This page has only limited features, please log in for full access.

Dr. Shogo Tsuruta
Department of Animal and Dairy Science, University of Georgia, United States

Basic Info


Research Keywords & Expertise

0 Genomic selection
0 Quantitative Traits
0 Genetic gain
0 genetic parameters
0 Breeding values

Fingerprints

Breeding values
Genomic predictions
Genomic selection
genetic parameters
Genetic gain

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Accepted manuscript
Published: 01 August 2021 in Journal of Animal Science
Reads 0
Downloads 0

It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.

ACS Style

Mary Kate Hollifield; Daniela Lourenco; Shogo Tsuruta; Matias Bermann; Jeremy T Howard; Ignacy Misztal. Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs. Journal of Animal Science 2021, 99, 1 .

AMA Style

Mary Kate Hollifield, Daniela Lourenco, Shogo Tsuruta, Matias Bermann, Jeremy T Howard, Ignacy Misztal. Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs. Journal of Animal Science. 2021; 99 (8):1.

Chicago/Turabian Style

Mary Kate Hollifield; Daniela Lourenco; Shogo Tsuruta; Matias Bermann; Jeremy T Howard; Ignacy Misztal. 2021. "Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs." Journal of Animal Science 99, no. 8: 1.

Journal article
Published: 01 May 2021 in Journal of Dairy Science
Reads 0
Downloads 0

The objective of this study was to assess the reliability and bias of estimated breeding values (EBV) from traditional BLUP with unknown parent groups (UPG), genomic EBV (GEBV) from single-step genomic BLUP (ssGBLUP) with UPG for the pedigree relationship matrix (A) only (SS_UPG), and GEBV from ssGBLUP with UPG for both A and the relationship matrix among genotyped animals (A22; SS_UPG2) using 6 large phenotype-pedigree truncated Holstein data sets. The complete data included 80 million records for milk, fat, and protein yields from 31 million cows recorded since 1980. Phenotype-pedigree truncation scenarios included truncation of phenotypes for cows recorded before 1990 and 2000 combined with truncation of pedigree information after 2 or 3 ancestral generations. A total of 861,525 genotyped bulls with progeny and cows with phenotypic records were used in the analyses. Reliability and bias (inflation/deflation) of GEBV were obtained for 2,710 bulls based on deregressed proofs, and on 381,779 cows born after 2014 based on predictivity (adjusted cow phenotypes). The BLUP reliabilities for young bulls varied from 0.29 to 0.30 across traits and were unaffected by data truncation and number of generations in the pedigree. Reliabilities ranged from 0.54 to 0.69 for SS_UPG and were slightly affected by phenotype-pedigree truncation. Reliabilities ranged from 0.69 to 0.73 for SS_UPG2 and were unaffected by phenotype-pedigree truncation. The regression coefficient of bull deregressed proofs on (G)EBV (i.e., GEBV and EBV) ranged from 0.86 to 0.90 for BLUP, from 0.77 to 0.94 for SS_UPG, and was 1.00 ± 0.03 for SS_UPG2. Cow predictivity ranged from 0.22 to 0.28 for BLUP, 0.48 to 0.51 for SS_UPG, and 0.51 to 0.54 for SS_UPG2. The highest cow predictivities for BLUP were obtained with the most extreme truncation, whereas for SS_UPG2, cow predictivities were also unaffected by phenotype-pedigree truncations. The regression coefficient of cow predictivities on (G)EBV was 1.02 ± 0.02 for SS_UPG2 with the most extreme truncation, which indicated the least biased predictions. Computations with the complete data set took 17 h with BLUP, 58 h with SS_UPG, and 23 h with SS_UPG2. The same computations with the most extreme phenotype-pedigree truncation took 7, 36, and 15 h, respectively. The SS_UPG2 converged in fewer rounds than BLUP, whereas SS_UPG took up to twice as many rounds. Thus, the ssGBLUP with UPG assigned to both A and A22 provided accurate and unbiased evaluations, regardless of phenotype-pedigree truncation scenario. Old phenotypes (before 2000 in this data set) did not affect the reliability of predictions for young selection candidates, especially in SS_UPG2.

ACS Style

A. Cesarani; Y. Masuda; S. Tsuruta; E.L. Nicolazzi; P.M. VanRaden; D. Lourenco; I. Misztal. Genomic predictions for yield traits in US Holsteins with unknown parent groups. Journal of Dairy Science 2021, 104, 5843 -5853.

AMA Style

A. Cesarani, Y. Masuda, S. Tsuruta, E.L. Nicolazzi, P.M. VanRaden, D. Lourenco, I. Misztal. Genomic predictions for yield traits in US Holsteins with unknown parent groups. Journal of Dairy Science. 2021; 104 (5):5843-5853.

Chicago/Turabian Style

A. Cesarani; Y. Masuda; S. Tsuruta; E.L. Nicolazzi; P.M. VanRaden; D. Lourenco; I. Misztal. 2021. "Genomic predictions for yield traits in US Holsteins with unknown parent groups." Journal of Dairy Science 104, no. 5: 5843-5853.

Journal article
Published: 01 February 2021 in Journal of Animal Science
Reads 0
Downloads 0

The stability of genomic evaluations depends on the amount of data and population parameters. When the dataset is large enough to estimate the value of nearly all independent chromosome segments (~10K in American Angus cattle), the accuracy and persistency of breeding values will be high. The objective of this study was to investigate changes in estimated breeding values (EBV) and genomic EBV (GEBV) across monthly evaluations for 1 yr in a large genotyped population of beef cattle. The American Angus data used included 8.2 million records for birth weight, 8.9 for weaning weight, and 4.4 for postweaning gain. A total of 10.1 million animals born until December 2017 had pedigree information, and 484,074 were genotyped. A truncated dataset included animals born until December 2016. To mimic a scenario with monthly evaluations, 2017 data were added 1 mo at a time to estimate EBV using best linear unbiased prediction (BLUP) and GEBV using single-step genomic BLUP with the algorithm for proven and young (APY) with core group fixed for 1 yr or updated monthly. Predictions from monthly evaluations in 2017 were contrasted with the predictions of the evaluation in December 2016 or the previous month for all genotyped animals born until December 2016 with or without their own phenotypes or progeny phenotypes. Changes in EBV and GEBV were similar across traits, and only results for weaning weight are presented. Correlations between evaluations from December 2016 and the 12 consecutive evaluations were ≥0.97 for EBV and ≥0.99 for GEBV. Average absolute changes for EBV were about two times smaller than for GEBV, except for animals with new progeny phenotypes (≤0.12 and ≤0.11 additive genetic SD [SDa] for EBV and GEBV). The maximum absolute changes for EBV (≤2.95 SDa) were greater than for GEBV (≤1.59 SDa). The average(maximum) absolute GEBV changes for young animals from December 2016 to January and December 2017 ranged from 0.05(0.25) to 0.10(0.53) SDa. Corresponding ranges for animals with new progeny phenotypes were from 0.05(0.88) to 0.11(1.59) SDa for GEBV changes. The average absolute change in EBV(GEBV) from December 2016 to December 2017 for sires with ≤50 progeny phenotypes was 0.26(0.14) and for sires with >50 progeny phenotypes was 0.25(0.16) SDa. Updating the core group in APY without adding data created an average absolute change of 0.07 SDa in GEBV. Genomic evaluations in large genotyped populations are as stable and persistent as the traditional genetic evaluations, with less extreme changes.

ACS Style

Jorge Hidalgo; Daniela Lourenco; Shogo Tsuruta; Yutaka Masuda; Stephen Miller; Matias Bermann; Andre L S Garcia; Ignacy Misztal. Changes in genomic predictions when new information is added. Journal of Animal Science 2021, 99, 1 .

AMA Style

Jorge Hidalgo, Daniela Lourenco, Shogo Tsuruta, Yutaka Masuda, Stephen Miller, Matias Bermann, Andre L S Garcia, Ignacy Misztal. Changes in genomic predictions when new information is added. Journal of Animal Science. 2021; 99 (2):1.

Chicago/Turabian Style

Jorge Hidalgo; Daniela Lourenco; Shogo Tsuruta; Yutaka Masuda; Stephen Miller; Matias Bermann; Andre L S Garcia; Ignacy Misztal. 2021. "Changes in genomic predictions when new information is added." Journal of Animal Science 99, no. 2: 1.

Journal article
Published: 25 January 2021 in Journal of Animal Science
Reads 0
Downloads 0

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.

ACS Style

Yutaka Masuda; Shogo Tsuruta; Matias Bermann; Heather L Bradford; Ignacy Misztal. Comparison of models for missing pedigree in single-step genomic prediction. Journal of Animal Science 2021, 99, 1 .

AMA Style

Yutaka Masuda, Shogo Tsuruta, Matias Bermann, Heather L Bradford, Ignacy Misztal. Comparison of models for missing pedigree in single-step genomic prediction. Journal of Animal Science. 2021; 99 (2):1.

Chicago/Turabian Style

Yutaka Masuda; Shogo Tsuruta; Matias Bermann; Heather L Bradford; Ignacy Misztal. 2021. "Comparison of models for missing pedigree in single-step genomic prediction." Journal of Animal Science 99, no. 2: 1.

Journal article
Published: 01 January 2021 in Journal of Dairy Science
Reads 0
Downloads 0

The objective of this study was to clarify how bias in genomic predictions is created by investigating a relationship among selection intensity, a change in heritability (Δh2), and assortative mating (ASM). A change in heritability, resulting from selection, reflects the impact that the Bulmer effect has on the reduction in between-family variation, whereas assortative mating impacts the within-family variance or Mendelian sampling variation. A partial data set up to 2014, including 841K genotyped animals, was used to calculate genomic predictions with a single-step genomic model for 18 linear type traits in US Holsteins. A full data set up to 2018, including 2.3 million genotyped animals, was used to calculate benchmark genomic predictions. Inbreeding and unknown parent groups for missing parents of animals were included in the model. Genomic evaluation was performed using 2 different genetic parameters: those estimated 14 yr ago, which have been used in the national genetic evaluation for linear type traits in the United States, and those newly estimated with recent records from 2015 to 2018 and those corresponding pedigrees. Genetic trends for 18 type traits were estimated for bulls with daughters and cows with phenotypes in 2018. Based on selection intensity and mating decisions, these traits can be categorized into 3 groups: (a) high directional selection, (b) moderate selection, and (c) intermediate optimum selection. The first 2 categories can be explained by positive assortative mating, and the last can be explained by negative assortative or disassortative mating. Genetic progress was defined by genetic gain per year based on average standardized genomic predictions for cows from 2000 to 2014. Traits with more genetic progress tended to have more "inflated" genomic predictions (i.e., "inflation" means here that genomic predictions are larger in absolute values than expected, whereas "deflation" means smaller than expected). Heritability estimates for 14 out of 18 traits declined in the last 16 yr, and Δh2 ranged from -0.09 to 0.04. Traits with a greater decline in heritability tended to have more deflated genomic predictions. Biases (inflation or deflation) in genomic predictions were not improved by using the latest genetic parameters, implying that bias in genomic predictions due to preselection was not substantial for a large-scale genomic evaluation. Moreover, the strong selection intensity was not fully responsible for bias in genomic predictions. The directional selection can decrease heritability; however, positive assortative mating, which was strongly associated with large genetic gains, could minimize the decline in heritability for a trait under strong selection and could affect bias in genomic predictions.

ACS Style

S. Tsuruta; T.J. Lawlor; D.A.L. Lourenco; I. Misztal. Bias in genomic predictions by mating practices for linear type traits in a large-scale genomic evaluation. Journal of Dairy Science 2021, 104, 662 -677.

AMA Style

S. Tsuruta, T.J. Lawlor, D.A.L. Lourenco, I. Misztal. Bias in genomic predictions by mating practices for linear type traits in a large-scale genomic evaluation. Journal of Dairy Science. 2021; 104 (1):662-677.

Chicago/Turabian Style

S. Tsuruta; T.J. Lawlor; D.A.L. Lourenco; I. Misztal. 2021. "Bias in genomic predictions by mating practices for linear type traits in a large-scale genomic evaluation." Journal of Dairy Science 104, no. 1: 662-677.

Journal article
Published: 19 November 2020 in Journal of Animal Science
Reads 0
Downloads 0

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.

ACS Style

Ignacy Misztal; Shogo Tsuruta; Ivan Pocrnic; Daniela Lourenco. Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction. Journal of Animal Science 2020, 98, 1 .

AMA Style

Ignacy Misztal, Shogo Tsuruta, Ivan Pocrnic, Daniela Lourenco. Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction. Journal of Animal Science. 2020; 98 (12):1.

Chicago/Turabian Style

Ignacy Misztal; Shogo Tsuruta; Ivan Pocrnic; Daniela Lourenco. 2020. "Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction." Journal of Animal Science 98, no. 12: 1.

Review
Published: 14 July 2020 in Genes
Reads 0
Downloads 0

Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data.

ACS Style

Daniela Lourenco; Andres Legarra; Shogo Tsuruta; Yutaka Masuda; Ignacio Aguilar; Ignacy Misztal. Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90. Genes 2020, 11, 790 .

AMA Style

Daniela Lourenco, Andres Legarra, Shogo Tsuruta, Yutaka Masuda, Ignacio Aguilar, Ignacy Misztal. Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90. Genes. 2020; 11 (7):790.

Chicago/Turabian Style

Daniela Lourenco; Andres Legarra; Shogo Tsuruta; Yutaka Masuda; Ignacio Aguilar; Ignacy Misztal. 2020. "Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90." Genes 11, no. 7: 790.

Journal article
Published: 23 June 2020 in Animals
Reads 0
Downloads 0

Our aims were to find a phenotypic variable to express mares’ lifetime reproductive performance after 6 breeding seasons (BS) in Italian Heavy Draught Horse breed (IHDH), and to estimate its heritability. At first, 1487 mares in a training dataset were used to implement and validate a set of predictive coefficients (LFR-C) or equations (LFR-E) to estimate a lifetime foaling rate (LFR) after 6 BS, i.e., the number of foals generated divided by the opportunities to do so. Then, 3033 mares in a dataset with at least 3 registered BS, was used to estimate LFR for mares with 3, 4, or 5 registered RS. This dataset contained actual (n = 1950) and estimated (n = 1443) LFR, obtained by LFR-C, and LFR-E; Arcsine transformation of LFR-C and LFR-E were also analyzed in single trait animal models to estimate heritability. Overall, the LFR showed a moderate but significant genetic variation, and the heritability of the trait was high (0.24) considering it is a fitness trait. The arcsine transformation of LFR did not show any improvement of heritability. The present study indicates the possible use of a linear LFR variable for breeding purposes in IHDH breed considering both complete and incomplete reproductive careers.

ACS Style

Roberto Mantovani; Fabio Folla; Giuseppe Pigozzi; Shogo Tsuruta; Cristina Sartori. Genetics of Lifetime Reproductive Performance in Italian Heavy Draught Horse Mares. Animals 2020, 10, 1085 .

AMA Style

Roberto Mantovani, Fabio Folla, Giuseppe Pigozzi, Shogo Tsuruta, Cristina Sartori. Genetics of Lifetime Reproductive Performance in Italian Heavy Draught Horse Mares. Animals. 2020; 10 (6):1085.

Chicago/Turabian Style

Roberto Mantovani; Fabio Folla; Giuseppe Pigozzi; Shogo Tsuruta; Cristina Sartori. 2020. "Genetics of Lifetime Reproductive Performance in Italian Heavy Draught Horse Mares." Animals 10, no. 6: 1085.

Journal article
Published: 06 May 2020 in Journal of Animal Science
Reads 0
Downloads 0

Reliable single-nucleotide polymorphisms (SNP) effects from genomic best linear unbiased prediction BLUP (GBLUP) and single-step GBLUP (ssGBLUP) are needed to calculate indirect predictions (IP) for young genotyped animals and animals not included in official evaluations. Obtaining reliable SNP effects and IP requires a minimum number of animals and when a large number of genotyped animals are available, the algorithm for proven and young (APY) may be needed. Thus, the objectives of this study were to evaluate IP with an increasingly larger number of genotyped animals and to determine the minimum number of animals needed to compute reliable SNP effects and IP. Genotypes and phenotypes for birth weight, weaning weight, and postweaning gain were provided by the American Angus Association. The number of animals with phenotypes was more than 3.8 million. Genotyped animals were assigned to three cumulative year-classes: born until 2013 (N = 114,937), born until 2014 (N = 183,847), and born until 2015 (N = 280,506). A three-trait model was fitted using the APY algorithm with 19,021 core animals under two scenarios: 1) core 2013 (random sample of animals born until 2013) used for all year-classes and 2) core 2014 (random sample of animals born until 2014) used for year-class 2014 and core 2015 (random sample of animals born until 2015) used for year-class 2015. GBLUP used phenotypes from genotyped animals only, whereas ssGBLUP used all available phenotypes. SNP effects were predicted using genomic estimated breeding values (GEBV) from either all genotyped animals or only core animals. The correlations between GEBV from GBLUP and IP obtained using SNP effects from core 2013 were ≥0.99 for animals born in 2013 but as low as 0.07 for animals born in 2014 and 2015. Conversely, the correlations between GEBV from ssGBLUP and IP were ≥0.99 for animals born in all years. IP predictive abilities computed with GEBV from ssGBLUP and SNP predictions based on only core animals were as high as those based on all genotyped animals. The correlations between GEBV and IP from ssGBLUP were ≥0.76, ≥0.90, and ≥0.98 when SNP effects were computed using 2k, 5k, and 15k core animals. Suitable IP based on GEBV from GBLUP can be obtained when SNP predictions are based on an appropriate number of core animals, but a considerable decline in IP accuracy can occur in subsequent years. Conversely, IP from ssGBLUP based on large numbers of phenotypes from non-genotyped animals have persistent accuracy over time.

ACS Style

Andre L S Garcia; Yutaka Masuda; Shogo Tsuruta; Stephen Miller; Ignacy Misztal; Daniela Lourenco. Indirect predictions with a large number of genotyped animals using the algorithm for proven and young. Journal of Animal Science 2020, 98, 1 .

AMA Style

Andre L S Garcia, Yutaka Masuda, Shogo Tsuruta, Stephen Miller, Ignacy Misztal, Daniela Lourenco. Indirect predictions with a large number of genotyped animals using the algorithm for proven and young. Journal of Animal Science. 2020; 98 (6):1.

Chicago/Turabian Style

Andre L S Garcia; Yutaka Masuda; Shogo Tsuruta; Stephen Miller; Ignacy Misztal; Daniela Lourenco. 2020. "Indirect predictions with a large number of genotyped animals using the algorithm for proven and young." Journal of Animal Science 98, no. 6: 1.

Journal article
Published: 30 January 2020 in Journal of Animal Science
Reads 0
Downloads 0

Genomic selection increases accuracy and decreases generation interval, speeding up genetic changes in the populations. However, intensive changes caused by selection can reduce the genetic variation and can strengthen undesirable genetic correlations. The purpose of this study was to investigate changes in genetic parameters for fitness traits related with prolificacy (FT1) and litter survival (FT2 and FT3), and for growth (GT1 and GT2) traits in pigs over time. The data set contained 21,269 (FT1), 23,246 (FT2), 23,246 (FT3), 150,492 (GT1), and 150,493 (GT2) phenotypic records obtained from 2009 to 2018. The pedigree file included 369,776 animals born between 2001 and 2018, of which 39,103 were genotyped. Genetic parameters were estimated with bivariate models (FT1-GT1, FT1-GT2, FT2-GT1, FT2-GT2, FT3-GT1, and FT3-GT2) using 3-yr sliding subsets. With a Bayesian implementation using the GIBBS3F90 program computations were performed as genomic analysis (GEN) or pedigree-based analysis (PED), that is, with or without genotypes, respectively. For GEN (PED), the changes in heritability from the first to the last year interval, that is, from 2009–2011 to 2015–2018 were 8.6 to 5.6 (7.9 to 8.8) for FT1, 7.8 to 7.2 (7.7 to 10.8) for FT2, 11.4 to 7.6 (10.1 to 7.5) for FT3, 35.1 to 16.5 (32.5 to 23.7) for GT1, and 35.9 to 16.5 (32.6 to 24.1) for GT2. Differences were also observed for genetic correlations as they changed from −0.31 to −0.58 (−0.28 to −0.73) for FT1-GT1, −0.32 to −0.50 (−0.29 to −0.74) for FT1-GT2, −0.27 to −0.45 (−0.30 to −0.65) for FT2-GT1, −0.28 to −0.45 (−0.32 to −0.66) for FT2-GT2, 0.14 to 0.17 (0.11 to 0.04) for FT3-GT1, and 0.14 to 0.18 (0.11 to 0.05) for FT3-GT2. Strong selection in pigs reduced heritabilities and emphasized the antagonistic genetic relationships between fitness and growth traits. With genotypes considered, heritability estimates were smaller and genetic correlations were greater than estimates with only pedigree and phenotypes. When selection is based on genomic information, genetic parameters estimated without this information can be biased because preselection is not accounted for by the model.

ACS Style

Jorge Hidalgo; Shogo Tsuruta; Daniela Lourenco; Yutaka Masuda; Yijian Huang; Kent A Gray; Ignacy Misztal. Changes in genetic parameters for fitness and growth traits in pigs under genomic selection. Journal of Animal Science 2020, 98, 1 .

AMA Style

Jorge Hidalgo, Shogo Tsuruta, Daniela Lourenco, Yutaka Masuda, Yijian Huang, Kent A Gray, Ignacy Misztal. Changes in genetic parameters for fitness and growth traits in pigs under genomic selection. Journal of Animal Science. 2020; 98 (2):1.

Chicago/Turabian Style

Jorge Hidalgo; Shogo Tsuruta; Daniela Lourenco; Yutaka Masuda; Yijian Huang; Kent A Gray; Ignacy Misztal. 2020. "Changes in genetic parameters for fitness and growth traits in pigs under genomic selection." Journal of Animal Science 98, no. 2: 1.

Journal article
Published: 01 November 2019 in Journal of Dairy Science
Reads 0
Downloads 0

The objectives of this study were to investigate bias in genomic predictions for dairy cattle and to find a practical approach to reduce the bias. The simulated data included phenotypes, pedigrees, and genotypes, mimicking a dairy cattle population (i.e., cows with phenotypes and bulls with no phenotypes) and assuming selection by breeding values or no selection. With the simulated data, genomic estimated breeding values (GEBV) were calculated with a single-step genomic BLUP and compared with true breeding values. Phenotypes and genotypes were simulated in 10 generations and in the last 4 generations, respectively. Phenotypes in the last generation were removed to predict breeding values for those individuals using only genomic and pedigree information. Complete pedigrees and incomplete pedigrees with 50% missing dams were created to construct the pedigree-based relationship matrix with and without inbreeding. With missing dams, unknown parent groups (UPG) were assigned in relationship matrices. Regression coefficients (b1) and coefficients of determination (R2) of true breeding values on (G)EBV were calculated to investigate inflation and accuracy in GEBV for genotyped animals, respectively. In addition to the simulation study, 18 linear type traits of US Holsteins were examined. For the 18 type traits, b1 and R2 of GEBV with full data sets on GEBV with partial data sets for young genotyped bulls were calculated. The results from the simulation study indicated inflation in GEBV for genotyped males that were evaluated with only pedigree and genomic information under BLUP selection. However, when UPG for only pedigree-based relationships were included, the inflation was reduced, accuracy was highest, and genetic trends had no bias. For the linear type traits, when UPG for only pedigree-based relationships were included, the results were generally in agreement with those from the simulation study, implying less bias in genetic trends. However, when including no UPG, UPG in pedigree-based relationships, or UPG in genomic relationships, inflation and accuracy in GEBV were similar. The results from the simulation and type traits suggest that UPG must be defined accurately to be estimable and inbreeding should be included in pedigree-based relationships. In dairy cattle, known pedigree information with inbreeding and estimable UPG plays an important role in improving compatibility between pedigree-based and genomic relationship matrices, resulting in more reliable genomic predictions.

ACS Style

S. Tsuruta; D.A.L. Lourenco; Y. Masuda; I. Misztal; T.J. Lawlor. Controlling bias in genomic breeding values for young genotyped bulls. Journal of Dairy Science 2019, 102, 9956 -9970.

AMA Style

S. Tsuruta, D.A.L. Lourenco, Y. Masuda, I. Misztal, T.J. Lawlor. Controlling bias in genomic breeding values for young genotyped bulls. Journal of Dairy Science. 2019; 102 (11):9956-9970.

Chicago/Turabian Style

S. Tsuruta; D.A.L. Lourenco; Y. Masuda; I. Misztal; T.J. Lawlor. 2019. "Controlling bias in genomic breeding values for young genotyped bulls." Journal of Dairy Science 102, no. 11: 9956-9970.

Journal article
Published: 14 December 2018 in Genetics Selection Evolution
Reads 0
Downloads 0

Catfish farming is the largest segment of US aquaculture and research is ongoing to improve production efficiency, including genetic selection programs to improve economically important traits. The objectives of this study were to investigate the use of genomic selection to improve breeding value accuracy and to identify major single nucleotide polymorphisms (SNPs) associated with harvest weight and residual carcass weight in a channel catfish population. Phenotypes were available for harvest weight (n = 27,160) and residual carcass weight (n = 6020), and 36,365 pedigree records were available. After quality control, genotypes for 54,837 SNPs were available for 2911 fish. Estimated breeding values (EBV) were obtained with traditional pedigree-based best linear unbiased prediction (BLUP) and genomic (G)EBV were estimated with single-step genomic BLUP (ssGBLUP). EBV and GEBV prediction accuracies were evaluated using different validation strategies. The ability to predict future performance was calculated as the correlation between EBV or GEBV and adjusted phenotypes. Compared to the pedigree BLUP, ssGBLUP increased predictive ability up to 28% and 36% for harvest weight and residual carcass weight, respectively; and GEBV were superior to EBV for all validation strategies tested. Breeding value inflation was assessed as the regression coefficient of adjusted phenotypes on breeding values, and the results indicated that genomic information reduced breeding value inflation. Genome-wide association studies based on windows of 20 adjacent SNPs indicated that both harvest weight and residual carcass weight have a polygenic architecture with no major SNPs (the largest SNPs explained 0.96 and 1.19% of the additive genetic variation for harvest weight and residual carcass weight respectively). Genomic evaluation improves the ability to predict future performance relative to traditional BLUP and will allow more accurate identification of genetically superior individuals within catfish families.

ACS Style

Andre L. S. Garcia; Brian Bosworth; Geoffrey Waldbieser; Ignacy Misztal; Shogo Tsuruta; Daniela A. L. Lourenco. Development of genomic predictions for harvest and carcass weight in channel catfish. Genetics Selection Evolution 2018, 50, 1 -12.

AMA Style

Andre L. S. Garcia, Brian Bosworth, Geoffrey Waldbieser, Ignacy Misztal, Shogo Tsuruta, Daniela A. L. Lourenco. Development of genomic predictions for harvest and carcass weight in channel catfish. Genetics Selection Evolution. 2018; 50 (1):1-12.

Chicago/Turabian Style

Andre L. S. Garcia; Brian Bosworth; Geoffrey Waldbieser; Ignacy Misztal; Shogo Tsuruta; Daniela A. L. Lourenco. 2018. "Development of genomic predictions for harvest and carcass weight in channel catfish." Genetics Selection Evolution 50, no. 1: 1-12.

Journal article
Published: 01 September 2017 in Journal of Dairy Science
Reads 0
Downloads 0

The objective of this study was to investigate the feasibility of genomic evaluation for cow mortality and milk production using a single-step methodology. Genomic relationships between cow mortality and milk production were also analyzed. Data included 883,887 (866,700) first-parity, 733,904 (711,211) second-parity, and 516,256 (492,026) third-parity records on cow mortality (305-d milk yields) of Holsteins from Northeast states in the United States. The pedigree consisted of up to 1,690,481 animals including 34,481 bulls genotyped with 36,951 SNP markers. Analyses were conducted with a bivariate threshold-linear model for each parity separately. Genomic information was incorporated as a genomic relationship matrix in the single-step BLUP. Traditional and genomic estimated breeding values (GEBV) were obtained with Gibbs sampling using fixed variances, whereas reliabilities were calculated from variances of GEBV samples. Genomic EBV were then converted into single nucleotide polymorphism (SNP) marker effects. Those SNP effects were categorized according to values corresponding to 1 to 4 standard deviations. Moving averages and variances of SNP effects were calculated for windows of 30 adjacent SNP, and Manhattan plots were created for SNP variances with the same window size. Using Gibbs sampling, the reliability for genotyped bulls for cow mortality was 28 to 30% in EBV and 70 to 72% in GEBV. The reliability for genotyped bulls for 305-d milk yields was 53 to 65% to 81 to 85% in GEBV. Correlations of SNP effects between mortality and 305-d milk yields within categories were the highest with the largest SNP effects and reached >0.7 at 4 standard deviations. All SNP regions explained less than 0.6% of the genetic variance for both traits, except regions close to the DGAT1 gene, which explained up to 2.5% for cow mortality and 4% for 305-d milk yields. Reliability for GEBV with a moderate number of genotyped animals can be calculated by Gibbs samples. Genomic information can greatly increase the reliability of predictions not only for milk but also for mortality. The existence of a common region on Bos taurus autosome 14 affecting both traits may indicate a major gene with a pleiotropic effect on milk and mortality. The objective of this study was to investigate the feasibility of genomic evaluation for cow mortality and milk production using a single-step methodology. Genomic relationships between cow mortality and milk production were also analyzed. Data included 883,887 (866,700) first-parity, 733,904 (711,211) second-parity, and 516,256 (492,026) third-parity records on cow mortality (305-d milk yields) of Holsteins from Northeast states in the United States. The pedigree consisted of up to 1,690,481 animals including 34,481 bulls genotyped with 36,951 SNP markers. Analyses were conducted with a bivariate threshold-linear model for each parity separately. Genomic information was incorporated as a genomic relationship matrix in the single-step BLUP. Traditional and genomic estimated breeding values (GEBV) were obtained with Gibbs sampling using fixed variances, whereas reliabilities were calculated from variances of GEBV samples. Genomic EBV were then converted into single nucleotide polymorphism (SNP) marker effects. Those SNP effects were categorized according to values corresponding to 1 to 4 standard deviations. Moving averages and variances of SNP effects were calculated for windows of 30 adjacent SNP, and Manhattan plots were created for SNP variances with the same window size. Using Gibbs sampling, the reliability for genotyped bulls for cow mortality was 28 to 30% in EBV and 70 to 72% in GEBV. The reliability for genotyped bulls for 305-d milk yields was 53 to 65% to 81 to 85% in GEBV. Correlations of SNP effects between mortality and 305-d milk yields within categories were the highest with the largest SNP effects and reached >0.7 at 4 standard deviations. All SNP regions explained less than 0.6% of the genetic variance for both traits, except regions close to the DGAT1 gene, which explained up to 2.5% for cow mortality and 4% for 305-d milk yields. Reliability for GEBV with a moderate number of genotyped animals can be calculated by Gibbs samples. Genomic information can greatly increase the reliability of predictions not only for milk but also for mortality. The existence of a common region on Bos taurus autosome 14 affecting both traits may indicate a major gene with a pleiotropic effect on milk and mortality.

ACS Style

S. Tsuruta; D.A.L. Lourenco; I. Misztal; T.J. Lawlor. Genomic analysis of cow mortality and milk production using a threshold-linear model. Journal of Dairy Science 2017, 100, 7295 -7305.

AMA Style

S. Tsuruta, D.A.L. Lourenco, I. Misztal, T.J. Lawlor. Genomic analysis of cow mortality and milk production using a threshold-linear model. Journal of Dairy Science. 2017; 100 (9):7295-7305.

Chicago/Turabian Style

S. Tsuruta; D.A.L. Lourenco; I. Misztal; T.J. Lawlor. 2017. "Genomic analysis of cow mortality and milk production using a threshold-linear model." Journal of Dairy Science 100, no. 9: 7295-7305.

Journal article
Published: 01 August 2015 in Journal of Dairy Science
Reads 0
Downloads 0

The objective of this study was to investigate genotype by environment interactions for culling rates and milk production in large and small dairy herds in 3 US regions, using genotypes, pedigree, and phenotypes. Single nucleotide polymorphism (SNP) marker variances were also estimated in these different environments. Culling rates including cow mortality were based on 6 Dairy Herd Improvement termination codes reported by dairy producers. Separate data sets for culling rates and 305-d milk yield were created for large and small dairy herds in the US regions of the Southeast (SE), Southwest (SW), and Northeast (NE) for the first 3 lactation cows that calved between 1999 and 2008. Genomic information from 42,503 SNP markers on 34,506 bulls was included in the analysis to predict genomic estimated breeding value (GEBV) of culling rates and 305-d milk yield with a single-step genomic BLUP using a bivariate threshold-linear model. Cow replacement rates in large SE and NE herds were higher. Heritability estimates of culling rates ranged from 0.03 to 0.11, but the differences were small between large and small herds and among the 3 US regions. Genetic correlations between culling rates and 305-d milk yield were medium to high for cows sold for poor production and reproduction problems. Correlations of GEBV for culling rates among the 3 US regions ranged from 0.34 to 0.92 and were lower between the SW and the other regions, especially in small herds. Correlations of GEBV between large and small herds ranged from 0.44 to 0.90 and were lower in the SW. These results indicate genotype by environment interactions of cow culling rate between the US regions and between large and small herds. Correlations of top 30 SNP marker effects for culling rates between 2 US regions ranged from 0.64 to 0.98 and were higher than those of more SNP marker effects except for a culling reason "sold for dairy purpose." Those correlations between large and small herds ranged from 0.67 to 0.98. High correlations of top SNP marker effects on culling reasons between the US regions and between large and small herds suggest that major markers can be useful for selection in different environments. The SNP variance shown in a marker gene segment on chromosome 14 was strongly associated with milk production in large and small herds in the NE but not in the SE and SW. Marker genes on chromosome 14 also showed a strong association with cow culling rates due to poor production and mortality in large herds in the NE.

ACS Style

S. Tsuruta; D.A.L. Lourenco; I. Misztal; T.J. Lawlor. Genotype by environment interactions on culling rates and 305-day milk yield of Holstein cows in 3 US regions. Journal of Dairy Science 2015, 98, 5796 -5805.

AMA Style

S. Tsuruta, D.A.L. Lourenco, I. Misztal, T.J. Lawlor. Genotype by environment interactions on culling rates and 305-day milk yield of Holstein cows in 3 US regions. Journal of Dairy Science. 2015; 98 (8):5796-5805.

Chicago/Turabian Style

S. Tsuruta; D.A.L. Lourenco; I. Misztal; T.J. Lawlor. 2015. "Genotype by environment interactions on culling rates and 305-day milk yield of Holstein cows in 3 US regions." Journal of Dairy Science 98, no. 8: 5796-5805.

Journal article
Published: 02 July 2015 in Genetics Selection Evolution
Reads 0
Downloads 0

As more and more genotypes become available, accuracy of genomic evaluations can potentially increase. However, the impact of genotype data on accuracy depends on the structure of the genotyped cohort. For populations such as dairy cattle, the greatest benefit has come from genotyping sires with high accuracy, whereas the benefit due to adding genotypes from cows was smaller. In broiler chicken breeding programs, males have less progeny than dairy bulls, females have more progeny than dairy cows, and most production traits are recorded for both sexes. Consequently, genotyping both sexes in broiler chickens may be more advantageous than in dairy cattle. We studied the contribution of genotypes from males and females using a real dataset with genotypes on 15 723 broiler chickens. Genomic evaluations used three training sets that included only males (4648), only females (8100), and both sexes (12 748). Realized accuracies of genomic estimated breeding values (GEBV) were used to evaluate the benefit of including genotypes for different training populations on genomic predictions of young genotyped chickens. Using genotypes on males, the average increase in accuracy of GEBV over pedigree-based EBV for males and females was 12 and 1 percentage points, respectively. Using female genotypes, this increase was 1 and 18 percentage points, respectively. Using genotypes of both sexes increased accuracies by 19 points for males and 20 points for females. For two traits with similar heritabilities and amounts of information, realized accuracies from cross-validation were lower for the trait that was under strong selection. Overall, genotyping males and females improves predictions of all young genotyped chickens, regardless of sex. Therefore, when males and females both contribute to genetic progress of the population, genotyping both sexes may be the best option.

ACS Style

Daniela A. L. Lourenco; Breno O. Fragomeni; Shogo Tsuruta; Ignacio Aguilar; Birgit Zumbach; Rachel J. Hawken; Andres Legarra; Ignacy Misztal. Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken. Genetics Selection Evolution 2015, 47, 1 -9.

AMA Style

Daniela A. L. Lourenco, Breno O. Fragomeni, Shogo Tsuruta, Ignacio Aguilar, Birgit Zumbach, Rachel J. Hawken, Andres Legarra, Ignacy Misztal. Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken. Genetics Selection Evolution. 2015; 47 (1):1-9.

Chicago/Turabian Style

Daniela A. L. Lourenco; Breno O. Fragomeni; Shogo Tsuruta; Ignacio Aguilar; Birgit Zumbach; Rachel J. Hawken; Andres Legarra; Ignacy Misztal. 2015. "Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken." Genetics Selection Evolution 47, no. 1: 1-9.

Journal article
Published: 01 September 2014 in Journal of Dairy Science
Reads 0
Downloads 0

Assigning unknown parent groups (UPG) in mixed-model equations using single-step genomic BLUP was investigated to reduce bias and to increase accuracy in genomic estimated breeding values (GEBV). The original UPG were defined based on the animal's birth year and the sex of the animal's unknown parents. Combining the last 2 UPG for the animals' birth years and separating the UPG for US and non-US Holsteins were considered in the redefinition. A full data set in the 2011 national genetic evaluation of final score in US Holsteins was used to calculate estimated breeding values (EBV) for validation, and a subset of the 2011 data, which excluded phenotypes recorded after 2007, was used to calculate GEBV for all animals, including 34,500 genotyped bulls. The EBV and GEBV in 2007 were compared with EBV in the 2011 full data. The last group effects for unknown sires and dams were overestimated with the GEBV model using the reduced 2007 data. The genetic trends from EBV in 2011 and GEBV in 2007 with the original UPG in the last few years demonstrated inflation, whereas GEBV with the redefined UPG by combining the last 2 groups showed deflation. On the other hand, the redefined UPG by separating for US and non-US Holsteins reduced the bias in GEBV. Regression coefficients smaller than unity for GEBV for young genotyped bulls with no daughters in 2007 on progeny deviations in 2011 also indicated inflation. The redefining of UPG reduced bias and slightly increased accuracy in GEBV for both US and non-US genotyped bulls. Rank correlations between GEBV in 2007 and in 2011 with the redefined UPG were higher than those with no UPG and the original UPG, especially for non-US bulls. Redefining of UPG in genomic evaluation could improve reliability of GEBV and provide correct genetic trends.

ACS Style

S. Tsuruta; I. Misztal; D.A.L. Lourenco; T.J. Lawlor. Assigning unknown parent groups to reduce bias in genomic evaluations of final score in US Holsteins. Journal of Dairy Science 2014, 97, 5814 -5821.

AMA Style

S. Tsuruta, I. Misztal, D.A.L. Lourenco, T.J. Lawlor. Assigning unknown parent groups to reduce bias in genomic evaluations of final score in US Holsteins. Journal of Dairy Science. 2014; 97 (9):5814-5821.

Chicago/Turabian Style

S. Tsuruta; I. Misztal; D.A.L. Lourenco; T.J. Lawlor. 2014. "Assigning unknown parent groups to reduce bias in genomic evaluations of final score in US Holsteins." Journal of Dairy Science 97, no. 9: 5814-5821.

Journal article
Published: 01 May 2013 in Journal of Dairy Science
Reads 0
Downloads 0

Currently, the US Department of Agriculture Animal Improvement Programs Laboratory utilizes a multi-step procedure in genomic evaluations for US Holstein bulls and cows, with adjustments for cows. We used a single-step procedure to investigate whether adding cows' genotypes could increase reliability in genomic breeding values for bulls while minimizing bias. The first data set to 2007 was used to calculate genomic estimated breeding values (GEBV) for animals, including young genotyped bulls with no daughters and young cows (heifers) with no records in 2007. The second data set to 2011 was used to calculate GEBV for the same animals, including those young bulls with daughters and young cows with records in 2011. Genotypes (42,503 single nucleotide polymorphism markers) for 34,506 bulls and 5,235 cows from 356,413 bulls and 9,245,619 cows in pedigree were used to calculate single-step GEBV (ssGEBV) and multi-step GEBV (msGEBV). Regression coefficients of 2007 GEBV on 2011 progeny deviations and coefficients of determination were used as indicators of bias and reliability in 2007 GEBV for bulls with no daughters and for cows with no records in 2007, using bull genotypes only and using bull and cow genotypes. Parent averages were also calculated from estimated breeding values of parents to compare with GEBV. For genotyped bulls, inflation was larger for ssGEBV than for msGEBV, whereas reliability was higher for ssGEBV. Using all genotyped bulls and cows, reliabilities were increased by 2 to 3%. Use of genotypes of high-profile cows improves reliability in ssGEBV and msGEBV for bulls.

ACS Style

S. Tsuruta; I. Misztal; T.J. Lawlor. Short communication: Genomic evaluations of final score for US Holsteins benefit from the inclusion of genotypes on cows. Journal of Dairy Science 2013, 96, 3332 -3335.

AMA Style

S. Tsuruta, I. Misztal, T.J. Lawlor. Short communication: Genomic evaluations of final score for US Holsteins benefit from the inclusion of genotypes on cows. Journal of Dairy Science. 2013; 96 (5):3332-3335.

Chicago/Turabian Style

S. Tsuruta; I. Misztal; T.J. Lawlor. 2013. "Short communication: Genomic evaluations of final score for US Holsteins benefit from the inclusion of genotypes on cows." Journal of Dairy Science 96, no. 5: 3332-3335.

Journal article
Published: 01 August 2011 in Journal of Dairy Science
Reads 0
Downloads 0

Currently, the USDA uses a single-trait (ST) model with several intermediate steps to obtain genomic evaluations for US Holsteins. In this study, genomic evaluations for 18 linear type traits were obtained with a multiple-trait (MT) model using a unified single-step procedure. The phenotypic type data on up to 18 traits were available for 4,813,726 Holsteins, and single nucleotide polymorphism markers from the Illumina BovineSNP50 genotyping Beadchip (Illumina Inc., San Diego, CA) were available on 17,293 bulls. Genomic predictions were computed with several genomic relationship matrices (G) that assumed different allele frequencies: equal, base, current, and current scaled. Computations were carried out with ST and MT models. Procedures were compared by coefficients of determination (R(2)) and regression of 2004 prediction of bulls with no daughters in 2004 on daughter deviations of those bulls in 2009. Predictions for 2004 also included parent averages without the use of genomic information. The R(2) for parent averages ranged from 10 to 34% for ST models and from 12 to 35% for MT models. The average R(2) for all G were 34 and 37% for ST and MT models, respectively. All of the regression coefficients were <1.0, indicating that estimated breeding values in 2009 of 1,307 genotyped young bulls' parents tended to be biased. The average regression coefficients ranged from 0.74 to 0.79 and from 0.75 to 0.80 for ST and MT models, respectively. When the weight for the inverse of the numerator relationship matrix (A(-1)) for genotyped animals was reduced from 1 to 0.7, R(2) remained almost identical while the regression coefficients increased by 0.11-0.26 and 0.12-0.23 for ST and MT models, respectively. The ST models required about 5s per iteration, whereas MT models required 3 (6) min per iteration for the regular (genomic) model. The MT single-step approach is feasible for 18 linear type traits in US Holstein cattle. Accuracy for genomic evaluation increases when switching ST models to MT models. Inflation of genomic evaluations for young bulls could be reduced by choosing a small weight for the A(-1) for genotyped bulls.

ACS Style

S. Tsuruta; I. Misztal; I. Aguilar; T.J. Lawlor. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science 2011, 94, 4198 -4204.

AMA Style

S. Tsuruta, I. Misztal, I. Aguilar, T.J. Lawlor. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science. 2011; 94 (8):4198-4204.

Chicago/Turabian Style

S. Tsuruta; I. Misztal; I. Aguilar; T.J. Lawlor. 2011. "Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins." Journal of Dairy Science 94, no. 8: 4198-4204.

Journal article
Published: 01 March 2005 in Journal of Dairy Science
Reads 0
Downloads 0

Data included 392,800 records for cows born between 1995 and 1997. Traits analyzed were milk, fat, and protein yields, somatic cell score, days open (DO), 18 linear type traits, final score, and several measures of longevity. Productive life (PL) was defined as the total number of days in milk up to 84 mo of age with a restriction of 305, 500, or 999 d per lactation (PL305, PL500, or PL999, respectively). Herd life was defined as the total number of days from the first calving date to the last (culling) date. A multiple-trait sire model including the effects of registration status, herd-year, age group, month of calving and stage of lactation, sire, and residual was used for parameter estimation. The average duration of the first lactation was 365 d for survivors and 386 d for culled cows. Lactation lengths for the survivors in the next 3 parities all exceeded 330 d. Heritability estimates of between 0.08 and 0.10 were obtained for all definitions of longevity. As maximum recordable PL was increased from 305 to 999 d per lactation, the genetic correlations with milk production increased (from −0.11 to +0.14) and with DO decreased (−0.62 to −0.27). Formulas for an indirect prediction of PL from correlated traits were developed. As maximum PL per lactation was increased, little change in the weights used to predict the various measures of PL, with the exception of DO was found. As the currently used value of PL305 does not properly account for the longer lactation lengths that are routinely occurring with today's cows, PL with longer lactations may be preferable in routine evaluation.

ACS Style

S. Tsuruta; I. Misztal; T.J. Lawlor. Changing Definition of Productive Life in US Holsteins: Effect on Genetic Correlations. Journal of Dairy Science 2005, 88, 1156 -1165.

AMA Style

S. Tsuruta, I. Misztal, T.J. Lawlor. Changing Definition of Productive Life in US Holsteins: Effect on Genetic Correlations. Journal of Dairy Science. 2005; 88 (3):1156-1165.

Chicago/Turabian Style

S. Tsuruta; I. Misztal; T.J. Lawlor. 2005. "Changing Definition of Productive Life in US Holsteins: Effect on Genetic Correlations." Journal of Dairy Science 88, no. 3: 1156-1165.

Journal article
Published: 01 May 2004 in Journal of Dairy Science
Reads 0
Downloads 0

Genetic correlations among milk, fat, and protein yields; body size composite (BSC); udder composite (UDC); and productive life (PL) in Holsteins were investigated over time. The data set contained 25,280 records of cows born in Wisconsin between 1979 and 1993. The multiple trait random regression (MT-RR) animal model included registration status, herd-year, age group, and stage of lactation as fixed effects; additive genetic effects with random regressions (RR) on year of birth using the first-order Legendre polynomial; and residual effects. Heterogeneous residual variances were considered in the model. Estimates of variance components and genetic correlations among traits from MT-RR were compared with those estimated with a multiple trait interval (MT-I) model, which assumed that every 3-yr interval was a separate trait and included the same effects as in the MT-RR model except for the RR. Genetic correlations estimated with MT-RR and MT-I models over time among all traits were compared with correlations among breeding values predicted with the single trait (ST) model without RR. Correlations among breeding values predicted with MT-RR, ST, and MT models were also calculated.Additive genetic and residual variances for all traits except PL increased over time; those for PL were constant. As a result, heritability estimates had no significant changes during the 15 yr. Genetic correlations of PL with milk, fat, protein, and BSC declined to zero or negative; those with UDC remained positive. Correlations among breeding values predicted with ST, MT, and MT-RR models were relatively high for all traits except PL.Genetic correlations between PL and other traits varied over time, with some correlations changing sign. For accurate indirect prediction of PL from other traits, the genetic correlations among the traits need to be re-estimated periodically.

ACS Style

S. Tsuruta; I. Misztal; T.J. Lawlor. Genetic Correlations Among Production, Body Size, Udder, and Productive Life Traits Over Time in Holsteins. Journal of Dairy Science 2004, 87, 1457 -1468.

AMA Style

S. Tsuruta, I. Misztal, T.J. Lawlor. Genetic Correlations Among Production, Body Size, Udder, and Productive Life Traits Over Time in Holsteins. Journal of Dairy Science. 2004; 87 (5):1457-1468.

Chicago/Turabian Style

S. Tsuruta; I. Misztal; T.J. Lawlor. 2004. "Genetic Correlations Among Production, Body Size, Udder, and Productive Life Traits Over Time in Holsteins." Journal of Dairy Science 87, no. 5: 1457-1468.