Biology subjects

Ros-Freixedes, R.

Publications and source records attributed to Ros-Freixedes, R..

15 recordsLinked to original sources

bioRxiv2024

Association between mitochondrial DNA copy number and production traits in pigs

Mitochondria are essential organelles in the regulation of cellular energetic metabolism. Mitochondrial DNA copy number (mtDNA_CN) can be used as a proxy for mitochondria number, size, and activity. The aims of our study are to evaluate the effect of mtDNA_CN and mitochondrial haploblocks on production traits in pigs, and to identify the genetic background of this cellular phenotype. We collected performance data of 234 pigs and extracted DNA from skeletal muscle. Whole-genome sequencing data was used to determine mtDNA_CN. We detected positive correlations of muscle mtDNA_CN with backfat thickness at 207 d (+0.14; p-value = 0.07), and negative correlations with carcase loin thickness (-0.14; p-value = 0.03). Pigs with less mtDNA_CN had greater loin thickness (+4.1 mm; p-value = 0.01) and lower backfat thickness (-1.1 mm; p-value = 0.08), which resulted in greater carcase lean percentage (+2.4%; p-value = 0.04) than pigs with high mtDNA_CN. These results support the hypothesis that a reduction of mitochondrial activity is associated with greater feed efficiency. Pork from pigs with less mtDNA_CN may also tend to have lower ultimate pH (correlation: +0.19; p-value < 0.01). We found no association of the most frequent mitochondrial haploblocks with mtDNA_CN or the production traits, but several genomic regions that harbour potential candidate genes with functions related to mitochondrial biogenesis and homeostasis were associated to mtDNA_CN. These regions provide new insights into the genetic background of this cellular phenotype but it is still uncertain if such associations translate into noticeable effects on the production traits.

genomics↗

bioRxiv2024

Long-read de novo assembly of the red-legged partridge (Alectoris rufa) genome

The red-legged partridge (Alectoris rufa) is a popular game bird species that is in decline in several regions of southwestern Europe. The introduction of farm-reared individuals of a distinct genetic make-up in hunting reserves can result in genetic swamping of wild populations. Here we present a de novo genome assembly for the red-legged partridge based on long-read sequencing technology. The assembled genome size is 1.14 Gb, with scaffold N50 of 37.6 Mb and contig N50 of 29.5 Mb. Our genome is highly contiguous and contains 97.06% of complete avian core genes. Overall, quality of this genome assembly is equivalent to those available for other close relatives such as the Japanese quail or the chicken. This genome assembly will contribute to the understanding of genetic dynamics of wild populations of red-legged partridges with releases of farm-reared reinforcements and to appropriate management decisions of such populations.

genomics↗

bioRxiv2024

An almost chromosome-level assembly and annotation of the Alectoris rufa genome

The red-legged partridge, Alectoris rufa (n=38 chromosomes) plays a crucial role in the ecosystem of southwestern Europe, and understanding its genetics is vital for conservation and management. Here we sequence, assemble, and annotate a highly contiguous and nearly complete version of it genome (115 scaffolds, L90=23). This assembly contains 96.9% (8078 out of 8332) orthologous genes from the BUSCO aves_odb10 dataset of single copy orthologous genes. We identify RNA and protein genes, 95% of which with functional annotation. This near-chromosome level assembly revealed significant chromosome rearrangements compared to quail (Coturnix japonica) and chicken (Gallus gallus), suggesting that A. rufa and C. japonica diverged 21 M-years ago and that their common ancestor diverged from G. gallus 37 M-years ago. The reported assembly is a significant step towards a complete reference genome for A. rufa, contributing to facilitate comparative avian genomics, and providing a valuable resource for future research and conservation efforts for the red-legged partridge.

genomics↗

bioRxiv2022

Using large-scale whole-genome sequence data for single-step genomic predictions in maternal and terminal pig lines

BackgroundWhole-genome sequence (WGS) data harbor causative variants that may not be present in the regular SNP chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k imputed sequenced animals per line. MethodsTwo maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1,365 to 1,491 in maternal lines and 381 to 1,865 in terminal lines. Imputation occurred within each line, and the number of animals imputed to sequence ranged from 66k to 76k in maternal lines and 29k to 104k in terminal lines. Two preselected SNP sets were generated based on genome-wide association study (GWAS). Top40k included the SNP with the lowest p-value in each of 40k genomic windows; ChipPlusSign included significant variants integrated into the regular porcine SNP chip. Single-step genomic predictions with equal or different SNP variances using those SNP sets were compared to the regular porcine SNP chip. ResultsIn maternal lines, ChipPlusSign, and Top40k showed, on average, 0.62%, and 4.9% increased accuracy compared to the regular porcine SNP chip. The greatest changes were for fertility traits with Top40k, where the initial accuracy based on the SNP chip was low. However, for terminal lines, Top40k resulted in a loss of accuracy of 1% on average. Only ChipPlusSign provided a positive, albeit small, gain (0.85%). Assigning different variances for SNP slightly improved accuracies when using variances obtained from BayesR; however, the increase was inconsistent across the lines and traits. ConclusionsThe benefit of using sequence data depends on the line, size of the genotyped population, and how the WGS variants are preselected. When WGS is available on hundreds of thousands of animals, the advantage of sequence data is present but limited in maternal and terminal pig lines.

genomics↗

bioRxiv2022

Genomic prediction with whole-genome sequence data in intensely selected pig lines

BackgroundEarly simulations indicated that whole-genome sequence data (WGS) could improve genomic prediction accuracy and its persistence across generations and breeds. However, empirical results have been ambiguous so far. Large data sets that capture most of the genome diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays, to identify scenarios in which WGS provides the largest advantage, and to identify potential pitfalls for its effective implementation. MethodsWe sequenced 6,931 individuals from seven commercial pig lines with different numerical size. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a marker array or variants preselected from WGS based on association tests. ResultsThe prediction accuracy with each set of preselected WGS variants was not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and used to preselect variants with statistically significant associations to the trait for augmenting the established marker array. With this method and training sets of around 80k individuals, average improvements of genomic prediction accuracy of 0.025 were observed in within-line scenarios. ConclusionsOur results showed that WGS has a small potential to improve genomic prediction accuracy compared to marker arrays in intensely selected pig lines in some settings. Thus, although we expect that more robust improvements could be attained with a combination of larger training sets and optimised pipelines, the use of WGS in the current implementations of genomic prediction should be carefully evaluated on a case-by-case basis against the cost of generating WGS at a large scale.

genomics↗

bioRxiv2022

Rare and population-specific functional variation across pig lines

BackgroundIt is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for phenotypic differences among breeds, genetic lines, and varieties of livestock and crop species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in livestock commercial populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction. MethodsWe re-sequenced the whole genome of 7,848 individuals from nine commercial pig breeding lines (average sequencing coverage: 4.1x) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of distribution along the genome, minor allele frequency, Wrights fixation index (FST), individual load, and association to production traits. ResultsOf the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower FST, and putatively functional and regulatory roles (including loss-of-function and deleterious missense variants). Only a small subset of low-prevalence variants was found at intermediate allele frequencies and had large estimated effects on production traits. Individuals on average carried less private deleterious missense alleles than expected compared to other predicted consequence types. A small subset of low-prevalence variants with intermediate allele frequencies and higher FST were detected as significantly associated to the production traits and explained small fractions of phenotypic variance (up to 3.2%). These associations were tagged by other more widespread variants, including intergenic variants. ConclusionsMost low-prevalence variants are kept at very low allele frequency and only a small subset contributed detectable fractions of phenotypic variance. Not accounting for low-prevalence variants is therefore unlikely to hinder across-breed analyses, in particular for genomic prediction of breeding values using reference populations of a different genetic background.

genomics↗

bioRxiv2020

Genetics of recombination rate variation in the pig

BackgroundIn this paper, we estimated recombination rate variation within the genome and between individuals in the pig using multiocus iterative peeling for 150,000 pigs across nine genotyped pedigrees. We used this to estimate the heritability of recombination and perform a genome-wide association study of recombination in the pig. ResultsOur results confirmed known features of the pig recombination landscape, including differences in chromosome length, and marked sex differences. The recombination landscape was repeatable between lines, but at the same time, the lines also showed differences in average genome-wide recombination rate. The heritability of genome-wide recombination was low but non-zero (on average 0.07 for females and 0.05 for males). We found three genomic regions associated with recombination rate, one of them harbouring the RNF212 gene, previously associated with recombination rate in several other species. ConclusionOur results from the pig agree with the picture of recombination rate variation in vertebrates, with low but nonzero heritability, and a major locus that is homologous to one detected in several other species. This work also highlights the utility of using large-scale livestock data to understand biological processes.

genetics↗

bioRxiv2019

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

BackgroundFor assembling large whole-genome sequence datasets to be used routinely in research and breeding, the sequencing strategy should be adapted to the methods that will later be used for variant discovery and imputation. In this study we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method well-suited for large livestock populations.\n\nMethodsWe simulated marker array and whole-genome sequence data for fifteen populations with simulated or real pedigrees that had different structures. In these populations we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population we considered four levels of investment in sequencing that were proportional to the size of the population.\n\nResultsImputation accuracy largely depended on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence. Additionally, it was critical to balance high imputation accuracy in early generations as well as in late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of around 2x rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2x provided high imputation accuracy. The gain in imputation accuracy from additional investment diminished with larger populations and larger levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones.\n\nConclusionsSuitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing around 2% of the population at a uniform coverage around 2x, distributed preferably from the third generation of the pedigree onwards. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

genomics↗

bioRxiv2019

Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations

BackgroundWe demonstrate high accuracy of whole-genome sequence imputation in large livestock populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage.\n\nMethodsWe used data from four pig populations of different sizes (18,349 to 107,815 individuals) that were broadly genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most at 1x or 2x and a small fraction at 30x; average coverage per individual: 4x). We imputed whole-genome sequence with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of a total of 284 individuals that had been sequenced at high coverage, using a leave-one-out design. We complemented these results with simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees.\n\nResultsImputation accuracy was high for the majority of individuals in all four populations (median individual-wise correlation was 0.97). Individuals in the earliest generations of each population had lower accuracy than the rest, likely due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status of the individual, the availability of marker array data for immediate ancestors, and the degree of connectedness of an individual to the rest of the population, but sequencing coverage had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. These results were validated with the empirical observations.\n\nConclusionsThe coupling of an appropriate sequencing strategy and imputation method, such as described and validated here, is a powerful strategy for generating whole-genome sequence data in large pedigreed populations with high accuracy. This is a critical step for the successful implementation of whole-genome sequence data for genomic predictions and fine-mapping of causal variants.

genomics↗

bioRxiv2018

A family-based phasing algorithm for sequence data

This paper describes a family-based phasing algorithm, for variable-coverage sequence data, that first minimises phasing errors and then maximises the proportion of alleles phased. This algorithm is one of the essential tools that underpin an overall strategy for generating highly accurate sequence data on whole populations at low cost.\n\nThe algorithm is called AlphaFamSeq. It uses sequence data on the focal individual and at least two generations of ancestors to phase alleles. In the first step, AlphaFamSeq calculates allele probabilities using iterative peeling. In subsequent steps, the alleles are phased using heuristics deriving information from the sequence data of parents, grandparents and progenies and, if available, from other families in the pedigree. AlphaFamSeq was tested on a range of simulated data sets.\n\nAlphaFamSeq gives low phasing error rates and, if there is sufficient sequence information and haplotype sharing amongst individuals, it can give a high yield of correctly phased alleles.\n\nThe allele threshold had a large effect and window size had a small effect on performance. When all individuals in a single family were sequenced at different coverages the highest correctly phased alleles reached 90% of the possible maximum (98.9%) at ~1/6 of the maximum aggregate coverage. Adding sequence information from other related individuals increased the percentage of correctly phased alleles. Imputation performance was high across all allele frequencies (average correlation by marker of 0.94), except for a slight decrease at very low frequencies ([≤]0.01 MAF).\n\nWithin an overall strategy for generating highly accurate sequence data on whole populations at low cost the role of AlphaFamSeq is to provide very accurately phased haplotypes on focal individuals, who are individuals whose haplotypes are very common in the population.

genomics↗

bioRxiv2018

Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

BackgroundInherent sources of error and bias that affect the quality of the sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing there is a need to understand the impact of these errors and bias on resulting genotype calls.\n\nResultsWe used a dataset of 26 pigs sequenced both at 2x with multiplexing and at 30x without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, a default and desired step for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points.\n\nConclusionsWe propose a simple pipeline to correct this bias and we recommend that users of low-coverage sequencing be wary of unexpected biases produced by tools designed for high-coverage sequencing.

bioinformatics↗

bioRxiv2018

Sequence variability, constraint and selection in the CD163 gene in pigs

BackgroundIn this paper, we investigate sequence variability, evolutionary constraint, and selection on the CD163 gene in pigs. The pig CD163 gene is required for infection by porcine reproductive and respiratory syndrome virus (PRRSV), a serious pathogen with major impact on pig production.\n\nResultsWe used targeted pooled sequencing of the exons of CD163 to detect sequence variants in 35,000 pigs of diverse genetic backgrounds and search for potential knock-out variants. We then used whole genome sequence data from three pig lines to calculate a variant intolerance score, which measures the tolerance of genes to protein coding variation, a selection test on protein coding variation over evolutionary time, and haplotype diversity statistics to detect recent selective sweeps during breeding.\n\nConclusionsWe performed a deep survey of sequence variation in the CD163 gene in domestic pigs. We found no potential knock-out variants. CD163 was moderately intolerant to variation, and showed evidence of positive selection in the lineage leading up to the pig, but no evidence of selective sweeps during breeding.

genomics↗

bioRxiv2017

Hybrid peeling for fast and accurate calling, phasing, and imputation with sequence data of any coverage in pedigrees

In this paper we extend multi-locus iterative peeling to be a computationally efficient method for calling, phasing, and imputing sequence data of any coverage in small or large pedigrees. Our method, called hybrid peeling, uses multi-locus iterative peeling to estimate shared chromosome segments between parents and their offspring, and then uses single-locus iterative peeling to aggregate genomic information across multiple generations. Using a synthetic dataset, we first analysed the performance of hybrid peeling for calling and phasing alleles in disconnected families, families which contained only a focal individual and its parents and grandparents. Second, we analysed the performance of hybrid peeling for calling and phasing alleles in the context of the full pedigree. Third, we analysed the performance of hybrid peeling for imputing whole genome sequence data to the remaining individuals in the population. We found that hybrid peeling substantially increase the number of genotypes that were called and phased by leveraging sequence information on related individuals. The calling rate and accuracy increased when the full pedigree was used compared to a reduced pedigree of just parents and grandparents. Finally, hybrid peeling accurately imputed whole genome sequence information to non-sequenced individuals. We believe that this algorithm will enable the generation of low cost and high accuracy whole genome sequence data in many pedigreed populations. We are making this algorithm available as a standalone program called AlphaPeel.

genetics↗

bioRxiv2017

Assessment of the performance of different hidden Markov models for imputation in animal breeding

In this paper we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. However, recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations. To test this, we evaluate here the accuracy and computational cost of several methods in a series of simulated populations and a real animal population. We first tested single-step (diploid) imputation, which performs both phasing and imputation. Then we tested pre-phasing followed by haploid imputation. We tested four diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and minimac3). We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, we found that pre-phasing with Eagle2 or HAPI-UR and imputing with minimac3 or IMPUTE2 gave the highest accuracies in both simulated and real data.

genetics↗

bioRxiv2017

A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals

BackgroundThis paper describes a heuristic method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Low-coverage sequencing assembles high-coverage sequence information for every individual by accumulating data from the genome segments that they share with many other individuals into consensus haplotypes. Deriving the consensus haplotypes accurately is critical for achieving a high phasing and imputation accuracy. In order to enable accurate phasing and imputation of sequence information for the whole population we allocate the available sequencing resources among individuals with existing phased genomic data by targeting the sequencing coverage of their haplotypes.\n\nResultsOur method, called AlphaSeqOpt, prioritizes haplotypes using a score function that is based on the frequency of the haplotypes in the sequencing set relative to the target coverage. AlphaSeqOpt has two steps: (1) selection of an initial set of individuals by iteratively choosing the individuals that have the maximum score conditional to the current set, and (2) refinement of the set through several rounds of exchanges of individuals. AlphaSeqOpt is very effective for distributing a fixed amount of sequencing resources evenly across haplotypes, which results in a reduction of the proportion of haplotypes that are sequenced below the target coverage. AlphaSeqOpt can provide a greater proportion of haplotypes sequenced at the target coverage by sequencing less individuals, as compared with other methods that use a score function based on the haplotypes population frequency. A refinement of the initially selected set can provide a larger more diverse set with more unique individuals, which is beneficial in the context of low-coverage sequencing. We extend the method with an approach to filter rare haplotypes based on their flanking haplotypes, so that only those that are likely to derive from a recombination event are targeted.\n\nConclusionsWe present a method for allocating sequencing resources so that a greater proportion of haplotypes are sequenced at a coverage that is sufficiently high for population-based imputation with low-coverage sequencing. The haplotype score function, the refinement step, and the new approach of filtering rare haplotypes make AlphaSeqOpt more effective for that purpose than methods reported previously for reducing sequencing redundancy.

genomics↗