Publications by authors named "Christian Maltecca"

57 Publications

Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet.

Genet Sel Evol 2022 Jul 27;54(1):55. Epub 2022 Jul 27.

GenPhySE, INRAE, ENVT, Université de Toulouse, 31320, Castanet Tolosan, France.

Background: Breeding pigs that can efficiently digest alternative diets with increased fiber content is a viable strategy to mitigate the feed cost in pig production. This study aimed at determining the contribution of the gut microbiota and host genetics to the phenotypic variability of digestive efficiency (DE) traits, such as digestibility coefficients of energy, organic matter and nitrogen, feed efficiency (FE) traits (feed conversion ratio and residual feed intake) and growth traits (average daily gain and daily feed intake). Data were available for 791 pigs fed a conventional diet and 735 of their full-sibs fed a high-fiber diet. Fecal samples were collected at 16 weeks of age to sequence the V3-V4 regions of the 16S ribosomal RNA gene and predict DE with near-infrared spectrometry. The proportions of phenotypic variance explained by the microbiota (microbiability) were estimated under three OTU filtering scenarios. Then, microbiability and heritability were estimated independently (models Micro and Gen) and jointly (model Micro+Gen) using a Bayesian approach for all traits. Breeding values were estimated in models Gen and Micro+Gen.

Results: Differences in microbiability estimates were significant between the two extreme filtering scenarios (14,366 and 803 OTU) within diets, but only for all DE. With the intermediate filtering scenario (2399 OTU) and for DE, microbiability was higher (> 0.44) than heritability (< 0.32) under both diets. For two of the DE traits, microbiability was significantly higher under the high-fiber diet (0.67 ± 0.06 and 0.68 ± 0.06) than under the conventional diet (0.44 ± 0.06). For growth and FE, heritability was higher (from 0.26 ± 0.06 to 0.44 ± 0.07) than microbiability (from 0.17 ± 0.05 to 0.35 ± 0.06). Microbiability and heritability estimates obtained with the Micro+Gen model did not significantly differ from those with the Micro and Gen models for all traits. Finally, based on their estimated breeding values, pigs ranked differently between the Gen and Micro+Gen models, only for the DE traits under both diets.

Conclusions: The microbiota explained a significant proportion of the phenotypic variance of the DE traits, which was even larger than that explained by the host genetics. Thus, the use of microbiota information could improve the selection of DE traits, and to a lesser extent, of growth and FE traits. In addition, our results show that, at least for DE traits, filtering OTU is an important step and influences the microbiability.
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http://dx.doi.org/10.1186/s12711-022-00742-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9327178PMC
July 2022

Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine.

J Anim Sci 2022 Jul 1. Epub 2022 Jul 1.

Department of Animal Science, North Carolina State University, Raleigh, NC 27607, USA.

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 days of age). Rectal swabs were taken from each animal at 158 ± 4 days of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including four kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), two dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and two ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.
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http://dx.doi.org/10.1093/jas/skac231DOI Listing
July 2022

Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine.

Genet Sel Evol 2022 Jun 7;54(1):42. Epub 2022 Jun 7.

Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA.

Background: Meat quality and composition traits have become valuable in modern pork production; however, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi-trait indirect selection based on cheaper indicator traits is an alternative for continued cost-effective genetic improvement.

Methods: Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three-way crossbred and purebred Duroc animals belonging to 28 half-sib families. We applied different methods to assess the value of using purebred and crossbred information (both genomic and phenotypic) to predict expensive-to-record traits measured on crossbred individuals. Estimation of multi-trait variance components set the basis for comparing the different scenarios, together with a fourfold cross-validation approach to validate the phenotyping schemes under four genotyping strategies.

Results: The benefit of including genomic information for multi-trait prediction depended on the breeding goal trait, the indicator traits included, and the source of genomic information. While some traits benefitted significantly from genotyping crossbreds (e.g., loin intramuscular fat content, backfat depth, and belly weight), multi-trait prediction was advantageous for some traits even in the absence of genomic information (e.g., loin muscle weight, subjective color, and subjective firmness).

Conclusions: Our results show the value of using different sources of phenotypic and genomic information. For most of the traits studied, including crossbred genomic information was more beneficial than performing multi-trait prediction. Thus, we recommend including crossbred individuals in the reference population when these are phenotyped for the breeding objective.
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http://dx.doi.org/10.1186/s12711-022-00736-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171933PMC
June 2022

Use of Host Feeding Behavior and Gut Microbiome Data in Estimating Variance Components and Predicting Growth and Body Composition Traits in Swine.

Genes (Basel) 2022 Apr 26;13(5). Epub 2022 Apr 26.

Department of Animal Science, North Carolina State University, 120 W Broughton Dr, Raleigh, NC 27607, USA.

The purpose of this study was to investigate the use of feeding behavior in conjunction with gut microbiome sampled at two growth stages in predicting growth and body composition traits of finishing pigs. Six hundred and fifty-one purebred boars of three breeds: Duroc (DR), Landrace (LR), and Large White (LW), were studied. Feeding activities were recorded individually from 99 to 163 days of age. The 16S rRNA gene sequences were obtained from each pig at 123 ± 4 and 158 ± 4 days of age. When pigs reached market weight, body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content were measured on live animals. Three models including feeding behavior (Model_FB), gut microbiota (Model_M), or both (Model_FB_M) as predictors, were investigated. Prediction accuracies were evaluated through cross-validation across genetic backgrounds using the leave-one-breed-out strategy and across rearing environments using the leave-one-room-out approach. The proportions of phenotypic variance of growth and body composition traits explained by feeding behavior ranged from 0.02 to 0.30, and from 0.20 to 0.52 when using gut microbiota composition. Overall prediction accuracy (averaged over traits and time points) of phenotypes was 0.24 and 0.33 for Model_FB, 0.27 and 0.19 for Model_M, and 0.40 and 0.35 for Model_FB_M for the across-breed and across-room scenarios, respectively. This study shows how feeding behavior and gut microbiota composition provide non-redundant information in predicting growth in swine.
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http://dx.doi.org/10.3390/genes13050767DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140470PMC
April 2022

A network modeling approach provides insights into the environment-specific yield architecture of wheat.

Genetics 2022 07;221(3)

Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA.

Wheat (Triticum aestivum) yield is impacted by a diversity of developmental processes which interact with the environment during plant growth. This complex genetic architecture complicates identifying quantitative trait loci that can be used to improve yield. Trait data collected on individual processes or components of yield have simpler genetic bases and can be used to model how quantitative trait loci generate yield variation. The objectives of this experiment were to identify quantitative trait loci affecting spike yield, evaluate how their effects on spike yield proceed from effects on component phenotypes, and to understand how the genetic basis of spike yield variation changes between environments. A 358 F5:6 recombinant inbred line population developed from the cross of LA-95135 and SS-MPV-57 was evaluated in 2 replications at 5 locations over the 2018 and 2019 seasons. The parents were 2 soft red winter wheat cultivars differing in flowering, plant height, and yield component characters. Data on yield components and plant growth were used to assemble a structural equation model to characterize the relationships between quantitative trait loci, yield components, and overall spike yield. The effects of major quantitative trait loci on spike yield varied by environment, and their effects on total spike yield were proportionally smaller than their effects on component traits. This typically resulted from contrasting effects on component traits, where an increase in traits associated with kernel number was generally associated with a decrease in traits related to kernel size. In all, the complete set of identified quantitative trait loci was sufficient to explain most of the spike yield variation observed within each environment. Still, the relative importance of individual quantitative trait loci varied dramatically. Path analysis based on coefficients estimated through structural equation model demonstrated that these variations in effects resulted from both different effects of quantitative trait loci on phenotypes and environment-by-environment differences in the effects of phenotypes on one another, providing a conceptual model for yield genotype-by-environment interactions in wheat.
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http://dx.doi.org/10.1093/genetics/iyac076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252273PMC
July 2022

Exploring the role of gut microbiota in host feeding behavior among breeds in swine.

BMC Microbiol 2022 01 3;22(1). Epub 2022 Jan 3.

Department of Animal Science, North Carolina State University, 120 W Broughton Dr, Raleigh, 27607, NC, USA.

Background: The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing.

Results: Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae_R-7_group, Ruminococcaceae_UCG-004, Dorea, Ruminococcaceae_UCG-014, and Marvinbryantia.

Conclusions: This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies.
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http://dx.doi.org/10.1186/s12866-021-02409-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8722167PMC
January 2022

Genetic diversity and population history of eight Italian beef cattle breeds using measures of autozygosity.

PLoS One 2021 25;16(10):e0248087. Epub 2021 Oct 25.

Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Firenze, Italy.

In the present study, GeneSeek GGP-LDv4 33k single nucleotide polymorphism chip was used to detect runs of homozygosity (ROH) in eight Italian beef cattle breeds, six breeds with distribution limited to Tuscany (Calvana, Mucca Pisana, Pontremolese) or Sardinia (Sarda, Sardo Bruna and Sardo Modicana) and two cosmopolitan breeds (Charolais and Limousine). ROH detection analyses were used to estimate autozygosity and inbreeding and to identify genomic regions with high frequency of ROH, which might reflect selection signatures. Comparative analysis among breeds revealed differences in length and distribution of ROH and inbreeding levels. The Charolais, Limousine, Sarda, and Sardo Bruna breeds were found to have a high frequency of short ROH (~ 15.000); Calvana and Mucca Pisana presented also runs longer than 16 Mbp. The highest level of average genomic inbreeding was observed in Tuscan breeds, around 0.3, while Sardinian and cosmopolitan breeds showed values around 0.2. The population structure and genetic distances were analyzed through principal component and multidimensional scaling analyses, and resulted in a clear separation among the breeds, with clusters related to productive purposes. The frequency of ROH occurrence revealed eight breed-specific genomic regions where genes of potential selective and conservative interest are located (e.g. MYOG, CHI3L1, CHIT1 (BTA16), TIMELESS, APOF, OR10P1, OR6C4, OR2AP1, OR6C2, OR6C68, CACNG2 (BTA5), COL5A2 and COL3A1 (BTA2)). In all breeds, we found the largest proportion of homozygous by descent segments to be those that represent inbreeding events that occurred around 32 generations ago, with Tuscan breeds also having a significant proportion of segments relating to more recent inbreeding.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248087PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8544844PMC
November 2021

Identification of unique ROH regions with unfavorable effects on production and fertility traits in Canadian Holsteins.

Genet Sel Evol 2021 Aug 30;53(1):68. Epub 2021 Aug 30.

Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.

Background: The advent of genomic information and the reduction in the cost of genotyping have led to the use of genomic information to estimate genomic inbreeding as an alternative to pedigree inbreeding. Using genomic measures, effects of genomic inbreeding on production and fertility traits have been observed. However, there have been limited studies on the specific genomic regions causing the observed negative association with the trait of interest. Our aim was to identify unique run of homozygosity (ROH) genotypes present within a given genomic window that display negative associations with production and fertility traits and to quantify the effects of these identified ROH genotypes.

Methods: In total, 50,575 genotypes based on a 50K single nucleotide polymorphism (SNP) array and 259,871 pedigree records were available. Of these 50,575 genotypes, 46,430 cows with phenotypic records for production and fertility traits and having a first calving date between 2008 and 2018 were available. Unique ROH genotypes identified using a sliding-window approach were fitted into an animal mixed model as fixed effects to determine their effect on production and fertility traits.

Results: In total, 133 and 34 unique ROH genotypes with unfavorable effects were identified for production and fertility traits, respectively, at a 1% genome-wise false discovery rate. Most of these ROH regions were located on bovine chromosomes 8, 13, 14 and 19 for both production and fertility traits. For production traits, the average of all the unfavorably identified unique ROH genotypes effects were estimated to decrease milk yield by 247.30 kg, fat yield by 11.46 kg and protein yield by 8.11 kg. Similarly, for fertility traits, an average 4.81-day extension in first service to conception, a 0.16 increase in number of services, and a - 0.07 incidence in 56-day non-return rate were observed. Furthermore, a ROH region located on bovine chromosome 19 was identified that, when homozygous, had a negative effect on production traits. Signatures of selection proximate to this region have implicated GH1 as a potential candidate gene, which encodes the growth hormone that binds the growth hormone receptor. This observed negative effect could be a consequence of unfavorable alleles in linkage disequilibrium with favorable alleles.

Conclusions: ROH genotypes with unfavorable effects on production and fertility traits were identified within and across multiple traits on most chromosomes. These identified ROH genotypes could be included in mate selection programs to minimize their frequency in future generations.
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http://dx.doi.org/10.1186/s12711-021-00660-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8406729PMC
August 2021

Microbial composition differs between production systems and is associated with growth performance and carcass quality in pigs.

Anim Microbiome 2021 Aug 28;3(1):57. Epub 2021 Aug 28.

Acuity Ag Solutions, 7475 State Route 127, Carlyle, IL, 62231, USA.

Background: The role of the microbiome in livestock production has been highlighted in recent research. Currently, little is known about the microbiome's impact across different systems of production in swine, particularly between selection nucleus and commercial populations. In this paper, we investigated fecal microbial composition in nucleus versus commercial systems at different time points.

Results: We identified microbial OTUs associated with growth and carcass composition in each of the two populations, as well as the subset common to both. The two systems were represented by individuals with sizeable microbial diversity at weaning. At later times microbial composition varied between commercial and nucleus, with species of the genus Lactobacillus more prominent in the nucleus population. In the commercial populations, OTUs of the genera Lactobacillus and Peptococcus were associated with an increase in both growth rate and fatness. In the nucleus population, members of the genus Succinivibrio were negatively correlated with all growth and carcass traits, while OTUs of the genus Roseburia had a positive association with growth parameters. Lactobacillus and Peptococcus OTUs showed consistent effects for fat deposition and daily gain in both nucleus and commercial populations. Similarly, OTUs of the Blautia genus were positively associated with daily gain and fat deposition. In contrast, an increase in the abundance of the Bacteroides genus was negatively associated with growth performance parameters.

Conclusions: The current study provides a first characterization of microbial communities' value throughout the pork production systems. It also provides information for incorporating microbial composition into the selection process in the quest for affordable and sustainable protein production in swine.
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http://dx.doi.org/10.1186/s42523-021-00118-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403435PMC
August 2021

Effect of Temperature and Maternal Age on Recombination Rate in Cattle.

Front Genet 2021 20;12:682718. Epub 2021 Jul 20.

Department of Animal and Avian Sciences, University of Maryland, College Park, College Park, MD, United States.

Meiotic recombination is a fundamental biological process that facilitates meiotic division and promotes genetic diversity. Recombination is phenotypically plastic and affected by both intrinsic and extrinsic factors. The effect of maternal age on recombination rates has been characterized in a wide range of species, but the effect's direction remains inconclusive. Additionally, the characterization of temperature effects on recombination has been limited to model organisms. Here we seek to comprehensively determine the impact of genetic and environmental factors on recombination rate in dairy cattle. Using a large cattle pedigree, we identified maternal recombination events within 305,545 three-generation families. By comparing recombination rate between parents of different ages, we found a quadratic trend between maternal age and recombination rate in cattle. In contrast to either an increasing or decreasing trend in humans, cattle recombination rate decreased with maternal age until 65 months and then increased afterward. Combining recombination data with temperature information from public databases, we found a positive correlation between environmental temperature during fetal development of offspring and recombination rate in female parents. Finally, we fitted a full recombination rate model on all related factors, including genetics, maternal age, and environmental temperatures. Based on the final model, we confirmed the effect of maternal age and environmental temperature during fetal development of offspring on recombination rate with an estimated heritability of 10% ( = 0.03) in cattle. Collectively, we characterized the maternal age and temperature effects on recombination rate and suggested the adaptation of meiotic recombination to environmental stimuli in cattle. Our results provided first-hand information regarding the plastic nature of meiotic recombination in a mammalian species.
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http://dx.doi.org/10.3389/fgene.2021.682718DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329537PMC
July 2021

Potential Use of Gut Microbiota Composition as a Biomarker of Heat Stress in Monogastric Species: A Review.

Animals (Basel) 2021 Jun 19;11(6). Epub 2021 Jun 19.

Department of Animal Science, North Carolina State University, Raleigh, NC 27695, USA.

Heat stress is a current challenge for livestock production, and its impact could dramatically increase if global temperatures continue to climb. Exposure of agricultural animals to high ambient temperatures and humidity would lead to substantial economic losses because it compromises animal performance, productivity, health, and welfare. The gut microbiota plays essential roles in nutrient absorption, energy balance, and immune defenses through profound symbiotic interactions with the host. The homeostasis of those diverse gut microorganisms is critical for the host's overall health and welfare status and also is sensitive to environmental stressors, like heat stress, reflected in altered composition and functionality. This article aims to summarize the research progress on the interactions between heat stress and gut microbiome and discuss the potential use of the gut microbiota composition as a biomarker of heat stress in monogastric animal species. A comprehensive understanding of the gut microbiota's role in responding to or regulating physiological activities induced by heat stress would contribute to developing mitigation strategies.
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http://dx.doi.org/10.3390/ani11061833DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235026PMC
June 2021

Trends in genetic diversity and the effect of inbreeding in American Angus cattle under genomic selection.

Genet Sel Evol 2021 Jun 16;53(1):50. Epub 2021 Jun 16.

Department of Animal Science, North Carolina State University, Raleigh, NC, 27607, USA.

Background: While the adoption of genomic evaluations in livestock has increased genetic gain rates, its effects on genetic diversity and accumulation of inbreeding have raised concerns in cattle populations. Increased inbreeding may affect fitness and decrease the mean performance for economically important traits, such as fertility and growth in beef cattle, with the age of inbreeding having a possible effect on the magnitude of inbreeding depression. The purpose of this study was to determine changes in genetic diversity as a result of the implementation of genomic selection in Angus cattle and quantify potential inbreeding depression effects of total pedigree and genomic inbreeding, and also to investigate the impact of recent and ancient inbreeding.

Results: We found that the yearly rate of inbreeding accumulation remained similar in sires and decreased significantly in dams since the implementation of genomic selection. Other measures such as effective population size and the effective number of chromosome segments show little evidence of a detrimental effect of using genomic selection strategies on the genetic diversity of beef cattle. We also quantified pedigree and genomic inbreeding depression for fertility and growth. While inbreeding did not affect fertility, an increase in pedigree or genomic inbreeding was associated with decreased birth weight, weaning weight, and post-weaning gain in both sexes. We also measured the impact of the age of inbreeding and found that recent inbreeding had a larger depressive effect on growth than ancient inbreeding.

Conclusions: In this study, we sought to quantify and understand the possible consequences of genomic selection on the genetic diversity of American Angus cattle. In both sires and dams, we found that, generally, genomic selection resulted in decreased rates of pedigree and genomic inbreeding accumulation and increased or sustained effective population sizes and number of independently segregating chromosome segments. We also found significant depressive effects of inbreeding accumulation on economically important growth traits, particularly with genomic and recent inbreeding.
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http://dx.doi.org/10.1186/s12711-021-00644-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207663PMC
June 2021

Genetic Parameters for Tolerance to Heat Stress in Crossbred Swine Carcass Traits.

Front Genet 2020 4;11:612815. Epub 2021 Feb 4.

Department of Animal Science, North Carolina State University, Raleigh, NC, United States.

Data for loin and backfat depth, as well as carcass growth of 126,051 three-way crossbred pigs raised between 2015 and 2019, were combined with climate records of air temperature, relative humidity, and temperature-humidity index. Environmental covariates with the largest impact on the studied traits were incorporated in a random regression model that also included genomic information. Genetic control of tolerance to heat stress and the presence of genotype by environment interaction were detected. Its magnitude was more substantial for loin depth and carcass growth, but all the traits studied showed a different impact of heat stress and different magnitude of genotype by environment interaction. For backfat depth, heritability was larger under comfortable conditions (no heat stress), as compared to heat stress conditions. Genetic correlations between extreme values of environmental conditions were lower (∼0.5 to negative) for growth and loin depth. Based on the solutions obtained from the model, sires were ranked on their breeding value for general performance and tolerance to heat stress. Antagonism between overall performance and tolerance to heat stress was moderate. Still, the models tested can provide valuable information to identify genetic material that is resilient and can perform equally when environmental conditions change. Overall, the results obtained from this study suggest the existence of genotype by environment interaction for carcass traits, as a possible genetic contributor to heat tolerance in swine.
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http://dx.doi.org/10.3389/fgene.2020.612815DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890262PMC
February 2021

Gut microbiome mediates host genomic effects on phenotypes: a case study with fat deposition in pigs.

Comput Struct Biotechnol J 2021 30;19:530-544. Epub 2020 Dec 30.

Department of Animal Science, North Carolina State University, Raleigh, NC, USA.

A large number of studies have highlighted the importance of gut microbiome composition in shaping fat deposition in mammals. Several studies have also highlighted how host genome controls the abundance of certain species that make up the gut microbiota. We propose a systematic approach to infer how the host genome can control the gut microbiome, which in turn contributes to the host phenotype determination. We implemented a mediation test that can be applied to measured and latent dependent variables to describe fat deposition in swine (). In this study, we identify several host genomic features having a microbiome-mediated effects on fat deposition. This demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. Host genomic variants identified through our analysis are different than the ones detected in a traditional genome-wide association study. In addition, the use of latent dependent variables allows for the discovery of additional host genomic features that do not show a significant effect on the measured variables. Microbiome-mediated host genomic effects can help understand the genetic determination of fat deposition. Since their contribution to the overall genetic variance is usually not included in association studies, they can contribute to filling the missing heritability gap and provide further insights into the host genome - gut microbiome interplay. Further studies should focus on the portability of these effects to other populations as well as their preservation when pro-/pre-/anti-biotics are used (i.e. remediation).
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http://dx.doi.org/10.1016/j.csbj.2020.12.038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809165PMC
December 2020

Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations.

Front Genet 2020 29;11:563393. Epub 2020 Sep 29.

Department of Animal Science, North Carolina State University, Raleigh, NC, United States.

The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull's progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations (r ) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: (i) LAB cows from the training set; (ii) FIELD cows from the training set; and (iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.
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http://dx.doi.org/10.3389/fgene.2020.563393DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550782PMC
September 2020

Leveraging Multiple Layers of Data To Predict Complex Traits.

G3 (Bethesda) 2020 12 3;10(12):4599-4613. Epub 2020 Dec 3.

Department of Biological Sciences and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC 27695.

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels ( = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes ( = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response ( = 0.15 and 0.14 for female and male genotypes, respectively; and = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 ( 0.39 for transcripts) and GO:0032870 ( 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three complex phenotypes.
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http://dx.doi.org/10.1534/g3.120.401847DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718734PMC
December 2020

Microbiability of meat quality and carcass composition traits in swine.

J Anim Breed Genet 2021 Mar 26;138(2):223-236. Epub 2020 Sep 26.

Department of Animal Science, North Carolina State University, Raleigh, NC, USA.

The impact of gut microbiome composition was investigated at different stages of production (weaning, Mid-test and Off-test) on meat quality and carcass composition traits of 1,123 three-way crossbred pigs. Data were analysed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off-test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analysed, belly weight (BEL) had a higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had a greater decrease (10%) in genomic heritability at Off-test. High microbial correlations were found among different traits, particularly with traits related to fat deposition with a decrease in the genomic correlation up to 20% for loin weight and BEL. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition. The results indicated that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process.
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http://dx.doi.org/10.1111/jbg.12504DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891674PMC
March 2021

Evidence for recombination variability in purebred swine populations.

J Anim Breed Genet 2021 Mar 25;138(2):259-273. Epub 2020 Sep 25.

Department of Animal Science, North Carolina State University, Raleigh, NC, USA.

This study aimed to investigate interpopulation variation due to sex, breed and age, and the intrapopulation variation in the form of genetic variance for recombination in swine. Genome-wide recombination rate and recombination occurrences (RO) were traits studied in Landrace (LR) and Large White (LW) male and female populations. Differences were found for sex, breed, sex-breed interaction, and age effects for genome-wide recombination rate and RO at one or more chromosomes. Dams were found to have a higher genome-wide recombination rate and RO at all chromosomes than sires. LW animals had higher genome-wide recombination rate and RO at seven chromosomes but lower at two chromosomes than LR individuals. The sex-breed interaction effect did not show any pattern not already observable by sex. Recombination increased with increasing parity in females, while in males no effect of age was observed. We estimated heritabilities and repeatabilities for both investigated traits and obtained the genetic correlation between male and female genome-wide recombination rate within each of the two breeds studied. Estimates of heritability and repeatability were low (h  = 0.01-0.26; r = 0.18-0.42) for both traits in all populations. Genetic correlations were high and positive, with estimates of 0.98 and 0.94 for the LR and LW breeds, respectively. We performed a GWAS for genome-wide recombination rate independently in the four sex/breed populations. The results of the GWAS were inconsistent across the four populations with different significant genomic regions identified. The results of this study provide evidence of variability for recombination in purebred swine populations.
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http://dx.doi.org/10.1111/jbg.12510DOI Listing
March 2021

Effect of recent and ancient inbreeding on production and fertility traits in Canadian Holsteins.

BMC Genomics 2020 Sep 1;21(1):605. Epub 2020 Sep 1.

Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.

Background: Phenotypic performances of livestock animals decline with increasing levels of inbreeding, however, the noticeable decline known as inbreeding depression, may not be due only to the total level of inbreeding, but rather could be distinctly associated with more recent or more ancient inbreeding. Therefore, splitting inbreeding into different age classes could help in assessing detrimental effects of different ages of inbreeding. Hence, this study sought to investigate the effect of recent and ancient inbreeding on production and fertility traits in Canadian Holstein cattle with both pedigree and genomic records. Furthermore, inbreeding coefficients were estimated using traditional pedigree measure (F) and genomic measures using segment based (F) and marker-by-marker (F) based approaches.

Results: Inbreeding depression was found for all production and most fertility traits, for example, every 1% increase in F, F and F was observed to cause a - 44.71, - 40.48 and - 48.72 kg reduction in 305-day milk yield (MY), respectively. Similarly, an extension in first service to conception (FSTC) of 0.29, 0.24 and 0.31 day in heifers was found for every 1% increase in F, F and F, respectively. Fertility traits that did not show significant depression were observed to move in an unfavorable direction over time. Splitting both pedigree and genomic inbreeding into age classes resulted in recent age classes showing more detrimental inbreeding effects, while more distant age classes caused more favorable effects. For example, a - 1.56 kg loss in 305-day protein yield (PY) was observed for every 1% increase in the most recent pedigree age class, whereas a 1.33 kg gain was found per 1% increase in the most distant pedigree age class.

Conclusions: Inbreeding depression was observed for production and fertility traits. In general, recent inbreeding had unfavorable effects, while ancestral inbreeding had favorable effects. Given that more negative effects were estimated from recent inbreeding when compared to ancient inbreeding suggests that recent inbreeding should be the primary focus of selection programs. Also, further work to identify specific recent homozygous regions negatively associated with phenotypic traits could be investigated.
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http://dx.doi.org/10.1186/s12864-020-07031-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466804PMC
September 2020

Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine.

Genet Sel Evol 2020 Jul 29;52(1):41. Epub 2020 Jul 29.

Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA.

Background: The objectives of this study were to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits in swine, and to evaluate the contribution of host-microbiome interactions to the prediction. Data were collected from Duroc-sired three-way crossbred individuals (n = 1123) that were genotyped with a 60 k SNP chip. Phenotypic information and fecal 16S rRNA microbial sequences at three stages of growth (Wean, Mid-test, and Off-test) were available for all these individuals. We used fourfold cross-validation with animals grouped based on sire relatedness. Five models with three sets of predictors (full, informatively reduced, and randomly reduced) were evaluated. 'Full' included information from all genetic markers and all operational taxonomic units (OTU), while 'informatively reduced' and 'randomly reduced' represented a reduced number of markers and OTU based on significance preselection and random sampling, respectively. The baseline model included the fixed effects of dam line, sex and contemporary group and the random effect of pen. The other four models were constructed by including only genomic information, only microbiome information, both genomic and microbiome information, and microbiome and genomic information and their interaction.

Results: Inclusion of microbiome information increased predictive ability of phenotype for most traits, in particular when microbiome information collected at a later growth stage was used. Inclusion of microbiome information resulted in higher accuracies and lower mean squared errors for fat-related traits (fat depth, belly weight, intramuscular fat and subjective marbling), objective color measures (Minolta a*, Minolta b* and Minolta L*) and carcass daily gain. Informative selection of markers increased predictive ability but decreasing the number of informatively reduced OTU did not improve model performance. The proportion of variation explained by the host-genome-by-microbiome interaction was highest for fat depth (~ 20% at Mid-test and Off-test) and shearing force (~ 20% consistently at Wean, Mid-test and Off-test), although the inclusion of the interaction term did not increase the accuracy of predictions significantly.

Conclusions: This study provides novel insight on the use of microbiome information for the phenotypic prediction of meat quality and carcass traits in swine. Inclusion of microbiome information in the model improved predictive ability of phenotypes for fat deposition and color traits whereas including a genome-by-microbiome term did not improve prediction accuracy significantly.
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http://dx.doi.org/10.1186/s12711-020-00561-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7388461PMC
July 2020

Gut microbiome composition differences among breeds impact feed efficiency in swine.

Microbiome 2020 07 22;8(1):110. Epub 2020 Jul 22.

Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA.

Background: Feed efficiency is a crucial parameter in swine production, given both its economic and environmental impact. The gut microbiota plays an essential role in nutrient digestibility and is, therefore, likely to affect feed efficiency. This study aimed to characterize feed efficiency, fatness traits, and gut microbiome composition in three major breeds of domesticated swine and investigate a possible link between feed efficiency and gut microbiota composition.

Results: Average daily feed intake (ADFI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), backfat, loin depth, and intramuscular fat of 615 pigs belonging to the Duroc (DR), Landrace (LR), and Large White (LW) breeds were measured. Gut microbiota composition was characterized by 16S rRNA gene sequencing. Orthogonal contrasts between paternal line (DR) and maternal lines (LR+LW) and between the two maternal lines (LR versus LW) were performed. Average daily feed intake and ADG were statistically different with DR having lower ADFI and ADG compared to LR and LW. Landrace and LW had a similar ADG and RFI, with higher ADFI and FCR for LW. Alpha diversity was higher in the fecal microbial communities of LR pigs than in those of DR and LW pigs for all time points considered. Duroc communities had significantly higher proportional representation of the Catenibacterium and Clostridium genera compared to LR and LW, while LR pigs had significantly higher proportions of Bacteroides than LW for all time points considered. Amplicon sequence variants from multiple genera (including Anaerovibrio, Bacteroides, Blautia, Clostridium, Dorea, Eubacterium, Faecalibacterium, Lactobacillus, Oscillibacter, and Ruminococcus) were found to be significantly associated with feed efficiency, regardless of the time point considered.

Conclusions: In this study, we characterized differences in the composition of the fecal microbiota of three commercially relevant breeds of swine, both over time and between breeds. Correlations between different microbiome compositions and feed efficiency were established. This suggests that the microbial community may contribute to shaping host productive parameters. Moreover, our study provides important insights into how the intestinal microbial community might influence host energy harvesting capacity. A deeper understanding of this process may allow us to modulate the gut microbiome in order to raise more efficient animals. Video Abstract.
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http://dx.doi.org/10.1186/s40168-020-00888-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376719PMC
July 2020

Genomics of Heat Tolerance in Reproductive Performance Investigated in Four Independent Maternal Lines of Pigs.

Front Genet 2020 30;11:629. Epub 2020 Jun 30.

Department of Animal Science, North Carolina State University, Raleigh, NC, United States.

Improving swine climatic resilience through genomic selection has the potential to minimize welfare issues and increase the industry profitability. The main objective of this study was to investigate the genetic and genomic determinism of tolerance to heat stress in four independent purebred populations of swine. Three female reproductive traits were investigated: total number of piglets born (TNB), number of piglets born alive (NBA) and average birth weight (ABW). More than 80,000 phenotypic and 12,000 genotyped individuals were included in this study. Genomic random-regression models were fitted regressing the phenotypes of interest on a set of 95 environmental covariates extracted from public weather station records. The models yielded estimates of (genomic) reactions norms for individual pigs, as indicator of heat tolerance. Heat tolerance is a heritable trait, although the heritabilities are larger under comfortable than heat-stress conditions (larger than 0.05 vs. 0.02 for TNB; 0.10 vs. 0.05 for NBA; larger than 0.20 vs. 0.10 for ABW). TNB showed the lowest genetic correlation (-38%) between divergent climatic conditions, being the trait with the strongest impact of genotype by environment interaction, while NBA and ABW showed values slightly negative or equal to zero reporting a milder impact of the genotype by environment interaction. After estimating genetic parameters, a genome-wide association study was performed based on the single-step GBLUP method. Heat tolerance was observed to be a highly polygenic trait. Multiple and non-overlapping genomic regions were identified for each trait based on the genomic breeding values for reproductive performance under comfortable or heat stress conditions. Relevant regions were found on chromosomes (SSC) 1, 3, 5, 6, 9, 11, and 12, although there were important regions across all autosomal chromosomes. The genomic region located on SSC9 appears to be of particular interest since it was identified for two traits (TNB and NBA) and in two independent populations. Heat tolerance based on reproductive performance indicators is a heritable trait and genetic progress for heat tolerance can be achieved through genetic or genomic selection. Various genomic regions and candidate genes with important biological functions were identified, which will be of great value for future functional genomic studies.
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http://dx.doi.org/10.3389/fgene.2020.00629DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338773PMC
June 2020

Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters.

Sci Rep 2020 06 23;10(1):10134. Epub 2020 Jun 23.

Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA.

Despite recent efforts to characterize longitudinal variation in the swine gut microbiome, the extent to which a host's genome impacts the composition of its gut microbiome is not yet well understood in pigs. The objectives of this study were: i) to identify pig gut microbiome features associated with growth and fatness, ii) to estimate the heritability of those features, and, iii) to conduct a genome-wide association study exploring the relationship between those features and single nucleotide polymorphisms (SNP) in the pig genome. A total of 1,028 pigs were characterized. Animals were genotyped with the Illumina PorcineSNP60 Beadchip. Microbiome samples from fecal swabs were obtained at weaning (Wean), at mid-test during the growth trial (MidTest), and at the end of the growth trial (OffTest). Average daily gain was calculated from birth to week 14 of the growth trial, from weaning to week 14, from week 14 to week 22, and from week 14 to harvest. Backfat and loin depth were also measured at weeks 14 and 22. Heritability estimates (±SE) of Operational Taxonomic Units ranged from 0.025 (±0.0002) to 0.139 (±0.003), from 0.029 (±0.003) to 0.289 (±0.004), and from 0.025 (±0.003) to 0.545 (±0.034) at Wean, MidTest, and OffTest, respectively. Several SNP were significantly associated with taxa at the three time points. These SNP were located in genomic regions containing a total of 68 genes. This study provides new evidence linking gut microbiome composition with growth and carcass traits in swine, while also identifying putative host genetic markers associated with significant differences in the abundance of several prevalent microbiome features.
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http://dx.doi.org/10.1038/s41598-020-66791-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7311463PMC
June 2020

Transcriptome analysis identifies genes and co-expression networks underlying heat tolerance in pigs.

BMC Genet 2020 04 21;21(1):44. Epub 2020 Apr 21.

Department of Animal Science, North Carolina State University, Raleigh, NC, 27695-7621, USA.

Background: Heat stress adversely affects pig growth and reproduction performance by reducing feed intake, weight gain, farrowing rate, and litter size. Heat tolerance is an important characteristic in pigs, allowing them to mitigate the negative effects of heat stress on their physiological activities. Yet, genetic variation and signaling pathways associated with the biological processes of heat-tolerant pigs are currently not fully understood. This study examined differentially expressed genes and constructed gene co-expression networks on mRNAs of pigs under different heat-stress conditions using whole transcriptomic RNA-seq analyses. Semen parameters, including total sperm number per ejaculate, motility, normal morphology rate, droplets, and rejected ejaculate rate, were measured weekly on 12 boars for two time periods: thermoneutral (January to May), and heat stress (July to October). Boars were classified into heat-tolerant (n = 6) and heat-susceptible (n = 6) groups based on the variation of their ejaculate parameters across the two periods. RNA was isolated from the blood samples collected from the thermoneutral and heat stress periods for gene expression analysis.

Results: Under heat stress, a total of 66 differentially expressed genes (25 down-regulated, 41 up-regulated) were identified in heat-tolerant pigs compared to themselves during the thermoneutral period. A total of 1041 differentially expressed genes (282 down-regulated, 759 up-regulated) were identified in the comparison between heat-tolerant pigs and heat-susceptible pigs under heat stress. Weighted gene co-expression network analysis detected 4 and 7 modules with genes highly associated (r > 0.50, p < 0.05) with semen quality parameters in heat-tolerant and heat-susceptible pigs under the effects of heat stress, respectively.

Conclusion: This study utilized the sensitivity of semen to heat stress to discriminate the heat-tolerance ability of pigs. The gene expression profiles under the thermoneutral and heat stress conditions were documented in heat-tolerant and heat-susceptible boars. Findings contribute to the understanding of genes and biological mechanisms related to heat stress response in pigs and provide potential biomarkers for future investigations on the reproductive performance of pigs.
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http://dx.doi.org/10.1186/s12863-020-00852-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171765PMC
April 2020

Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations.

J Dairy Sci 2020 Jun 8;103(6):5183-5199. Epub 2020 Apr 8.

Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1 Canada; Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern 3001, Switzerland. Electronic address:

Genetic diversity in livestock populations is a significant contributor to the sustainability of animal production. Also, genetic diversity allows animal production to become more responsive to environmental changes and market demands. The loss of genetic diversity can result in a plateau in production and may also result in loss of fitness or viability in animal production. In this study, we investigated the rate of inbreeding (ΔF), rate of coancestry (Δf), and effective population size (N) as important quantitative indicators of genetic diversity and evaluated the effect of the recent implementation of genomic selection on the loss of genetic diversity in North American Holstein and Jersey dairy cattle. To estimate the rate of inbreeding and coancestry, inbreeding and coancestry coefficients were calculated using the traditional pedigree method and genomic methods estimated from segment- and marker-based approaches. Furthermore, we estimated N from the rate of inbreeding and coancestry and extent of linkage disequilibrium. A total of 205,755 and 89,238 pedigreed and genotyped animals born between 1990 and 2018 inclusively were available for Holsteins and Jerseys, respectively. The estimated average pedigree inbreeding coefficients were 7.74 and 7.20% for Holsteins and Jerseys, respectively. The corresponding values for the segment and marker-by-marker genomic inbreeding coefficients were 13.61, 15.64, and 31.40% for Holsteins and 21.16, 22.54, and 42.62% for Jerseys, respectively. The average coancestry coefficients were 8.33 and 15.84% for Holsteins and 9.23 and 23.46% for Jerseys with pedigree and genomic measures, respectively. Generation interval for the whole 29-yr time period averaged approximately 5 yr for all selection pathways combined. The ΔF per generation based on pedigree, segment, and marker-by-marker genomic measures for the entire 29-yr period was estimated to be 0.75, 1.10, 1.16, and 1.02% for Holstein animals and 0.67, 0.62, 0.63, and 0.59% for Jersey animals, respectively. The Δf was estimated to be 0.98 and 0.98% for Holsteins and 0.73 and 0.78% for Jerseys with pedigree and genomic measures, respectively. These ΔF and Δf translated to an N that ranged from 43 to 66 animals for Holsteins and 64 to 85 animals for Jerseys. In addition, the N based on linkage disequilibrium was 58 and 120 for Holsteins and Jerseys, respectively. The 10-yr period that involved the application of genomic selection resulted in an increased ΔF per generation with ranges from 1.19 to 2.06% for pedigree and genomic measures in Holsteins. Given the rate at which inbreeding is increasing after the implementation of genomic selection, there is a need to implement measures and means for controlling the rate of inbreeding per year, which will help to manage and maintain farm animal genetic resources.
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http://dx.doi.org/10.3168/jds.2019-18013DOI Listing
June 2020

The future of phenomics in dairy cattle breeding.

Anim Front 2020 Apr 1;10(2):37-44. Epub 2020 Apr 1.

Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.

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http://dx.doi.org/10.1093/af/vfaa007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7111603PMC
April 2020

GWAS and fine-mapping of livability and six disease traits in Holstein cattle.

BMC Genomics 2020 Jan 13;21(1):41. Epub 2020 Jan 13.

Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.

Background: Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data.

Results: We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability.

Conclusions: Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.
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http://dx.doi.org/10.1186/s12864-020-6461-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6958677PMC
January 2020

Genome-wide association study for carcass quality traits and growth in purebred and crossbred pigs1.

J Anim Sci 2020 Jan;98(1)

Department of Animal Science, North Carolina State University, Raleigh, NC.

Carcass quality traits such as back fat (BF), loin depth (LD), and ADG are of extreme economic importance for the swine industry. This study aimed to (i) estimate the genetic parameters for such traits and (ii) conduct a single-step genome-wide association study (ssGWAS) to identify genomic regions that affect carcass quality and growth traits in purebred (PB) and three-way crossbred (CB) pigs. A total of 28,497 PBs and 135,768 CBs pigs were phenotyped for BF, LD, and ADG. Of these, 4,857 and 3,532 were genotyped using the Illumina PorcineSNP60K Beadchip. After quality control, 36,328 SNPs were available and were used to perform an ssGWAS. A bootstrap analysis (n = 1,000) and a signal enrichment analysis were performed to declare SNP significance. Genome regions were based on the variance explained by significant 10-SNP sliding windows. Estimates of PB heritability (SE) were 0.42 (0.019) for BF, 0.39 (0.020) for LD, and 0.35 (0.021) for ADG. Estimates of CB heritability were 0.49 (0.042) for BF, 0.27 (0.029) for LD, and 0.12 (0.021) for ADG. Genetic correlations (SE) across the two populations were 0.81 (0.02), 0.79 (0.04), and 0.56 (0.05), for BF, LD, and ADG, respectively. The variance explained by significant regions for each trait in PBs ranged from 1.51% to 1.35% for BF, from 4.02% to 3.18% for LD, and from 2.26% to 1.45% for ADG. In CBs, the variance explained by significant regions ranged from 1.88% to 1.37% for BF, from 1.29% to 1.23% for LD, and from 1.54% to 1.32% for ADG. In this study, we have described regions of the genome that determine carcass quality and growth traits of PB and CB pigs. These results provide evidence that there are overlapping and nonoverlapping regions in the genome influencing carcass quality and growth traits in PBs and three-way CB pigs.
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http://dx.doi.org/10.1093/jas/skz360DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6978898PMC
January 2020

The interaction between microbiome and pig efficiency: A review.

J Anim Breed Genet 2020 Jan 1;137(1):4-13. Epub 2019 Oct 1.

Department of Animal Science, North Carolina State University, Raleigh, NC, USA.

The existence of genetic control over the abundance of particular taxa and the link of these to energy balance and growth has been documented in model organisms and humans as well as several livestock species. Preliminary evidence of the same mechanisms is currently under investigation in pigs. Future research should expand these results and elicit the extent of genetic control of the gut microbiome population in swine and its relationship with growth efficiency. The quest for a more efficient pig at the interface between the host and its metagenome rests on the central hypothesis that the gut microbiome is an essential component of the variability of growth in all living organisms. Swine do not escape this general rule, and the identification of the significance of the interaction between host and its gut microbiota in the growth process could be a game-changer in the achievement of sustainable and efficient lean meat production. Standard sampling protocols, sequencing techniques, bioinformatic pipelines and methods of analysis will be paramount for the portability of results across experiments and populations. Likewise, characterizing and accounting for temporal and spatial variability will be a necessary step if microbiome is to be utilized routinely as an aid to selection.
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http://dx.doi.org/10.1111/jbg.12443DOI Listing
January 2020

Genetic parameters of meat quality, carcass composition, and growth traits in commercial swine.

J Anim Sci 2019 Sep;97(9):3669-3683

Department of Animal Science, North Carolina State University, Raleigh, NC.

Swine industry breeding goals are mostly directed towards meat quality and carcass traits due to their high economic value. Yet, studies on meat quality and carcass traits including both phenotypic and genotypic information remain limited, particularly in commercial crossbred swine. The objectives of this study were to estimate the heritabilities for different carcass composition traits and meat quality traits and to estimate the genetic and phenotypic correlations between meat quality, carcass composition, and growth traits in 2 large commercial swine populations: The Maschhoffs LLC (TML) and Smithfield Premium Genetics (SPG), using genotypes and phenotypes data. The TML data set consists of 1,254 crossbred pigs genotyped with 60K SNP chip and phenotyped for meat quality, carcass composition, and growth traits. The SPG population included over 35,000 crossbred pigs phenotyped for meat quality, carcass composition, and growth traits. For TML data sets, the model included fixed effects of dam line, contemporary group (CG), gender, as well as random additive genetic effect and pen nested within CG. For the SPG data set, fixed effects included parity, gender, and CG, as well as random additive genetic effect and harvest group. Analyses were conducted using BLUPF90 suite of programs. Univariate and bivariate analyses were implemented to estimate heritabilities and correlations among traits. Primal yield traits were uniquely created in this study. Heritabilities [high posterior density interval] of meat quality traits ranged from 0.08 [0.03, 0.16] for pH and 0.08 [0.03, 0.1] for Minolta b* to 0.27 [0.22, 0.32] for marbling score, except intramuscular fat with the highest estimate of 0.52 [0.40, 0.62]. Heritabilities of primal yield traits were higher than that of primal weight traits and ranged from 0.17 [0.13, 0.25] for butt yield to 0.45 [0.36, 0.55] for ham yield. The genetic correlations of meat quality and carcass composition traits with growth traits ranged from moderate to high in both directions. High genetic correlations were observed for male and female for all traits except pH. The genetic parameter estimates of this study indicate that a multitrait approach should be considered for selection programs aimed at meat quality and carcass composition in commercial swine populations.
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http://dx.doi.org/10.1093/jas/skz247DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735811PMC
September 2019
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