Impact of inclusion rates of crossbred phenotypes and genotypes in nucleus selection programs

Abstract Numerous methods have been suggested to incorporate crossbred (CB) phenotypes and genotypes into swine selection programs, yet little research has focused on the implicit trade-off decisions between generating data at the nucleus or commercial level. The aim of this study was to investigate the impact of altering the proportion of purebred (PB) and CB phenotypes and genotypes in genetic evaluations on the response to selection of CB performance. Assuming CB and PB performance with moderate heritabilities (h2=0.4), a three-breed swine crossbreeding scheme was simulated and selection was practiced for six generations, where the goal was to increase CB performance. Phenotypes, genotypes, and pedigrees for three PB breeds (25 and 175 mating males and females for each breed, respectively), F1 crosses (400 mating females), and terminal cross progeny (2,500) were simulated. The genome consisted of 18 chromosomes with 1,800 quantitative trait loci and 72k single nucleotide polymorphism (SNP) markers. Selection was performed in PB breeds using estimated breeding value for each phenotyping/genotyping strategy. Strategies investigated were: 1) increasing the proportion of CB with genotypes, phenotypes, and sire pedigree relationships, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB performance (rpc). Each unique rpc scenario and data collection strategy was replicated 10 times. Results showed that including CB data improved the CB performance regardless of rpc or data collection strategy compared with when no CB data were included. Compared with using only PB information, including 10% of CB progeny per generation with sire pedigrees and phenotypes increased the response in CB phenotype by 134%, 55%, 33%, 23%, and 21% when rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. When the same 10% of CB progeny were also genotyped, CB performance increased by 243%, 54%, 38%, 23%, and 20% when the rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, compared with when no CB data were utilized. Minimal change was observed in the average CB phenotype when PB phenotypes were included or proportionally removed when CB were genotyped. Removal of both PB phenotypes and genotypes when CB were genotyped greatly reduced the response in CB performance. In practice, the optimal inclusion rate of CB and PB data depends upon the genetic correlation between CB and PB animals and the expense of additional CB data collection compared with the economic benefit associated with increased CB performance.

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Impact of birth and rearing type, as well as inaccuracy of recording, on pre-weaning lamb phenotypic and genetic merit for live weight1

Translational Animal Science ◽

10.2527/tas2017.0015 ◽

2017 ◽

Vol 1 (2) ◽

pp. 137-145 ◽

Cited By ~ 6

Author(s):

N. McHugh ◽

T. Pabiou ◽

K. McDermott ◽

E. Wall ◽

D. P. Berry

Keyword(s):

Birth Weight ◽

Average Daily Gain ◽

Live Weight ◽

Breeding Value ◽

Parity Number ◽

Recorded Data ◽

Genetic Evaluations ◽

The Impact ◽

Selection Of ◽

Birth Type

Abstract The objective of the present study was to quantify the impact of the systematic environmental effects of both birth and rearing type on pre-weaning lamb live weight, and to evaluate the repercussions of inaccurate recording of birth and rearing type on subsequent genetic evaluations. A total of 32,548 birth weight records, 35,770 forty-day weight records and 32,548 records for average daily gain (ADG) between birth and 40-day weight from the Irish national sheep database were used. For each lamb, a new variable, birth-rearing type, reflecting both the birth and rearing type of a lamb was generated by concatenating both parameters. The association between birth-rearing type and birth weight, 40-day weight, and ADG was estimated using linear mixed models. The repercussions of inaccurate recording of birth type were determined by quantifying the impact on sire estimated breeding value (EBV; with an accuracy of ≥ 35%), where one of the lambs born in a selection of twin litter births was assumed to have died at birth but the farmer recorded the birth and rearing type as a singleton. The heaviest mean birth weight was associated with lambs born and subsequently reared as singles (5.47 kg); the lightest mean birth weight was associated with lambs born and reared as triplets (4.10 kg). The association between birth-rearing type and 40-day weight differed by dam parity (P < 0.001). Lambs reared by first parity dams as singles, irrespective of birth type were, on average, heavier at 40-day weighing than lambs reared as multiples, but as parity number increased, single-born lambs reared as twins outperformed triplet-born lambs reared as singles. Irrespective of the trait evaluated, the correlation between sire EBV estimated from the accurately recorded data and sire EBV estimated from the data with recording errors was strong ranging from 0.93 (birth weight) to 0.97 (ADG). The EBV for sires with progeny data manipulated were 0.14 kg, 0.34 kg and 5.56 g/d less for birth weight, 40-day weight and ADG, respectively, compared to their equivalent EBV calculated using accurately recorded data. Results from this study highlight the importance of precise recording of birth-rearing type by producers for the generation of accurate genetic evaluations.

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Effects of single nucleotide polymorphism ascertainment on population structure inferences

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab128 ◽

2021 ◽

Author(s):

Kotaro Dokan ◽

Sayu Kawamura ◽

Kosuke M Teshima

Keyword(s):

Single Nucleotide Polymorphism ◽

Population Structure ◽

Natural Populations ◽

Snp Markers ◽

Ascertainment Bias ◽

Snp Discovery ◽

Nucleotide Polymorphism ◽

Frequency Spectra ◽

Single Nucleotide ◽

Selection Of

Abstract Single nucleotide polymorphism (SNP) data are widely used in research on natural populations. Although they are useful, SNP genotyping data are known to contain bias, normally referred to as ascertainment bias, because they are conditioned by already confirmed variants. This bias is introduced during the genotyping process, including the selection of populations for novel SNP discovery and the number of individuals involved in the discovery panel and selection of SNP markers. It is widely recognized that ascertainment bias can cause inaccurate inferences in population genetics and several methods to address these bias issues have been proposed. However, especially in natural populations, it is not always possible to apply an ideal ascertainment scheme because natural populations tend to have complex structures and histories. In addition, it was not fully assessed if ascertainment bias has the same effect on different types of population structure. Here we examine the effects of bias produced during the selection of population for SNP discovery and consequent SNP marker selection processes under three demographic models: the island, stepping-stone, and population split models. Results show that site frequency spectra and summary statistics contain biases that depend on the joint effect of population structure and ascertainment schemes. Additionally, population structure inferences are also affected by ascertainment bias. Based on these results, it is recommended to evaluate the validity of the ascertainment strategy prior to the actual typing process because the direction and extent of ascertainment bias vary depending on several factors.

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Efficiency of using SNP markers in the MSTN gene in the selection of the Pushkin breed chickens

Vavilov Journal of Genetics and Breeding ◽

10.18699/vj19.575 ◽

2020 ◽

Vol 23 (8) ◽

pp. 993-998 ◽

Cited By ~ 1

Author(s):

N. V. Dementeva ◽

A. B. Vakhrameev ◽

T. A. Larkina ◽

O. V. Mitrofanova

Keyword(s):

Molecular Genetic ◽

Live Weight ◽

Snp Markers ◽

Experimental Population ◽

Adult Bird ◽

Nucleotide Polymorphisms ◽

Poultry Industry ◽

Single Nucleotide ◽

Low Efficiency ◽

Selection Of

In the poultry industry, indicators reflecting the growth rate of young stock and the exterior characteristics of chickens are important benchmarks for breeding. Traditional selection based on phenotypic evaluation is characterized by low efficiency with a low character inheritance ratio and is difficult to apply in small groups of animals and birds bred in bioresource collections. The use of molecular genetic markers associated with economically important traits makes it possible to carry out early selection of birds. This entails an increase in the profitability of the poultry industry. Recently, single nucleotide polymorphisms (SNPs) have served as convenient markers for selection purposes. For five generations (P1–P5), an experimental selection of hens of the Pushkin breed was carried out for live weight. It was based on selection for single nucleotide polymorphism rs313744840 in the MSTN gene. As a result, a significant increase in the frequency of allele A in this gene, from 0.11 to 0.50, took place. The association of SNP markers with meat qualities in the experimental group led to changes in the exterior profile of an adult bird at 330 days of age. The individuals with the AA and AG genotypes had the greatest live weight and longest body. As a result of selection, the bird on average became larger due to an increase in the number of heterozygous individuals with long bodies and large chest girths. The depth of the chest and the width of the pelvis increased due to an increase in the frequency of allele A in the experimental population. A tendency towards an increase in these indicators with the substitution of G with A in the genotype was found. Saturation of the population with desirable alleles led to an increase in the average live weight of the chickens. Analysis of the exterior parameters of adult birds showed that this growth is achieved by increasing the depth and volume of the bird body, and not by increasing the length of the limbs. Thus, marker selection carried out for five generations in the experimental population of Pushkin breed chickens to increase body weight has reliably (p < 0.001) changed the exterior profile of adult birds.

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Bias in variance component estimation in swine crossbreeding schemes using selective genotyping and phenotyping strategies

Journal of Animal Science ◽

10.1093/jas/skab293 ◽

2021 ◽

Author(s):

Garrett M See ◽

Benny E Mote ◽

Matthew L Spangler

Keyword(s):

Variance Components ◽

Snp Markers ◽

Selective Genotyping ◽

Sib Families ◽

Component Estimation ◽

Selection For ◽

Estimated Breeding Values ◽

Genetic Evaluations ◽

The Impact ◽

True Values

Abstract Selective genotyping of crossbred (CB) animals to include in traditionally purebred (PB) dominated genetic evaluations has been shown to provide an increase in the response to selection for CB performance. However, the inclusion of phenotypes from selectively genotyped CB animals, without the phenotypes of their non-genotyped cohorts, could cause bias in estimated variance components (VC) and subsequent estimated breeding values (EBV). The objective of the study was to determine the impact of selective CB genotyping on VC estimates and subsequent bias in EBV when non-genotyped CB animals are not included in genetic evaluations. A swine crossbreeding scheme producing 3-way CB animals was simulated to create selectively genotyped datasets. The breeding scheme consisted of three PB breeds each with 25 males and 450 females, F1 crosses with 1200 females and 12,000 CB progeny. Eighteen chromosomes each with 100 QTL and 4k SNP markers were simulated. Both PB and CB performance were considered to be moderately heritable (h2=0.4). Factors evaluated were, 1) CB phenotype and genotype inclusion of 15% (n=1800) or 35% (n=4200), 2) genetic correlation between PB and CB performance (rpc=0.1, 0.5 or 0.7) and 3) selective genotyping strategy. Genotyping strategies included: a) Random: random CB selection, b) Top: highest CB phenotype and c) Extreme: half highest and half lowest CB phenotypes. Top and Extreme selective genotyping strategies were considered by selecting animals in full-sib (FS) families or among the CB population (T). In each generation, 4320 PB selection candidates contributed phenotypic and genotypic records. Each scenario was replicated 15 times. VC were estimated for PB and CB performance utilizing bivariate models using pedigree relationships with dams of CB animals considered to be unknown. Estimated values of VC for PB performance were not statistically different from true values. Top selective genotyping strategies produced deflated estimates of phenotypic VC for CB performance compared to true values. When using estimated VC, Top_T and Extreme_T produced the most biased EBV, yet EBV of PB selection candidates for CB performance were most accurate when using Extreme_T. Results suggest that randomly selecting CB animals to genotype or selectively genotyping Top or Extreme CB animals within full-sib families can lead to accurate estimates of additive genetic VC for CB performance and unbiased EBV.

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Predicting survival and longevity of sows using purebred and crossbred data1

Translational Animal Science ◽

10.1093/tas/txaa073 ◽

2020 ◽

Vol 4 (2) ◽

pp. 993-1005

Author(s):

Maja W Iversen ◽

Øyvind Nordbø ◽

Eli Gjerlaug-Enger ◽

Eli Grindflek ◽

Theodorus H E Meuwissen

Keyword(s):

Genetic Correlation ◽

Prediction Accuracy ◽

Genetic Evaluation ◽

Breeding Value ◽

Breeding Values ◽

Breeding Goal ◽

Definition Of ◽

F1 Cross ◽

Economic Standpoint ◽

Selection Of

Abstract Survival and longevity are very important traits in pig breeding. From an economic standpoint, it is favorable to keep the sows for another parity instead of replacing them and, from the animal’s perspective, better welfare is achieved if they do not experience health problems. It is challenging to record longevity in purebred (PB) nucleus herds because animals are more likely to be replaced based on breeding value and high replacement rates rather than inability to produce. Crossbred (CB) sows are, however, submitted to lower replacement rates and are more likely to be kept in the farm longer if they can produce large and robust litters. Therefore, the objective of this study was to investigate whether the use of CB phenotypes could improve prediction accuracy of longevity for PBs. In addition, a new definition of survival was investigated. The analyzed data included phenotypes from two PB dam lines and their F1 cross. Three traits were evaluated: 1) whether or not the sow got inseminated for a second litter within 85 d of first farrowing (Longevity 1–2), 2) how many litters the sow can produce within 570 d of first farrowing [Longevity 1–5 (LGY15)], and 3) a repeatability trait that indicates whether or not the sow survived until the next parity (Survival). Traits were evaluated both as the same across breeds and as different between breeds. Results indicated that longevity is not the same trait in PB and CB animals (low genetic correlation). In addition, there were differences between the two PB lines in terms of which trait definition gave the greatest prediction accuracy. The repeatability trait (Survival) gave the greatest prediction accuracy for breed B, but LGY15 gave the greatest prediction accuracy for breed A. Prediction accuracy for CBs was generally poor. The Survival trait is recorded earlier in life than LGY15 and seemed to give a greater prediction accuracy for young animals than LGY15 (until own phenotype was available). Thus, for selection of young animals for breeding, Survival would be the preferred trait definition. In addition, results indicated that lots of data were needed to get accurate estimates of breeding values and that, if CB performance is the breeding goal, CB phenotypes should be used in the genetic evaluation.

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Genome prediction accuracy of common bean via Bayesian models

Ciência Rural ◽

10.1590/0103-8478cr20170497 ◽

2018 ◽

Vol 48 (8) ◽

Author(s):

Leiri Daiane Barili ◽

Naine Martins do Vale ◽

Fabyano Fonseca e Silva ◽

José Eustáquio de Souza Carneiro ◽

Hinayah Rojas de Oliveira ◽

...

Keyword(s):

Common Bean ◽

Prediction Accuracy ◽

Bayesian Models ◽

Snp Markers ◽

Genomic Information ◽

Stay Green ◽

Single Nucleotide ◽

Genome Prediction ◽

Heritability Estimation ◽

Selection Of

ABSTRACT: We aimed to apply genomic information based on SNP (single nucleotide polymorphism) markers for the genetic evaluation of the traits “stay-green” (SG), plant architecture (PA), grain aspect (GA) and grain yield (GY) in common bean through Bayesian models. These models were compared in terms of prediction accuracy and ability for heritability estimation for each one of the mentioned traits. A total of 80 cultivars were genotyped for 377 SNP markers, whose effects were estimated by five different Bayesian models: Bayes A (BA), B (BB), C (BC), LASSO (BL) e Ridge regression (BRR). Although, prediction accuracies calculated by means of cross-validation have been similar within each trait, the BB model stood out for the trait SG, whereas the BRR was indicated for the remaining traits. The heritability estimates for the traits SG, PA, GA and GY were 0.61, 0.28, 0.32 and 0.29, respectively. In summary, the Bayesian methods applied here were effective and ease to be implemented. The used SNP markers can help in the early selection of promising genotypes, since incorporating genomic information increase the prediction accuracy of the estimated genetic merit.

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Development of optimal genetic evaluations for teat and udder structure in Canadian Angus cattle

Journal of Animal Science ◽

10.1093/jas/skz314 ◽

2019 ◽

Vol 97 (11) ◽

pp. 4445-4452 ◽

Cited By ~ 2

Author(s):

Kajal Devani ◽

Tiago S Valente ◽

John J Crowley ◽

Karin Orsel

Keyword(s):

Fixed Effects ◽

Genetic Effect ◽

Information Criteria ◽

Breeding Value ◽

Additive Genetic Effect ◽

Multiple Trait ◽

Angus Cattle ◽

Genotypic Correlations ◽

Genetic Evaluations ◽

The Impact

Abstract Despite their heritability and influence on female productivity, there are currently no genetic evaluations for teat and udder structure in Canadian Angus cattle. The objective of this study was to develop optimal genetic evaluations for these traits in the Canadian Angus population. Guidelines recommended by Beef Improvement Federation (BIF) were used to score teat and udder structure in 1,735 Canadian Angus cows from 10 representative herds. Cows scored ranged in parity from 1 to 13; however, >70% of cows were parity ≤4. Scores ranged from 1 (large, bottle shaped) to 9 (very small) for teats and from 1 (very pendulous) to 9 (very tight) for udders. Consistent with parity distribution, >70% of teat and udder scores were ≥6. Teat and udder scores (TS9 and US9, respectively) were modeled using a multiple trait animal model with random effects of contemporary group (herd-year-season) and additive genetic effect, and fixed effects of breed, parity group, and days between calving and scoring. To test good versus poor structure, a binary classification of 1 or 2 (TS2, US2) [comprised of scores 1 to 5 = 1 (poor structure) and scores 6 to 9 = 2 (good structure)] was created. Further, to assess the impact of grouping less frequently observed poor scores, a 1 to 7 scale (TS7, US7) was created by combining teat and udder scores 1 to 3. Analyses for teat and udder scores on scales TS9, US9, TS7, US7, and TS2, US2 were compared. In addition, both threshold and linear animal models were used to estimate variance components for the traits. Data treatment and models were evaluated based on correlation of resulting estimated breeding value (EBV) with corrected phenotypes, Spearman’s rank correlation coefficient, average EBV accuracies (r), and deviance information criteria (DIC). TS9, US9 scales for teat and udder scores and linear models performed best. Estimates of heritability (SE) for teat and udder score were 0.32 (0.06) and 0.15 (0.04), respectively, indicating these traits were moderately heritable and that genetic improvement for teat and udder scores was possible. Estimates of phenotypic and genotypic correlations for teat and udder score were 0.46 (0.02) and 0.71 (0.09), respectively. Estimates of genotypic correlations with birth weight (BW), weaning weight (WW), and yearling weight (YW), ranged from −0.04 (0.10) to −0.20 (0.12), verifying the importance of selecting for improved teat and udder score as individual traits, alongside performance traits.

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S-SAD phasing of monoclinic histidine kinase fromBrucella abortuscombining data from multiple crystals and orientations: an example of data-collection strategy anda posteriorianalysis of different data combinations

Acta Crystallographica Section D Biological Crystallography ◽

10.1107/s1399004715007622 ◽

2015 ◽

Vol 71 (7) ◽

pp. 1433-1443 ◽

Cited By ~ 8

Author(s):

Sebastián Klinke ◽

Nicolas Foos ◽

Jimena J. Rinaldi ◽

Gastón Paris ◽

Fernando A. Goldbaum ◽

...

Keyword(s):

Data Collection ◽

Structure Determination ◽

Histidine Kinase ◽

Structural Information ◽

Data Sets ◽

X Rays ◽

X Ray Crystallography ◽

Data Collection Strategy ◽

Sad Phasing ◽

The Impact

The histidine kinase (HK) domain belonging to the light–oxygen–voltage histidine kinase (LOV-HK) fromBrucella abortusis a member of the HWE family, for which no structural information is available, and has low sequence identity (20%) to the closest HK present in the PDB. The `off-edge' S-SAD method in macromolecular X-ray crystallography was used to solve the structure of the HK domain from LOV-HK at low resolution from crystals in a low-symmetry space group (P21) and with four copies in the asymmetric unit (∼108 kDa). Data were collected both from multiple crystals (diffraction limit varying from 2.90 to 3.25 Å) and from multiple orientations of the same crystal, using the κ-geometry goniostat on SOLEIL beamline PROXIMA 1, to obtain `true redundancy'. Data from three different crystals were combined for structure determination. An optimized HK construct bearing a shorter cloning artifact yielded crystals that diffracted X-rays to 2.51 Å resolution and that were used for final refinement of the model. Moreover, a thorougha posteriorianalysis using several different combinations of data sets allowed us to investigate the impact of the data-collection strategy on the success of the structure determination.

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Development and characterization of 57 SNP markers in Misgurnus anguillicaudatus

10.21203/rs.3.rs-201840/v1 ◽

2021 ◽

Author(s):

Guiyun Huang ◽

Fengying Gao ◽

Zhigang Liu ◽

Jianmeng Cao ◽

Gang Chen ◽

...

Keyword(s):

Snp Markers ◽

Freshwater Species ◽

Nucleotide Polymorphisms ◽

Misgurnus Anguillicaudatus ◽

Population Genetic Analysis ◽

Single Nucleotide ◽

Natural Resource Conservation ◽

Hardy Weinberg Equilibrium ◽

Selection Of

Abstract The dojo loach Misgurnus anguillicaudatus is an endemic freshwater species to Asia. The effective conservation and molecular-aided selection of M. anguillicaudatus have been limited without sufficient molecular markers. In this study, 112 novel single nucleotide polymorphisms (SNPs) were screened based on 2b-RAD sequencing database, and 57 SNP markers were developed and characterized by genotyping 40 individuals using SNaPshot method. The observed heterozygosity (Ho) ranged from 0.025 to 0.675, while the expected heterozygosity (He) varied from 0.025 to 0.500. The minor allele frequency (MAF) ranged from 0.013 to 0.500. Among these SNPs, 18 loci were found to deviate significantly from the Hardy–Weinberg equilibrium after Bonferroni correction (P < 0.05). The first set of SNP markers developed from M. anguillicaudatus will provide valuable information in further population genetic analysis and natural resource conservation.

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39 Impact of inclusion rates of crossbred phenotypes and genotypes in nucleus selection programs

Journal of Animal Science ◽

10.1093/jas/skaa278.015 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 8-8

Author(s):

Garrett See ◽

Benny E Mote ◽

Matthew L Spangler

Keyword(s):

Genetic Correlation ◽

Cost Benefit ◽

Snp Markers ◽

Minimal Change ◽

Pedigree Information ◽

Benefit Ratio ◽

Cost Benefit Ratio ◽

Genetic Evaluations ◽

The Cost

Abstract The aim of this study was to investigate different inclusion rates of purebred (PB) and CB phenotypes and genotypes in genetic evaluations. Assuming PB and CB traits with moderate heritabilities (h2 = 0.4), a three-way swine crossbreeding scheme was simulated, and selection was practiced for 6 generations. The goal was to increase the CB phenotype. Phenotypes, genotypes and pedigrees for three purebred breeds (each consisting of 25 males and 175 females), F1 crosses (400 females) and terminal cross progeny (2500) were simulated using AlphaSimR. The genome consisted of 18 chromosomes with 1,800 QTL and 59.4k SNP markers. Selection was performed using EBV produced by the BLUPf90 suite of programs for each phenotyping/genotyping strategy. Strategies investigated were 1) increasing the proportion of CB with genotypes, phenotypes and sire pedigree information, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB traits (rpc). Each strategy was replicated 15 times. Results showed that including CB performance improved the CB phenotype regardless of rpc or phenotyping/genotyping strategy. Compared to using only PB information, including 10% of possible CB animals per generation with sire pedigrees and phenotypes increased the response in CB phenotype when rpc was 0.1, 0.3, 0.5, 0.7, and 0.9 by 192, 64, 41, 25 and 21%, respectively. Including CB genotypes dramatically improved the previously mentioned increases in response. Minimal change was observed in the CB phenotype when PB phenotypes were included or removed, if CB phenotypes, genotypes and sire pedigrees were included. PB genotypes were more informative than phenotypes in enabling prediction for CB traits. In practice, the inclusion rates of CB and PB data depends upon the degree of connectedness between CB animals and PB selection candidates and the cost-benefit ratio of increased CB performance and genotyping/phenotyping costs.

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