Ancestral haplotype-based association mapping with generalized linear mixed models accounting for stratification

Genetic and genomic analysis of Belgian Blue's susceptibility for psoroptic mange

BACKGROUND

Psoroptic mange, caused by Psoroptes ovis mites, is affecting Belgian Blue cattle's welfare and production potential. The Belgian Blue cattle-known for its high degree of muscling, low feed conversion ratio and high beef quality-is highly susceptible for this disease.

RESULTS

In this study, we phenotyped 1975 Belgian Blue cattle from more than 100 different groups on commercial beef farms for their psoroptic mange susceptibility. Substantial individual differences were observed within these management groups, with lesion extent differences up to ± 15%. Animal models showed that estimated heritabilities were low for lesion extent and severe lesion extent (0.07 and 0.09, respectively) and 0.12 for the number of mites. A genome wide association study for mange susceptibility revealed signals on BTA6, BTA11, BTA15 and BTA24. In these regions, candidate genes GBA3, RAG2, and TRAF6 were identified.

CONCLUSIONS

Despite the challenges in phenotyping for psoroptic mange due to the timing of screening, the continuous evolution of lesions and different management conditions, we successfully conducted a study on the genetic susceptibility to psoroptic mange in Belgian Blue cattle. Our results clearly indicate that psoroptic mange is under polygenic control and the underlying candidate genes should be studied more thoroughly. This is the first study providing candidate genes for this complex disease. These results are already valuable for Belgian Blue breeding, however, further research is needed to unravel the architecture of this disease and to identify causal mutations.

Genetic dissection and genomic prediction for pork cuts and carcass morphology traits in pig

BACKGROUND

As pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts.

RESULTS

We identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs.

CONCLUSIONS

We performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs.

High-resolution structural variants catalogue in a large-scale whole genome sequenced bovine family cohort data

BACKGROUND

Structural variants (SVs) are chromosomal segments that differ between genomes, such as deletions, duplications, insertions, inversions and translocations. The genomics revolution enabled the discovery of sub-microscopic SVs via array and whole-genome sequencing (WGS) data, paving the way to unravel the functional impact of SVs. Recent human expression QTL mapping studies demonstrated that SVs play a disproportionally large role in altering gene expression, underlining the importance of including SVs in genetic analyses. Therefore, this study aimed to generate and explore a high-quality bovine SV catalogue exploiting a unique cattle family cohort data (total 266 samples, forming 127 trios).

RESULTS

We curated 13,731 SVs segregating in the population, consisting of 12,201 deletions, 1,509 duplications, and 21 multi-allelic CNVs (> 50-bp). Of these, we validated a subset of copy number variants (CNVs) utilising a direct genotyping approach in an independent cohort, indicating that at least 62% of the CNVs are true variants, segregating in the population. Among gene-disrupting SVs, we prioritised two likely high impact duplications, encompassing ORM1 and POPDC3 genes, respectively. Liver expression QTL mapping results revealed that these duplications are likely causing altered gene expression, confirming the functional importance of SVs. Although most of the accurately genotyped CNVs are tagged by single nucleotide polymorphisms (SNPs) ascertained in WGS data, most CNVs were not captured by individual SNPs obtained from a 50K genotyping array.

CONCLUSION

We generated a high-quality SV catalogue exploiting unique whole genome sequenced bovine family cohort data. Two high impact duplications upregulating the ORM1 and POPDC3 are putative candidates for postpartum feed intake and hoof health traits, thus warranting further investigation. Generally, CNVs were in low LD with SNPs on the 50K array. Hence, it remains crucial to incorporate CNVs via means other than tagging SNPs, such as investigation of tagging haplotypes, direct imputation of CNVs, or direct genotyping as done in the current study. The SV catalogue and the custom genotyping array generated in the current study will serve as valuable resources accelerating utilisation of full spectrum of genetic variants in bovine genomes.

Benchmarking phasing software with a whole-genome sequenced cattle pedigree

Background

Accurate haplotype reconstruction is required in many applications in quantitative and population genomics. Different phasing methods are available but their accuracy must be evaluated for samples with different properties (population structure, marker density, etc.). We herein took advantage of whole-genome sequence data available for a Holstein cattle pedigree containing 264 individuals, including 98 trios, to evaluate several population-based phasing methods. This data represents a typical example of a livestock population, with low effective population size, high levels of relatedness and long-range linkage disequilibrium.

Results

After stringent filtering of our sequence data, we evaluated several population-based phasing programs including one or more versions of AlphaPhase, ShapeIT, Beagle, Eagle and FImpute. To that end we used 98 individuals having both parents sequenced for validation. Their haplotypes reconstructed based on Mendelian segregation rules were considered the gold standard to assess the performance of population-based methods in two scenarios. In the first one, only these 98 individuals were phased, while in the second one, all the 264 sequenced individuals were phased simultaneously, ignoring the pedigree relationships. We assessed phasing accuracy based on switch error counts (SEC) and rates (SER), lengths of correctly phased haplotypes and the probability that there is no phasing error between a pair of SNPs as a function of their distance. For most evaluated metrics or scenarios, the best software was either ShapeIT4.1 or Beagle5.2, both methods resulting in particularly high phasing accuracies. For instance, ShapeIT4.1 achieved a median SEC of 50 per individual and a mean haplotype block length of 24.1 Mb (scenario 2). These statistics are remarkable since the methods were evaluated with a map of 8,400,000 SNPs, and this corresponds to only one switch error every 40,000 phased informative markers. When more relatives were included in the data (scenario 2), FImpute3.0 reconstructed extremely long segments without errors.

Conclusions

We report extremely high phasing accuracies in a typical livestock sample. ShapeIT4.1 and Beagle5.2 proved to be the most accurate, particularly for phasing long segments and in the first scenario. Nevertheless, most tools achieved high accuracy at short distances and would be suitable for applications requiring only local haplotypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08354-6.

Genome-wide association studies for production, respiratory disease, and immune-related traits in Landrace pigs

Identification of a quantitative trait locus (QTL) related to a chronic respiratory disease such as Mycoplasmal pneumonia of swine (MPS) and immune-related traits is important for the genetic improvement of disease resistance in pigs. The objective of this study was to detect a novel QTL for a total of 22 production, respiratory disease, and immune-related traits in Landrace pigs. A total of 874 Landrace purebred pigs, which were selected based on MPS resistance, were genotyped using the Illumina PorcineSNP60 BeadChip. We performed single nucleotide polymorphism (SNP)-based and haplotype-based genome-wide association studies (GWAS) to detect a novel QTL and to evaluate the possibility of a pleiotropic QTL for these traits. SNP-based GWAS detected a total of six significant regions in backfat thickness, ratio of granular leucocytes to lymphatic cells, plasma concentration of cortisol at different ages, and complement alternative pathway activity in serum. The significant region detected by haplotype-based GWAS was overlapped across the region detected by SNP-based GWAS. Most of these detected QTL regions were novel regions with some candidate genes located in them. With regard to a pleiotropic QTL among traits, only three of these detected QTL regions overlapped among traits, and many detected regions independently affected the traits.

A 12 kb multi-allelic copy number variation encompassing a GC gene enhancer is associated with mastitis resistance in dairy cattle

Clinical mastitis (CM) is an inflammatory disease occurring in the mammary glands of lactating cows. CM is under genetic control, and a prominent CM resistance QTL located on chromosome 6 was reported in various dairy cattle breeds. Nevertheless, the biological mechanism underpinning this QTL has been lacking. Herein, we mapped, fine-mapped, and discovered the putative causal variant underlying this CM resistance QTL in the Dutch dairy cattle population. We identified a ~12 kb multi-allelic copy number variant (CNV), that is in perfect linkage disequilibrium with a lead SNP, as a promising candidate variant. By implementing a fine-mapping and through expression QTL mapping, we showed that the group-specific component gene (GC), a gene encoding a vitamin D binding protein, is an excellent candidate causal gene for the QTL. The multiplicated alleles are associated with increased GC expression and low CM resistance. Ample evidence from functional genomics data supports the presence of an enhancer within this CNV, which would exert cis-regulatory effect on GC. We observed that strong positive selection swept the region near the CNV, and haplotypes associated with the multiplicated allele were strongly selected for. Moreover, the multiplicated allele showed pleiotropic effects for increased milk yield and reduced fertility, hinting that a shared underlying biology for these effects may revolve around the vitamin D pathway. These findings together suggest a putative causal variant of a CM resistance QTL, where a cis-regulatory element located within a CNV can alter gene expression and affect multiple economically important traits.

Tracing founder haplotypes of Japanese apple varieties: application in genomic prediction and genome-wide association study

Haplotypes provide useful information for genomics-based approaches, genomic prediction, and genome-wide association study. As a small number of superior founders have contributed largely to the breeding history of fruit trees, the information of founder haplotypes may be relevant for performing the genomics-based approaches in these plants. In this study, we proposed a method to estimate 14 haplotypes from 7 founders and automatically trace the haplotypes forward to apple parental (185 varieties) and breeding (659 F1 individuals from 16 full-sib families) populations based on 11,786 single-nucleotide polymorphisms, by combining multiple algorithms. Overall, 92% of the single-nucleotide polymorphisms information in the parental and breeding populations was characterized by the 14 founder haplotypes. The use of founder haplotype information improved the accuracy of genomic prediction in 7 traits and the resolution of genome-wide association study in 13 out of 27 fruit quality traits analyzed in this study. We also visualized the significant propagation of the founder haplotype with the largest genetic effect in genome-wide association study over the pedigree tree of the parental population. These results suggest that the information of founder haplotypes can be useful for not only genetic improvement of fruit quality traits in apples but also for understanding the selection history of founder haplotypes in the breeding program of Japanese apple varieties.

Maternal effect on body measurement and meat production traits in purebred Duroc pigs

We investigated maternal effect on nine body measurement traits (body height, body length, front width (FW), chest width (CW), hind width (HW), chest depth, chest girth (CHG), front cannon circumference (FCC) and rear cannon circumference (RCC)) measured at the end of performance testing and five meat production traits (ages at the start and end of performance testing (D30 and D105), average daily gain (ADG), backfat thickness and loin muscle area) in purebred Duroc pigs. Genetic parameters for each trait were estimated by using six single-trait models with and without common litter environmental effect, maternal genetic effect and direct-maternal genetic correlation. The value of Akaike's information criterion was lowest with the model including direct additive genetic and common litter environmental effects for 10 traits. The estimated proportion of common litter environmental variance to phenotypic variance was approximately ≥0.1 for D30, D105, ADG, FW, CW, HW, CHG, FCC and RCC. Using a model without common litter environmental effect would overestimate the direct heritability of most traits. Standard errors of estimated genetic parameters tended to be larger in models including maternal genetic effect. The results indicate that a compromise could be made for accurate genetic parameter estimation for body measurement traits, as well as meat production traits, in pigs by considering common litter environmental effect.

Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle

BACKGROUND

Genomic selection has been successfully implemented in many livestock and crop species. The genomic best linear unbiased predictor (GBLUP) approach, assigning equal variance to all SNP effects, is one of the reference methods. When large-effect variants contribute to complex traits, it has been shown that genomic prediction methods that assign a higher variance to subsets of SNP effects can achieve higher prediction accuracy. We herein compared the efficiency of several such approaches, including the Adaptive MultiBLUP (AM-BLUP) that uses local genomic relationship matrices (GRM) to automatically identify and weight genomic regions with large effects, to predict genetic merit in Belgian Blue beef cattle.

RESULTS

We used a population of approximately 10,000 genotyped cows and their phenotypes for 14 traits, mostly related to muscular development and body dimensions. According to the trait, we found that 4 to 25% of the genetic variance could be associated with 2 to 12 genomic regions harbouring large-effect variants. Noteworthy, three previously identified recessive deleterious variants presented heterozygote advantage and were among the most significant SNPs for several traits. The AM-BLUP resulted in increased reliability of genomic predictions compared to GBLUP (+ 2%), but Bayesian methods proved more efficient (+ 3%). Overall, the reliability gains remained thus limited although higher gains were observed for skin thickness, a trait affected by two genomic regions having particularly large effects. Higher accuracies than those from the original AM-BLUP were achieved when applying the Bayesian Sparse Linear Mixed Model to pre-select groups of SNPs with large effects and subsequently use their estimated variance to build a weighted GRM. Finally, the single-step GBLUP performed best and could be further improved (+ 3% prediction accuracy) by using these weighted GRM.

CONCLUSIONS

The AM-BLUP is an attractive method to automatically identify and weight genomic regions with large effects on complex traits. However, the method was less accurate than Bayesian methods. Overall, weighted methods achieved modest accuracy gains compared to GBLUP. Nevertheless, the computational efficiency of the AM-BLUP might be valuable at higher marker density, including with whole-genome sequencing data. Furthermore, weighted GRM are particularly useful to account for large variance loci in the single-step GBLUP.

Genome-Wide Association Study Using Individual Single-Nucleotide Polymorphisms and Haplotypes for Erythrocyte Traits in Alpine Merino Sheep

Adaptation to high-altitude hypoxia is essential for domestic animals, such as yak, Tibetan chicken, and Tibetan sheep, living on high plateaus, as it ensures efficient oxygen absorption and utilization. Red blood cells are the primary medium for transporting oxygen in the blood. However, little is known about the genetic mechanism of erythrocyte traits. Genome-wide association studies (GWASs) based on single markers or haplotypes have identified potential mechanisms for genetic variation and quantitative traits. To identify loci associated with erythrocyte traits, we performed a GWAS based on the method of the single marker and haplotype in 498 Alpine Merino sheep for six erythrocyte traits: red blood cell count (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and RBC volume distribution width coefficient of variation (RWD_CV). Forty-two significant single-nucleotide polymorphisms (SNPs) associated with the six erythrocyte traits were detected by means of a single-marker GWAS, and 34 significant haplotypes associated with five erythrocyte traits were detected by means of haplotype analysis. We identified six genes (DHCR24, SPATA9, FLI1, PLCB1, EFNB2, and SH2B3) as potential genes of interest via gene function annotations, location, and expression variation. In particular, FLI1 and PLCB1 were associated with hematopoiesis and erythropoiesis, respectively. These results provide a theoretical basis for analyzing erythrocyte traits and high-altitude hypoxia adaptation in Alpine Merino sheep and will be a useful reference for future studies of plateau-dwelling livestock.

Genetic architecture of individual variation in recombination rate on the X chromosome in cattle

Meiotic recombination is an essential biological process that ensures proper chromosome segregation and creates genetic diversity. Individual variation in global recombination rates has been shown to be heritable in several species, and variants significantly associated with this trait have been identified. Recombination on the sex chromosome has often been ignored in these studies although this trait may be particularly interesting as it may correspond to a biological process distinct from that on autosomes. For instance, recombination in males is restricted to the pseudo-autosomal region (PAR). We herein used a large cattle pedigree with more than 100,000 genotyped animals to improve the genetic map of the X chromosome and to study the genetic architecture of individual variation in recombination rate on the sex chromosome (XRR). The length of the genetic map was 46.4 and 121.2 cM in males and females, respectively, but the recombination rate in the PAR was six times higher in males. The heritability of CO counts on the X chromosome was comparable to that of autosomes in males (0.011) but larger than that of autosomes in females (0.024). XRR was highly correlated (0.76) with global recombination rate (GRR) in females, suggesting that both traits might be governed by shared variants. In agreement, a set of eleven previously identified variants associated with GRR had correlated effects on female XRR (0.86). In males, XRR and GRR appeared to be distinct traits, although more accurate CO counts on the PAR would be valuable to confirm these results.

RAINBOW: Haplotype-based genome-wide association study using a novel SNP-set method

Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named "RAINBOW" and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named "RAINBOWR" and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).

Fine mapping of genomic regions associated with female fertility in Nellore beef cattle based on sequence variants from segregating sires

BACKGROUND

Impaired fertility in cattle limits the efficiency of livestock production systems. Unraveling the genetic architecture of fertility traits would facilitate their improvement by selection. In this study, we characterized SNP chip haplotypes at QTL blocks then used whole-genome sequencing to fine map genomic regions associated with reproduction in a population of Nellore (Bos indicus) heifers.

METHODS

The dataset comprised of 1337 heifers genotyped using a GeneSeek® Genomic Profiler panel (74677 SNPs), representing the daughters from 78 sires. After performing marker quality control, 64800 SNPs were retained. Haplotypes carried by each sire at six previously identified QTL on BTAs 5, 14 and 18 for heifer pregnancy and BTAs 8, 11 and 22 for antral follicle count were constructed using findhap software. The significance of the contrasts between the effects of every two paternally-inherited haplotype alleles were used to identify sires that were heterozygous at each QTL. Whole-genome sequencing data localized to the haplotypes from six sires and 20 other ancestors were used to identify sequence variants that were concordant with the haplotype contrasts. Enrichment analyses were applied to these variants using KEGG and MeSH libraries.

RESULTS

A total of six (BTA 5), six (BTA 14) and five (BTA 18) sires were heterozygous for heifer pregnancy QTL whereas six (BTA 8), fourteen (BTA 11), and five (BTA 22) sires were heterozygous for number of antral follicles' QTL. Due to inadequate representation of many haplotype alleles in the sequenced animals, fine mapping analysis could only be reliably performed for the QTL on BTA 5 and 14, which had 641 and 3733 concordant candidate sequence variants, respectively. The KEGG "Circadian rhythm" and "Neurotrophin signaling pathway" were significantly associated with the genes in the QTL on BTA 5 whereas 32 MeSH terms were associated with the QTL on BTA 14. Among the concordant sequence variants, 0.2% and 0.3% were classified as missense variants for BTAs 5 and 14, respectively, highlighting the genes MTERF2, RTMB, ENSBTAG00000037306 (miRNA), ENSBTAG00000040351, PRKDC, and RGS20. The potential causal mutations found in the present study were associated with biological processes such as oocyte maturation, embryo development, placenta development and response to reproductive hormones.

CONCLUSIONS

The identification of heterozygous sires by positionally phasing SNP chip data and contrasting haplotype effects for previously detected QTL can be used for fine mapping to identify potential causal mutations and candidate genes. Genomic variants on genes MTERF2, RTBC, miRNA ENSBTAG00000037306, ENSBTAG00000040351, PRKDC, and RGS20, which are known to have influence on reproductive biological processes, were detected.

Phylogenetic Origin of Primary and Secondary Metabolic Pathway Genes Revealed by C. maxima and C. reticulata Diagnostic SNPs

Modern cultivated Citrus species and varieties result from interspecific hybridization between four ancestral taxa. Among them, Citrus maxima and Citrus reticulata, closely associated with the pummelo and mandarin horticultural groups, respectively, were particularly important as the progenitors of sour and sweet oranges (Citrus aurantium and Citrus sinensis), grapefruits (Citrus paradisi), and hybrid types resulting from modern breeding programs (tangors, tangelos, and orangelos). The differentiation between the four ancestral taxa and the phylogenomic structure of modern varieties widely drive the phenotypic diversity's organization. In particular, strong phenotypic differences exist in the coloration and sweetness and represent important criteria for breeders. In this context, focusing on the genes of the sugar, carotenoid, and chlorophyll biosynthesis pathways, the aim of this work was to develop a set of diagnostic single-nucleotide polymorphism (SNP) markers to distinguish the ancestral haplotypes of C. maxima and C. reticulata and to provide information at the intraspecific diversity level (within C. reticulata or C. maxima). In silico analysis allowed the identification of 3,347 SNPs from selected genes. Among them, 1,024 were detected as potential differentiation markers between C. reticulata and C. maxima. A total of 115 SNPs were successfully developed using a competitive PCR technology. Their transferability among all Citrus species and the true citrus genera was very good, with only 0.87% of missing data. The ancestral alleles of the SNPs were identified, and we validated the usefulness of the developed markers for tracing the ancestral haplotype in large germplasm collections and sexually recombined progeny issued from the C. reticulata/C. maxima admixture gene pool. These markers will pave the way for targeted association studies based on ancestral haplotypes.

Rediscover and Refine QTLs for Pig Scrotal Hernia by Increasing a Specially Designed F3 Population and Using Whole-Genome Sequence Imputation Technology

Pig scrotal hernia is one of the most common congenital defects triggered by both genetic and environmental factors, leading to severe economic loss as well as poor animal welfare in the pig industry. Identification and implementation of genomic regions controlling scrotal hernia in breeding is of great appeal to reduce incidences of hernia in pig production. The aim of this study was to identify such regions or molecular markers affecting scrotal hernia in pigs. First of all, we summarized and analyzed the results of some international teams on scrotal hernia and designed a specially population which contains 246 male individuals. We then performed genome-wide association study (GWAS) in this specially designed population using two scenarios, i.e., the target panel data before and after imputation, which contain 42,365 SNPs and 18,756,672 SNPs, respectively. In addition, a series of methods including genetic differentiation analysis, linkage disequilibrium and linkage analysis (LDLA), and haplotype sharing analysis were appropriate to provide for further analysis to identify the potential gene underlying the QTL. The GWAS in this report detected a highly significant region affecting scrotal hernia within a 24.8Mb region (114.1-138.9Mb) on SSC8. And the result of genetic differentiation analysis also showed a strong genetic differentiation signal between 116.1 and 132.7Mb on SSC8. In addition, the QTL interval was refined to 2.99Mb by combining LDLA and genetic differentiation analysis. Finally, two susceptibility haplotypes were identified through haplotype sharing analysis, with one potential causal gene in it. Our study provided deeper insights into the genetic architecture of pig scrotal hernia and contributed to further fine-mapping and characterize haplotype and gene that influence scrotal hernia in pigs.

Haplotype Heritability Mapping Method Uncovers Missing Heritability of Complex Traits

We propose a novel approach to analyze genomic data that incorporates haplotype information for detecting rare variants within a regional heritability mapping framework. The performance of our approach was tested in a simulation study based on human genotypes. The phenotypes were simulated by generating regional variance using either SNP(s) or haplotype(s). Regional genomic relationship matrices, constructed with either a SNP-based or a haplotype-based estimator, were employed to estimate the regional variance. The results from the study show that haplotype heritability mapping captures the regional effect, with its relative performance decreasing with increasing analysis window size. The SNP-based regional mapping approach often misses the effect of causal haplotype(s); however, it has a greater power to detect simulated SNP-based-variants. Heritability estimates suggest that the haplotype heritability mapping estimates the simulated regional heritability accurately for all phenotypes and analysis windows. However, the SNP-based analysis overestimates the regional heritability and performs less well than our haplotype-based approach for the simulated rare haplotype-based-variant. We conclude that haplotype heritability mapping is a useful tool to capture the effect of rare variants, and explain a proportion of the missing heritability.

Age-based partitioning of individual genomic inbreeding levels in Belgian Blue cattle

Background

Inbreeding coefficients can be estimated either from pedigree data or from genomic data, and with genomic data, they are either global or local (when the linkage map is used). Recently, we developed a new hidden Markov model (HMM) that estimates probabilities of homozygosity-by-descent (HBD) at each marker position and automatically partitions autozygosity in multiple age-related classes (based on the length of HBD segments). Our objectives were to: (1) characterize inbreeding with our model in an intensively selected population such as the Belgian Blue Beef (BBB) cattle breed; (2) compare the properties of the model at different marker densities; and (3) compare our model with other methods.

Results

When using 600 K single nucleotide polymorphisms (SNPs), the inbreeding coefficient (probability of sampling an HBD locus in an individual) was on average 0.303 (ranging from 0.258 to 0.375). HBD-classes associated to historical ancestors (with small segments ≤ 200 kb) accounted for 21.6% of the genome length (71.4% of the total length of the genome in HBD segments), whereas classes associated to more recent ancestors accounted for only 22.6% of the total length of the genome in HBD segments. However, these recent classes presented more individual variation than more ancient classes. Although inbreeding coefficients obtained with low SNP densities (7 and 32 K) were much lower (0.060 and 0.093), they were highly correlated with those obtained at higher density (r = 0.934 and 0.975, respectively), indicating that they captured most of the individual variation. At higher SNP density, smaller HBD segments are identified and, thus, more past generations can be explored. We observed very high correlations between our estimates and those based on homozygosity (r = 0.95) or on runs-of-homozygosity (r = 0.95). As expected, pedigree-based estimates were mainly correlated with recent HBD-classes (r = 0.56).

Conclusions

Although we observed high levels of autozygosity associated with small HBD segments in BBB cattle, recent inbreeding accounted for most of the individual variation. Recent autozygosity can be captured efficiently with low-density SNP arrays and relatively simple models (e.g., two HBD classes). The HMM framework provides local HBD probabilities that are still useful at lower SNP densities.

Electronic supplementary material

The online version of this article (10.1186/s12711-017-0370-x) contains supplementary material, which is available to authorized users.

Genomic evaluation using SNP- and haplotype-based genomic relationship matrices in a closed line of Duroc pigs

A simulation analysis and real phenotype analysis were performed to evaluate the impact of three different relationship matrices on heritability estimation and prediction accuracy in a closed-line breeding of Duroc pigs. The numerator relationship matrix (NRM), single nucleotide polymorphism (SNP)-based genomic relationship matrix (GRM) (G_S ), and haplotype-based GRM (G_H ) were applied in this study. We used PorcineSNP60 genotype array data (38 114 SNPs) of 831 Duroc pigs with four selection traits. In both heritability estimation and prediction accuracy, the accuracy depended on the number of animals with records. For heritability estimation, a large difference in the results among three relationship matrices was not shown, but the trend of the estimated heritabilities between GRMs, that is G_S  < G_H , was shown in this population. For the accuracy of prediction values in test animals, the accuracies of prediction values obtained by two GRMs were higher than that by the NRM in this population. The accuracies obtained by GRMs using animals with no records were lower than that by the NRM using animals with their performance records, but were close to that by the NRM using animals with full-sib testing records.

A strategy to improve phasing of whole-genome sequenced individuals through integration of familial information from dense genotype panels

Background

Haplotype reconstruction (phasing) is an essential step in many applications, including imputation and genomic selection. The best phasing methods rely on both familial and linkage disequilibrium (LD) information. With whole-genome sequence (WGS) data, relatively small samples of reference individuals are generally sequenced due to prohibitive sequencing costs, thus only a limited amount of familial information is available. However, reference individuals have many relatives that have been genotyped (at lower density). The goal of our study was to improve phasing of WGS data by integrating familial information from haplotypes that were obtained from a larger genotyped dataset and to quantify its impact on imputation accuracy.

Results

Aligning a pre-phased WGS panel [~5 million single nucleotide polymorphisms (SNPs)], which is based on LD information only, to a 50k SNP array that is phased with both LD and familial information (called scaffold) resulted in correctly assigning parental origin for 99.62% of the WGS SNPs, their phase being determined unambiguously based on parental genotypes. Without using the 50k haplotypes as scaffold, that value dropped as expected to 50%. Correctly phased segments were on average longer after alignment to the genotype phase while the number of switches decreased slightly. Most of the incorrectly assigned segments, and subsequent switches, were due to singleton errors. Imputation from 50k SNP array to WGS data with improved phasing had a marginal impact on imputation accuracy (measured as r²), i.e. on average, 90.47% with traditional techniques versus 90.65% with pre-phasing integrating familial information. Differences were larger for SNPs located in chromosome ends and rare variants. Using a denser WGS panel (~13 millions SNPs) that was obtained with traditional variant filtering rules, we found similar results although performances of both phasing and imputation accuracy were lower.

Conclusions

We present a phasing strategy for WGS data, which indirectly integrates familial information by aligning WGS haplotypes that are pre-phased with LD information only on haplotypes obtained with genotyping data, with both LD and familial information and on a much larger population. This strategy results in very few mismatches with the phase obtained by Mendelian segregation rules. Finally, we propose a strategy to further improve phasing accuracy based on haplotype clusters obtained with genotyping data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0321-6) contains supplementary material, which is available to authorized users.

Genomewide association studies for hematological traits and T lymphocyte subpopulations in a Duroc × Erhualian F resource population

It has been shown that hematological traits can act as important indicators of immune function in both humans and livestock. T lymphocytes are key components of the adaptive immune system, playing a critical role in immune response. To identify genomic regions affecting hematological traits and T lymphocyte subpopulations, we performed both a SNP-based genomewide association study (GWAS) and a haplotype analysis for 20 hematological traits and 8 T cell subpopulations at 3 different time points (d 20, 33, and 35) in a Duroc × Erhualian F intercross population. Bonferroni correction was used to calculate the threshold -values for suggestive and 5% genomewide significance levels. In total, for SNP-based GWAS, we detected 96 significant SNP, including 15 genomewide-significant SNP, associated with 23 hematological traits and 234 significant SNP, including 27 genomewide-significant SNP, associated with 8 T cell subpopulations. Meanwhile, we identified 563 significant SNP, including 7 genomewide-significant SNP, associated with 5 hematological traits and 2,407 significant SNP, including 1,261 genomewide-significant SNP, associated with 8 T cell subpopulations by haplotype analysis. Among the significant regions detected, we propose both the () gene and the () gene on SSC3 as plausible candidate genes associated with CD/CD T lymphocytes at d 20. The findings provide insights into the basis of molecular mechanisms that are involved with immune response in the domestic pig and would aid further identification of causative mutations.

Genome-wide Association Studies in Maize: Praise and Stargaze

Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype-phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.

A practical approach to detect ancestral haplotypes in livestock populations

Background

The effects of different evolutionary forces are expected to lead to the conservation, over many generations, of particular genomic regions (haplotypes) due to the development of linkage disequilibrium (LD). The detection and identification of early (ancestral) haplotypes can be used to clarify the evolutionary dynamics of different populations as well as identify selection signatures and genomic regions of interest to be used both in conservation and breeding programs. The aims of this study were to develop a simple procedure to identify ancestral haplotypes segregating across several generations both within and between populations with genetic links based on whole-genome scanning. This procedure was tested with simulated and then applied to real data from different genotyped populations of Spanish, Fleckvieh, Simmental and Brown-Swiss cattle.

Results

The identification of ancestral haplotypes has shown coincident patterns of selection across different breeds, allowing the detection of common regions of interest on different bovine chromosomes and mirroring the evolutionary dynamics of the studied populations. These regions, mainly located on chromosomes BTA5, BTA6, BTA7 and BTA21 are related with certain animal traits such as coat colour and milk protein and fat content.

Conclusion

In agreement with previous studies, the detection of ancestral haplotypes provides useful information for the development and comparison of breeding and conservation programs both through the identification of selection signatures and other regions of interest, and as indicator of the general genetic status of the populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0405-2) contains supplementary material, which is available to authorized users.

SNP- and haplotype-based genome-wide association studies for growth, carcass, and meat quality traits in a Duroc multigenerational population

Background

The aim of the present study was to compare the power of single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) and haplotype-based GWAS for quantitative trait loci (QTL) detection, and to detect novel candidate genes affecting economically important traits in a purebred Duroc population comprising seven-generation pedigree. First, we performed a simulation analysis using real genotype data of this population to compare the power (based on the null hypothesis) of the two methods. We then performed GWAS using both methods and real phenotype data comprising 52 traits, which included growth, carcass, and meat quality traits.

Results

In total, 836 animals were genotyped using the Illumina PorcineSNP60 BeadChip and 14 customized SNPs from regions of known candidate genes related to the traits of interest. The power of SNP-based GWAS was greater than that of haplotype-based GWAS in a simulation analysis. In real data analysis, a larger number of significant regions was obtained by SNP-based GWAS than by haplotype-based GWAS. For SNP-based GWAS, 23 genome-wide significant SNP regions were detected for 17 traits, and 120 genome-wide suggestive SNP regions were detected for 27 traits. For haplotype-based GWAS, 6 genome-wide significant SNP regions were detected for four traits, and 11 genome-wide suggestive SNP regions were detected for eight traits. All genome-wide significant SNP regions detected by haplotype-based GWAS were located in regions also detected by SNP-based GWAS. Four regions detected by SNP-based GWAS were significantly associated with multiple traits: on Sus scrofa chromosome (SSC) 1 at 304 Mb; and on SSC7 at 35–39 Mb, 41–42 Mb, and 103 Mb. The vertnin gene (VRTN) in particular, was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths. Mapped QTL regions contain some candidate genes involved in skeletal formation (FUBP3; far upstream element binding protein 3) and fat deposition (METTL3; methyltransferase like 3).

Conclusion

Our results show that a multigenerational pig population is useful for detecting QTL, which are typically segregated in a purebred population. In addition, a novel significant region could be detected by SNP-based GWAS as opposed to haplotype-based GWAS.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0368-3) contains supplementary material, which is available to authorized users.

Genome-Wide Association Analysis for Blood Lipid Traits Measured in Three Pig Populations Reveals a Substantial Level of Genetic Heterogeneity

Serum lipids are associated with myocardial infarction and cardiovascular disease in humans. Here we dissected the genetic architecture of blood lipid traits by applying genome-wide association studies (GWAS) in 1,256 pigs from Laiwu, Erhualian and Duroc × (Landrace × Yorkshire) populations, and a meta-analysis of GWAS in more than 2,400 pigs from five diverse populations. A total of 22 genomic loci surpassing the suggestive significance level were detected on 11 pig chromosomes (SSC) for six blood lipid traits. Meta-analysis of GWAS identified 5 novel loci associated with blood lipid traits. Comparison of GWAS loci across the tested populations revealed a substantial level of genetic heterogeneity for porcine blood lipid levels. We further evaluated the causality of nine polymorphisms nearby or within the APOB gene on SSC3 for serum LDL-C and TC levels. Of the 9 polymorphisms, an indel showed the most significant association with LDL-C and TC in Laiwu pigs. But the significant association was not identified in the White Duroc × Erhualian F2 resource population, in which the QTL for LDL-C and TC was also detected on SSC3. This indicates that population-specific signals may exist for the SSC3 QTL. Further investigations are warranted to validate this assumption.

Genome-wide next-generation DNA and RNA sequencing reveals a mutation that perturbs splicing of the phosphatidylinositol glycan anchor biosynthesis class H gene (PIGH) and causes arthrogryposis in Belgian Blue cattle

BACKGROUND

Cattle populations are characterized by regular outburst of genetic defects as a result of the extensive use of elite sires. The causative genes and mutations can nowadays be rapidly identified by means of genome-wide association studies combined with next generation DNA sequencing, provided that the causative mutations are conventional loss-of-function variants. We show in this work how the combined use of next generation DNA and RNA sequencing allows for the rapid identification of otherwise difficult to identify splice-site variants.

RESULTS

We report the use of haplotype-based association mapping to identify a locus on bovine chromosome 10 that underlies autosomal recessive arthrogryposis in Belgian Blue Cattle. We identify 31 candidate mutations by resequencing the genome of four cases and 15 controls at ~10-fold depth. By analyzing RNA-Seq data from a carrier fetus, we observe skipping of the second exon of the PIGH gene, which we confirm by RT-PCR to be fully penetrant in tissues from affected calves. We identify - amongst the 31 candidate variants - a C-to-G transversion in the first intron of the PIGH gene (c211-10C > G) that is predicted to affect its acceptor splice-site. The resulting PIGH protein is likely to be non-functional as it lacks essential domains, and hence to cause arthrogryposis.

CONCLUSIONS

This work illustrates how the growing arsenal of genome exploration tools continues to accelerate the identification of an even broader range of disease causing mutations, therefore improving the management and control of genetic defects in livestock.

Genome-wide association analyses reveal significant loci and strong candidate genes for growth and fatness traits in two pig populations

BACKGROUND

Recently, genome-wide association studies (GWAS) have been reported on various pig traits. We performed a GWAS to analyze 22 traits related to growth and fatness on two pig populations: a White Duroc × Erhualian F2 intercross population and a Chinese Sutai half-sib population.

RESULTS

We identified 14 and 39 loci that displayed significant associations with growth and fatness traits at the genome-wide level and chromosome-wide level, respectively. The strongest association was between a 750 kb region on SSC7 (SSC for Sus scrofa) and backfat thickness at the first rib. This region had pleiotropic effects on both fatness and growth traits in F2 animals and contained a promising candidate gene HMGA1 (high mobility group AT-hook 1). Unexpectedly, population genetic analysis revealed that the allele at this locus that reduces fatness and increases growth is derived from Chinese indigenous pigs and segregates in multiple Chinese breeds. The second strongest association was between the region around 82.85 Mb on SSC4 and average backfat thickness. PLAG1 (pleiomorphic adenoma gene 1), a gene under strong selection in European domestic pigs, is proximal to the top SNP and stands out as a strong candidate gene. On SSC2, a locus that significantly affects fatness traits mapped to the region around the IGF2 (insulin-like growth factor 2) gene but its non-imprinting inheritance excluded IGF2 as a candidate gene. A significant locus was also detected within a recombination cold spot that spans more than 30 Mb on SSCX, which hampered the identification of plausible candidate genes. Notably, no genome-wide significant locus was shared by the two experimental populations; different loci were observed that had both constant and time-specific effects on growth traits at different stages, which illustrates the complex genetic architecture of these traits.

CONCLUSIONS

We confirm several previously reported QTL and provide a list of novel loci for porcine growth and fatness traits in two experimental populations with Chinese Taihu and Western pigs as common founders. We showed that distinct loci exist for these traits in the two populations and identified HMGA1 and PLAG1 as strong candidate genes on SSC7 and SSC4, respectively.

A genome-wide association study reveals a novel candidate gene for sperm motility in pigs

Sperm motility is one of the most widely used parameters in order to evaluate boar semen quality. However, this trait can only be measured after puberty. Thus, the use of genomic information appears as an appealing alternative to evaluate and improve selection for boar fertility traits earlier in life. With this study we aimed to identify SNPs with significant association with sperm motility in two different commercial pig populations and to identify possible candidate genes within the identified QTL regions. We performed a single-SNP genome-wide association study using genotyped animals from a Landrace-based (L1) and a Large White-based (L2) pig populations. For L1, a total of 602 animals genotyped for 42,551 SNPs were used in the association analysis. For L2, a total of 525 animals genotyped for 40,890 SNPs were available. After the association analysis, a false discovery rate q-value ≤0.05 was used as the threshold for significant association. No SNPs were significantly associated with sperm motility in L1, while six SNPs on Sus scrofa chromosome 1 (position 117.26-119.56Mb) were significant in L2. The mitochondrial methionyl-tRNA formyltransferase (MTFMT) gene, which affects translation efficiency of proteins in sperm cells, was identified as a putative candidate gene. The significant markers identified in this study may be useful to enhance the genetic improvement of sperm motility by selection of boars at an earlier age under a marker assisted selection strategy.

Evaluation of the causality of the low-density lipoprotein receptor gene (LDLR) for serum lipids in pigs

A significant quantitative trait locus (QTL) for low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) was identified around the LDLR gene on chromosome 2 (SSC2) in a White Duroc × Erhualian F2 resource population and Sutai pigs in our previous study. However, in previous reports, the causality of LDLR with serum lipids is controversial in pigs. To systematically assess the causality of LDLR with serum lipids, association analyses were successively performed in three populations: Sutai pigs, a White Duroc × Erhualian F2 resource population and a Duroc × (Landrace × Large White) population. We first performed a haplotype-based association study with 60K SNP genotyping data and evidenced the significant association with LDL-C and TC around the LDLR gene region. We also found that there is more than one QTL for LDL-C and TC on SSC2. Then, we evaluated the causalities of two missense mutations, c.1812C>T and c.1520A>G, with LDL-C and TC. We revealed that the c.1812C>T SNP showed the strongest association with LDL-C (P = 5.40 × 10(-11) ) and TC (P = 3.64 × 10(-8) ) and explained all the QTL effect in Sutai pigs. Haplotype analysis found that two missense SNPs locate within a 1.93-Mb haplotype block. One major haplotype showed the strongest significant association with LDL-C (P = 4.62 × 10(-18) ) and TC (P = 1.06 × 10(-9) ). However, the c.1812C>T SNP was not identified in the White Duroc × Erhualian intercross, and the association of c.1520A>G with both LDL-C and TC did not achieve significance in this F2 population, suggesting population heterogeneity. Both missense mutations were identified in the Duroc × (Landrace × Large White) population and showed significant associations with LDL-C and TC. Our data give evidence that the LDLR gene should be a candidate causative gene for LDL-C and TC in pigs, but heterogeneity exists in different populations.

Genome-wide association studies for hematological traits in Chinese Sutai pigs

Background

It has been shown that hematological traits are strongly associated with the metabolism and the immune system in domestic pig. However, little is known about the genetic architecture of hematological traits. To identify quantitative trait loci (QTL) controlling hematological traits, we performed single marker Genome-wide association studies (GWAS) and haplotype analysis for 15 hematological traits in 495 Chinese Sutai pigs.

Results

We identified 161 significant SNPs including 44 genome-wide significant SNPs associated with 11 hematological traits by single marker GWAS. Most of them were located on SSC2. Meanwhile, we detected 499 significant SNPs containing 154 genome-wide significant SNPs associated with 9 hematological traits by haplotype analysis. Most of the identified loci were located on SSC7 and SSC9.

Conclusions

We detected 4 SNPs with pleiotropic effects on SSC2 by single marker GWAS and (or) on SSC7 by haplotype analysis. Furthermore, through checking the gene functional annotations, positions and their expression variation, we finally selected 7 genes as potential candidates. Specially, we found that three genes (TRIM58, TRIM26 and TRIM21) of them originated from the same gene family and executed similar function of innate and adaptive immune. The findings will contribute to dissection the immune gene network, further identification of causative mutations underlying the identified QTLs and providing insights into the molecular basis of hematological trait in domestic pig.

A missense mutation accelerating the gating of the lysosomal Cl-/H+-exchanger ClC-7/Ostm1 causes osteopetrosis with gingival hamartomas in cattle

Chloride-proton exchange by the lysosomal anion transporter ClC-7/Ostm1 is of pivotal importance for the physiology of lysosomes and bone resorption. Mice lacking either ClC-7 or Ostm1 develop a lysosomal storage disease and mutations in either protein have been found to underlie osteopetrosis in mice and humans. Some human disease-causing CLCN7 mutations accelerate the usually slow voltage-dependent gating of ClC-7/Ostm1. However, it has remained unclear whether the fastened kinetics is indeed causative for the disease. Here we identified and characterized a new deleterious ClC-7 mutation in Belgian Blue cattle with a severe symptomatology including perinatal lethality and in most cases gingival hamartomas. By autozygosity mapping and genome-wide sequencing we found a handful of candidate variants, including a cluster of three private SNPs causing the substitution of a conserved tyrosine in the CBS2 domain of ClC-7 by glutamine. The case for ClC-7 was strengthened by subsequent examination of affected calves that revealed severe osteopetrosis. The Y750Q mutation largely preserved the lysosomal localization and assembly of ClC-7/Ostm1, but drastically accelerated its activation by membrane depolarization. These data provide first evidence that accelerated ClC-7/Ostm1 gating per se is deleterious, highlighting a physiological importance of the slow voltage-activation of ClC-7/Ostm1 in lysosomal function and bone resorption.

Use of ancestral haplotypes in genome-wide association studies

We herein present a haplotype-based method to perform genome-wide association studies. The method relies on hidden Markov models to describe haplotypes from a population as a mosaic of a set of ancestral haplotypes. For a given position in the genome, haplotypes deriving from the same ancestral haplotype are also likely to carry the same risk alleles. Therefore, the model can be used in several applications such as haplotype reconstruction, imputation, association studies or genomic predictions. We illustrate then the model with two applications: the fine-mapping of a QTL affecting live weight in cattle and association studies in a stratified cattle population. Both applications show the potential of the method and the high linkage disequilibrium between ancestral haplotypes and causative variants.