Rank-invariant estimation of inbreeding coefficients.
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ABSTRACT:
SUBMITTER: Zhang QS
PROVIDER: S-EPMC8733021 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationEstimating the individual inbreeding coefficient and pairwise kinship is an important problem in human genetics (e.g. in disease mapping) and in animal and plant genetics (e.g. inbreeding design). Existing methods, such as sample correlation-based genetic relationship matrix, KING, and UKin, are either biased, or not able to estimate inbreeding coefficients, or produce a large proportion of negative estimates that are difficult to interpret. This limitation of existing methods is partly due to failure to explicitly model inbreeding. Since all humans are inbred to various degrees by virtue of shared ancestries, it is prudent to account for inbreeding when inferring kinship between individuals.ResultsWe present "Kindred," an approach that estimates inbreeding and kinship by modeling latent identity-by-descent states that accounts for all possible allele sharing-including inbreeding-between two individuals. Kindred used non-negative least squares method to fit the model, which not only increases computation efficiency compared to the maximum likelihood method, but also guarantees non-negativity of the kinship estimates. Through simulation, we demonstrate the high accuracy and non-negativity of kinship estimates by Kindred. By selecting a subset of SNPs that are similar in allele frequencies across different continental populations, Kindred can accurately estimate kinship between admixed samples. In addition, we demonstrate that the realized kinship matrix estimated by Kindred is effective in reducing genomic control values via linear mixed model in genome-wide association studies. Finally, we demonstrate that Kindred produces sensible heritability estimates on an Australian height dataset.Availability and implementationKindred is implemented in C with multi-threading. It takes vcf file or stream as input and works seamlessly with bcftools. Kindred is freely available at https://github.com/haplotype/kindred.
| S-EPMC10902678 | biostudies-literature
Project description:Understanding the effects of inbreeding and genetic drift within populations and hybridization between genetically differentiated populations is important for many basic and applied questions in ecology and evolutionary biology. The magnitudes and even the directions of these effects can be influenced by various factors, especially by the current and historical population size (i.e. inbreeding rate). Using Drosophila littoralis as a model species, we studied the effect of inbreeding rate over a range of inbreeding levels on (i) mean fitness of a population (relative to that of an outbred control population), (ii) within-population inbreeding depression (reduction in fitness of offspring from inbred versus random mating within a population) and (iii) heterosis (increase in fitness of offspring from interpopulation versus within-population random mating). Inbreeding rate was manipulated by using three population sizes (2, 10 and 40), and fitness was measured as offspring survival and fecundity. Fast inbreeding (smaller effective population size) resulted in greater reduction in population mean fitness than slow inbreeding, when populations were compared over similar inbreeding coefficients. Correspondingly, populations with faster inbreeding expressed more heterosis upon interpopulation hybridization. Inbreeding depression within the populations did not have a clear relationship with either the rate or the level of inbreeding.
| S-EPMC4231599 | biostudies-literature
Project description:Patterns of population genetic variation have frequently been understood as consequences of life history covariates such as dispersal ability and breeding systems (e.g. selfing). For example, marine invertebrates show enormous variation in life history traits that are correlated with the extent of gene flow between populations and the magnitude of differentiation among populations at neutral genetic markers (FST). Here we document an unexpected correlation between marine invertebrate life histories and deviation from Hardy-Weinberg equilibrium (non-zero values of FIS, the inbreeding coefficient). FIS values were significantly higher in studies of species with free-spawned planktonic sperm than in studies of species that copulate or have some form of direct sperm transfer to females or benthic egg masses. This result was robust to several different analytical approaches. We note several mechanisms that might contribute to this pattern, and appeal for more studies and ideas that might help to explain our observations.
| S-EPMC1626360 | biostudies-literature
Project description:Wright's inbreeding coefficient, FST, is a fundamental measure in population genetics. Assuming a predefined population subdivision, this statistic is classically used to evaluate population structure at a given genomic locus. With large numbers of loci, unsupervised approaches such as principal component analysis (PCA) have, however, become prominent in recent analyses of population structure. In this study, we describe the relationships between Wright's inbreeding coefficients and PCA for a model of K discrete populations. Our theory provides an equivalent definition of FST based on the decomposition of the genotype matrix into between and within-population matrices. The average value of Wright's FST over all loci included in the genotype matrix can be obtained from the PCA of the between-population matrix. Assuming that a separation condition is fulfilled and for reasonably large data sets, this value of FST approximates the proportion of genetic variation explained by the first (K - 1) principal components accurately. The new definition of FST is useful for computing inbreeding coefficients from surrogate genotypes, for example, obtained after correction of experimental artifacts or after removing adaptive genetic variation associated with environmental variables. The relationships between inbreeding coefficients and the spectrum of the genotype matrix not only allow interpretations of PCA results in terms of population genetic concepts but extend those concepts to population genetic analyses accounting for temporal, geographical and environmental contexts.
| S-EPMC8320931 | biostudies-literature
Project description:Inbreeding depression, the deterioration in mean trait value in progeny of related parents, is a fundamental quantity in genetics, evolutionary biology, animal and plant breeding, and conservation biology. The magnitude of inbreeding depression can be quantified by the inbreeding load, typically measured in numbers of lethal equivalents, a population genetic quantity that allows for comparisons between environments, populations or species. However, there is as yet no quantitative assessment of which combinations of statistical models and metrics of inbreeding can yield such estimates. Here, we review statistical models that have been used to estimate inbreeding load and use population genetic simulations to investigate how unbiased estimates can be obtained using genomic and pedigree-based metrics of inbreeding. We use simulated binary viability data (i.e., dead versus alive) as our example, but the concepts apply to any trait that exhibits inbreeding depression. We show that the increasingly popular generalized linear models with logit link do not provide comparable and unbiased population genetic measures of inbreeding load, independent of the metric of inbreeding used. Runs of homozygosity result in unbiased estimates of inbreeding load, whereas inbreeding measured from pedigrees results in slight overestimates. Due to widespread use of models that do not yield unbiased measures of the inbreeding load, some estimates in the literature cannot be compared meaningfully. We surveyed the literature for reliable estimates of the mean inbreeding load from wild vertebrate populations and found an average of 3.5 haploid lethal equivalents for survival to sexual maturity. To obtain comparable estimates, we encourage researchers to use generalized linear models with logarithmic links or maximum-likelihood estimation of the exponential equation, and inbreeding coefficients calculated from runs of homozygosity, provided an assembled reference genome of sufficient quality and enough genetic marker data are available.
| S-EPMC6346663 | biostudies-literature
Project description:Inference of individual ancestry coefficients, which is important for population genetic and association studies, is commonly performed using computer-intensive likelihood algorithms. With the availability of large population genomic data sets, fast versions of likelihood algorithms have attracted considerable attention. Reducing the computational burden of estimation algorithms remains, however, a major challenge. Here, we present a fast and efficient method for estimating individual ancestry coefficients based on sparse nonnegative matrix factorization algorithms. We implemented our method in the computer program sNMF and applied it to human and plant data sets. The performances of sNMF were then compared to the likelihood algorithm implemented in the computer program ADMIXTURE. Without loss of accuracy, sNMF computed estimates of ancestry coefficients with runtimes ?10-30 times shorter than those of ADMIXTURE.
| S-EPMC3982712 | biostudies-literature
Project description:The inbreeding coefficient (F) of individuals can be estimated from molecular marker data, such as SNPs, using measures of homozygosity of individual markers or runs of homozygosity (ROH) across the genome. These different measures of F can then be used to estimate the rate of inbreeding depression (ID) for quantitative traits. Some recent simulation studies have investigated the accuracy of this estimation with contradictory results. Whereas some studies suggest that estimates of inbreeding from ROH account more accurately for ID, others suggest that inbreeding measures from SNP-by-SNP homozygosity giving a large weight to rare alleles are more accurate. Here, we try to give more light on this issue by carrying out a set of computer simulations considering a range of population genetic parameters and population sizes. Our results show that the previous studies are indeed not contradictory. In populations with low effective size, where relationships are more tight and selection is relatively less intense, F measures based on ROH provide very accurate estimates of ID whereas SNP-by-SNP-based F measures with high weight to rare alleles can show substantial upwardly biased estimates of ID. However, in populations of large effective size, with more intense selection and trait allele frequencies expected to be low if they are deleterious for fitness because of purifying selection, average estimates of ID from SNP-by-SNP-based F values become unbiased or slightly downwardly biased and those from ROH-based F values become slightly downwardly biased. The noise attached to all these estimates, nevertheless, can be very high in large-sized populations. We also investigate the relationship between the different F measures and the homozygous mutation load, which has been suggested as a proxy of inbreeding depression.
| S-EPMC7896712 | biostudies-literature
Project description:BackgroundThe calculation of demographic measures is a useful tool for evaluating the genomic architecture of dog breeds and enables ranking dog breeds in terms of genetic diversity. To achieve this for the German Dalmatian dog population, 307 purebred animals of this breed were genotyped on the Illumina Canine high density BeadChip. The analysis of pedigree-based inbreeding was performed based on a pedigree with 25,761 dogs including the genotyped dogs.ResultsThe effective population size derived from squared correlation coefficients between SNP alleles (r 2) was 69. The maximum value of r 2 was 0.56, resulting in a 50% decay value of 0.28 at a marker distance of 37.5 kb. The effective population size calculated from pedigree data using individual increase in inbreeding over equivalent generations was 116. The pedigree inbreeding coefficient was 0.026. The genomic inbreeding coefficient based on the length of runs of homozygosity (ROH) was calculated for seven length categories of ROHs, and ranged from 0.08 to 0.28. The fixation coefficients FIS_PED and FIS_GENO were at 0.017 and 0.004. PANTHER statistical overrepresentation analysis of genes located in consensus ROHs revealed highly underrepresented biological processes in 50% of the investigated dogs. One of those is the 0.28 fold enriched "immune response", which might be associated to the high prevalence of allergic dermatitis in the breed. Candidate genes for congenital sensorineural deafness (CCSD, a highly prevalent disease in the Dalmatian) were discovered in consensus ROHs.ConclusionsThe fast decay of r 2 and the moderate inbreeding coefficients indicate that the German Dalmatian dog population is rather diverse. Pedigree- and genomic-based inbreeding measures were highly correlated and therefore prove good reliability for the given population. Analyses of consensus ROHs with genes coding for deafness and other breed-defining traits, such as hyperuricosuria, indicate that those ROH became fixed in the Dalmatian population about 500 years ago. In case of the Dalmatian dog, a ROH of 40 SNPs length is enough to investigate signatures of selection (e.g. the ROH with the fixed hyperuricosuria mutation) as far back as the breed formation point approximately 500 years ago.
| S-EPMC7371805 | biostudies-literature
Project description:BackgroundGenomic technologies, such as high-throughput genotyping based on SNP arrays, have great potential to decipher the genetic architecture of complex traits and provide background information concerning genome structure in domestic animals, including the extent of linkage disequilibrium (LD) and haplotype blocks. The objective of this study was to estimate LD, the population evolution (past effective population size) and the level of inbreeding in Spanish Churra sheep.ResultsA total of 43,784 SNPs distributed in the ovine autosomal genome was analyzed in 1,681 Churra ewes. LD was assessed by measuring r2 between all pairs of loci. For SNPs up to 10 ?kb apart, the average r2 was 0.329; for SNPs separated by 200-500? kb the average r2 was 0.061. When SNPs are separated by more than 50 Mbp, the average r2 is the same as between non-syntenic SNP pairs (0.003). The effective population size has decreased through time, faster from 1,000 to 100 ?years ago and slower since the selection scheme started (15-25 generations ago). In the last generation, four years ago, the effective population size was estimated to be 128 animals. Inbreeding coefficients, although differed depending on the estimation approaches, were generally low and showed the same trend, which indicates that since 2003, inbreeding has been slightly increasing in the studied resource population.ConclusionsThe extent of LD in Churra sheep persists over much more limited distances than reported in dairy cattle and seems to be similar to other ovine populations. Churra sheep show a wide genetic base, with a long-term viable effective population size that has been slightly decreasing since selection scheme began in 1986. The genomic dataset analyzed provided useful information for identifying low-level inbreeding in the sample, whereas based on the parameters reported here, a higher marker density than that analyzed here will be needed to successfully conduct accurate mapping of genes underlying production traits and genomic selection prediction in this sheep breed. Although the Ovine Assembly development is still in a draft stage and future refinements will provide a more accurate physical map that will improve LD estimations, this work is a first step towards the understanding of the genetic architecture in sheep.
| S-EPMC3431250 | biostudies-literature
Project description:Many statistical methods gain robustness and flexibility by sacrificing convenient computational structures. In this paper, we illustrate this fundamental tradeoff by studying a semi-parametric graph estimation problem in high dimensions. We explain how novel computational techniques help to solve this type of problem. In particular, we propose a nonparanormal neighborhood pursuit algorithm to estimate high dimensional semiparametric graphical models with theoretical guarantees. Moreover, we provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Though this paper focuses on the problem of graph estimation, the proposed methodology is widely applicable to other problems with similar structures. We also report thorough experimental results on text, stock, and genomic datasets.
| S-EPMC4219653 | biostudies-literature