Biology subjects

Lloret-Villas, A.

Publications and source records attributed to Lloret-Villas, A..

5 recordsLinked to original sources

bioRxiv2023

Genome-wide association study and regional heritability mapping of protein efficiency and performance traits in Swiss Large White pigs

BackgroundThe improvement of protein efficiency (PE) is a key factor for a sustainable pig production as nitrogen excretion contributes substantially to environmental pollution. Protein efficiency has been shown to be clearly heritable and genetically correlated with some performance traits, such as feed conversion ratio (FCR) and average daily feed intake (ADFI). This study aimed to identify genomic regions associated with these traits through genome-wide association studies (GWAS) and regional heritability mapping (RHM) using whole-genome sequence variants from low-pass sequencing of more than 1,000 Swiss Large White pigs. ResultsThe genomic-based heritability estimates using [~]15 million variants were moderate to high, ranging from 0.33 to 0.47. Using GWAS, no significant variants were found at the genome-wide thresholds for PE and FCR, but 45 variants were identified for ADFI on chromosome 1and one for ADG on chromosome 14. No region was found to be significantly associated with PE and FCR using RHM. We identified five suggestive regions on chromosome 1 for ADFI and one on chromosome 14 for ADG. Combining both analyses, we were able to highlight putative candidate genes for PE, which included PHYKPL, COL23A1, PPFIBP2, GVIN1, SYT9, RBMXL2, ZNF215 and olfactory receptor genes. ConclusionsCombining GWAS and RHM allowed us to suggest genomic regions that potentially influence PE and production traits. The apparent difficulty in detecting significant regions for these traits probably reflects the relatively small sample size, differences in genetic architecture across experimental conditions, and the possibility that polymorphisms explaining large parts of the trait variation may not segregate in the population. Nevertheless, we identified plausible functional candidate genes in the highlighted regions, including those involved in nutrient sensing, the urea cycle, and metabolic pathways, in particular IGF1-insulin), and have previously been reported in association with nitrogen metabolism in cattle, muscle and adipose tissue metabolism and feed intake in pigs. We also highlighted a range of noncoding RNAs, and their targets and gene regulation in general in this context should be investigated in the future.

genomics↗

bioRxiv2023

Molecular quantitative trait loci in reproductive tissues impact male fertility in a large mammal

Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 417k molecular phenotypes and 21M genome-wide variants to identify 41k regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.

genomics↗

bioRxiv2023

Structural variants and short tandem repeats impact gene expression and splicing in bovine testis tissue

Structural variants (SVs) and short tandem repeats (STRs) are significant sources of genetic variation. However, the impacts of these variants on gene regulation have not been investigated in cattle. Here, we genotyped and characterized 19,408 SVs and 374,821 STRs in 183 bovine genomes and investigated their impact on molecular phenotypes derived from testis transcriptomes. We found that 71% STRs were multiallelic. The vast majority (95%) of STRs and SVs were in intergenic and intronic regions. Only 37% SVs and 40% STRs were in high LD (R2>0.8) with surrounding SNPs/Indels, indicating that SNP-based association testing and genomic prediction are blind to a non-negligible portion of genetic variation. We showed that both SVs and STRs were more than two-fold enriched among expression and splicing QTL (e/sQTL) relative to SNPs/Indels and were often associated with differential expression and splicing of multiple genes. Deletions and duplications had larger impacts on splicing and expression than any other type of structural variant. Exonic duplications predominantly increased gene expression either through alternative splicing or other mechanisms, whereas expression- and splicing-associated STRs primarily resided in intronic regions and exhibited bimodal effects on the molecular phenotypes investigated. Most e/sQTL resided within 100 kb of the affected genes or splicing junctions. We pinpoint candidate causal STRs and SVs associated with the expression of SLC13A4 and TTC7B, and alternative splicing of a lncRNA and CAPP1. We provide a catalogue of STRs and SVs for taurine cattle and show that these variants contribute substantially to gene expression and splicing variation.

genomics↗

bioRxiv2023

Size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle

BackgroundLow-pass sequencing followed by sequence variant genotype imputation is an alternative to the routine microarray-based genotyping in cattle. However, the impact of haplotype reference panel composition and its interplay with the coverage of low-pass whole-genome sequencing data has not been sufficiently explored in typical livestock settings where only a small number of reference samples are available. MethodsSequence variant genotyping accuracy was compared between two variant callers, GATK and DeepVariant, in 50 Brown Swiss cattle with sequencing coverages ranging from 4 to 63-fold. Haplotype reference panels of varying sizes and composition were built with DeepVariant considering 501 cattle from nine breeds. High coverage sequencing data of 24 Brown Swiss cattle was downsampled to between 0.01- and 4-fold coverage to mimic low-pass sequencing. GLIMPSE was used to infer sequence variant genotypes from the low-pass sequencing data using different haplotype reference panels. The accuracy of the sequence variant genotypes imputed inferred from low-pass sequencing data was compared with sequence variant genotypes called from high-coverage data. ResultsDeepVariant was used to establish bovine haplotype reference panels because it outperformed GATK in all evaluations. Same-breed haplotype reference panels were better suited to impute sequence variant genotypes from low-pass sequencing than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9, implying high harmonic means of recall and precision of called genotypes, were achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) were used. In absence of such large same-breed haplotype panels, variant genotyping accuracy from low-pass sequencing could be increased either by adding non-related samples to the haplotype reference panel or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low pass sequencing was substantially less accurate when the reference panel lacks individuals from the target breed. ConclusionsVariant genotyping is more accurate with Deep-Variant than GATK. DeepVariant is therefore suitable to establish bovine haplotype reference panels. Medium-sized breed-specific haplotype reference panels and large multibreed haplotype reference panels enable accurate imputation of low-pass sequencing data in a typical cattle breed.

genomics↗

bioRxiv2021

Investigating the impact of reference assembly choice on genomic analyses in a cattle breed

BackgroundReference-guided read alignment and variant genotyping are prone to reference allele bias, particularly for samples that are greatly divergent from the reference genome. A Hereford-based assembly is the widely accepted bovine reference genome. Haplotype-resolved genomes that exceed the current bovine reference genome in quality and continuity have been assembled for different breeds of cattle. Using whole genome sequencing data of 161 Brown Swiss cattle, we compared the accuracy of read mapping and sequence variant genotyping as well as downstream genomic analyses between the bovine reference genome (ARS-UCD1.2) and a highly continuous Angus-based assembly (UOA_Angus_1). ResultsRead mapping accuracy did not differ notably between the ARS-UCD1.2 and UOA_Angus_1 assemblies. We discovered 22,744,517 and 22,559,675 high-quality variants from ARS-UCD1.2 and UOA_Angus_1, respectively. The concordance between sequence- and array-called genotypes was high and the number of variants deviating from Hardy-Weinberg proportions was low at segregating sites for both assemblies. More artefactual INDELs were genotyped from UOA_Angus_1 than ARS-UCD1.2 alignments. Using the composite likelihood ratio test, we detected 40 and 33 signatures of selection from ARS-UCD1.2 and UOA_Angus_1, respectively, but the overlap between both assemblies was low. Using the 161 sequenced Brown Swiss cattle as a reference panel, we imputed sequence variant genotypes into a mapping cohort of 30,499 cattle that had microarray-derived genotypes. The accuracy of imputation (Beagle R2) was very high (0.87) for both assemblies. Genome-wide association studies between imputed sequence variant genotypes and six dairy traits as well as stature produced almost identical results from both assemblies. ConclusionsThe ARS-UCD1.2 and UOA_Angus_1 assemblies are suitable for reference-guided genome analyses in Brown Swiss cattle. Although differences in read mapping and genotyping accuracy between both assemblies are negligible, the choice of the reference genome has a large impact on detecting signatures of selection using the composite likelihood ratio test. We developed a workflow that can be adapted and reused to compare the impact of reference genomes on genome analyses in various breeds, populations and species.

genomics↗