggrey ◽  
...  
Keyword(s):  
Common Ancestor ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Recent Common Ancestor ◽  
Pedigree Information ◽  
Grid Search ◽  
Genomic Information ◽  
The Impact ◽  
Daily Gain ◽  
Harmful Effects

Abstract Background Although inbreeding caused by the mating of animals related through a recent common ancestor is expected to have more harmful effects on phenotypes than ancient inbreeding (old inbreeding), estimating these effects requires a clear definition of recent (new) and ancient (old) inbreeding. Several methods have been proposed to classify inbreeding using pedigree and genomic data. Unfortunately, these methods are largely based on heuristic criteria such as the number of generations from a common ancestor or length of runs of homozygosity (ROH) segments. To mitigate these deficiencies, this study aimed to develop a method to classify pedigree and genomic inbreeding into recent and ancient classes based on a grid search algorithm driven by the assumption that new inbreeding tends to have a more pronounced detrimental effect on traits. The proposed method was tested using a cattle population characterized by a deep pedigree. Results Effects of recent and ancient inbreeding were assessed on four growth traits (birth, weaning and yearling weights and average daily gain). Thresholds to classify inbreeding into recent and ancient classes were trait-specific and varied across traits and sources of information. Using pedigree information, inbreeding generated in the last 10 to 11 generations was considered as recent. When genomic information (ROH) was used, thresholds ranged between four to seven generations, indicating, in part, the ability of ROH segments to characterize the harmful effects of inbreeding in shorter periods of time. Nevertheless, using the proposed classification method, the discrimination between new and old inbreeding was less robust when ROH segments were used compared to pedigree. Using several model comparison criteria, the proposed approach was generally better than existing methods. Recent inbreeding appeared to be more harmful across the growth traits analyzed. However, both new and old inbreeding were found to be associated with decreased yearling weight and average daily gain. Conclusions The proposed method provided a more objective quantitative approach for the classification of inbreeding. The proposed method detected a clear divergence in the effects of old and recent inbreeding using pedigree data and it was superior to existing methods for all analyzed traits. Using ROH data, the discrimination between old and recent inbreeding was less clear and the proposed method was superior to existing approaches for two out of the four analyzed traits. Deleterious effects of recent inbreeding were detected sooner (fewer generations) using genomic information than pedigree. Difference in the results using genomic and pedigree information could be due to the dissimilarity in the number of generations to a common ancestor. Additionally, the uncertainty associated with the identification of ROH segments and associated inbreeding could have an effect on the results. Potential biases in the estimation of inbreeding effects may occur when new and old inbreeding are discriminated based on arbitrary thresholds. To minimize the impact of inbreeding, mating designs should take the different inbreeding origins into consideration.

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 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.

149 Integration of Selection Signatures Analyses and Weighted Single-step GWAS to Prioritize Candidate Genes for Body Conformation Traits in Pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 76-77
Author(s):  
Seyed Milad Vahedi ◽  
Siavash Salek Ardestani ◽  
Duy Ngoc Do ◽  
Karim Karimi ◽  
Younes Miar
Keyword(s):  
Candidate Genes ◽  
Complex Traits ◽  
Body Height ◽  
Single Step ◽  
Fixation Index ◽  
Nucleotide Polymorphisms ◽  
Selection Signatures ◽  
Body Conformation ◽  
Yorkshire Pigs ◽  
Estimated Breeding Values

Abstract Body conformation traits such as body height (BH) and body length (BL) have been included in the swine industry’s selection criteria. The objective of this study was to identify the quantitative trait loci (QTLs) and candidate genes for pig conformation traits using an integration of selection signatures analyses and weighted single-step GWAS (WssGWAS). Body measurement records of 5,593 Yorkshire pigs of which 598 animals were genotyped with Illumina 50K panel were used. Estimated breeding values (EBVs) for BH and BL were computed using univariate animal models. Genotyped animals were grouped into top 5% and bottom 5% based on their EBVs, and selection signatures analyses were performed using fixation index (Fst), FLK, hapFLK, and Rsb statistics, which were then combined as a Mahalanobis distance (Md) framework. The WssGWAS was conducted to detect the single nucleotide polymorphisms (SNPs) associated with the studied traits. The top 1% SNPs (n=530) from Md distribution that overlapped with the top 1% SNPs from WssGWAS (n = 530) were used to detect the candidate genes. A total of 31 and six overlapped SNPs were found to be associated with BH and BL, respectively. Several candidate genes were identified for BH (PARVA, DCDC1, SYT1, CASTOR2, RGSL1, RGS8, RBMS3, TGFBR2, and HS6ST1) and BL (SNTB1, AK7, PAPOLA, KSR1, CHODL, and BMP2), explaining 2.58% and 0.42% of the trait’s genetic variation, respectively. Our results indicated that integrating data from the signatures of selection tests with WssGWAS could help elucidate genomic regions underlying complex traits.

scholarly journals Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires

2022 ◽  
Author(s):  
Gabriel Soares Campos ◽  
Fernando Flores Cardoso ◽  
Claudia Cristina Gulias Gomes ◽  
Robert Domingues ◽  
Luciana Correia de Almeida Regitano ◽  
...  
Keyword(s):  
Reference Population ◽  
Single Step ◽  
Linear Regression Method ◽  
Effective Population ◽  
Genetic Progress ◽  
Angus Cattle ◽  
Improvement Programs ◽  
Snp Panels ◽  
Estimate Variance ◽  
Genomic Predictions

Abstract Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo® breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k SNP panels. After imputation and quality control, 61,666 SNP were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent SNPs across all chromosomes was 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

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