Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey

Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific Abalone Haliotis discus hannai

Low-coverage whole-genome sequencing (lcWGS) followed by imputation is emerging as a cost-effective method for generating a substantial number of single nucleotide polymorphism (SNP) in aquatic species with highly heterozygous and complex genomes. This study represents the first systematic investigation into the application of low-coverage whole-genome sequencing (lcWGS) combined with imputation for genotyping in Pacific abalone (Haliotis discus hannai) without a reference panel. We utilized 1059 Pacific abalone individuals sequenced at an average depth of 7.86×, as well as 16 individuals sequenced at 20×, as sample materials. To assess the genotype imputation accuracy for lcWGS without a reference panel, we simulated data with varying sequencing depths (0.5-4×) and examined the effects of sample size, chromosome length, and minor allele frequency (MAF) using BaseVar and STITCH strategies. Results showed that STITCH achieved high accuracy when the sample size exceeded 400, with a genotype correlation (R2) of 0.98 ± 0.002 and genotype concordance (GC) of 0.99 ± 0.001. Imputation accuracy plateaued when the sample size exceeded 400 and sequencing depth surpassed 1×. Chromosome length had minimal effects, with all three chromosomes achieving an accuracy of approximately 0.98. However, the accuracy for rare MAF (<0.05) was lower, falling below 0.99. A second imputation with Beagle significantly increased SNP detection by 3.9-8.3 folds for a sequencing depth of 0.5-4×, apparently without sacrificing accuracy. To our knowledge, this is the first study of lcWGS analysis conducted in abalone. The findings demonstrate that lcWGS with imputation can achieve high accuracy with moderate sample sizes (n ≥ 400) in Pacific abalone, offering a cost-effective approach for genotyping in aquaculture species.

FST-Based Marker Prioritization Within Quantitative Trait Loci Regions and Its Impact on Genomic Selection Accuracy: Insights from a Simulation Study with High-Density Marker Panels for Bovines

BACKGROUND/OBJECTIVES

Genomic selection (GS) has improved accuracy compared to traditional methods. However, accuracy tends to plateau beyond a certain marker density. Prioritizing influential SNPs could further enhance the accuracy of GS. The fixation index (FST) allows for the identification of SNPs under selection pressure. Although the FST method was shown to be able to prioritize SNPs across the whole genome and to increase accuracy, its performance could be further improved by focusing on the prioritization process within QTL regions.

METHODS

A trait with heritability of 0.1 and 0.4 was generated under different simulation scenarios (number of QTL, size of SNP windows around QTL, and number of selected SNPs within a QTL region). In total, six simulation scenarios were analyzed. Each scenario was replicated five times. The population comprised 30K animals from the last 2 generations (G9-G10) of a 10-generation (G1-G10) selection process. All animals in G9-10 were genotyped with a 600K SNP panel. FST scores were calculated for all 600K SNPs. Two prioritization scenarios were used: (1) selecting the top 1% SNPs with the highest FST scores, and (2) selecting a predetermined number of SNPs within each QTL window. GS accuracy was evaluated using the correlation between true and estimated breeding values for 5000 randomly selected animals from G10.

RESULTS

Prioritizing SNPs using FST scores within QTL window regions increased accuracy by 5 to 18%, with the 50-SNP windows showing the best performance.

CONCLUSIONS

The increase in GS accuracy warrants the testing of the algorithm when the number and position of QTL are unknown.

Genomic selection and weighted single-step genome-wide association study of sheep body weight and milk yield: Imputing low-coverage sequencing data with similar genetic background panels

Low-coverage whole-genome sequencing (LcWGS), a cost-effective genotyping method, offers greater flexibility in variant detection than SNP chips. However, to our knowledge, no studies have explored the application of LcWGS in sheep. This study aimed to evaluate the feasibility of implementing LcWGS and genotype imputation and assess their applicability in genomic studies of body weight and milk yield in sheep. A total of 45,787 birth weight (BiW), 31,135 weaning daily gain (WDG), 8,928 milk yield (MY), and 4,918 milk yield per unit of metabolic body weight (MWMY) data records were analyzed. Among these, 2,366 sheep had imputed high-density genotypes. Simulated sequencing depths from 0.1× to 3× were imputed using reference panels of 100 to 600 individuals. Genotype concordance with true data improved from 0.8875 to 0.9852 as the sequencing depth and panel size increased. The single-step GBLUP method applied to the imputed data yielded higher accuracy for BiW, WDG, MY, and MWMY than the classical pedigree-based BLUP, and notably increased MY accuracy from 0.61 to 0.66. Furthermore, a weighted single-step genome-wide association study identified key genes associated with BiW (ANKS1B, OPRM1, CSMD1), WDG (TKDP5, GRP, RAX, IGFBP7), MY (CCSER1, FGGY, HOOK1), and MWMY (NDUFA10, ZNF385D, NWD1), revealing the importance of multiple pathways in sheep growth and milk production. This is the first study to assess the feasibility of combining LcWGS with genotype imputation for sheep genomic selection, balancing economic costs and imputation efficiency. Furthermore, we demonstrate an effective approach for identifying genetic variants linked to body weight and milk production, offering a cost-effective strategy for dairy sheep breeding.

DPImpute: A Genotype Imputation Framework for Ultra-Low Coverage Whole-Genome Sequencing and its Application in Genomic Selection

Whole-genome sequencing is pivotal for elucidating the complex relationships between genotype and phenotype. However, its widespread application is hindered by the high sequencing depth and large sample sizes required, especially for genomic selection (GS) reliant on precise phenotype prediction from high-density genotype data. To address this, DPImpute (Dual-Phase Impute) is developed, an two-step imputation pipeline enabling accurate whole-genome SNP genotyping under ultra-low coverage whole-genome sequencing (ulcWGS) depths, small testing sample sizes, and limited reference populations. DPImpute achieved 98.06% SNP imputation accuracy with minimal testing samples (≤10), reference samples (≤100), and an ultra-low sequencing depth of 0.3X, surpassing the accuracy of existing imputation methods. Moreover, this high accuracy is maintained across multi-ancestry human populations. Remarkably, DPImpute demonstrated accurate SNP imputation from low-coverage sequencing data from single blood cells and single blastocyst cells, highlighting its potential in embryo GS. To enhance the accessibility of DPImpute, a user-friendly web server (https://agdb.ecenr.com/DPImpute/home) is developed and a Docker container for seamless implementation. In summary, DPImpute can significantly expedite breeding programs through precise and cost-effective genotyping and serve as a valuable tool for diverse population genotyping, encompassing both human and animal studies.

Improvement of the accuracy of breeding value prediction for egg production traits in Muscovy duck using low-coverage whole-genome sequence data

Low-coverage whole genome sequencing (lcWGS) is an effective low-cost genotyping technology when combined with genotype imputation approaches. It facilitates cost-effective genomic selection (GS) programs in agricultural animal populations. GS based on lcWGS data has been successfully applied to livestock such as pigs and donkeys. However, its effectiveness in poultry is poorly reported. Furthermore, due to the high linkage disequilibrium (LD) between markers and the high marker density in lcWGS data, it is necessary to explore how to effectively utilize lcWGS data for genomic prediction. Phenotypic data for egg production traits were collected from a population of 1491 Muscovy ducks, with 975 of them sequenced using low-coverage whole genomic sequencing at an average depth of ∼0.84x. In the prediction, we compared the pedigree-based best linear unbiased prediction (PBLUP) method, the genomic best linear unbiased prediction (GBLUP) method utilizing SNP marker data, and the single-step genomic best linear unbiased prediction (SSGBLUP) method, which integrates both pedigree and SNP marker information. Among the SNP-based approaches, we further extended our analysis by applying LD-based weighting of SNPs and employing a Gaussian kernel model to capture epistatic genetic effects. The result showed that the estimated heritability of egg production traits in Muscovy duck ranged from 0.071 to 0.573. Compared to the PBLUP, integrating lcWGS data and pedigree data through a single-step genetic evaluation improved the accuracy of genomic prediction for all traits in this study, with accuracy improvement ranging from 12.3 % to 43.9 % in random cross-validation. Additionally, compared to the GBLUP, the extended method of GBLUP that controls for LD heterogeneity and accounts for epistatic effects using lcWGS data showed a superior prediction performance, with accuracy improvement ranging from 0.6 %∼75.1 % in the optimal scenario. This study demonstrates that utilization of lcWGS data is a promising approach for genomic prediction of egg production traits in Muscovy duck. Our findings provide valuable strategies for optimizing genomic prediction methods using lcWGS data.

Imputation strategies for low-coverage whole-genome sequencing data and their effects on genomic prediction and genome-wide association studies in pigs

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31-1.04% compared to the chip data, and by 0.7-1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs

Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R² , concordance rate, and dosage R² were 0.959, 0.952, and 0.933, respectively. The heritability values of a*_45 min , b*_45 min , L*_45 min , C*, and H⁰ were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea

Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

Background

Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses.

Results

Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding.

Conclusions

We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00766-y.