Evaluation of the linkage-disequilibrium method for the estimation of effective population size when generations overlap: an empirical case

Genomic diversity of the locally developed Latvian Darkheaded sheep breed

The Latvian Darkheaded is the only locally developed sheep breed. The breed was formed at the beginning of the 20th century by crossing local coarse-wooled sheep with the British Shropshire and Oxfordshire breeds. The breed was later improved by adding Ile-de-France, Texel, German blackheads, and Finnsheep to achieve higher prolificacy and better meat quality. Previous studies have reported the Latvian Darkheaded sheep to be closely related to Estonian and Lithuanian Blackface breeds, according to microsatellite data. To expand our knowledge of the genetic resources of the Latvian Darkheaded breed, we conducted a whole-genome resequencing analysis on 40 native sheep. The investigation showed that local sheep harbor genetic diversity levels similar to those observed among other improved breeds of European origin, including Charollais and Suffolk. Genome-wide nucleotide diversity (π) in Latvian Darkheaded sheep was 3.91 × 10-3, whereas the average observed heterozygosity among the 40 animals was 0.267 and 0.438 within the subsample of unrelated individuals. The Ne has rapidly decreased to 200 ten generations ago with a recent drop to Ne 73 four generations ago. However, inbreeding levels based on runs of homozygosity were, on average, low, with FROH ranging between 0.016 and 0.059. The analysis of the genomic composition of the breed confirmed shared ancestry with sheep of British origin, reflecting the history of the breed. Nevertheless, Latvian Darkheaded sheep were genetically separable. The contemporary Latvian Darkheaded sheep population is genetically diverse with a low inbreeding rate. However, further development of breed management programs is necessary to prevent an increase in inbreeding, loss of genetic diversity, and depletion of breed-specific genetic resources, ensuring the preservation of the native Latvian Darkheaded sheep.

The genetic status and rescue measure for a geographically isolated population of Amur tigers

The Amur tiger is currently confronted with challenges of anthropogenic development, leading to its population becoming fragmented into two geographically isolated groups: smaller and larger ones. Small and isolated populations frequently face a greater extinction risk, yet the small tiger population's genetic status and survival potential have not been assessed. Here, a total of 210 samples of suspected Amur tiger feces were collected from this small population, and the genetic background and population survival potentials were assessed by using 14 microsatellite loci. Our results demonstrated that the mean number of alleles in all loci was 3.7 and expected heterozygosity was 0.6, indicating a comparatively lower level of population genetic diversity compared to previously reported studies on other subspecies. The genetic estimates of effective population size (Ne) and the Ne/N ratio were merely 7.6 and 0.152, respectively, representing lower values in comparison to the Amur tiger population in Sikhote-Alin (the larger group). However, multiple methods have indicated the possibility of genetic divergence within our isolated population under study. Meanwhile, the maximum kinship recorded was 0.441, and the mean inbreeding coefficient stood at 0.0868, both of which are higher than those observed in other endangered species, such as the African lion and the grey wolf. Additionally, we have identified a significant risk of future extinction if the lethal equivalents were to reach 6.26, which is higher than that of other large carnivores. Further, our simulation results indicated that an increase in the number of breeding females would enhance the prospects of this population. In summary, our findings provide a critical theoretical basis for further bailout strategies concerning Amur tigers.

Practical application of the linkage disequilibrium method for estimating contemporary effective population size: A review

The method to estimate contemporary effective population size (Ne ) based on patterns of linkage disequilibrium (LD) at unlinked loci has been widely applied to natural and managed populations. The underlying model makes many simplifying assumptions, most of which have been evaluated in numerous studies published over the last two decades. Here, these performance evaluations are reviewed and summarized, with a focus on information that facilitates practical application to real populations in nature. Potential sources of bias that are discussed include calculation of r2 (a measure of LD), adjustments for sampling error, physical linkage, age structure, migration and spatial structure, mutation and selection, mating systems, changes in abundance, rare alleles, missing data, genotyping errors, data filtering choices and methods for combining multiple Ne estimates. Factors that affect precision are reviewed, including pseudoreplication that limits the information gained from large genomics datasets, constraints imposed by small samples of individuals, and the challenges in obtaining robust estimates for large populations. Topics that merit further research include the potential to weight r2 values by allele frequency, lump samples of individuals, use genotypic likelihoods rather than called genotypes, prune large LD values and apply the method to species practising partial monogamy.

Estimation of the contemporary effective population size from SNP data while accounting for mating structure

A new method is developed to estimate the contemporary effective population size (Ne ) from linkage disequilibrium (LD) between SNPs without information on their location, which is the usual scenario in non-model species. The general theory of linkage disequilibrium is extended to include the contribution of full-sibs to the measure of LD, leading naturally to the estimation of Ne in monogamous and polygamous mating systems, as well as in multiparous species, and with non-random distributions of full-sib family size due to selection or other causes. Prediction of confidence intervals for Ne estimates was solved using a small artificial neural network trained on a dataset of over 105 simulation results. The method, implemented in a user-friendly and fast software (currentNe), is able to estimate Ne even in problematic scenarios with large population sizes or small sample sizes and provides confidence intervals that are more consistent than resampling methods.

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

In genomic recurrent selection, the more markers, the better because they buffer the linkage disequilibrium losses caused by recombination over cycles, and consequently, provide higher responses to selection. Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, studies that focus on long selective breeding cycles generally do not consider GS models with the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models [additive, additive-dominant, general (GCA), and this plus specific combining ability (GCA + SCA)]. Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or GCA + SCA for reciprocal.

Molecular tracking and prevalence of the red colour morph restricted to a harvested leopard population in South Africa

The red leopard (Panthera pardus) colour morph is a colour variant that occurs only in South Africa, where it is confined to the Central Bushveld bioregion. Red leopards have been spreading over the past 40 years, which raises the speculation that the prevalence of this phenotype is related to low dispersal of young individuals owing to high off-take in the region. Intensive selective hunting tends to remove large resident male leopards from the breeding population, which gives young male leopards the chance to mate with resident female leopards that are more likely to be their relatives, eventually increasing the frequency of rare genetic variants. To investigate the genetic mechanisms underlying the red coat colour morph in leopards, and whether its prevalence in South Africa relates to an increase in genetic relatedness in the population, we sequenced exons of six coat colour-associated genes and 20 microsatellite loci in twenty Wild-type and four red leopards. The results were combined with demographic data available from our study sites. We found that red leopards own a haplotype in homozygosity identified by two SNPs and a 1 bp deletion that causes a frameshift in the tyrosinase-related protein 1 (TYRP1), a gene known to be involved in the biosynthesis of melanin. Microsatellite analyses indicate clear signs of a population bottleneck and a relatedness of 0.11 among all pairwise relationships, eventually supporting our hypothesis that a rare colour morph in the wild has increased its local frequency due to low natal dispersal, while subject to high human-induced mortality rate.

The estimates of effective population size based on linkage disequilibrium are virtually unaffected by natural selection

The effective population size (Ne) is a key parameter to quantify the magnitude of genetic drift and inbreeding, with important implications in human evolution. The increasing availability of high-density genetic markers allows the estimation of historical changes in Ne across time using measures of genome diversity or linkage disequilibrium between markers. Directional selection is expected to reduce diversity and Ne, and this reduction is modulated by the heterogeneity of the genome in terms of recombination rate. Here we investigate by computer simulations the consequences of selection (both positive and negative) and recombination rate heterogeneity in the estimation of historical Ne. We also investigate the relationship between diversity parameters and Ne across the different regions of the genome using human marker data. We show that the estimates of historical Ne obtained from linkage disequilibrium between markers (NeLD) are virtually unaffected by selection. In contrast, those estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, which could have important consequences for the estimation of human demography. The simulation results are supported by the analysis of human data. The estimates of NeLD obtained for particular genomic regions do not correlate, or they do it very weakly, with recombination rate, nucleotide diversity, proportion of polymorphic sites, background selection statistic, minor allele frequency of SNPs, loss of function and missense variants and gene density. This suggests that NeLD measures mainly reflect demographic changes in population size across generations.

Estimates of recent and historical effective population size in turbot, seabream, seabass and carp selective breeding programmes

Background

The high fecundity of fish species allows intense selection to be practised and therefore leads to fast genetic gains. Based on this, numerous selective breeding programmes have been started in Europe in the last decades, but in general, little is known about how the base populations of breeders have been built. Such knowledge is important because base populations can be created from very few individuals, which can lead to small effective population sizes and associated reductions in genetic variability. In this study, we used genomic information that was recently made available for turbot (Scophthalmus maximus), gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and common carp (Cyprinus carpio) to obtain accurate estimates of the effective size for commercial populations.

Methods

Restriction-site associated DNA sequencing data were used to estimate current and historical effective population sizes. We used a novel method that considers the linkage disequilibrium spectrum for the whole range of genetic distances between all pairs of single nucleotide polymorphisms (SNPs), and thus accounts for potential fluctuations in population size over time.

Results

Our results show that the current effective population size for these populations is small (equal to or less than 50 fish), potentially putting the sustainability of the breeding programmes at risk. We have also detected important drops in effective population size about five to nine generations ago, most likely as a result of domestication and the start of selective breeding programmes for these species in Europe.

Conclusions

Our findings highlight the need to broaden the genetic composition of the base populations from which selection programmes start, and suggest that measures designed to increase effective population size within all farmed populations analysed here should be implemented in order to manage genetic variability and ensure the sustainability of the breeding programmes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00680-9.

The value of genomic relationship matrices to estimate levels of inbreeding

BACKGROUND

Genomic relationship matrices are used to obtain genomic inbreeding coefficients. However, there are several methodologies to compute these matrices and there is still an unresolved debate on which one provides the best estimate of inbreeding. In this study, we investigated measures of inbreeding obtained from five genomic matrices, including the Nejati-Javaremi allelic relationship matrix (FNEJ), the Li and Horvitz matrix based on excess of homozygosity (FL&H), and the VanRaden (methods 1, FVR1, and 2, FVR2) and Yang (FYAN) genomic relationship matrices. We derived expectations for each inbreeding coefficient, assuming a single locus model, and used these expectations to explain the patterns of the coefficients that were computed from thousands of single nucleotide polymorphism genotypes in a population of Iberian pigs.

RESULTS

Except for FNEJ, the evaluated measures of inbreeding do not match with the original definitions of inbreeding coefficient of Wright (correlation) or Malécot (probability). When inbreeding coefficients are interpreted as indicators of variability (heterozygosity) that was gained or lost relative to a base population, both FNEJ and FL&H led to sensible results but this was not the case for FVR1, FVR2 and FYAN. When variability has increased relative to the base, FVR1, FVR2 and FYAN can indicate that it decreased. In fact, based on FYAN, variability is not expected to increase. When variability has decreased, FVR1 and FVR2 can indicate that it has increased. Finally, these three coefficients can indicate that more variability than that present in the base population can be lost, which is also unreasonable. The patterns for these coefficients observed in the pig population were very different, following the derived expectations. As a consequence, the rate of inbreeding depression estimated based on these inbreeding coefficients differed not only in magnitude but also in sign.

CONCLUSIONS

Genomic inbreeding coefficients obtained from the diagonal elements of genomic matrices can lead to inconsistent results in terms of gain and loss of genetic variability and inbreeding depression estimates, and thus to misleading interpretations. Although these matrices have proven to be very efficient in increasing the accuracy of genomic predictions, they do not always provide a useful measure of inbreeding.

Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

Background

One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV).

Results

The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP.

Conclusion

These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07404-9.

The Identification of Runs of Homozygosity Gives a Focus on the Genetic Diversity and Adaptation of the "Charolais de Cuba" Cattle

Simple Summary

The Charolais de Cuba cattle is a tropical adapted breed founded in Cuba around 120 years ago from Charolais French specimens. Nowadays, it is still a closed breed and remains as a small population. In this work, we analyzed the inbreeding and diversity patterns, as well as the population size, of this recent adapted breed via a run of homozygosity (ROH) analysis. We found that the genomic inbreeding levels are higher in the Charolais de Cuba breed compared to French and British Charolais populations. Nevertheless, we detected that the effective population size experienced a very similar decline during the last century in the three Charolais populations studied. Finally, a number of regions with exceptional patterns of long homozygosity were identified in this breed, and these could be related to processes of adaptation to tropical conditions.

Abstract

Inbreeding and effective population size (Ne) are fundamental indicators for the management and conservation of genetic diversity in populations. Genomic inbreeding gives accurate estimates of inbreeding, and the Ne determines the rate of the loss of genetic variation. The objective of this work was to study the distribution of runs of homozygosity (ROHs) in order to estimate genomic inbreeding (F_ROH) and an effective population size using 38,789 Single Nucleotide Polymorphisms (SNPs) from the Illumina Bovine 50K BeadChip in 86 samples from populations of Charolais de Cuba (n = 40) cattle and to compare this information with French (n = 20) and British Charolais (n = 26) populations. In the Cuban, French, and British Charolais populations, the average estimated genomic inbreeding values using the F_ROH statistics were 5.7%, 3.4%, and 4%, respectively. The dispersion measured by variation coefficient was high at 43.9%, 37.0%, and 54.2%, respectively. The effective population size experienced a very similar decline during the last century in Charolais de Cuba (from 139 to 23 individuals), in French Charolais (from 142 to 12), and in British Charolais (from 145 to 14) for the ~20 last generations. However, the high variability found in the ROH indicators and F_ROH reveals an opportunity for maintaining the genetic diversity of this breed with an adequate mating strategy, which can be favored with the use of molecular markers. Moreover, the detected ROH were compared to previous results obtained on the detection of signatures of selection in the same breed. Some of the observed signatures were confirmed by the ROHs, emphasizing the process of adaptation to tropical climate experienced by the Charolais de Cuba population.

Assessing Accuracy of Genomic Predictions for Resistance to Infectious Hematopoietic Necrosis Virus With Progeny Testing of Selection Candidates in a Commercial Rainbow Trout Breeding Population

Infectious hematopoietic necrosis (IHN) is an economically important disease of salmonid fish caused by the IHN virus (IHNV). Under industrial aquaculture settings, IHNV can cause substantial mortality and losses. Actually, there is no confirmed and cost-effective method for IHNV control. Clear Springs Foods, Inc. has been performing family-based selective breeding to increase genetic resistance to IHNV in their rainbow trout breeding program. In an earlier study, we used siblings cross-validation to estimate the accuracy of genomic prediction (GP) for IHNV resistance in this breeding population. In the present report, we used empirical progeny testing data to evaluate whether genomic selection (GS) can improve the accuracy of breeding value predictions over traditional pedigree-based best linear unbiased predictions (PBLUP). We found that the GP accuracy with single-step GBLUP (ssGBLUP) outperformed PBLUP by 15% (from 0.33 to 0.38). Furthermore, we found that ssGBLUP had higher GP accuracy than weighted ssGBLUP (wssGBLUP) and single-step Bayesian multiple regression (ssBMR) models with BayesB and BayesC priors which supports our previous findings that the underlying liability of genetic resistance against IHNV in this breeding population might be polygenic. Our results show that GS can be more effective than either the traditional pedigree-based PBLUP model or the marker-assisted selection approach for improving genetic resistance against IHNV in this commercial rainbow trout population.

Marker genotyping error effects on genomic predictions under different genetic architectures

This study aimed to determine the effect of different rates of marker genotyping error on the accuracy of genomic prediction that was examined under distinct marker and quantitative trait loci (QTL) densities and different heritability estimates using a stochastic simulation approach. For each scenario of simulation, a reference population with phenotypic and genotypic records and a validation population with only genotypic records were considered. Marker effects were estimated in the reference population, and then their genotypic records were used to predict genomic breeding values in the validation population. The prediction accuracy was calculated as the correlation between estimated and true breeding values. The prediction bias was examined by computing the regression of true genomic breeding value on estimated genomic breeding value. The accuracy of the genomic evaluation was the highest in a scenario with no marker genotyping error and varied from 0.731 to 0.934. The accuracy of the genomic evaluation was the lowest in a scenario with marker genotyping error equal to 20% and changed from 0.517 to 0.762. The unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value were obtained in the reference and validation populations when the rate of marker genotyping error was equal to zero. The results showed that marker genotyping error can reduce the accuracy of genomic evaluations. Moreover, marker genotyping error can provide biased estimates of genomic breeding values. Therefore, for obtaining accurate results it is recommended to minimize the marker genotyping errors to zero in genomic evaluation programs.

Recent Demographic History Inferred by High-Resolution Analysis of Linkage Disequilibrium

Inferring changes in effective population size (Ne) in the recent past is of special interest for conservation of endangered species and for human history research. Current methods for estimating the very recent historical Ne are unable to detect complex demographic trajectories involving multiple episodes of bottlenecks, drops, and expansions. We develop a theoretical and computational framework to infer the demographic history of a population within the past 100 generations from the observed spectrum of linkage disequilibrium (LD) of pairs of loci over a wide range of recombination rates in a sample of contemporary individuals. The cumulative contributions of all of the previous generations to the observed LD are included in our model, and a genetic algorithm is used to search for the sequence of historical Ne values that best explains the observed LD spectrum. The method can be applied from large samples to samples of fewer than ten individuals using a variety of genotyping and DNA sequencing data: haploid, diploid with phased or unphased genotypes and pseudohaploid data from low-coverage sequencing. The method was tested by computer simulation for sensitivity to genotyping errors, temporal heterogeneity of samples, population admixture, and structural division into subpopulations, showing high tolerance to deviations from the assumptions of the model. Computer simulations also show that the proposed method outperforms other leading approaches when the inference concerns recent timeframes. Analysis of data from a variety of human and animal populations gave results in agreement with previous estimations by other methods or with records of historical events.

Genomic diversity, linkage disequilibrium and selection signatures in European local pig breeds assessed with a high density SNP chip

Genetic characterization of local breeds is essential to preserve their genomic variability, to advance conservation policies and to contribute to their promotion and sustainability. Genomic diversity of twenty European local pig breeds and a small sample of Spanish wild pigs was assessed using high density SNP chips. A total of 992 DNA samples were analyzed with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip. Genotype data was employed to compute genetic diversity, population differentiation and structure, genetic distances, linkage disequilibrium and effective population size. Our results point out several breeds, such as Turopolje, Apulo Calabrese, Casertana, Mora Romagnola and Lithuanian indigenous wattle, having the lowest genetic diversity, supported by low heterozygosity and very small effective population size, demonstrating the need of enhanced conservation strategies. Principal components analysis showed the clustering of the individuals of the same breed, with few breeds being clearly isolated from the rest. Several breeds were partially overlapped, suggesting genetic closeness, which was particularly marked in the case of Iberian and Alentejana breeds. Spanish wild boar was also narrowly related to other western populations, in agreement with recurrent admixture between wild and domestic animals. We also searched across the genome for loci under diversifying selection based on F_ST outlier tests. Candidate genes that may underlie differences in adaptation to specific environments and productive systems and phenotypic traits were detected in potentially selected genomic regions.

Whole Genome Linkage Disequilibrium and Effective Population Size in a Coho Salmon (Oncorhynchus kisutch) Breeding Population Using a High-Density SNP Array

The estimation of linkage disequilibrium between molecular markers within a population is critical when establishing the minimum number of markers required for association studies, genomic selection, and inferring historical events influencing different populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon breeding population using a high-density SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the breeding population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r² was 0.10. The Average r² value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at a distance lower than 1 kb and up to 10 Mb, respectively. An r² threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (>0.20 at distances lower than 1 Mb). Average r² values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective population size of 43 was estimated for the population 10 generations ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the SNP panel used allowed us to capture high-resolution information in the farmed coho salmon population. Furthermore, based on the contemporary N_e, a new mate allocation strategy is suggested to increase the effective population size.

Development of genomic predictions for harvest and carcass weight in channel catfish

Background

Catfish farming is the largest segment of US aquaculture and research is ongoing to improve production efficiency, including genetic selection programs to improve economically important traits. The objectives of this study were to investigate the use of genomic selection to improve breeding value accuracy and to identify major single nucleotide polymorphisms (SNPs) associated with harvest weight and residual carcass weight in a channel catfish population. Phenotypes were available for harvest weight (n = 27,160) and residual carcass weight (n = 6020), and 36,365 pedigree records were available. After quality control, genotypes for 54,837 SNPs were available for 2911 fish. Estimated breeding values (EBV) were obtained with traditional pedigree-based best linear unbiased prediction (BLUP) and genomic (G)EBV were estimated with single-step genomic BLUP (ssGBLUP). EBV and GEBV prediction accuracies were evaluated using different validation strategies. The ability to predict future performance was calculated as the correlation between EBV or GEBV and adjusted phenotypes.

Results

Compared to the pedigree BLUP, ssGBLUP increased predictive ability up to 28% and 36% for harvest weight and residual carcass weight, respectively; and GEBV were superior to EBV for all validation strategies tested. Breeding value inflation was assessed as the regression coefficient of adjusted phenotypes on breeding values, and the results indicated that genomic information reduced breeding value inflation. Genome-wide association studies based on windows of 20 adjacent SNPs indicated that both harvest weight and residual carcass weight have a polygenic architecture with no major SNPs (the largest SNPs explained 0.96 and 1.19% of the additive genetic variation for harvest weight and residual carcass weight respectively).

Conclusions

Genomic evaluation improves the ability to predict future performance relative to traditional BLUP and will allow more accurate identification of genetically superior individuals within catfish families.

Conservation Genomics of the Declining North American Bumblebee Bombus terricola Reveals Inbreeding and Selection on Immune Genes

The yellow-banded bumblebee Bombus terricola was common in North America but has recently declined and is now on the IUCN Red List of threatened species. The causes of B. terricola's decline are not well understood. Our objectives were to create a partial genome and then use this to estimate population data of conservation interest, and to determine whether genes showing signs of recent selection suggest a specific cause of decline. First, we generated a draft partial genome (contig set) for B. terricola, sequenced using Pacific Biosciences RS II at an average depth of 35×. Second, we sequenced the individual genomes of 22 bumblebee gynes from Ontario and Quebec using Illumina HiSeq 2500, each at an average depth of 20×, which were used to improve the PacBio genome calls and for population genetic analyses. The latter revealed that several samples had long runs of homozygosity, and individuals had high inbreeding coefficient F, consistent with low effective population size. Our data suggest that B. terricola's effective population size has decreased orders of magnitude from pre-Holocene levels. We carried out tests of selection to identify genes that may have played a role in ameliorating environmental stressors underlying B. terricola's decline. Several immune-related genes have signatures of recent positive selection, which is consistent with the pathogen-spillover hypothesis for B. terricola's decline. The new B. terricola contig set can help solve the mystery of bumblebee decline by enabling functional genomics research to directly assess the health of pollinators and identify the stressors causing declines.

Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium-density single nucleotide polymorphism (SNP) array. Here, the impact of lower-density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)-flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree-based prediction (0.50-0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL-flanking SNP (0.65-0.72) was similar to the panel with 35K SNP (0.65-0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r2  ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long-range LD likely contributed to the accurate genomic predictions with the low-density SNP panels. Population structure analysis supported the hypothesis that long-range LD in this population may be caused by admixture. Results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.

Demographic Trends in Korean Native Cattle Explained Using Bovine SNP50 Beadchip

Linkage disequilibrium (LD) is the non-random association between the loci and it could give us a preliminary insight into the genetic history of the population. In the present study LD patterns and effective population size (Ne) of three Korean cattle breeds along with Chinese, Japanese and Mongolian cattle were compared using the bovine Illumina SNP50 panel. The effective population size (Ne) is the number of breeding individuals in a population and is particularly important as it determines the rate at which genetic variation is lost. The genotype data in our study comprised a total of 129 samples, varying from 4 to 39 samples. After quality control there were ~29,000 single nucleotide polymorphisms (SNPs) for which r² value was calculated. Average distance between SNP pairs was 1.14 Mb across all breeds. Average r² between adjacent SNP pairs ranged between was 0.1 for Yanbian to 0.3 for Qinchuan. Effective population size of the breeds based on r² varied from 16 in Hainan to 226 in Yanbian. Amongst the Korean native breeds effective population size of Brindle Hanwoo was the least with Ne = 59 and Brown Hanwoo was the highest with Ne = 83. The effective population size of the Korean cattle breeds has been decreasing alarmingly over the past generations. We suggest appropriate measures to be taken to prevent these local breeds in their native tracts.

Deciphering the regulation of porcine genes influencing growth, fatness and yield-related traits through genetical genomics

Genetical genomics approaches aim at identifying quantitative trait loci for molecular traits, also known as intermediate phenotypes, such as gene expression, that could link variation in genetic information to physiological traits. In the current study, an expression GWAS has been carried out on an experimental Iberian × Landrace backcross in order to identify the genomic regions regulating the gene expression of those genes whose expression is correlated with growth, fat deposition, and premium cut yield measures in pig. The analyses were conducted exploiting Porcine 60K SNP BeadChip genotypes and Porcine Expression Microarray data hybridized on mRNA from Longissimus dorsi muscle. In order to focus the analysis on productive traits and reduce the number of analyses, only those probesets whose expression showed significant correlation with at least one of the seven phenotypes of interest were selected for the eGWAS. A total of 63 eQTL regions were identified with effects on 36 different transcripts. Those eQTLs overlapping with phenotypic QTLs on SSC4, SSC9, SSC13, and SSC17 chromosomes previously detected in the same animal material were further analyzed. Moreover, candidate genes and SNPs were analyzed. Among the most promising results, a long non-coding RNA, ALDBSSCG0000001928, was identified, whose expression is correlated with premium cut yield. Association analysis and in silico sequence domain annotation support TXNRD3 polymorphisms as candidate to regulate ALDBSSCG0000001928 expression, which can be involved in the transcriptional regulation of surrounding genes, affecting productive and meat quality traits.

Prediction and estimation of effective population size

Effective population size (Ne) is a key parameter in population genetics. It has important applications in evolutionary biology, conservation genetics and plant and animal breeding, because it measures the rates of genetic drift and inbreeding and affects the efficacy of systematic evolutionary forces, such as mutation, selection and migration. We review the developments in predictive equations and estimation methodologies of effective size. In the prediction part, we focus on the equations for populations with different modes of reproduction, for populations under selection for unlinked or linked loci and for the specific applications to conservation genetics. In the estimation part, we focus on methods developed for estimating the current or recent effective size from molecular marker or sequence data. We discuss some underdeveloped areas in predicting and estimating Ne for future research.

Genomic inbreeding estimation in small populations: evaluation of runs of homozygosity in three local dairy cattle breeds

In the local breeds with small population size, one of the most important problems is the increase of inbreeding coefficient (F). High levels of inbreeding lead to reduced genetic diversity and inbreeding depression. The availability of high-density single nucleotide polymorphism (SNP) arrays has facilitated the quantification of F by genomic markers in farm animals. Runs of homozygosity (ROH) are contiguous lengths of homozygous genotypes and represent an estimate of the degree of autozygosity at genome-wide level. The current study aims to quantify the genomic F derived from ROH (F ROH) in three local dairy cattle breeds. F ROH values were compared with F estimated from the genomic relationship matrix (F GRM), based on the difference between observed v. expected number of homozygous genotypes (F HOM) and the genomic homozygosity of individual i (F MOL i ). The molecular coancestry coefficient (f MOL ij ) between individuals i and j was also estimated. Individuals of Cinisara (71), Modicana (72) and Reggiana (168) were genotyped with the 50K v2 Illumina BeadChip. Genotypes from 96 animals of Italian Holstein cattle breed were also included in the analysis. We used a definition of ROH as tracts of homozygous genotypes that were >4 Mb. Among breeds, 3661 ROH were identified. Modicana showed the highest mean number of ROH per individual and the highest value of F ROH, whereas Reggiana showed the lowest ones. Differences among breeds existed for the ROH lengths. The individuals of Italian Holstein showed high number of short ROH segments, related to ancient consanguinity. Similar results showed the Reggiana with some extreme animals with segments covering 400 Mb and more of genome. Modicana and Cinisara showed similar results between them with the total length of ROH characterized by the presence of large segments. High correlation was found between F HOM and F ROH ranged from 0.83 in Reggiana to 0.95 in Cinisara and Modicana. The correlations among F ROH and other estimated F coefficients were generally lower ranged from 0.45 (F MOL i -F ROH) in Cinisara to 0.17 (F GRM-F ROH) in Modicana. On the basis of our results, recent inbreeding was observed in local breeds, considering that 16 Mb segments are expected to present inbreeding up to three generations ago. Our results showed the necessity of implementing conservation programs to control the rise of inbreeding and coancestry in the three Italian local dairy cattle breeds.