38 Using pooled data for single-step genomic prediction: Impact of within-pool variance and size

Abstract Economically relevant traits (ERT) are routinely collected within commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, which would be costly; pooling DNA and phenotypic data provides a cost-effective solution. A simulated beef cattle population consisting of 15 generations was genotyped with approximately 50k markers (841 quantitative trait loci were located across the genome) and phenotyped for a moderately heritable trait. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step GBLUP model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when no gap between genotyped parents and pooled offspring occurred. The EBV accuracies resulting from pools were greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. Pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared to individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P > 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to EBV accuracies that were statistically different than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible, although differences exist depending on pool size and pool formation strategy. The USDA is an equal opportunity employer.

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Genomic prediction using pooled data in a single-step genomic best linear unbiased prediction framework

Journal of Animal Science ◽

10.1093/jas/skaa184 ◽

2020 ◽

Vol 98 (6) ◽

Author(s):

Johnna L Baller ◽

Stephen D Kachman ◽

Larry A Kuehn ◽

Matthew L Spangler

Keyword(s):

Phenotypic Variation ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Pool Size ◽

Individual Data ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

Best Linear Unbiased ◽

Genetic Evaluations ◽

Unbiased Prediction

Abstract Economically relevant traits are routinely collected within the commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, but this would be costly; therefore, pooling DNA and phenotypic data provide a cost-effective solution. Pedigree, phenotypic, and genomic data were simulated for a beef cattle population consisting of 15 generations. Genotypes mimicked a 50k marker panel (841 quantitative trait loci were located across the genome, approximately once per 3 Mb) and the phenotype was moderately heritable. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step genomic best linear unbiased prediction model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation within pools), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when there was no gap between genotyped parents and pooled offspring. The EBV accuracies resulting from pools were usually greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. A pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared with individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P > 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to statistical differences in EBV accuracy than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Largest numerical increases in EBV accuracy resulting from pooling compared with no data from generation 15 were seen with sires with prior low EBV accuracy (those born in generation 14). Pooling of any size led to larger EBV accuracies of the pools than individual data when minimizing phenotypic variation. Resulting EBV for the pools could be used to inform management decisions of those pools. Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible although differences exist depending on pool size and pool formation strategy.

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Changes in genomic predictions when new information is added

Journal of Animal Science ◽

10.1093/jas/skab004 ◽

2021 ◽

Vol 99 (2) ◽

Author(s):

Jorge Hidalgo ◽

Daniela Lourenco ◽

Shogo Tsuruta ◽

Yutaka Masuda ◽

Stephen Miller ◽

...

Keyword(s):

Single Step ◽

Pedigree Information ◽

Weaning Weight ◽

Breeding Values ◽

Core Group ◽

Absolute Change ◽

Angus Cattle ◽

Average Maximum ◽

Estimated Breeding Values ◽

Genetic Evaluations

Abstract 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.

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A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

Canadian Journal of Animal Science ◽

10.1139/cjas-2017-0176 ◽

2018 ◽

Vol 98 (3) ◽

pp. 565-575 ◽

Cited By ~ 5

Author(s):

Mario L. Piccoli ◽

Luiz F. Brito ◽

José Braccini ◽

Fernanda V. Brito ◽

Fernando F. Cardoso ◽

...

Keyword(s):

Beef Cattle ◽

Production Systems ◽

Simulated Data ◽

Single Step ◽

Breeding Values ◽

True Breeding ◽

Blending Method ◽

Estimated Breeding Values ◽

Genetic Evaluations ◽

Genomic Predictions

The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

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Selective genotyping and phenotypic data inclusion strategies of crossbred progeny for combined crossbred and purebred selection in swine breeding

Journal of Animal Science ◽

10.1093/jas/skab041 ◽

2021 ◽

Author(s):

Garrett M See ◽

Benny E Mote ◽

Matthew L Spangler

Keyword(s):

Genetic Gain ◽

Production Systems ◽

Snp Markers ◽

Selective Genotyping ◽

Phenotypic Data ◽

Breeding Values ◽

True Breeding ◽

Estimated Breeding Values ◽

Highly Correlated ◽

Genetic Evaluations

Abstract Inclusion of crossbred (CB) data into traditionally purebred (PB) genetic evaluations has been shown to increase the response in CB performance. Currently it is unrealistic to collect data on all CB animals in swine production systems, thus, a subset of CB animals must be selected to contribute genomic/phenotypic information. The aim of this study was to evaluate selective genotyping strategies in a simulated 3-way swine crossbreeding scheme. The swine crossbreeding scheme was simulated and produced 3-way CB animals for 6 generations with three distinct purebred breeds each with 25 and 175 mating males and females, respectively. F1 crosses (400 mating females) produced 4,000 terminal CB progeny which were subjected to selective genotyping. The genome consisted of 18 chromosomes with 1,800 QTL and 72k SNP markers. Selection was performed using estimated breeding values (EBV) for CB performance. It was assumed that both PB and CB performance was moderately heritable (h2=0.4). Several scenarios altering the genetic correlation between PB and CB performance (rpc=0.1, 0.3, 0.5, 0.7 or 0.9) were considered. CB animals were chosen based on phenotypes to select 200, 400 or 800 CB animals to genotype per generation. Selection strategies included: 1) Random: random selection, 2) Top: highest phenotype, 3) Bottom: lowest phenotype, 4) Extreme: half highest and half lowest phenotypes, and 5) Middle: average phenotype. Each selective genotyping strategy, except for Random, was considered by selecting animals in half-sib (HS) or full-sib (FS) families. The number of PB animals with genotypes and phenotypes each generation was fixed at 1680. Each unique genotyping strategy and rpc scenario was replicated 10 times. Selection of CB animals based on the Extreme strategy resulted in the highest (P<0.05) rates of genetic gain in CB performance (ΔG) when rpc<0.9. For highly correlated traits (rpc=0.9) selective genotyping did not impact (P>0.05) ΔG. No differences (P>0.05) were observed in ΔG between Top, Bottom or Middle when rpc>0.1. Higher correlations between true breeding values (TBV) and EBV were observed using Extreme when rpc<0.9. In general, family sampling method did not impact ΔG or the correlation between TBV and EBV. Overall, the Extreme genotyping strategy produced the greatest genetic gain and the highest correlations between TBV and EBV, suggesting that two tailed sampling of CB animals is the most informative when CB performance is the selection goal.

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Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population

Veterinary World ◽

10.14202/vetworld.2021.3119-3125 ◽

2021 ◽

pp. 3119-3125

Author(s):

Piriyaporn Sungkhapreecha ◽

Ignacy Misztal ◽

Jorge Hidalgo ◽

Daniela Lourenco ◽

Sayan Buaban ◽

...

Keyword(s):

Linear Regression ◽

Milk Yield ◽

Heat Tolerance ◽

Selection Response ◽

Single Step ◽

Linear Regression Method ◽

Genomic Information ◽

Phenotypic Data ◽

Breeding Values ◽

Estimated Breeding Values

Background and Aim: Genomic selection improves accuracy and decreases the generation interval, increasing the selection response. This study was conducted to assess the benefits of using single-step genomic best linear unbiased prediction (ssGBLUP) for genomic evaluations of milk yield and heat tolerance in Thai-Holstein cows and to test the value of old phenotypic data to maintain the accuracy of predictions. Materials and Methods: The dataset included 104,150 milk yield records collected from 1999 to 2018 from 15,380 cows. The pedigree contained 33,799 animals born between 1944 and 2016, of which 882 were genotyped. Analyses were performed with and without genomic information using ssGBLUP and BLUP, respectively. Statistics for bias, dispersion, the ratio of accuracies, and the accuracy of estimated breeding values were calculated using the linear regression (LR) method. A partial dataset excluded the phenotypes of the last generation, and 66 bulls were identified as validation individuals. Results: Bias was considerable for BLUP (0.44) but negligible (–0.04) for ssGBLUP; dispersion was similar for both techniques (0.84 vs. 1.06 for BLUP and ssGBLUP, respectively). The ratio of accuracies was 0.33 for BLUP and 0.97 for ssGBLUP, indicating more stable predictions for ssGBLUP. The accuracy of predictions was 0.18 for BLUP and 0.36 for ssGBLUP. Excluding the first 10 years of phenotypic data (i.e., 1999-2008) decreased the accuracy to 0.09 for BLUP and 0.32 for ssGBLUP. Genomic information doubled the accuracy and increased the persistence of genomic estimated breeding values when old phenotypes were removed. Conclusion: The LR method is useful for estimating accuracies and bias in complex models. When the population size is small, old data are useful, and even a small amount of genomic information can substantially improve the accuracy. The effect of heat stress on first parity milk yield is small.

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

Journal of Animal Science ◽

10.1093/jas/skab019 ◽

2021 ◽

Vol 99 (2) ◽

Author(s):

Yutaka Masuda ◽

Shogo Tsuruta ◽

Matias Bermann ◽

Heather L Bradford ◽

Ignacy Misztal

Keyword(s):

Density Function ◽

Single Step ◽

Joint Density ◽

Pedigree Information ◽

Relationship Matrix ◽

Genomic Information ◽

Breeding Values ◽

Joint Density Function ◽

Information Updates ◽

Genetic Trends

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

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An efficient solution for the single-step synthesis of 4CaO·Al2O3·Fe2O3 powders

Journal of Materials Research ◽

10.1557/jmr.2009.0019 ◽

2009 ◽

Vol 24 (1) ◽

pp. 245-252 ◽

Cited By ~ 9

Author(s):

Robert Ianoş

Keyword(s):

Combustion Synthesis ◽

Solution Combustion Synthesis ◽

Cost Effective ◽

Fuel Mixture ◽

Single Step ◽

Combustion Reaction ◽

Exothermic Effect ◽

Solution Combustion ◽

Time Saving ◽

X Ray

Single-phase nanocrystalline 4CaO·Al2O3·Fe2O3 powders were prepared directly from the combustion reaction using a new cost-effective, time-saving, and environmentally friendly version of solution combustion synthesis. Instead of a single fuel, a fuel mixture of urea and β-alanine was used. It was shown by x-ray diffraction, energy-dispersive x-ray analysis, thermogravimetric analysis, and optical microscopy that this new version of the solution combustion synthesis allows the maximization of the exothermic effect associated with the combustion reaction. On the other hand, it was shown that the traditional version of combustion synthesis involving the use of a single fuel, such as urea or β-alanine, does not ensure the formation of Ca4Al2Fe2O10 unless subsequent thermal treatments are applied. It was suggested that the occurrence of combustion reactions cannot be regarded only in terms of adiabatic temperature, as the kinetic aspects overrule the thermodynamic ones.

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Genome-wide identification of loci associated with growth in rainbow trout

10.21203/rs.2.17035/v1 ◽

2019 ◽

Author(s):

Ali Ali ◽

Rafet Al-Tobasei ◽

Daniela Lourenco ◽

Tim Leeds ◽

Brett Kenney ◽

...

Keyword(s):

Additive Genetic Variance ◽

Chromatin Modification ◽

Bodyweight Gain ◽

Single Step ◽

Inositol Polyphosphate ◽

Gene Encoding ◽

Phenotypic Data ◽

Developmental Processes ◽

Sliding Windows ◽

Genome Wide

Abstract Background Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout.Results A 50K, gene-transcribed SNP chip was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 46 SNPs were identified in association with bodyweight gain. The highest SNP associated with this trait was identified in a gene coding for thrombospondin-1 (THBS1) (R 2 = 0.09).Conclusion The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.

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Combining F-Scan and GAITRite® for use in Clinical Gait Analysis: A Validation Study.

10.21203/rs.3.rs-93976/v1 ◽

2020 ◽

Author(s):

Stephanie Speight ◽

Selina Reidy ◽

John Stephenson ◽

Sarah Reel

Keyword(s):

Gait Analysis ◽

Correlation Coefficient ◽

Intraclass Correlation Coefficient ◽

Intraclass Correlation ◽

Cost Effective ◽

Quality Data ◽

Concordance Correlation Coefficient ◽

Concordance Correlation ◽

Mean Values ◽

Level Of Agreement

Abstract BackgroundClinical gait analysis is widely used to aid the assessment and diagnosis of symptomatic pathologies. Equipment based analysis provides clinicians with a more comprehensive assessment using pressure systems such as F-scan, or analysis of the spatial-temporal parameters of gait using GAITRite. There are systems however such as Strideway™ that can measure these parameters simultaneously, but can be expensive. This study aimed to determine whether standalone systems can be used collectively while still providing quality data, as a cost-effective alternative.MethodsTwenty-six participants walked on a standard floor and a GAITRite walkway, three times wearing the F-Scan system. Mid gait protocols were utilised by analysing the contact pressure of the 2 nd metatarsophalangeal joint of the third, fifth and seventh step from each walk. The Bland-Altman method was used to determine a level of agreement between the two surfaces, using mean values from all walks of all participants who successfully completed all required walks. The intraclass correlation coefficient and Lin’s concordance correlation coefficient were calculated as indices of reliability.ResultsThe intraclass correlation coefficient was calculated to be 0.991 and Lin’s concordance correlation coefficient for the data was calculated to be 0.956, indicating very good reproducibility.ConclusionsThe level of agreement in plantar pressures observed on the two surfaces was very high, suggesting that it is feasible to use F-Scan and GAITRite® together in a clinical setting, as an alternative to other less cost-effective standalone systems.

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Green Chemical Synthesis of N-Cholyl-L-Cysteine Encapsulated Gold Nanoclusters For Fluorometric Detection of Mercury Ions

10.21203/rs.3.rs-984847/v1 ◽

2021 ◽

Author(s):

Kasthuri Jayapalan ◽

Sivasamy Arumugam ◽

Rajendiran Nagappan

Keyword(s):

Uv Light ◽

Stokes Shift ◽

Gold Nanoclusters ◽

Green Emission ◽

Fluorescence Analysis ◽

Cost Effective ◽

Single Step ◽

Fluorometric Detection ◽

Transmission Electron ◽

Green Chemical Synthesis

Abstract Here we report a simple, single-step, cost-effective, environmentally friendly, and biocompatible approach using sodium salt of N-cholyl-L-cysteine (NaCysC) capped gold nanoclusters (AuNCs) with green emission properties at above the CMC in aqueous medium under UV-light irradiation. The primary and secondary CMC of NaCysC was found to be 4.6 and 10.7 mM respectively using pyrene as fluorescent probe. The synthesized AuNCs exhibit strong emission maxima at 520 nm upon excitation of 375 nm with a large Stokes shift of 145 nm. The surface functionality and morphology of NCs are studied by Fourier transform infrared spectroscopy, dymanic light scattering studies and transmission electron microscopy. The formation of AuNCs was completed within 5 h and exhibit high stability for more than 6 months. The NaCysC templated AuNCs selectively quenches the Hg2+ ions with higher sensitivity in aqueous solution over the other metal ions. The fluorescence analysis of Hg2+ showed a wide linear range from 15 to 120 µM and a detection limit was found to be 15 nM.

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