Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.

OBJECTIVE: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). METHODS: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. RESULTS: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. CONCLUSION: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms.

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

Population differentiated copy number variation between Eurasian wild boar and domesticated pig populations.

Sus scrofa is a globally distributed livestock species that still maintains two different ways of life: wild and domesticated. Herein, we detected copy number variation (CNV) of 328 animals using short read alignment on Sscrofa11.1. We compared CNV among five groups of porcine populations: Asian domesticated (AD), European domesticated (ED), Asian wild (AW), European wild (EW), and Near Eastern wild (NEW). In total, 21,673 genes were identified on 154,872 copy number variation region (CNVR). Differences in gene copy numbers between populations were measured by considering the variance-based value [Formula: see text] and the one-way ANOVA test followed by Scheffe test. As a result, 111 genes were suggested as copy number variable genes. Abnormally gained copy number on EEA1 in all populations was suggested the presence of minor CNV in the reference genome assembly, Sscrofa11.1. Copy number variable genes were related to meat quality, immune response, and reproduction traits. Hierarchical clustering of all individuals and mean pairwise [Formula: see text] in breed level were visualized genetic relationship of 328 individuals and 56 populations separately. Our findings have shown how the complex history of pig evolution appears in genome-wide CNV of various populations with different regions and lifestyles.

Genetic Parameter Estimation and Genome-Wide Association Analysis of Social Genetic Effects on Average Daily Gain in Purebreds and Crossbreds.

Average daily gain (ADG) is an important growth trait in the pig industry. The direct genetic effect (DGE) has been studied mainly to assess the association between genetic information and economic traits. The social genetic effect (SGE) has been shown to affect ADG simultaneously with the DGE because of group housing systems. We conducted this study to elucidate the genetic characteristics and relationships of the DGE and SGE of purebred Korean Duroc and crossbred pigs by single-step genomic best linear unbiased prediction and a genome-wide association study. We used the genotype, phenotype, and pedigree data of 1779, 6022, and 7904 animals, respectively. Total heritabilities on ADG were 0.19 ± 0.04 and 0.39 ± 0.08 for purebred and crossbred pigs, respectively. The genetic correlation was the greatest (0.77 ± 0.12) between the SGE of purebred and DGE of crossbred pigs. We found candidate genes located in the quantitative trait loci (QTLs) for the SGE that were associated with behavior and neurodegenerative diseases, and candidate genes in the QTLs for DGE that were related to body mass, size of muscle fiber, and muscle hypertrophy. These results suggest that the genomic selection of purebred animals could be applied for crossbred performance.

Genome-Wide Assessment of a Korean Composite Pig Breed, Woori-Heukdon.

A Korean synthetic pig breed, Woori-Heukdon (WRH; F3), was developed by crossing parental breeds (Korean native pig [KNP] and Korean Duroc [DUC]) with their crossbred populations (F1 and F2). This study in genome-wide assessed a total of 2,074 pigs which include the crossbred and the parental populations using the Illumina PorcineSNP60 BeadChip. After quality control of the initial datasets, we performed population structure, genetic diversity, and runs of homozygosity (ROH) analyses. Population structure analyses showed that crossbred populations were genetically influenced by the parental breeds according to their generation stage in the crossbreeding scheme. Moreover, principal component analysis showed the dispersed cluster of WRH, which might reflect introducing a new breeding group into the previous one. Expected heterozygosity values, which were used to assess genetic diversity, were .365, .349, .336, .330, and .211 for WRH, F2, F1, DUC, and KNP, respectively. The inbreeding coefficient based on ROH was the highest in KNP (.409), followed by WRH (.186), DUC (.178), F2 (.107), and F1 (.035). Moreover, the frequency of short ROH decreased according to the crossing stage (from F1 to WRH). Alternatively, the frequency of medium and long ROH increased, which indicated recent inbreeding in F2 and WRH. Furthermore, gene annotation of the ROH islands in WRH that might be inherited from their parental breeds revealed several interesting candidate genes that may be associated with adaptation, meat quality, production, and reproduction traits in pigs.

Genome-wide investigation of a Korean synthetic breed, Woori-Heukdon using the Illumina PorcineSNP60K BeadChip.

BACKGROUND: Woori-Heukdon (KWH) is a Korean synthetic pig breed generated using Chookjin-Duroc (KCD), Chookjin-Chamdon (KCC), and their crossbreds. Currently, there is a severe lack of studies investigating the Korean breed populations including wild boars (KWB) throughout the genome. OBJECTIVE: This study was performed to investigate the genetic characteristics of Korean pig populations at the genome-wide level. METHODS: Using the SNP dataset derived from genotyped and downloaded datasets using the Illumina PorcineSNP60K BeadChip, we compared the genomes of 532 individuals derived from 23 pig breeds to assess the genetic diversity, inbreeding coefficient, genetic differentiation, and population structure. RESULTS: KWB showed the lowest average expected heterozygosity (HE = 0.1904), while KWH showed the highest genetic diversity (HE = 0.02859) among Korean populations. We verified that the genetic composition of KWH, showing USD of 74.8% and KCC of 25.2% in ADMIXTURE analysis. In population structure analyses, KCC was consistently shown to be separated from other pig populations. In addition, we observed gene flow from Western pigs to a part of Chinese populations. CONCLUSION: This study showed that Korean native pigs, KCC have genetic differences in comparison with Chinese and Western pigs; despite some historical records and recent genetic studies, we could not find any clear evidence that KCC was significantly influenced by Chinese or Western breeds in this study. We also verified the theoretical genomic composition of KWH at the molecular level in structure analyses. To our knowledge, this is the first genomic study to investigate the genomic characteristics of KWH and KCC.

Single-step genome-wide association study for social genetic effects and direct genetic effects on growth in Landrace pigs.

In livestock social interactions, social genetic effects (SGE) represent associations between phenotype of one individual and genotype of another. Such associations occur when the trait of interest is affected by transmissible phenotypes of social partners. The aim of this study was to estimate SGE and direct genetic effects (DGE, genetic effects of an individual on its own phenotype) on average daily gain (ADG) in Landrace pigs, and to conduct single-step genome-wide association study using SGE and DGE as dependent variables to identify quantitative trait loci (QTLs) and their positional candidate genes. A total of 1,041 Landrace pigs were genotyped using the Porcine SNP 60K BeadChip. Estimates of the two effects were obtained using an extended animal model. The SGE contributed 16% of the total heritable variation of ADG. The total heritability estimated by the extended animal model including both SGE and DGE was 0.52. The single-step genome-wide association study identified a total of 23 QTL windows for the SGE on ADG distributed across three chromosomes (i.e., SSC1, SSC2, and SSC6). Positional candidate genes within these QTL regions included PRDM13, MAP3K7, CNR1, HTR1E, IL4, IL5, IL13, KIF3A, EFHD2, SLC38A7, mTOR, CNOT1, PLCB2, GABRR1, and GABRR2, which have biological roles in neuropsychiatric processes. The results of biological pathway and gene network analyses also support the association of the neuropsychiatric processes with SGE on ADG in pigs. Additionally, a total of 11 QTL windows for DGE on ADG in SSC2, 3, 6, 9, 10, 12, 14, 16, and 17 were detected with positional candidate genes such as ARL15. We found a putative pleotropic QTL for both SGE and DGE on ADG on SSC6. Our results in this study provide important insights that can help facilitate a better understanding of the molecular basis of SGE for socially affected traits.

Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs.

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2-3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

Assessment of Genetic Diversity, Relationships and Structure among Korean Native Cattle Breeds Using Microsatellite Markers.

Four Korean native cattle (KNC) breeds-Hanwoo, Chikso, Heugu, and Jeju black-are entered in the Domestic Animal Diversity Information System of the United Nations Food and Agriculture Organization (FAO). The objective of this study was to assess the genetic diversity, phylogenetic relationships and population structure of these KNC breeds (n = 120) and exotic breeds (Holstein and Charolais, n = 56). Thirty microsatellite loci recommended by the International Society for Animal Genetics/FAO were genotyped. These genotypes were used to determine the allele frequencies, allelic richness, heterozygosity and polymorphism information content per locus and breed. Genetic diversity was lower in Heugu and Jeju black breeds. Phylogenetic analysis, Factorial Correspondence Analysis and genetic clustering grouped each breed in its own cluster, which supported the genetic uniqueness of the KNC breeds. These results will be useful for conservation and management of KNC breeds as animal genetic resources.

Update on Renin-Angiotensin-aldosterone blockade in heart failure.

There is convincing evidence that the renin-angiotensin-aldosterone system (RAAS) plays an important role in heart failure (HF), from the events that result in its inception through advanced disease. In particular, RAAS activation is one of the major pathways involved in maladaptive cardiac remodeling, a process that results in progressive cardiac dysfunction. Not surprisingly, strategies targeting the RAAS have substantial benefits in HF. These therapies, which include angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and aldosterone receptor antagonists, have emerged as cornerstones of HF therapy, particularly in patients with systolic dysfunction. Their role in HF with preserved ejection fraction, however, is less certain. New information and approaches targeting RAAS activation continue to emerge. Direct renin inhibitors are hypothesized to have beneficial effects in HF, but further studies are needed to evaluate their efficacy.

Low back pain in older adults: are we utilizing healthcare resources wisely?

OBJECTIVES: 1) To examine recent change in prevalence and Medicare-associated charges for non-invasive/minimally invasive evaluation and treatment of nonspecific low back pain (LBP); and 2) to examine magnetic resonance imaging (MRI) utilization appropriateness in older adults with chronic low back pain (CLBP). DESIGN: Two cross-sectional surveys of 1) national (1991-2002) and Pennsylvania (2000-2002) Medicare data; and 2) patients aged >or= 65 years with CLBP. SETTING: Outpatient data. PARTICIPANTS: Patients aged >or= 65 years with LBP. MEASUREMENTS: Study 1: Outpatient national and Pennsylvania Part A Medicare data were examined for number of patients and charges for all patients, and for those with nonspecific LBP. Total number of visits and charges for imaging studies, physical therapy (PT), and spinal injections was also examined for Pennsylvania. Study 2: 111 older adults with CLBP were interviewed regarding presence of red flags necessitating imaging and history of having a lumbar MRI, neurogenic claudication (NC), and back surgery. RESULTS: Study 1: Between 1991 and 2002, there was a 42.5% increase in total Medicare patients, 131.7% increase in LBP patients, 310% increase in total charges, and 387.2% increase in LBP charges. In Pennsylvania (2000-2002), there was a 5.5% increase in LBP patients and 33.2% increase in charges (0.2% for PT, 59.4% for injections, 41.9% for MRI/CT, and 19.3% for X rays). Study 2: None of the 111 participants had red flags and 61% had undergone MRIs (29% with NC, 24% with failed back surgery syndrome). CONCLUSION: LBP documentation and diagnostic studies are increasing in Medicare beneficiaries, and evidence suggests that MRIs may often be ordered unnecessarily. Injection procedures appear to account for a significant proportion of LBP-associated costs. More studies are needed to examine the appropriateness with which imaging procedures and non-invasive/minimally invasive treatments are utilized, and their effect on patient outcomes.

Do medical factors predict disability in older adults with persistent low back pain?

Persistent low back pain (LBP) is one of the most common and challenging persistent pain conditions in older adults. Medical comorbidity also is common in these individuals, but its impact on disability has not been examined. The purpose of this study was, using a cross-sectional design, to examine the functional impact of pain-related and general medical comorbidity on 100 community dwelling older adults (mean age 74.3) with persistent mechanical LBP. Subjects received a structured history and physical examination, lumbosacral spine X-rays, and standardized tests of physical function. Pain-related variables included intensity, duration, extent, and lumbar motion-induced pain. General medical variables included age, comorbidity, number of medications, depressive symptoms, back range of motion, body mass index, and severity of radiographic pathology. Function/disability measures included self-reported disability, gait speed, and mean number of static lifts and amount of work performed during a dynamic lifting task. Structural equation modeling was used to evaluate the influence of pain and medical variables on function/disability. The overall regression model indicated pain and medical variables were significantly associated with function/disability measures (R2=0.45, P<0.01). Individual regression coefficients, however, indicated that only pain duration (r=-0.36, P<0.05) and pain severity (r=0.37, P<0.001) were significantly associated with function/disability. Despite the prevalence of medical co-morbidities in older PLBP subjects, they appear to be of limited utility in understanding level of disability. These findings also underscore the need to optimize pain treatment in independent older adults to optimize physical function and delay the onset of dependent living status.