Imputation-Based Whole-Genome Sequence Association Study Rediscovered the Missing QTL for Lumbar Number in Sutai Pigs

Whole-Genome Resequencing of Ujimqin Sheep Identifies Genes Associated with Vertebral Number

The number of vertebrae is a crucial economic trait that can significantly impact the carcass length and meat production in animals. However, our understanding of the quantitative trait loci (QTLs) and candidate genes associated with the vertebral number in sheep (Ovis aries) remains limited. To identify these candidate genes and QTLs, we collected 73 Ujimqin sheep with increased numbers of vertebrae (T13L7, T14L6, and T14L7) and 23 sheep with normal numbers of vertebrae (T13L6). Through high-throughput genome resequencing, we obtained a total of 24,130,801 effective single-nucleotide polymorphisms (SNPs). By conducting a selective-sweep analysis, we discovered that the most significantly selective region was located on chromosome 7. Within this region, we identified several genes, including VRTN, SYNDIG1L, LTBP2, and ABCD4, known to regulate the spinal development and morphology. Further, a genome-wide association study (GWAS) performed on sheep with increased and normal vertebral numbers confirmed that ABCD4 is a candidate gene for determining the number of vertebrae in sheep. Additionally, the most significant SNP on chromosome 7 was identified as a candidate QTL. Moreover, we detected two missense mutations in the ABCD4 gene; one of these mutations (Chr7: 89393414, C > T) at position 22 leads to the conversion of arginine (Arg) to glutamine (Gln), which is expected to negatively affect the protein's function. Notably, a transcriptome expression profile in mouse embryonic development revealed that ABCD4 is highly expressed during the critical period of vertebral formation (4.5-7.5 days). Our study highlights ABCD4 as a potential major gene influencing the number of vertebrae in Ujimqin sheep, with promising prospects for future genome-assisted breeding improvements in sheep.

Advancements in Genetic Marker Exploration for Livestock Vertebral Traits with a Focus on China

In livestock breeding, the number of vertebrae has gained significant attention due to its impact on carcass quality and quantity. Variations in vertebral traits have been observed across different animal species and breeds, with a strong correlation to growth and meat production. Furthermore, vertebral traits are classified as quantitative characteristics. Molecular marker techniques, such as marker-assisted selection (MAS), have emerged as efficient tools to identify genetic markers associated with vertebral traits. In the current review, we highlight some key potential genes and their polymorphisms that play pivotal roles in controlling vertebral traits (development, length, and number) in various livestock species, including pigs, donkeys, and sheep. Specific genetic variants within these genes have been linked to vertebral development, number, and length, offering valuable insights into the genetic mechanisms governing vertebral traits. This knowledge has significant implications for selective breeding strategies to enhance structural characteristics and meat quantity and quality in livestock, ultimately improving the efficiency and quality of the animal husbandry industry.

Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs

Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.

Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data

Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Imputation to whole-genome sequence and its use in genome-wide association studies for pork colour traits in crossbred and purebred pigs

Imputed whole-genome sequence (WGS) has been proposed to improve genome-wide association studies (GWAS), since all causative mutations responsible for phenotypic variation are expected to be present in the data. This approach was applied on a large number of purebred (PB) and crossbred (CB) pigs for 18 pork color traits to evaluate the impact of using imputed WGS relative to medium-density marker panels. The traits included Minolta A*, B*, and L* for fat (FCOL), quadriceps femoris muscle (QFCOL), thawed loin muscle (TMCOL), fresh ham gluteus medius (GMCOL), ham iliopsoas muscle (ICOL), and longissimus dorsi muscle on the fresh loin (FMCOL). Sequence variants were imputed from a medium-density marker panel (61K for CBs and 50K for PBs) in all genotyped pigs using BeagleV5.0. We obtained high imputation accuracy (average of 0.97 for PBs and 0.91 for CBs). GWAS were conducted for three datasets: 954 CBs and 891 PBs, and the combined CBs and PBs. For most traits, no significant associations were detected, regardless of panel density or population type. However, quantitative trait loci (QTL) regions were only found for a few traits including TMCOL Minolta A* and GMCOL Minolta B* (CBs), FMCOL Minolta B*, FMCOL Minolta L*, and ICOL Minolta B* (PBs) and FMCOL Minolta A*, FMCOL Minolta B*, GMCOL Minolta B*, and ICOL Minolta B* (Combined dataset). More QTL regions were identified with WGS (n = 58) relative to medium-density marker panels (n = 22). Most of the QTL were linked to previously reported QTLs or candidate genes that have been previously reported to be associated with meat quality, pH and pork color; e.g., VIL1, PRKAG3, TTLL4, and SLC11A1, USP37. CTDSP1 gene on SSC15 has not been previously associated with meat color traits in pigs. The findings suggest any added value of WGS was only for detecting novel QTL regions when the sample size is sufficiently large as with the Combined dataset in this study. The percentage of phenotypic variance explained by the most significant SNPs also increased with WGS compared with medium-density panels. The results provide additional insights into identification of a number of candidate regions and genes for pork color traits in different pig populations.

Exploring the optimal strategy of imputation from SNP array to whole-genome sequencing data in farm animals

Genotype imputation from BeadChip to whole-genome sequencing (WGS) data is a cost-effective method of obtaining genotypes of WGS variants. Beagle, one of the most popular imputation software programs, has been widely used for genotype inference in humans and non-human species. A few studies have systematically and comprehensively compared the performance of beagle versions and parameter settings of farm animals. Here, we investigated the imputation performance of three representative versions of Beagle (Beagle 4.1, Beagle 5.0, and Beagle 5.4), and the effective population size (Ne) parameter setting for three species (cattle, pig, and chicken). Six scenarios were investigated to explore the impact of certain key factors on imputation performance. The results showed that the default Ne (1,000,000) is not suitable for livestock and poultry in small reference or low-density arrays of target panels, with 2.47%–10.45% drops in accuracy. Beagle 5 significantly reduced the computation time (4.66-fold–13.24-fold) without an accuracy loss. In addition, using a large combined-reference panel or high-density chip provides greater imputation accuracy, especially for low minor allele frequency (MAF) variants. Finally, a highly significant correlation in the measures of imputation accuracy can be obtained with an MAF equal to or greater than 0.05.

PHARP: a pig haplotype reference panel for genotype imputation

Pigs not only function as a major meat source worldwide but also are commonly used as an animal model for studying human complex traits. A large haplotype reference panel has been used to facilitate efficient phasing and imputation of relatively sparse genome-wide microarray chips and low-coverage sequencing data. Using the imputed genotypes in the downstream analysis, such as GWASs, TWASs, eQTL mapping and genomic prediction (GS), is beneficial for obtaining novel findings. However, currently, there is still a lack of publicly available and high-quality pig reference panels with large sample sizes and high diversity, which greatly limits the application of genotype imputation in pigs. In response, we built the pig Haplotype Reference Panel (PHARP) database. PHARP provides a reference panel of 2012 pig haplotypes at 34 million SNPs constructed using whole-genome sequence data from more than 49 studies of 71 pig breeds. It also provides Web-based analytical tools that allow researchers to carry out phasing and imputation consistently and efficiently. PHARP is freely accessible at http://alphaindex.zju.edu.cn/PHARP/index.php . We demonstrate its applicability for pig commercial 50 K SNP arrays, by accurately imputing 2.6 billion genotypes at a concordance rate value of 0.971 in 81 Large White pigs (~ 17 × sequencing coverage). We also applied our reference panel to impute the low-density SNP chip into the high-density data for three GWASs and found novel significantly associated SNPs that might be casual variants.

An imputation-based genome-wide association study for growth and fatness traits in Sujiang pigs

Sujiang pigs are a synthetic breed derived from Jiangquhai, Fengjing, and Duroc pigs. In this study, we sequenced the genome of 62 pigs with a coverage depth of 10× to 20×, including 27 Sujiang and 35 founder breed pigs, and we collected 360 global pigs' genome sequence data from public databases including 39 Duroc pigs. We obtained a high-quality variant dataset of 365 Sujiang pigs by imputing the porcine 80 K single nucleotide polymorphism (SNP) Beadchip to the whole-genome scale with a total of 422 pigs as a reference panel. A dataset of 365 imputated Sujiang pigs was used to perform single-trait genome-wide association study (GWAS) and meta-analyses for growth and fatness traits. Single-trait GWAS identified 1 907, 18, and 14 SNPs surpassing the suggestively significant threshold for backfat thickness, chest circumference, and chest width, respectively. Meta-analyses identified 2 400 genome-wide significant SNPs and 520 suggestively significant SNPs for backfat thickness and chest circumference, and 719 genome-wide significant SNPs and 1 225 suggestively significant SNPs for all seven traits. According to the meta-analysis of backfat thickness and chest circumference, a remarkable region of 2.69 Mb on Sus scrofa chromosome 4 containing FAM110B, IMPAD1, LYN, MOS, PENK, PLAG1, SDR16C5 and XKR4 was identified as a candidate region. The haplotype heat map of the 2.69 Mb region verified that Sujiang pigs were derived from Duroc and Chinese indigenous pigs, especially Jiangquhai pigs. The Kruskal-Wallis test showed that haplotypes of the 2.69 Mb region significantly affected backfat thickness and chest circumference traits. We then focused on PLAG1, an important growth-related gene, and identified two synonymous SNPs with obvious differences among different breeds in the PLAG1 gene. We then performed genotyping of 365 Sujiang, 150 Duroc, 95 Jiangquhai, and 100 Fengjing pigs to confirm the above result and verified that the two variants significantly affected phenotypes of growth and fatness traits. Our findings not only provide insights into the genetic architecture of porcine growth and fatness traits but also provide potential markers for selective breeding of these traits in Sujiang pigs.

Development and validation of a horse reference panel for genotype imputation

Background

Genotype imputation is a cost-effective method to generate sequence-level genotypes for a large number of animals. Its application can improve the power of genomic studies, provided that the accuracy of imputation is sufficiently high. The purpose of this study was to develop an optimal strategy for genotype imputation from genotyping array data to sequence level in German warmblood horses, and to investigate the effect of different factors on the accuracy of imputation. Publicly available whole-genome sequence data from 317 horses of 46 breeds was used to conduct the analyses.

Results

Depending on the size and composition of the reference panel, the accuracy of imputation from medium marker density (60K) to sequence level using the software Beagle 5.1 ranged from 0.64 to 0.70 for horse chromosome 3. Generally, imputation accuracy increased as the size of the reference panel increased, but if genetically distant individuals were included in the panel, the accuracy dropped. Imputation was most precise when using a reference panel of multiple but related breeds and the software Beagle 5.1, which outperformed the other two tested computer programs, Impute 5 and Minimac 4. Genome-wide imputation for this scenario resulted in a mean accuracy of 0.66. Stepwise imputation from 60K to 670K markers and subsequently to sequence level did not improve the accuracy of imputation. However, imputation from higher density (670K) was considerably more accurate (about 0.90) than from medium density. Likewise, imputation in genomic regions with a low marker coverage resulted in a reduced accuracy of imputation.

Conclusions

The accuracy of imputation in horses was influenced by the size and composition of the reference panel, the marker density of the genotyping array, and the imputation software. Genotype imputation can be used to extend the limited amount of available sequence-level data from horses in order to boost the power of downstream analyses, such as genome-wide association studies, or the detection of embryonic lethal variants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00740-8.

Genomic Prediction Using LD-Based Haplotypes in Combined Pig Populations

The size of reference population is an important factor affecting genomic prediction. Thus, combining different populations in genomic prediction is an attractive way to improve prediction ability. However, combining multireference population roughly cannot increase the prediction accuracy as well as expected in pig. This may be due to different linkage disequilibrium (LD) pattern differences between population. In this study, we used the imputed whole-genome sequencing (WGS) data to construct LD-based haplotypes for genomic prediction in combined population to explore the impact of different single-nucleotide polymorphism (SNP) densities, variant representation (SNPs or haplotype alleles), and reference population size on the prediction accuracy for reproduction traits. Our results showed that genomic best linear unbiased prediction (GBLUP) using the WGS data can improve prediction accuracy in multi-population but not within-population. Not only the genomic prediction accuracy of the haplotype method using 80 K chip data in multi-population but also GBLUP for the multi-population (3.4–5.9%) was higher than that within-population (1.2–4.3%). More importantly, we have found that using the haplotype method based on the WGS data in multi-population has better genomic prediction performance, and our results showed that building haploblock in this scenario based on low LD threshold (r² = 0.2–0.3) produced an optimal set of variables for reproduction traits in Yorkshire pig population. Our results suggested that whether the use of the haplotype method based on the chip data or GBLUP (individual SNP method) based on the WGS data were beneficial for genomic prediction in multi-population, while simultaneously combining the haplotype method and WGS data was a better strategy for multi-population genomic evaluation.

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

Increasing calling accuracy, coverage, and read-depth in sequence data by the use of haplotype blocks

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing genotyping technologies when resources are limited. In this work, we are proposing a new imputation pipeline (“HBimpute”) that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and subsequently use the reads of all locally similar lines in the variant calling for a specific line. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced at 0.5X read-depth. The overall imputing error rates are cut in half compared to state-of-the-art software like BEAGLE and STITCH, while the average read-depth is increased to 83X, thus enabling the calling of copy number variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance of sequence data in common breeding applications to that of genomic data generated with a genotyping array. For both genome-wide association studies and genomic prediction, results are on par or even slightly better than results obtained with high-density array data (600k). In particular for genomic prediction, we observe slightly higher data quality for the sequence data compared to the 600k array in the form of higher prediction accuracies. This occurred specifically when reducing the data panel to the set of overlapping markers between sequence and array, indicating that sequencing data can benefit from the same marker ascertainment as used in the array process to increase the quality and usability of genomic data.

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics and breeding. Cost, precision, and throughput must be balanced to achieve optimal efficiency given available technologies and finite resources. Although genotyping arrays are still considered the gold standard in high-throughput quantitative genetics, recent advances in sequencing provide new opportunities. Both the quality and cost of genomic data generated based on sequencing are highly dependent on the used read-depth. In this work, we propose a new imputation pipeline (“HBimpute”) that uses haplotype blocks to detect individuals of the same genetic origin and subsequently uses all reads of those individuals in the variant calling. Thus, the obtained virtual read-depth is artificially increased, leading to higher calling accuracy, coverage, and the ability to call copy number variation based on low read-depth sequencing data. To conclude, our approach makes sequencing a cost-competitive alternative to genotyping arrays with the added benefit of allowing the calling of structural variation.

An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population

Sequencing-based genome-wide association studies (GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F₂ population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide (QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes, CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future.

Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy

BACKGROUND

Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels.

RESULTS

We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks.

CONCLUSIONS

Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.

A Single-Step Genome Wide Association Study on Body Size Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs

The body shape of a pig is the most direct production index, which can fully reflect the pig’s growth status and is closely related to important economic traits. In this study, a genome-wide association study on seven body size traits, the body length (BL), height (BH), chest circumference (CC), abdominal circumference (AC), cannon bone circumference (CBC), rump width (RW), and chest width (CW), were conducted in Yorkshire pigs. Illumina Porcine 80K SNP chips were used to genotype 589 of 5,572 Yorkshire pigs with body size records, and then the chip data was imputed to sequencing data. After quality control of imputed sequencing data, 784,267 SNPs were obtained, and the averaged linkage disequilibrium (r²) was 0.191. We used the single-trait model and the two-trait model to conduct single-step genome wide association study (ssGWAS) on seven body size traits; a total of 198 significant SNPS were finally identified according to the P-value and the contribution to the genetic variance of individual SNP. 11 candidate genes (CDH13, SIL1, CDC14A, TMRPSS15, TRAPPC9, CTNND2, KDM6B, CHD3, MUC13, MAPK4, and HMGA1) were found to be associated with body size traits in pigs; KDM6B and CHD3 jointly affect AC and CC, and MUC13 jointly affect RW and CW. These genes are involved in the regulation of bone growth and development as well as the absorption of nutrients and are associated with obesity. HMGA1 is proposed as a strong candidate gene for body size traits because of its important function and high consistency with other studies regarding the regulation of body size traits. Our results could provide valuable information for pig breeding based on molecular breeding.

A further look at quantitative trait loci for growth and fatness traits in a White Duroc × Erhualian F3 intercross population

Genetic analysis of porcine growth and fatness traits is beneficial to the swine industry and provides a reference to understand human obesity. Here, we obtained 29 growth and fatness traits for 473 individuals from a White Duroc × Erhualian F₃ intercross population. Basic statistical analyses showed that: (1) Positive correlations between different-stage body weights were detected, the shorter the time interval the stronger the correlation. (2) Strong correlations existed in the paired fatness traits. (3) With the growth of age, the correlation between fatness and body weight was increasing. All pigs were genotyped by Illumina 50 K SNP chips and their whole-genome genotypes were imputed referred to 109 re-sequencing data. We performed common and imputation-based GWASs for these traits. Two genome-wide significant loci on swine chromosome (SSC) 4 and 7 were repeatedly detected. The strongest association (P = 3.24 × 10^-19) was detected at 31.96 Mb on SSC7 for leaf fat weight. On this locus, seven major haplotypes were identified, of which two were novel and had an increasing-fatness effect. In the imputation-based GWAS, three new loci were identified. Our findings provide further insights into and enhance our understanding of genetic mechanism of porcine growth and fat deposition.

Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics

Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics

Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds

Whole-genome sequencing of 217 animals from three Danish commercial pig breeds (Duroc, Landrace [LL], and Yorkshire [YY]) was performed. Twenty-six million single-nucleotide polymorphisms (SNPs) and 8 million insertions or deletions (indels) were uncovered. Among the SNPs, 493,099 variants were located in coding sequences, and 29,430 were predicted to have a high functional impact such as gain or loss of stop codon. Using the whole-genome sequence dataset as the reference, the imputation accuracy for pigs genotyped with high-density SNP chips was examined. The overall average imputation accuracy for all biallelic variants (SNP and indel) was 0.69, while it was 0.83 for variants with minor allele frequency > 0.1. This study provides whole-genome reference data to impute SNP chip-genotyped animals for further studies to fine map quantitative trait loci as well as improving the prediction accuracy in genomic selection. Signatures of selection were identified both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during breed development or subsequent divergent selection. However, the fixation indices did not indicate a strong divergence among these three breeds. In LL and YY, the integrated haplotype score identified genomic regions under recent selection. These regions contained genes for olfactory receptors and oxidoreductases. Olfactory receptor genes that might have played a major role in the domestication were previously reported to have been under selection in several species including cattle and swine.

Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

Background

Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy.

Results

The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R²dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R²dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants.

Conclusions

Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel and MAF of the imputed variants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07184-8.

Animal-ImputeDB: a comprehensive database with multiple animal reference panels for genotype imputation

Animal-ImputeDB (http://gong_lab.hzau.edu.cn/Animal_ImputeDB/) is a public database with genomic reference panels of 13 animal species for online genotype imputation, genetic variant search, and free download. Genotype imputation is a process of estimating missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs) and thus can be widely used in large-scale genome-wide association studies (GWASs) using relatively inexpensive and low-density SNP arrays. However, most animals except humans lack high-quality reference panels, which greatly limits the application of genotype imputation in animals. To overcome this limitation, we developed Animal-ImputeDB, which is dedicated to collecting genotype data and whole-genome resequencing data of nonhuman animals from various studies and databases. A computational pipeline was developed to process different types of raw data to construct reference panels. Finally, 13 high-quality reference panels including ∼400 million SNPs from 2265 samples were constructed. In Animal-ImputeDB, an easy-to-use online tool consisting of two popular imputation tools was designed for the purpose of genotype imputation. Collectively, Animal-ImputeDB serves as an important resource for animal genotype imputation and will greatly facilitate research on animal genomic selection and genetic improvement.

Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population

Combining different swine populations in genomic prediction can be an important tool, leading to an increased accuracy of genomic prediction using single nucleotide polymorphism (SNP) chip data compared with within-population genomic. However, the expected higher accuracy of multi-population genomic prediction has not been realized. This may be due to an inconsistent linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTL) across populations, and the weak genetic relationships across populations. In this study, we determined the impact of different genomic relationship matrices, SNP density and pre-selected variants on prediction accuracy using a combined Yorkshire pig population. Our objective was to provide useful strategies for improving the accuracy of genomic prediction within a combined population. Results showed that the accuracy of genomic best linear unbiased prediction (GBLUP) using imputed whole-genome sequencing (WGS) data in the combined population was always higher than that within populations. Furthermore, the use of imputed WGS data always resulted in a higher accuracy of GBLUP than the use of 80K chip data for the combined population. Additionally, the accuracy of GBLUP with a non-linear genomic relationship matrix was markedly increased (0.87% to 15.17% for 80K chip data, and 0.43% to 4.01% for imputed WGS data) compared with that obtained with a linear genomic relationship matrix, except for the prediction of XD population in the combined population using imputed WGS data. More importantly, the application of pre-selected variants based on fixation index (Fst) scores improved the accuracy of multi-population genomic prediction, especially for 80K chip data. For BLUP|GA (BLUP approach given the genetic architecture), the use of a linear method with an appropriate weight to build a weight-relatedness matrix led to a higher prediction accuracy compared with the use of only pre-selected SNPs for genomic evaluations, especially for the total number of piglets born. However, for the non-linear method, BLUP|GA showed only a small increase or even a decrease in prediction accuracy compared with the use of only pre-selected SNPs. Overall, the best genomic evaluation strategy for reproduction-related traits for a combined population was found to be GBLUP performed with a non-linear genomic relationship matrix using variants pre-selected from the 80K chip data based on Fst scores.

Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits

BACKGROUND

Genome wide association studies (GWAS) on residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) were conducted in a population of 7573 animals from multiple beef cattle breeds based on 7,853,211 imputed whole genome sequence variants. The GWAS results were used to elucidate genetic architectures of the feed efficiency related traits in beef cattle.

RESULTS

The DNA variant allele substitution effects approximated a bell-shaped distribution for all the traits while the distribution of additive genetic variances explained by single DNA variants followed a scaled inverse chi-squared distribution to a greater extent. With a threshold of P-value < 1.00E-05, 16, 72, 88, and 116 lead DNA variants on multiple chromosomes were significantly associated with RFI, DMI, ADG, and MWT, respectively. In addition, lead DNA variants with potentially large pleiotropic effects on DMI, ADG, and MWT were found on chromosomes 6, 14 and 20. On average, missense, 3'UTR, 5'UTR, and other regulatory region variants exhibited larger allele substitution effects in comparison to other functional classes. Intergenic and intron variants captured smaller proportions of additive genetic variance per DNA variant. Instead 3'UTR and synonymous variants explained a greater amount of genetic variance per DNA variant for all the traits examined while missense, 5'UTR and other regulatory region variants accounted for relatively more additive genetic variance per sequence variant for RFI and ADG, respectively. In total, 25 to 27 enriched cellular and molecular functions were identified with lipid metabolism and carbohydrate metabolism being the most significant for the feed efficiency traits.

CONCLUSIONS

RFI is controlled by many DNA variants with relatively small effects whereas DMI, ADG, and MWT are influenced by a few DNA variants with large effects and many DNA variants with small effects. Nucleotide polymorphisms in regulatory region and synonymous functional classes play a more important role per sequence variant in determining variation of the feed efficiency traits. The genetic architecture as revealed by the GWAS of the imputed 7,853,211 DNA variants will improve our understanding on the genetic control of feed efficiency traits in beef cattle.

Improving Imputation Quality in BEAGLE for Crop and Livestock Data

Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

Landscape of Loci and Candidate Genes for Muscle Fatty Acid Composition in Pigs Revealed by Multiple Population Association Analysis

Genome wide association analyses in diverse populations can identify complex trait loci that are specifically present in one population or shared across multiple populations, which help to better understand the genetic architecture of complex traits in a broader genetic context. In this study, we conducted genome-wide association studies and meta-analysis for 38 fatty acid composition traits with 12–19 million imputed genome sequence SNPs in 2446 pigs from six populations, encompassing White Duroc × Erhualian F₂, Sutai, Duroc-Landrace-Yorkshire (DLY) three-way cross, Laiwu, Erhualian, and Bamaxiang pigs that were originally genotyped with 60 K or 1.4 million single nucleotide polymorphism (SNP) chips. The analyses uncovered 285 lead SNPs (P < 5 × 10^-8), among which 78 locate more than 1 Mb to the lead chip SNPs were considered as novel, largely augmented the landscape of loci for porcine muscle fatty acid composition. Meta-analysis enhanced the association significance at loci near FADS2, ABCD2, ELOVL5, ELOVL6, ELOVL7, SCD, and THRSP genes, suggesting possible existence of population shared mutations underlying these loci. Further haplotype analysis at SCD loci identified a shared 3.7 kb haplotype in F₂, Sutai and DLY pigs showing consistent effects of decreasing C18:0 contents in the three populations. In contrast, at FASN loci, we found an Erhualian specific haplotype explaining the population specific association signals in Erhualian pigs. This study refines our understanding on landscape of loci and candidate genes for fatty acid composition traits of pigs.

Using imputation-based whole-genome sequencing data to improve the accuracy of genomic prediction for combined populations in pigs

BACKGROUND

For genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs.

RESULTS

In total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations.

CONCLUSIONS

The use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs

The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R ²) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.

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.

Multi-trait meta-analyses reveal 25 quantitative trait loci for economically important traits in Brown Swiss cattle

Background

Little is known about the genetic architecture of economically important traits in Brown Swiss cattle because only few genome-wide association studies (GWAS) have been carried out in this breed. Moreover, most GWAS have been performed for single traits, thus not providing detailed insights into potentially existing pleiotropic effects of trait-associated loci.

Results

To compile a comprehensive catalogue of large-effect quantitative trait loci (QTL) segregating in Brown Swiss cattle, we carried out association tests between partially imputed genotypes at 598,016 SNPs and daughter-derived phenotypes for more than 50 economically important traits, including milk production, growth and carcass quality, body conformation, reproduction and calving traits in 4578 artificial insemination bulls from two cohorts of Brown Swiss cattle (Austrian-German and Swiss populations). Across-cohort multi-trait meta-analyses of the results from the single-trait GWAS revealed 25 quantitative trait loci (QTL; P < 8.36 × 10^− 8) for economically relevant traits on 17 Bos taurus autosomes (BTA). Evidence of pleiotropy was detected at five QTL located on BTA5, 6, 17, 21 and 25. Of these, two QTL at BTA6:90,486,780 and BTA25:1,455,150 affect a diverse range of economically important traits, including traits related to body conformation, calving, longevity and milking speed. Furthermore, the QTL at BTA6:90,486,780 seems to be a target of ongoing selection as evidenced by an integrated haplotype score of 2.49 and significant changes in allele frequency over the past 25 years, whereas either no or only weak evidence of selection was detected at all other QTL.

Conclusions

Our findings provide a comprehensive overview of QTL segregating in Brown Swiss cattle. Detected QTL explain between 2 and 10% of the variation in the estimated breeding values and thus may be considered as the most important QTL segregating in the Brown Swiss cattle breed. Multi-trait association testing boosts the power to detect pleiotropic QTL and assesses the full spectrum of phenotypes that are affected by trait-associated variants.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6066-6) contains supplementary material, which is available to authorized users.

Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens

BACKGROUND

Poultry breeding programs have been focused on improvement of growth and carcass traits, however, this has resulted in correlated changes in internal organ weights and increased incidence of metabolic disorders. These disorders can affect feed efficiency or even cause death. We used a high density SNP array (600 K, Affymetrix) to estimate genomic heritability, perform genome-wide association analysis, and identify genomic regions and positional candidate genes (PCGs) associated with internal organ traits in an F2 chicken population. We integrated knowledge of haplotype blocks, selection signature regions and sequencing data to refine the list of PCGs.

RESULTS

Estimated genomic heritability for internal organ traits in chickens ranged from low (LUNGWT, 0.06) to high (GIZZWT, 0.45). A total of 20 unique 1 Mb windows identified on GGA1, 2, 4, 7, 12, 15, 18, 19, 21, 27 and 28 were significantly associated with intestine length, and weights or percentages of liver, gizzard or lungs. Within these windows, 14 PCGs were identified based on their biological functions: TNFSF11, GTF2F2, SPERT, KCTD4, HTR2A, RB1, PCDH7, LCORL, LDB2, NR4A2, GPD2, PTPN11, ITGB4 and SLC6A4. From those genes, two were located within haplotype blocks and three overlapped with selection signature regions. A total of 13,748 annotated sequence SNPs were in the 14 PCGs, including 156 SNPs in coding regions (124 synonymous, 26 non-synonymous, and 6 splice variants). Seven deleterious SNPs were identified in TNFSF11, NR4A2 or ITGB4 genes.

CONCLUSIONS

The results from this study provide novel insights to understand the genetic architecture of internal organ traits in chickens. The QTL detection performed using a high density SNP array covered the whole genome allowing the discovery of novel QTL associated with organ traits. We identified PCGs within the QTL involved in biological processes that may regulate internal organ growth and development. Potential functional genetic variations were identified generating crucial information that, after validation, might be used in poultry breeding programs to reduce the occurrence of metabolic disorders.

Whole-Genome Resequencing Reveals Loci Associated With Thoracic Vertebrae Number in Sheep

The number of vertebrae, especially thoracic vertebrae, is an important economic trait that may influence carcass length and meat production in animals. However, the genetic basis of vertebrae number in sheep is still poorly understood. To detect the candidate genes, 400 increased number of thoracic vertebrae (T14L6) and 200 normal (T13L6) Kazakh sheep were collected. We generated and sequenced 60 pools of genomic DNA (each pool prepared by mixing genomic DNA from 10 sheep with the same thoracic traits), with an average depth of coverage of 25.65×. We identified a total of 42,075,402 SNPs and 11 putatively selected genomic regions, including the VRTN gene and the HoxA gene family that regulate vertebral development. The most prominent areas of selective elimination were located in a region of chromosome 7, including VRTN, which regulates spinal development and morphology. Further investigation indicated that the expression level of the VRTN gene during fetal development was significantly higher in sheep with more thoracic vertebrae than in those with a normal number of thoracic vertebrae. A genome-wide comparison between sheep with increased and normal numbers of thoracic vertebrae showed that the VRTN gene is the major selection locus for the number of thoracic vertebrae in sheep and has the potential to be utilized in sheep breeding in the future.

Imputation-Based Whole-Genome Sequence Association Study Reveals Constant and Novel Loci for Hematological Traits in a Large-Scale Swine F2 Resource Population

The whole-genome sequences of progenies with low-density single-nucleotide polymorphism (SNP) genotypes can be imputed with high accuracy based on the deep-coverage sequences of key ancestors. With this imputation technology, a more powerful genome-wide association study (GWAS) can be carried out using imputed whole-genome variants and the phenotypes of interest to overcome the shortcomings of low-power detection and the large confidence interval derived from low-density SNP markers in classic association studies. In this study, 19 ancestors of a large-scale swine F₂ White Duroc × Erhualian population were deeply sequenced for their genome with an average coverage of 25×. Considering 98 pigs from 10 different breeds with high-quality deep sequenced genomes, we imputed the whole genomic variants of 1020 F₂ pigs genotyped by the PorcineSNP60 BeadChip with high accuracy and obtained 14,851,440 sequence variants after quality control. Based on this, 87 novel quantitative traits loci (QTLs) for 18 hematological traits at three different physiological stages of the F₂ pigs were identified, among which most of the novel QTLs have been repeated in two of the three stages. Literature mining pinpointed that the FGF14 and LCLAT1 genes at SSC11 and SSC3 may affect the MCH at day 240 and MCV at day 18, respectively. The present study shows that combining high-quality imputed genomic variants and correlated phenomic traits into GWAS can improve the capability to detect QTL considerably. The large number of different QTLs for hematological traits identified at multiple growth stages implies the complexity and time specificity of these traits.