New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens.

Poultry feed constitutes the largest cost in poultry production, estimated to be up to 70% of the total cost. Moreover, there is pressure on the poultry industry to increase production to meet the protein demand of humans and simultaneously reduce emissions to protect the environment. Therefore, improving feed efficiency plays an important role to improve profits and the environmental footprint in broiler production. In this study, using imputed whole-genome sequencing data, genome-wide association analysis (GWAS) was performed to identify single-nucleotide polymorphisms (SNPs) and genes associated with residual feed intake (RFI) and its component traits. Furthermore, a transcriptomic analysis between the high-RFI and the low-RFI groups was performed to validate the candidate genes from GWAS. The results showed that the heritability estimates of average daily gain (ADG), average daily feed intake (ADFI), and RFI were 0.29 (0.004), 0.37 (0.005), and 0.38 (0.004), respectively. Using imputed sequence-based GWAS, we identified seven significant SNPs and five candidate genes [MTSS I-BAR domain containing 1, folliculin, COP9 signalosome subunit 3, 5',3'-nucleotidase (mitochondrial), and gametocyte-specific factor 1] associated with RFI, 20 significant SNPs and one candidate gene (inositol polyphosphate multikinase) associated with ADG, and one significant SNP and one candidate gene (coatomer protein complex subunit alpha) associated with ADFI. After performing a transcriptomic analysis between the high-RFI and the low-RFI groups, both 38 up-regulated and 26 down-regulated genes were identified in the high-RFI group. Furthermore, integrating regional conditional GWAS and transcriptome analysis, ras-related dexamethasone induced 1 was the only overlapped gene associated with RFI, which also suggested that the region (GGA14: 4767015-4882318) is a new quantitative trait locus associated with RFI. In conclusion, using imputed sequence-based GWAS is an efficient method to identify significant SNPs and candidate genes in chicken. Our results provide valuable insights into the genetic mechanisms of RFI and its component traits, which would further improve the genetic gain of feed efficiency rapidly and cost-effectively in the context of marker-assisted breeding selection.

Genome-Wide Association Study for Reproductive Traits in a Duroc Pig Population.

In the pig industry, reproductive traits constantly influence the production efficiency. To identify markers and candidate genes underlying porcine reproductive traits, a genome-wide association study (GWAS) was performed in a Duroc pig population. In total, 1067 pigs were genotyped using single-nucleotide polymorphism (SNP) chips, and four reproductive traits, including litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), and litter weight at weaning (LWW), were examined. The results showed that 20 potential SNPs reached the level of suggestive significance and were associated with these traits of interest. Several important candidate genes, including TXN2, KCNA1, ENSSSCG00000003546, ZDHHC18, MAP2K6, BICC1, FAM135B, EPHB2, SEMA4D, ST3GAL1, KCTD3, FAM110A, TMEM132D, TBX3, and FAM110A, were identified and might compose the underlying genetic architecture of porcine reproductive traits. These findings help to understand the genetic basis of porcine reproductive traits and provide important information for molecular breeding in pigs.

Strategies for Obtaining and Pruning Imputed Whole-Genome Sequence Data for Genomic Prediction.

Genomic prediction with imputed whole-genome sequencing (WGS) data is an attractive approach to improve predictive ability with low cost. However, high accuracy has not been realized using this method in livestock. In this study, we imputed 435 individuals from 600K single nucleotide polymorphism (SNP) chip data to WGS data using different reference panels. We also investigated the prediction accuracy of genomic best linear unbiased prediction (GBLUP) using imputed WGS data from different reference panels, linkage disequilibrium (LD)-based marker pruning, and pre-selected variants based on Genome-wide association society (GWAS) results. Results showed that the imputation accuracies from 600K to WGS data were 0.873 ± 0.038, 0.906 ± 0.036, and 0.979 ± 0.010 for the internal, external, and combined reference panels, respectively. In most traits of chickens, the prediction accuracy of imputed WGS data obtained from the internal reference panel was greater than or equal to that of the combined reference panel; the external reference panel had the lowest prediction accuracy. Compared with 600K chip data, GBLUP with imputed WGS data had only a small increase (1-3%) in prediction accuracy. Using only variants selected from imputed WGS data based on GWAS results resulted in almost no increase for most traits and even increased the bias of the regression coefficient. The impact of the degree of LD of selected and remaining variants on prediction accuracy was different. For average daily gain (ADG), residual feed intake (RFI), intestine length (IL), and body weight in 91 days (BW91), the accuracy of GBLUP increased as the degree of LD of selected variants decreased, but the opposite relationship occurred for the remaining variants. But for breast muscle weight (BMW) and average daily feed intake (ADFI), the accuracy of GBLUP increased as the degree of LD of selected variants increased, and the degree of LD of remaining variants had a small effect on prediction accuracy. Overall, the optimal imputation strategy to obtain WGS data for genomic prediction should consider the relationship between selected individuals and target population individuals to avoid heterogeneity of imputation. LD-based marker pruning can be used to improve the accuracy of genomic prediction using imputed WGS data.

Genome-wide association studies for the number of animals born alive and dead in duroc pigs.

Litter size is one of the most important economic traits for pig production as it is directly related to the production efficiency. As an important litter size trait in pigs, the number of piglets born alive at birth (NBA) receives widespread interests in the pig industry. However, traits of piglets born dead, including the number of stillborn piglets (NS) and the piglets mummified at birth (NM) should be noted to explain the loss of reproduction. Herein, in the present study, a total of 803 producing sows were sampled and 2807 farrowing records for NBA, NM, and NS traits were collected in a Duroc swine population. Subsequently, a genome-wide association study (GWAS) was performed for NBA, NS and NM in parity groups 1 to 5. In total, 10 putative regions were found associated with these traits. After stepwise conditional analyses around the putative regions, eight independent signals were ultimately identified for NBA, NS, and NM, and there were seven promising candidate genes related to these traits, including ARID1A, RXRG, NFATC4, ABTB2, GRAMD1B, NDRG1, and APC. Our findings contribute to the understanding of the significant genetic causes of piglets born alive and dead, and could have a positive effect on pig production efficiency and economic profits.

Genetic Diversity of Indigenous Pigs from South China Area Revealed by SNP Array.

To investigate the genetic diversity, population structure, extent of linkage disequilibrium (LD), effective population size (Ne), and selection signatures in indigenous pigs from Guangdong and Guangxi in China, 226 pigs belonging to ten diverse populations were genotyped using single nucleotide polymorphism (SNP) chips. The genetic divergence between Chinese and Western pigs was determined based on the SNP chip data. Low genetic diversity of Dahuabai (DHB), Luchuan (LC), Lantang (LT), and Meihua (MH) pigs, and introgression of Western pigs into Longlin (LL), MH, and Yuedonghei (YDH) pigs were detected. Analysis of the extent of LD showed that indigenous pigs had low LD when pairwise SNP distance was short and high LD when pairwise SNP distance was long. Effective population size analysis showed a rapid decrease for Chinese indigenous pigs, and some pig populations had a relatively small Ne. This result indicated the loss of genetic diversity in indigenous pigs, and introgression from Western commercial pigs. Selection signatures detected in this study overlapped with meat quality traits, such as drip loss, intramuscular fat content, meat color b*, and average backfat thickness. Our study deepened understanding of the conservation status and domestication of Chinese indigenous pigs.

Genome-Wide Signatures of Selection Detection in Three South China Indigenous Pigs.

South China indigenous pigs are famous for their superior meat quality and crude feed tolerance. Saba and Baoshan pigs without saddleback were located in the high-altitude area of Yunnan Province, while Tunchang and Ding'an pigs with saddleback were located in the low-altitude area of Hainan Province. Although these pigs are different in appearance, the underlying genetic differences have not been investigated. In this study, based on the single-nucleotide polymorphism (SNP) genotypes of 124 samples, both the cross-population extended haplotype homozygosity (XP-EHH) and the fixation index (FST) statistic were used to identify potential signatures of selection in these pig breeds. We found nine potential signatures of selection detected simultaneously by two methods, annotated 22 genes in Hainan pigs, when Baoshan pigs were used as the reference group. In addition, eleven potential signatures of selection detected simultaneously by two methods, annotated 24 genes in Hainan pigs compared with Saba pigs. These candidate genes were most enriched in GO: 0048015~phosphatidylinositol-mediated signaling and ssc00604: Glycosphingolipid biosynthesis-ganglio series. These selection signatures were likely to overlap with quantitative trait loci associated with meat quality traits. Furthermore, one potential selection signature, which was associated with different coat color, was detected in Hainan pigs. These results contribute to a better understanding of the underlying genetic architecture of South China indigenous pigs.

Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population.

BACKGROUND: Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation. RESULTS: We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study. CONCLUSIONS: In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.

Profiling the genome-wide DNA methylation pattern of porcine ovaries using reduced representation bisulfite sequencing.

Substantial evidence has shown that DNA methylation regulates the initiation of ovarian and sexual maturation. Here, we investigated the genome-wide profile of DNA methylation in porcine ovaries at single-base resolution using reduced representation bisulfite sequencing. The biological variation was minimal among the three ovarian replicates. We found hypermethylation frequently occurred in regions with low gene abundance, while hypomethylation in regions with high gene abundance. The DNA methylation around transcriptional start sites was negatively correlated with their own CpG content. Additionally, the methylation level in the bodies of genes was higher than that in their 5' and 3' flanking regions. The DNA methylation pattern of the low CpG content promoter genes differed obviously from that of the high CpG content promoter genes. The DNA methylation level of the porcine ovary was higher than that of the porcine intestine. Analyses of the genome-wide DNA methylation in porcine ovaries would advance the knowledge and understanding of the porcine ovarian methylome.

Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model.

BACKGROUND: In recent years, with the development of high-throughput sequencing technology and the commercial availability of genotyping bead chips, more attention is being directed towards the utilization of abundant genetic markers in animal and plant breeding programs, human disease risk prediction and personal medicine. Several useful approaches to accomplish genomic prediction have been developed and used widely, but still have room for improvement to gain more accuracy. In this study, an improved Bayesian approach, termed BayesBπ, which differs from the original BayesB in priors assigning, is proposed. An effective method for calculating the locus-specific π by converting p-values from association between SNPs and traits' phenotypes is given and systemically validated using a German Holstein dairy cattle population. Furthermore, the new method is applied to a loblolly pine (Pinus taeda) dataset. RESULTS: Compared with the original BayesB, BayesBπ can improve the accuracy of genomic prediction up to 7.62% for milk fat percentage, a trait which shows a large effect of quantitative trait loci (QTL). For milk yield, which is controlled by small to moderate effect genes, the accuracy of genomic prediction can be improved up to 4.94%. For somatic cell score, of which no large effect QTL has been reported, GBLUP performs better than Bayesian methods. BayesBπ outperforms BayesCπ in 10 out of 12 scenarios in the dairy cattle population, especially in small to moderate population sizes where accuracy of BayesCπ are dramatically low. Results of the loblolly pine dataset show that BayesBπ outperforms BayesB in 14 out of 17 traits and BayesCπ in 8 out of 17 traits, respectively. CONCLUSIONS: For traits controlled by large effect genes, BayesBπ can improve the accuracy of genomic prediction and unbiasedness of BayesB in moderate size populations. Knowledge of traits' genetic architectures can be integrated into practices of genomic prediction by assigning locus-specific priors to markers, which will help Bayesian approaches perform better in variable selection and marker effects shrinkage.

A functional mutation at position -155 in porcine APOE promoter affects gene expression.

BACKGROUND: Apolipoprotein E, a component of the plasma lipoproteins, plays an important role in the transport and metabolism of cholesterol and other lipids. Three single nucleotide polymorphisms (SNPs) -491A>T, -219T>G and +113G>C in the regulatory region of human apolipoprotein E gene (APOE) change the promoter activity and are associated with a wide variety of disorders including Alzheimer disease (AD). Functional SNPs in porcine APOE gene 5' regulatory region have not been explored. RESULTS: We examined SNPs within this region (from -831 to +855), and the analysis revealed that the T>A SNP at position -155 among these three polymorphism sites (-440, -155, +501) was found to exert a marked influence on the transcription of the porcine APOE gene. Electrophoretic mobility shift assays showed that the binding affinity of oligonucletides containing the -155A to transcription factor(s) was stronger than that of the -155T. Transient transfection assays and quantitative real-time PCR results revealed that the -155T>A variant enhanced the activity of the APOE promoter and was associated with increased APOE mRNA levels in vivo. CONCLUSIONS: These data suggest that the -155T>A mutation in the promoter region of the porcine APOE gene is an important functional variant. The results provided new insights into aspects of pig genetics and might also facilitate the application of pigs in biomedical studies addressing important human diseases.

[Founding and genetic analysis of Landrace x Lantang pig resource population for QTL mapping].

With F2 design, 16 Chinese Lantang sows crossbred with eight highly improved Landrace boars to establish a resource population including 40 F1 sows, eight F1 boars, and 232 F2 pigs. Genetic analysis of the resource population showed that the 32 performance traits displayed some degree of variation, and coefficients of variation of the majority of economic traits exceeded 10%. The variance component analysis revealed that the ratios of additive genetic variance to phenotypic variance of the majority of economic traits were high. Of the 22 microsatellite DNA markers, only 12 microsatellite DNA markers are polymorphisic in this populations. The average heterozygosity of these markers and PIC were 0.53 and 0.46 respectively. The results showed that these markers can provide enough information for QTL mapping. In conclusion,the F2 pigs were sufficiently segregated, and were capable of serving as a resource population for QTL mapping.

Genetic effects of IGF-1 gene on the performance in Landrace x Lantang pig resource population.

A Landrace x Lantang resource population (LL-SCAU) including 216 F2 pigs was founded by F2 design for analysis of IGF-1 gene polymorphism by PCR-RFLP. The least square means of divergent IGF-1 genotypes for the measured traits were estimated with the fixed model. The genetic effects of IGF-1 gene were estimated with a mixed model and the additive and dominant effects of IGF-1 gene were accordingly calculated. The results of the fixed model and the mixed model showed that IGF-1 locus significantly affected average daily gain after weaning. Average daily gain after weaning for AA genotype on IGF-1 locus was 20.58 g (P = 0.0347) higher than for AB. The additive and dominant effects attributed to IGF-1 were 1.78 g and -18.81 g respectively. IGF-1 locus also significantly affected carcass composition. Bone percentages for AA and AB on IGF-1 locus were lower than for BB by 5.22% (P = 0.0008) and 5.19% (P = 0.0007) respectively, and the additive and dominant effects were -2.61% and -2.58% respectively. Amount of carcass lean for AA on IGF-1 locus was less than for AB by 0.45 kg (P = 0.0264), and the additive and dominant effects were 0.16 kg and 0.61 kg respectively. Skin and fat percentage for AA and AB on IGF-1 locus was higher than for BB by 8.81% (P = 0.0206) and 7.64% (P = 0.0431) respectively, and the additive and dominant effects were 4.41% and 3.24% respectively. The genetic analysis of IGF-1 gene showed that IGF-1 locus significantly affected average daily gain after weaning, bone percentage, carcass lean, skin and fat percentage. The estimated additive and dominant effects showed that only the additive effect of IGF-1 locus on skin and fat percentage was much higher than dominant effect, and IGF-1 gene can be used as a major gene for effective selection of these traits.