Past, present, and future developments in single-step genomic models

Bermann, Matias; Cesarani, Alberto; Misztal, I.; Lourenço, Daniela

doi:10.1080/1828051x.2022.2053366

Cited by 7 publications

(4 citation statements)

References 96 publications

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“…For conventional multistep genetic evaluations, the drawbacks for instance of biased or inaccurate predictions for genotyped animals, the absence of gain in accuracy for ungenotyped animals, and incompatibility between EBVs for genotyped and ungenotyped animals (Bermann et al, 2022), undermine the prediction performance of the multiple-step method and may yield lower accuracy compared with ssGBLUP that includes both genotyped and ungenotyped animals simultaneously in a single genetic evaluation. In this study, we improved the prediction performance of the multistep method with Absorption approach, which achieved similar accuracy as ssGBLUP and in general lower bias.…”

Section: Practical Implementation Of the Multistep Methods Based On A...mentioning

confidence: 99%

“…With extensive research and the development of efficient computing algorithms to solve the challenges that limit the practical implementation of ssGBLUP, for instance metafounder approach (Kudinov et al, 2020; Legarra et al, 2015) and the use of the J factor (Belay et al, 2022; Stranden et al, 2022) to improve the compatibility between genomic and pedigree information, ssGBLUP has become the most popular methodology for genetic evaluations including genotyped and ungenotyped animals and has been successfully implemented in almost all livestock populations (Bermann et al, 2022). However, even though ssGBLUP took over the multistep method as the most chosen genetic evaluation methodology, the multistep method may show merits for instance the more straightforward extension of the method to variable selection models.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐trait genomic prediction in pigs using single and multistep methods based on the absorption of ungenotyped animals

Luan

Nordbø

Andersen-Ranberg

et al. 2023

J Animal Breeding Genetics

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Many quantitative traits measured in breeding programs are genetically correlated. The genetic correlations between the traits indicate that the measurement of one trait carries information on others. To benefit from this information, multi‐trait genomic prediction (MTGP) is preferable to use. However, MTGP is more difficult to implement compared to single‐trait genomic prediction (STGP), and even more challenging for the goal to exploit not only the information on other traits but also the information on ungenotyped animals. This could be accomplished using both single and multistep methods. The single‐step method was achieved by implementing a single‐step genomic best linear unbiased prediction (ssGBLUP) approach using a multi‐trait model. Here, we examined a multistep analysis based on an approach called “Absorption” to achieve this goal. The Absorption approach absorbed all available information including the phenotypic information on ungenotyped animals as well as the information on other traits if applicable, into mixed model equations of genotyped animals. The multistep analysis included (1) to apply the Absorption approach that exploits all available information and (2) to implement genomic BLUP (GBLUP) prediction on the absorbed dataset. In this study, the ssGBLUP and multistep analysis were applied to 5 traits in Duroc pigs, which were slaughter percentage, feed consumption from 40 to 120 kg (FC40_120), days of growth from 40 to 120 kg (D40_120), age at 40 kg (A40) and lean meat percentage. The results showed that MTGP yielded higher accuracy than STGP, which on average was 0.057 higher for the multistep method and 0.045 higher for ssGBLUP. The multistep method achieved similar prediction accuracy as ssGBLUP. However, the prediction bias of the multistep method was in general lower than that of ssGBLUP.

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Section: Practical Implementation Of the Multistep Methods Based On A...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐trait genomic prediction in pigs using single and multistep methods based on the absorption of ungenotyped animals

Luan

Nordbø

Andersen-Ranberg

et al. 2023

J Animal Breeding Genetics

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“…Genomic information is valuable for selecting low‐heritability traits and is widely employed in animal breeding programs. Currently, the single‐step genomic best linear unbiased prediction (ssGBLUP) is the most popular method for genetic evaluations of populations with both genotyped and non‐genotyped animals (Bermann et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation

Chen

Atashi

Mota

et al. 2023

J Animal Breeding Genetics

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Nitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single‐step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs.

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“…This is two-step procedure. Recently, single-step genomic BLUP combining information on both genotyped and non-genotyped animals is widely implemented (Lourenco et al, 2020;Mäntysaari et al, 2020;Bermann et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Perspectives for the use of genomic selection for genetic improvement of dairy cattle in Ukraine

Ruban¹,

Danshin²

2023

UBSRAS

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An important problem in modern dairy cattle breeding is the achievement of a high level of genetic progress in economically important traits through the implementation of effective breeding programs. For this purpose, genomic selection is currently used in many countries of the world. The aim of the study was to investigate possibilities of use of genomic selection in dairy cattle breeding in Ukraine. On the basis of analysis of “Catalogue of sires of dairy and dual-purpose breeds for reproduction of cows in 2020” (sperm of these sires was used in Ukraine) two methods of breeding value estimation were compared: 1) traditional method based on pedigree and performance of progeny; 2) genomic method based on effects of SNPs. Considerable advantage of sires with genomic evaluations was proved. These sires excel sires with traditional evaluation for milk yield by 1.6 times, for fat percentage by 2.2 times, for fat yield by 1.7 times, for protein percentage by 2.1 times and for protein yield by 1.7 times. Using estimates of breeding values of sires pare-wise genetic correlations between main genetic traits were computed. The negative genetic relationship between milk yield and fat and protein percentages was revealed. Values of energy corrected milk (ECM) of daughters and dams of sires across breeds and countries of origin were calculated. It was shown that dams of sires of Holstein and Jersey breeds had highest values of energy corrected milk (9,132.0 kg and 8,041 kg, respectively) while dams of sires of Ukrainian Black-and-White dairy breed had lowest values of this trait (5,848.1 kg). According to country-of-origin daughters of sire’s form USA, Canada and the Netherlands had highest values of energy corrected milk. Values of response to selection using traditional breeding program and genomic selection were compared. It was proved that by means of shortening generation intervals on pathways of genetic improvement “sires of bulls”, “sires of cows” and “dams of bulls” using genomic selection it is possible to increase rate of genetic progress for milk yield from 100.1 kg to 180.0 kg that is by 80%

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Past, present, and future developments in single-step genomic models

Cited by 7 publications

References 96 publications

Multi‐trait genomic prediction in pigs using single and multistep methods based on the absorption of ungenotyped animals

Multi‐trait genomic prediction in pigs using single and multistep methods based on the absorption of ungenotyped animals

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation

Perspectives for the use of genomic selection for genetic improvement of dairy cattle in Ukraine

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