Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data

Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes

BACKGROUND

Body conformation, including withers height, is a major selection criterion in horse breeding and is associated with other important traits, such as health and performance. However, little is known about the genomic background of equine conformation. Therefore, the aim of this study was to use imputed sequence-level genotypes from up to 4891 German Warmblood horses to identify genomic regions associated with withers height and linear conformation traits. Furthermore, the traits were genetically characterised and putative causal variants for withers height were detected.

RESULTS

A genome-wide association study (GWAS) for withers height confirmed the presence of a previously known quantitative trait locus (QTL) on Equus caballus (ECA) chromosome 3 close to the LCORL/NCAPG locus, which explained 16% of the phenotypic variance for withers height. An additional significant association signal was detected on ECA1. Further investigations of the region on ECA3 identified a few promising candidate causal variants for withers height, including a nonsense mutation in the coding sequence of the LCORL gene. The estimated heritability for withers height was 0.53 and ranged from 0 to 0.34 for the conformation traits. GWAS identified significantly associated variants for more than half of the investigated conformation traits, among which 13 showed a peak on ECA3 in the same region as withers height. Genetic parameter estimation revealed high genetic correlations between these traits and withers height for the QTL on ECA3.

CONCLUSIONS

The use of imputed sequence-level genotypes from a large study cohort led to the discovery of novel QTL associated with conformation traits in German Warmblood horses. The results indicate the high relevance of the QTL on ECA3 for various conformation traits, including withers height, and contribute to deciphering causal mutations for body size in horses.

Identifying distant relatives using benchtop-scale sequencing

The genetic component of forensic genetic genealogy (FGG) is an estimate of kinship, often conducted at genome scales between a great number of individuals. The promise of FGG is substantial: in concert with genealogical records and other nongenetic information, it can indirectly identify a person of interest. A downside of FGG is cost, as it is currently expensive and requires chemistries uncommon to forensic genetic laboratories (microarrays and high throughput sequencing). The more common benchtop sequencers can be coupled with a targeted PCR assay to conduct FGG, though such approaches have limited resolution for kinship. This study evaluates low-pass sequencing, an alternative strategy that is accessible to benchtop sequencers and can produce resolutions comparable to high-pass sequencing. Samples from a three-generation pedigree were augmented to include up to 7th degree relatives (using whole genome pedigree simulations) and the ability to recover the true kinship coefficient was assessed using algorithms qualitatively similar to those found in GEDmatch. We show that up to 7th degree relatives can be reliably inferred from 1 × whole genome sequencing obtainable from desktop sequencers.

AGIDB: a versatile database for genotype imputation and variant decoding across species

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

Genome-Wide Association Study on Reproductive Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs

Reproductive traits have a key impact on production efficiency in the pig industry. It is necessary to identify the genetic structure of potential genes that influence reproductive traits. In this study, a genome-wide association study (GWAS) based on chip and imputed data of five reproductive traits, namely, total number born (TNB), number born alive (NBA), litter birth weight (LBW), gestation length (GL), and number of weaned (NW), was performed in Yorkshire pigs. In total, 272 of 2844 pigs with reproductive records were genotyped using KPS Porcine Breeding SNP Chips, and then chip data were imputed to sequencing data using two online software programs: the Pig Haplotype Reference Panel (PHARP v2) and Swine Imputation Server (SWIM 1.0). After quality control, we performed GWAS based on chip data and the two different imputation databases by using fixed and random model circulating probability unification (FarmCPU) models. We discovered 71 genome-wide significant SNPs and 25 potential candidate genes (e.g., SMAD4, RPS6KA2, CAMK2A, NDST1, and ADCY5). Functional enrichment analysis revealed that these genes are mainly enriched in the calcium signaling pathway, ovarian steroidogenesis, and GnRH signaling pathways. In conclusion, our results help to clarify the genetic basis of porcine reproductive traits and provide molecular markers for genomic selection in pig breeding.

A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software

Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data.

GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data

Total number born (TNB), number of stillborn (NSB), and gestation length (GL) are economically important traits in pig production, and disentangling the molecular mechanisms associated with traits can provide valuable insights into their genetic structure. Genotype imputation can be used as a practical tool to improve the marker density of single-nucleotide polymorphism (SNP) chips based on sequence data, thereby dramatically improving the power of genome-wide association studies (GWAS). In this study, we applied Beagle software to impute the 50 K chip data to the whole-genome sequencing (WGS) data with average imputation accuracy (R2) of 0.876. The target pigs, 2655 Large White pigs introduced from Canadian and French lines, were genotyped by a GeneSeek Porcine 50K chip. The 30 Large White reference pigs were the key ancestral individuals sequenced by whole-genome resequencing. To avoid population stratification, we identified genetic variants associated with reproductive traits by performing within-population GWAS and cross-population meta-analyses with data before and after imputation. Finally, several genes were detected and regarded as potential candidate genes for each of the traits: for the TNB trait: NOTCH2, KLF3, PLXDC2, NDUFV1, TLR10, CDC14A, EPC2, ORC4, ACVR2A, and GSC; for the NSB trait: NUB1, TGFBR3, ZDHHC14, FGF14, BAIAP2L1, EVI5, TAF1B, and BCAR3; for the GL trait: PPP2R2B, AMBP, MALRD1, HOXA11, and BICC1. In conclusion, expanding the size of the reference population and finding an optimal imputation strategy to ensure that more loci are obtained for GWAS under high imputation accuracy will contribute to the identification of causal mutations in pig breeding.