Networks of inbreeding coefficients in a selected population of rabbits.

The correlation between pedigree and genomic-based inbreeding coefficients is usually discussed in the literature. However, some of these correlations could be spurious. Using partial correlations and information theory, it is possible to distinguish a significant association between two variables which is independent from associations with a third variable. The objective of this study is to implement partial correlations and information theory to assess the relationship between different inbreeding coefficients using a selected population of rabbits. Data from pedigree and genomic information from a 200K SNP chip were available. After applying filtering criteria, the data set comprised 437 animals genotyped for 114,604 autosomal SNP. Fifteen pedigree- and genome-based inbreeding coefficients were estimated and used to build a network. Recent inbreeding coefficient based on runs of homozygosity had 9 edges linking it with different inbreeding coefficients. Partial correlations and information theory approach allowed to infer meaningful associations between inbreeding coefficients and highlighted the importance of the recent inbreeding based on runs of homozygosity, but a good proxy of it could be those pedigree-based definitions reflecting recent inbreeding.

Strong selection pressures maintain divergence on genomic islands in Atlantic cod (Gadus morhua L.) populations.

BACKGROUND: Two distinct populations have been extensively studied in Atlantic cod (Gadus morhua L.): the Northeast Arctic cod (NEAC) population and the coastal cod (CC) population. The objectives of the current study were to identify genomic islands of divergence and to propose an approach to quantify the strength of selection pressures using whole-genome single nucleotide polymorphism (SNP) data. After applying filtering criteria, information on 93 animals (9 CC individuals, 50 NEAC animals and 34 CC × NEAC crossbred individuals) and 3,123,434 autosomal SNPs were used. RESULTS: Four genomic islands of divergence were identified on chromosomes 1, 2, 7 and 12, which were mapped accurately based on SNP data and which extended in size from 11 to 18 Mb. These regions differed considerably between the two populations although the differences in the rest of the genome were small due to considerable gene flow between the populations. The estimates of selection pressures showed that natural selection was substantially more important than genetic drift in shaping these genomic islands. Our data confirmed results from earlier publications that suggested that genomic islands are due to chromosomal rearrangements that are under strong selection and reduce recombination between rearranged and non-rearranged segments. CONCLUSIONS: Our findings further support the hypothesis that selection and reduced recombination in genomic islands may promote speciation between these two populations although their habitats overlap considerably and migrations occur between them.

A fast indirect method to compute functions of genomic relationships concerning genotyped and ungenotyped individuals, for diversity management.

BACKGROUND: Pedigree-based management of genetic diversity in populations, e.g., using optimal contributions, involves computation of the [Formula: see text] type yielding elements (relationships) or functions (usually averages) of relationship matrices. For pedigree-based relationships [Formula: see text], a very efficient method exists. When all the individuals of interest are genotyped, genomic management can be addressed using the genomic relationship matrix [Formula: see text]; however, to date, the computational problem of efficiently computing [Formula: see text] has not been well studied. When some individuals of interest are not genotyped, genomic management should consider the relationship matrix [Formula: see text] that combines genotyped and ungenotyped individuals; however, direct computation of [Formula: see text] is computationally very demanding, because construction of a possibly huge matrix is required. Our work presents efficient ways of computing [Formula: see text] and [Formula: see text], with applications on real data from dairy sheep and dairy goat breeding schemes. RESULTS: For genomic relationships, an efficient indirect computation with quadratic instead of cubic cost is [Formula: see text], where Z is a matrix relating animals to genotypes. For the relationship matrix [Formula: see text], we propose an indirect method based on the difference between vectors [Formula: see text], which involves computation of [Formula: see text] and of products such as [Formula: see text] and [Formula: see text], where [Formula: see text] is a working vector derived from [Formula: see text]. The latter computation is the most demanding but can be done using sparse Cholesky decompositions of matrix [Formula: see text], which allows handling very large genomic and pedigree data files. Studies based on simulations reported in the literature show that the trends of average relationships in [Formula: see text] and [Formula: see text] differ as genomic selection proceeds. When selection is based on genomic relationships but management is based on pedigree data, the true genetic diversity is overestimated. However, our tests on real data from sheep and goat obtained before genomic selection started do not show this. CONCLUSIONS: We present efficient methods to compute elements and statistics of the genomic relationships [Formula: see text] and of matrix [Formula: see text] that combines ungenotyped and genotyped individuals. These methods should be useful to monitor and handle genomic diversity.

Associated effects of copy number variants on economically important traits in Spanish Holstein dairy cattle.

Copy number variants (CNV) are structural variants consisting of duplications or deletions of genomic fragments longer than 1 kb that present variability in the population and are heritable. The objective of this study was to identify CNV regions (CNVR) associated with 7 economically important traits (production, functional, and type traits) in Holstein cattle: fat yield, protein yield, somatic cell count, days open, stature, foot angle, and udder depth. Copy number variants were detected by using deep-sequencing data from 10 sequenced bulls and the Bovine SNP chip array hybridization signals. To reduce the number of false-positive calls, only CNV identified by both sequencing and Bovine SNP chip assays were kept in the final data set. This resulted in 823 CNVR. After filtering by minor allele frequency >0.01, a total of 90 CNVR appeared segregating in the bulls that had phenotypic data. Linear and quadratic CNVR effects were estimated using Bayesian approaches. A total of 15 CNVR were associated with the traits included in the analysis. One CNVR was associated with fat and protein yield, another 1 with fat yield, 3 with stature, 1 with foot angle, 7 with udder depth, and only 1 with days open. Among the genes located within these regions, highlighted were the MTHFSD gene that belongs to the folate metabolism genes, which play critical roles in regulating milk protein synthesis; the SNRPE gene that is related to several morphological pathologies; and the NF1 gene, which is associated with potential effects on fertility traits. The results obtained in the current study revealed that these CNVR segregate in the Holstein population, and therefore some potential exists to increase the frequencies of the favorable alleles in the population after independent validation of results in this study. However, genetic variance explained by the variants reported in this study was small.

Improving the inference of population genetic structure in the presence of related individuals.

It is well known that the presence of related individuals can affect the inference of population genetic structure from molecular data. This has been verified, for example, on the unsupervised Bayesian clustering algorithm implemented in the software STRUCTURE. This methodology assumes, among others, Hardy-Weinberg and linkage equilibrium within subpopulations. The existence of groups of close relatives, such as full-sib families, may prevent these assumptions to be fulfilled, causing the algorithm to work suboptimally. The purpose of this study was to evaluate the effect of the presence of related individuals on a different methodology (implemented in CLUSTER_DIST) for population genetic structure inference. This approach arranges individuals to maximize the genetic distance between groups and does not make Hardy-Weinberg and linkage equilibrium assumptions. We study the robustness of this approach to the presence of close relatives in a sample using simulated scenarios involving combinations of several factors, including the number of subpopulations, the level of differentiation between them, the number, size and type (full or half-sibs) of families in a sample, and the type and number of molecular markers available for clustering analysis. Results indicate that the methodology that maximizes the genetic distance between subpopulations is less influenced by the presence of related individuals than the program STRUCTURE. Therefore, the former can be used, in combination with the program STRUCTURE, to analyse population genetic structure when related individuals are suspected to be present in a sample.

QTL detection for Aeromonas salmonicida resistance related traits in turbot (Scophthalmus maximus).

BACKGROUND: Interactions between fish and pathogens, that may be harmless under natural conditions, often result in serious diseases in aquaculture systems. This is especially important due to the fact that the strains used in aquaculture are derived from wild strains that may not have had enough time to adapt to new disease pressures. The turbot is one of the most promising European aquaculture species. Furunculosis, caused by the bacterium Aeromonas salmonicida, produces important losses to turbot industry. An appealing solution is to achieve more robust broodstock, which can prevent or diminish the devastating effects of epizooties. Genomics strategies have been developed in turbot to look for candidate genes for resistance to furunculosis and a genetic map with appropriate density to screen for genomic associations has been also constructed. In the present study, a genome scan for QTL affecting resistance and survival to A. salmonicida in four turbot families was carried out. The objectives were to identify consistent QTL using different statistical approaches (linear regression and maximum likelihood) and to locate the tightest associated markers for their application in genetic breeding strategies. RESULTS: Significant QTL for resistance were identified by the linear regression method in three linkage groups (LGs 4, 6 and 9) and for survival in two LGs (6 and 9). The maximum likelihood methodology identified QTL in three LGs (5, 6 and 9) for both traits. Significant association between disease traits and genotypes was detected for several markers, some of them explaining up to 17% of the phenotypic variance. We also identified candidate genes located in the detected QTL using data from previously mapped markers. CONCLUSIONS: Several regions controlling resistance to A. salmonicida in turbot have been detected. The observed concordance between different statistical methods at particular linkage groups gives consistency to our results. The detected associated markers could be useful for genetic breeding strategies. A finer mapping will be necessary at the detected QTL intervals to narrow associations and around the closely associated markers to look for candidate genes through comparative genomics or positional cloning strategies. The identification of associated variants at specific genes will be essential, together with the QTL associations detected in this study, for future marker assisted selection programs.

Characterization of Autozygosity in Pigs in Three-Way Crossbreeding.

Crossbreeding in livestock can be used to increase genetic diversity. The resulting increase in variability is related to the heterozygosity of the crossbred animal. The evolution of diversity during crossbreeding can be assessed using genomic data. The objective of this study was to describe patterns of runs of homozygosity (ROH) in animals resulting from three-way crossbreeding, from parental pure lines, and in their crossbred offspring. The crossbreeding scheme consisted of a first crossbreeding Pietrain boars and Large White sows, after which the offspring of the Pietrain × Large White were crossed with Duroc boars. The offspring of the second crossbreeding are called G0, the offspring of G0 boars and G0 sows are called G1. All the animals were genotyped using the Illumina SNP60 porcine chip. After filtering, analyses were performed with 2,336 animals and 48,579 autosomal single nucleotide polymorphism (SNP). The mean ROH-based inbreeding coefficients were shown to be 0.27 ± 0.05, 0.23 ± 0.04, and 0.26 ± 0.04 for Duroc, Large White, and Pietrain, respectively. ROH were detected in the Pietrain × Large White crossbred but the homozygous segments were fewer and smaller than in their parents. Similar results were obtained in the G0 crossbred. However, in the G1 crossbreds the number and the size of ROH were higher than in G0 parents. Similar ROH hotspots were detected on SSC1, SSC4, SSC7, SSC9, SSC13, SSC14, and SSC15 in both G0 and G1 animals. Long ROH (>16 Mb) were observed in G1 animals, suggesting regions with low recombination rates. The conservation of these homozygous segments in the three crossbred populations means that some haplotypes were shared between parental breeds. Gene annotation in ROH hotspots in G0 animals identified genes related to production traits including carcass composition and reproduction. These findings advance our understanding of how to manage genetic diversity in crossbred populations.

Assessing population genetic structure via the maximisation of genetic distance.

BACKGROUND: The inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics. METHODS: In this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a simulated annealing algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set. RESULTS: The simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for F(ST) >or= 0.03, but only STRUCTURE estimates the correct number of clusters for F(ST) as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found. CONCLUSION: This new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present.

Identification of Quantitative Trait Loci Associated with the Skeletal Deformity LSK complex in Gilthead Seabream (Sparus aurata L.).

Morphological abnormalities, especially skeletal deformities, are some of the most important problems affecting gilthead seabream (Sparus aurata L.) aquaculture industry. In this study, a QTL analysis for LSK complex deformity in gilthead seabream is reported. LSK complex is a severe deformity consisting of a consecutive repetition of three vertebral deformities: lordosis, scoliosis, and kyphosis. Seventy-eight offspring from six breeders from a mass-spawning were analyzed: five full-sibling families, three maternal, and two paternal half-sibling families. They had shown a significant association with the LSK complex prevalence in a previous segregation analysis. Fish were genotyped using a set of multiplex PCRs (ReMsa1-13), which includes 106 microsatellite markers. Two methods were used to perform the QTL analysis: a linear regression with the GridQTL software and a linear mixed model with the Qxpak software. A total of 18 QTL were identified. Four of them (QTLSK3, 6, 12, and 14), located in LG5, 8, 17, and 20, respectively, were the most solid ones. These QTL were significant at genome level and showed an extremely large effect (>35%) with both methods. Markers close to the identified QTL showed a strong association with phenotype. Two of these molecular markers (DId-03-T and Bt-14-F) were considered as potential linked-to-this-deformity markers. The detection of these QTL supposes a critical step in the implementation of marker-assisted selection in this species, which could decrease the incidence of this deformity and other related deformities. The identification of these QTL also represents a major step towards the study of the etiology of skeletal deformities in this species.

Identification of quantitative trait loci associated with resistance to viral haemorrhagic septicaemia (VHS) in turbot (Scophthalmus maximus ): a comparison between bacterium, parasite and virus diseases.

One of the main objectives of genetic breeding programs in turbot industry is to reduce disease-related mortality. In the present study, a genome scan to detect quantitative trait loci (QTL) affecting resistance and survival to viral haemorrhagic septicaemia (VHS) was carried out. Three full-sib families with approximately 90 individuals each were genotyped and evaluated by linear regression and maximum likelihood approaches. In addition, a comparison between QTL detected for resistance and survival time to other important bacterial and parasite diseases affecting turbot (furunculosis and scuticociliatosis) was also carried out. Finally, the relationship between QTL affecting resistance/survival time to the virus and growth-related QTL was also evaluated. Several genomic regions controlling resistance and survival time to VHS were detected. Also significant associations between the evaluated traits and genotypes at particular markers were identified, explaining up to 14 % of the phenotypic variance. Several genomic regions controlling general and specific resistance to different diseases in turbot were detected. A preliminary gene mining approach identified candidate genes related to general or specific immunity. This information will be valuable to develop marker-assisted selection programs and to discover candidate genes related to disease resistance to improve turbot production.

The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis.

The inference of population genetic structures is essential in many research areas in population genetics, conservation biology and evolutionary biology. Recently, unsupervised Bayesian clustering algorithms have been developed to detect a hidden population structure from genotypic data, assuming among others that individuals taken from the population are unrelated. Under this assumption, markers in a sample taken from a subpopulation can be considered to be in Hardy-Weinberg and linkage equilibrium. However, close relatives might be sampled from the same subpopulation, and consequently, might cause Hardy-Weinberg and linkage disequilibrium and thus bias a population genetic structure analysis. In this study, we used simulated and real data to investigate the impact of close relatives in a sample on Bayesian population structure analysis. We also showed that, when close relatives were identified by a pedigree reconstruction approach and removed, the accuracy of a population genetic structure analysis can be greatly improved. The results indicate that unsupervised Bayesian clustering algorithms cannot be used blindly to detect genetic structure in a sample with closely related individuals. Rather, when closely related individuals are suspected to be frequent in a sample, these individuals should be first identified and removed before conducting a population structure analysis.

Analysis and management of gene and allelic diversity in subdivided populations using the software program METAPOP.

METAPOP (http://webs.uvigo.es/anpefi/metapop/) is a desktop application that provides an analysis of gene and allelic diversity in subdivided populations from molecular genotype or coancestry data as well as a tool for the management of genetic diversity in conservation programs. A partition of gene and allelic diversity is made within and between subpopulations, in order to assess the contribution of each subpopulation to global diversity for descriptive population genetics or conservation purposes. In the context of management of subdivided populations in in situ conservation programs, the software also determines the optimal contributions (i.e., number of offspring) of each individual, the number of migrants, and the particular subpopulations involved in the exchange of individuals in order to maintain the largest level of gene diversity in the whole population with a desired control in the rate of inbreeding. The partition of gene and allelic diversity within and between subpopulations is illustrated with microsatellite and SNP data from human populations.