Rate Of Inbreeding Research Articles

The effect of phenotyping, adult selection, and mating strategies on genetic gain and rate of inbreeding in black soldier fly breeding programs

BackgroundThe aim of this study was to compare genetic gain and rate of inbreeding for different mass selection breeding programs with the aim of increasing larval body weight (LBW) in black soldier flies. The breeding programs differed in: (1) sampling of individuals for phenotyping (either random over the whole population or a fixed number per full sib family), (2) selection of adult flies for breeding (based on an adult individual’s phenotype for LBW or random from larvae preselected based on LBW), and (3) mating strategy (mating in a group with unequal male contributions or controlled between two females and one male). In addition, the numbers of phenotyped and preselected larvae were varied. The sex of an individual was unknown during preselection and females had higher LBW, resulting in more females being preselected.ResultsSelecting adult flies based on their phenotype for LBW increased genetic gain by 0.06 genetic standard deviation units compared to randomly selecting from the preselected larvae. Fixing the number of phenotyped larvae per family increased the rate of inbreeding by 0.15 to 0.20% per generation. Controlled mating compared to group mating decreased the rate of inbreeding by 0.02 to 0.03% per generation. Phenotyping more than 4000 larvae resulted in a lack of preselected males due to the sexual dimorphism. Preselecting both too few and too many larvae could negatively impact genetic gain, depending on the breeding program.ConclusionsA mass selection breeding programs in which the adult fly is selected based on their larval phenotype, breeding animals mate in a group and sampling larvae for phenotyping at random over the whole population is recommended for black soldier flies, considering the positive effect on rates of genetic gain and inbreeding. The number of phenotyped and preselected larvae should be calculated based on the expected female weight deviation to ensure sufficient male and female candidates are selected.

Optimization of breeding program design through stochastic simulation with evolutionary algorithms.

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

Mitochondrial Genomes of Korean Native Black Goats Reveal Shared Phylogeographic Patterns and Demographic History.

This study explores the phylogeny of Korean native black goats through analysis of their complete mitochondrial DNA. The National Institute of Animal Science has gathered genetic material on purebred goats from isolated regions such as Tongyeong, Dangjin, and Jangsu, and is actively breeding them on a national level. These populations, however, are small and exhibit high inbreeding rates, highlighting the urgent need to preserve genetic diversity. The haplotype diversity within this native group is 0.659, with 39 haplotypes identified. By contrast, including international breeds in the analysis increases the overall haplotype diversity to 0.925 with 203 haplotypes identified, highlighting the limited genetic diversity among native black goats. For phylogenetic assessment, a neighbor-joining tree and median-joining network were constructed using identified haplogroups (A, B, C, D, G, and F) from prior studies. The results pinpoint the native black goats as closely related to, but distinct from, Haplogroup A with a bootstrap value of 98, establishing them as a separate clade (A'). This supports the notion of a shared ancestry with various global populations. This research provides essential data on the origins and evolutionary history of Korean native black goats, supporting conservation and breeding efforts aimed at enhancing genetic diversity.

Pedigree-based analysis of population structure and genetic diversity in high-milch Vrindavani crossbred cattle of India.

The present study aimed to elucidate the population structure and genetic diversity along with the estimation of genealogical parameters in Vrindavani cattle using pedigree data. The study was based on pedigree data on 12,718 animals, spread across multiple generations during a 52-year period (between 1971 and 2023). The pedigree data was used to estimate different population genealogical parameters including the generation interval; pedigree completeness; rate and level of inbreeding; effective population size; and parameters characterizing the probabilities of gene origin. The ENDOG program was used for estimation of different parameters while using population after 2010as reference cohort. The results revealed the maximum number of generations (MG) to be 13, while the numbers of completed (CG) and equivalent generations (EqG) were 3.23 and 1.95, respectively. The mean generation interval for the population was 6.9years. The average inbreeding coefficient of animals in the whole and reference population was 1.11 and 3.44%, respectively; with 0.68% rate of inbreeding per generation. The average additive relationship among all the animals and those in the reference population was 1.16 and 5.49%, respectively. The average effective population sizes for the maximum, equivalent, and complete generations were 115.56, 56.42, and 46.02, respectively. The effective population size on the basis of regression and log-regression on birth date was 77.40 and 71.24, respectively. The probabilities of gene origin were estimated by the effective number of founders (fe) and ancestors (fa), which was 115 and 78, respectively. The fe/fa ratio in the reference population was 1.20, indicating that occasional bottlenecks may have occurred in the population. The analysis revealed a loss of 5.3% of total heterozygosity as compared to base population, though significant variability exists in the latest generations. The results revealed that considerable genetic variability exists within the population that may be exploited through appropriate breed improvement programs targeting various economic traits.

Population genetic analysis of the Lviv region based on surname data

Aim. The study aimed to determine indicators characterizing the population structure of the Lviv region. Methods. The study employed the method of describing the genetic structure of the population using surnames, as proposed by I. Barrai. Results. Indicators of population structure in district populations of the Lviv region exhibited a wide range of values. The basic index, the isonymy index (I), varied between 2.3 and 26.5 (×10-4). The inbreeding coefficient (FST) ranged from 11.2 to 66.1 (×10-5), the migration index (v) from 70.8 to 212.9 (10-4), the diversity index (a) from 3.8 to 22.9 (×102), the entropy index (H) from 10.0 to 13.0, and the redundancy of surnames (R) from 26.1 to 36.2. In some settlements, there is an accumulation of bearers of the same surname, which can be explained by the founder effect. Conclusions. The rates of isonymy, inbreeding, and migration in district populations were several times higher than those observed in the regional population. The level of isonymy was lowest in areas with high tourist and transit activity.

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs

BackgroundIn this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS).MethodsStochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates.ResultsBased on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies.ConclusionsGenotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

Joint identity among loci under mutation and regular inbreeding

This study describes a compact method for determining joint probabilities of identity-by-state (IBS) within and between loci in populations evolving under genetic drift, crossing-over, mutation, and regular inbreeding (partial self-fertilization). Analogues of classical indices of associations among loci arise as functions of these joint identities. This coalescence-based analysis indicates that multi-locus associations reflect simultaneous coalescence events across loci. Measures of association depend on genetic diversity rather than allelic frequencies, as do linkage disequilibrium and its relatives. Scaled indices designed to show monotonic dependence on rates of crossing-over, inbreeding, and mutation may prove useful for interpreting patterns of genome-scale variation.

The number of drones to inseminate a queen with has little potential for optimization of honeybee breeding programs

BackgroundMating control is a crucial aspect of honeybee breeding. Instrumental insemination of queens gives the breeder maximum control over the genetic origin of the involved drones. However, in addition to the drones’ descent, the breeder’s control also extends over the number of drones to use for inseminations. Thus far, this aspect has largely been ignored in attempts to optimize honeybee breeding schemes. The literature provides some comparisons between single drone inseminations (SDI) and multi drone inseminations (MDI) but it is unclear whether the number of drones used in MDI is a relevant parameter for the optimization of honeybee breeding programs.MethodsBy computer simulations, we investigated the effect of the number of drones per inseminated queen in breeding programs that relied on best linear unbiased prediction (BLUP) breeding values. We covered a range of 1 to 50 drones per queen and observed the developments of genetic gain and inbreeding over a period of 20 years. Hereby, we focused on insemination schemes that take the drones for one queen from a single colony.ResultsSDI strategies led to 5.46% to 14.19% higher genetic gain than MDI at the cost of 6.1% to 30.2% higher inbreeding rates. The number of drones used in MDI settings had only a negligible impact on the results. There was a slight tendency that more drones lead to lower genetic gain and lower inbreeding rates but whenever more than five drones were used for inseminations, no significant differences could be observed.ConclusionThe opportunities to optimize breeding schemes via the number of drones used in inseminations are very limited. SDI can be a viable strategy in situations where breeders are interested in genetically homogeneous offspring or precise pedigree information. However, such strategies have to account for the fact that the semen from a single drone is insufficient to fill a queen’s spermatheca, whence SDI queens will not build full-strength colonies. When deciding for MDI, breeders should focus on collecting enough semen for a succesful insemination, regardless of how many drones they need for this purpose.

Estimating the Inbreeding Rate in Natural Populations of The Black-Naped Fruit Dove (Ptilinopus melanasphila)

This research aimed to determine the population of the Black-Naped Fruit Dove (Ptilinopus melanasphila) by estimating population size, sex ratio, effective population, and inbreeding rate, providing a basis for ex-situ design considerations. Data collection employed survey techniques such as the line transect and concentration count point methods. Data analysis focused on determining population size, sex ratio, effective population, and inbreeding rate. The research was conducted in the Sula Islands, North Maluku. Observations and discussions were conducted separately for each location to obtain empirical and comprehensive data serving as a reference for further research. Results revealed the inbreeding rates at the research locations were observed as follows: Soamole village (1.42%), Wai Ipa village (1.56%), and Bega village (1.60%). Sex ratios were observed as follows: Soamole village (male : female ratio of 22.17 : 85.38), Wai Ipa village (male : female ratio of 19.48 : 73.78), and Bega village (male : female ratio of 19.61:80.39). Effective populations were observed as follows: Soamole village (22.39 heads), Wai Ipa village (16.48 heads), and Bega village (16.08 heads). The inbreeding rate is in the good category, while the population number, sex ratio and effective population are in the medium category, however habitat destruction and continuous excessive hunting can result in decline and extinction.

Assessment of genetic diversity and population structure of U.S. Polypay sheep from breed origins to future genomic selection.

Knowledge of past and present genetic diversity within a breed is critical for the design and optimization of breeding programs as well as the development of strategies for the conservation of genetic resources. The Polypay sheep breed was developed at the U.S. Sheep Experiment Station (USSES) in 1968 with the goal of improving productivity in Western U.S. range flocks. It has since flourished in the more intensively managed production systems throughout the U.S. The genetic diversity of the breed has yet to be documented. Therefore, the primary objective of this study was to perform a comprehensive evaluation of the genetic diversity and population structure of U.S. Polypay sheep using both pedigree- and genomic-based methods. Pedigree data from 193 Polypay flocks participating in the National Sheep Improvement Program (NSIP) were combined with pedigree records from USSES (n = 162,997), tracing back to the breed's origin. A subset of these pedigreed sheep from 32 flocks born from 2011 to 2023 were genotyped with the GGP Ovine 50K BeadChip containing 51,867 single nucleotide polymorphisms (SNPs). Four subgroups were used for the pedigree-based analyses: 1) the current generation of animals born in 2020-2022 (n = 20,701), 2) the current generation with a minimum of four generations of known ancestors (n = 12,685), 3) only genotyped animals (n = 1,856), and 4) the sires of the current generation (n = 509). Pedigree-based inbreeding for the full population was 2.2%, with a rate of inbreeding of 0.22% per generation. Pedigree-based inbreeding, Wright's inbreeding, and genomic inbreeding based on runs of homozygosity were 2.9%, 1.3%, and 5.1%, respectively, for the genotyped population. The effective population size ranged from 41 to 249 for the pedigree-based methods and 118 for the genomic-based estimate. Expected and observed heterozygosity levels were 0.409 and 0.403, respectively. Population substructure was evident based on the fixation index (FST), principal component analysis, and model-based population structure. These analyses provided evidence of differentiation from the foundation flock (USSES). Overall, the Polypay breed exhibited substantial genetic diversity and the presence of a population substructure that provides a basis for the implementation of genomic selection in the breed.

Effect of germplasm exchange strategies on genetic gain, homozygosity, and genetic diversity in dairy stud populations: A simulation study

While genomic selection has led to considerable improvements in genetic gain, it has also seemingly led to increased rates of inbreeding and homozygosity, which can negatively affect genetic diversity and the long-term sustainability of dairy populations. Using genotypes from US Holstein animals from 3 distinct stud populations, we performed a simulation study consisting of 10 rounds of selection, with each breeding population composed of 200 males and 2000 females. The investigated selection strategies consisted of selection using true breeding values (TBV), estimated breeding values (EBV), estimated breeding values penalized for the average future genomic inbreeding of progeny (PEN-EBV), or random selection (RAND). We also simulated several germplasm exchange strategies where the germplasm of males from other populations was used for breeding. These strategies included exchanging males based on EBV, PEN-EBV, low genomic future inbreeding of progeny (GFI), or randomly (RAND). Variations of several parameters, such as the correlation between the selection objectives of populations and the size of the exchange, were simulated. Penalizing genetic merit to minimize genomic inbreeding of progeny provided similar genetic gain and reduced the average homozygosity of populations compared with the EBV strategy. Germplasm exchange was found to generally provide long-term benefits to all stud populations. In both the short and long-term, germplasm exchange using the EBV or PEN-EBV strategies provided more cumulative genetic progress than the no-exchange strategy; the amount of long-term genetic progress achieved with germplasm exchange using these strategies was higher for scenarios with a higher genetic correlation between the traits selected by the studs and for a larger size of the exchange. Both the PEN-EBV and GFI exchange strategies allowed decreases in homozygosity and provided significant benefits to genetic diversity compared with other strategies, including larger average minor allele frequencies and smaller proportions of markers near fixation. Overall, this study showed the value of breeding strategies to balance genetic progress and genetic diversity and the benefits of cooperation between studs to ensure the sustainability of their respective breeding programs.

Assessing different metrics of pedigree and genomic inbreeding and inbreeding effect on growth, fertility, and feed efficiency traits in a closed-herd Nellore cattle population

BackgroundThe selection of individuals based on their predicted breeding values and mating of related individuals can increase the proportion of identical-by-descent alleles. In this context, the objectives of this study were to estimate inbreeding coefficients based on alternative metrics and data sources such as pedigree (FPED), hybrid genomic relationship matrix H (FH), and ROH of different length (FROH); and calculate Pearson correlations between the different metrics in a closed Nellore cattle population selected for body weight adjusted to 378 days of age (W378). In addition to total FROH (all classes) coefficients were also estimated based on the size class of the ROH segments: FROH1 (1–2 Mb), FROH2 (2–4 Mb), FROH3 (4–8 Mb), FROH4 (8–16 Mb), and FROH5 (> 16 Mb), and for each chromosome (FROH_CHR). Furthermore, we assessed the effect of each inbreeding metric on birth weight (BW), body weights adjusted to 210 (W210) and W378, scrotal circumference (SC), and residual feed intake (RFI). We also evaluated the chromosome-specific effects of inbreeding on growth traits.ResultsThe correlation between FPED and FROH was 0.60 while between FH and FROH and FH and FPED were 0.69 and 0.61, respectively. The annual rate of inbreeding was 0.16% for FPED, 0.02% for FH, and 0.16% for FROH. A 1% increase in FROH5 resulted in a reduction of up to -1.327 ± 0.495 kg in W210 and W378. Four inbreeding coefficients (FPED, FH, FROH2, and FROH5) had a significant effect on W378, with reductions of up to -3.810 ± 1.753 kg per 1% increase in FROH2. There was an unfavorable effect of FPED on RFI (0.01 ± 0.0002 kg dry matter/day) and of FROH on SC (-0.056 ± 0.022 cm). The FROH_CHR coefficients calculated for BTA3, BTA5, and BTA8 significantly affected the growth traits.ConclusionsInbreeding depression was observed for all traits evaluated. However, these effects were greater for the criterion used for selection of the animals (i.e., W378). The increase in the genomic inbreeding was associated with a higher inbreeding depression on the traits evaluated when compared to pedigree-based inbreeding. Genomic information should be used as a tool during mating to optimize control of inbreeding and, consequently, minimize inbreeding depression in Nellore cattle.

Ultraviolet signaling in a butterfly is preferred by females and conveys male genetic quality.

Indicator models of sexual selection posit that females choose males on the basis of traits that reveal male genetic quality and thereby enjoy increased offspring production. Here, we report that females of the butterfly Eurema hecabe receive indirect benefits from choosing males based on their ultraviolet (UV) wing coloration, a heritable and condition-dependent trait in this species. We first used a large laboratory-bred pedigree to demonstrate a per-family association between inbreeding and male UV trait value. Females exerted choice for UV-bright males within this protocol, and the average male UV trait value increased over six consecutive generations, presumably due to such selection and despite an increasing rate of pedigree-wide inbreeding. We then experimentally imposed a standard strength of inbreeding upon lines of divergent male UV trait values. Inbreeding depressed the siring performance of low UV treatment males more severely and resulted in a marginal reduction of their UV brightness, which rebounded sharply following subsequent outcrossing. These findings are consistent with the ornament-based signaling of genetic quality as a function of underlying individual-level mutational load.

FnR: R package for computing inbreeding and numerator relationship coefficients

BackgroundInbreeding and relationship coefficients are essential for conservation and breeding programs. Whether dealing with a small conserved population or a large commercial population, monitoring the inbreeding rate and designing mating plans that minimize the inbreeding rate and maximize the effective population size is important. Free, open-source, and efficient software may greatly contribute to conservation and breeding programs and help students and researchers. Efficient methods exist for calculating inbreeding coefficients. Therefore, an efficient way of calculating the numerator relationship coefficients is via the inbreeding coefficients. i.e., the relationship coefficient between parents is twice the inbreeding coefficient of their progeny. A dummy progeny is introduced where no progeny exists for a pair of individuals. Calculating inbreeding coefficients is very fast, and finding whether a pair of individuals has a progeny and picking one from multiple progenies is computationally more demanding. Therefore, the R package introduces a dummy progeny for any pair of individuals whose relationship coefficient is of interest, whether they have a progeny or not.ResultsRuntime and peak memory usage were benchmarked for calculating relationship coefficients between two sets of 250 and 800 animals (200,000 dummy progenies) from a pedigree of 2,721,252 animals. The program performed efficiently (200,000 relationship coefficients, which involved calculating 2,721,252 + 200,000 inbreeding coefficients) within 3:45 (mm:ss). Providing the inbreeding coefficients (for real animals), the runtime was reduced to 1:08. Furthermore, providing the diagonal elements of D in A=TDT′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{A} = \ extbf{TDT}^{\\prime }$$\\end{document} (d), the runtime was reduced to 54s. All the analyses were performed on a machine with a total memory size of 1 GB.ConclusionsThe R package FnR is free and open-source software with implications in conservation and breeding programs. It proved to be time and memory efficient for large populations and many dummy progenies. Calculation of inbreeding coefficients can be resumed for new animals in the pedigree. Thus, saving the latest inbreeding coefficient estimates is recommended. Calculation of d coefficients (from scratch) was very fast, and there was limited value in storing those for future use.

Effective population size in field pea

BackgroundEffective population size (Ne) is a pivotal parameter in population genetics as it can provide information on the rate of inbreeding and the contemporary status of genetic diversity in breeding populations. The population with smaller Ne can lead to faster inbreeding, with little potential for genetic gain making selections ineffective. The importance of Ne has become increasingly recognized in plant breeding, which can help breeders monitor and enhance the genetic variability or redesign their selection protocols. Here, we present the first Ne estimates based on linkage disequilibrium (LD) in the pea genome.ResultsWe calculated and compared Ne using SNP markers from North Dakota State University (NDSU) modern breeding lines and United States Department of Agriculture (USDA) diversity panel. The extent of LD was highly variable not only between populations but also among different regions and chromosomes of the genome. Overall, NDSU had a higher and longer-range LD than the USDA that could extend up to 500 Kb, with a genome-wide average r2 of 0.57 (vs 0.34), likely due to its lower recombination rates and the selection background. The estimated Ne for the USDA was nearly three-fold higher (Ne = 174) than NDSU (Ne = 64), which can be confounded by a high degree of population structure due to the selfing nature of pea.ConclusionsOur results provided insights into the genetic diversity of the germplasm studied, which can guide plant breeders to actively monitor Ne in successive cycles of breeding to sustain viability of the breeding efforts in the long term.

Assessing the population structure and genetic variability of Kenyan native goats under extensive production system

Indigenous goats are important to many livelihoods. Despite this, they are subjected to indiscriminate crossbreeding. This affects their genetic variability which is needed to survive in current regime of climate change. The study assessed population structure and genetic diversity of Galla and Small East African goats (SEA) using pedigree information. A total of 7384 animals, 5222 of the Galla and 2162 of the SEA breeds, born between the years 1983 and 2022, were utilized. Individuals with known parents were defined as reference population. From the results, the maximum generation traced for Galla and SEA populations was 14.6 and 14.5, respectively. However, only 6 and 5 generations for Galla and SEA were complete. Pedigree completeness increased with the increasing number of generations. The average generation interval (GI) for Galla and SEA was 3.84 ± 0.04 and 4.4 ± 0.13 years. The average increase in the rate of inbreeding per generation for Galla and SEA was 0.04 and 0.05, with the effective ancestors and founders (fa/fe) ratio being same (1.00) for both breeds. Fifty percent (50%) of the genetic variability in the populations was contributed by 3 and 1 ancestor for Galla SEA, respectively. The effective population size (Ne) was 5.19 and 4.77 for Galla and SEA. Therefore, the current breeding programs should be changed to avoid future genetic bottlenecks in this population. These findings offer an opportunity to enhance the current genetic status and management of Kenyan native goats and other regions with similar production systems.

Population structure and genetic diversity in males of the autochthonous Busha cattle in Croatia

Busha is recognized as the most numerous autochthonous cattle breed in Croatia. In small populations, using the same sire repeatedly can increase inbreeding rates. This can lead to fewer high-quality, and high inbred breeding sires over time. Genealogical records of the Busha cattle breed registered from 1987 to 2022 were used to investigate the population structure and the genetic variability of the male individuals. The dataset included 10,411 animals, of which 3,962 were males. The average equivalent complete generation reached 2.92 in the total population and 3.05 in the reference population, when considering only living animals. The average inbreeding coefficient in the male population was 1.59%. Over the studied period, inbreeding rose to an average value of 2.07% in the last birth year cohort (2017-2022). The unequal contribution of the effective number of founders indicates the more frequent use of particular breeding sire lines. Nevertheless, despite the controlled inbreeding rate observed among breeding sires, and the absence of any evident population bottleneck, our study highlighted the need for breeding strategies to optimize the contribution of breeding animals in the next generations to ensure long-term conservation of the Busha cattle breed.

Using Runs of Homozygosity and Machine Learning to Disentangle Sources of Inbreeding and Infer Self-Fertilization Rates.

Runs of homozygosity (ROHs) are indicative of elevated homozygosity and inbreeding due to mating of closely related individuals. Self-fertilization can be a major source of inbreeding which elevates genome-wide homozygosity and thus should also create long ROHs. While ROHs are frequently used to understand inbreeding in the context of conservation and selective breeding, as well as for consanguinity of populations and their demographic history, it remains unclear how ROH characteristics are altered by selfing and if this confounds expected signatures of inbreeding due to demographic change. Using simulations, we study the impact of the mode of reproduction and demographic history on ROHs. We apply random forests to identify unique characteristics of ROHs, indicative of different sources of inbreeding. We pinpoint distinct features of ROHs that can be used to better characterize the type of inbreeding the population was subjected to and to predict outcrossing rates and complex demographic histories. Using additional simulations and four empirical datasets, two from highly selfing species and two from mixed-maters, we predict the selfing rate and validate our estimations. We find that self-fertilization rates are successfully identified even with complex demography. Population genetic summary statistics improve algorithm accuracy particularly in the presence of additional inbreeding, e.g. from population bottlenecks. Our findings highlight the importance of ROHs in disentangling confounding factors related to various sources of inbreeding and demonstrate situations where such sources cannot be differentiated. Additionally, our random forest models provide a novel tool to the community for inferring selfing rates using genomic data.

Response to Selection of Indigenous Chicken in Rwanda Using Within-Breed Selection Strategy

The study evaluated response to selection from within-breed selection strategy for conventional (<I>CBS</I>) and genomic (<I>GBS</I>) breeding schemes. These breeding schemes were evaluated in both high-health environments (nucleus) and smallholder farms (commercial). Breeding goal was to develop a dual-purpose IC for both eggs and meat through selective breeding. Breeding objectives were body weight (BW), egg weight (EW), egg number (EN) and resistance to Newcastle disease (AbR). A deterministic simulation was performed to evaluate rates of genetic gain and inbreeding. Base population in the nucleus was made up of 40 cockerels and 200 pullets. Selection pressure was 4% and 20% in the males and the females, respectively. The impact of nucleus size and selection pressure on rates of genetic gain and inbreeding of the breeding program was investigated through sensitivity analysis. SelAction software was used to predict rates of genetic gain and inbreeding. Results showed that using <I>CBS</I> in the nucleus, the breeding goal was 340.41\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\$ and 1.13 times higher than that in the commercial flock. Inbreeding rate per generation of selected chicken in the nucleus was 1.45% and lower by 1.32 times compared to their offspring under smallholder farms. Genetic gains per generation in the nucleus for BW and EN traits were 141.10 g and 1.07 eggs and 1.12 and 1.10 times greater than those in smallholder farms, respectively. With <I>GBS</I>, the breeding goal was increased by 3.00 times whereas inbreeding rate was reduced by 3.15 times. Besides, using <I>GBS</I>, the selected birds in the nucleus were relatively similar to those in a commercial environment. Finally, the study revealed that the nucleus size and mating ratio influence the rates of genetic gain and inbreeding in both <I>GBS</I> and <I>CBS.</I> This study shows that IC in Rwanda have the potential to be improved through within-breed selection strategy using either <I>CBS</I> or <I>GBS</I>.

Species-specific erosion of genetic diversity in grassland butterflies depends on landscape land cover

Changes in land cover, particularly agricultural intensification, are a primary cause of pollinator decline. Decline and isolation can reduce genetic diversity, reducing adaptive potential and unmasking genetic load. Maintenance of functional connectivity in changing landscapes is necessary to ensure persistence, but how land cover impacts genetic diversity and functional connectivity in butterflies remains poorly understood. We assess to which extent land cover functionally isolates grassland butterfly populations and what consequences isolation has for genetic diversity using whole genome and land cover data. We assess species-specific patterns across three butterfly species that vary in habitat specialization and mobility - Polyommatus icarus, Plebejus argus and Cyaniris semiargus - using samples from 6 to 11 populations across a ~25,000 km2 area in southern Sweden. We find that while generalist Po. icarus is nearly panmictic and heathland specialist Pl. argus maintains modest functional connectivity, grassland specialist C. semiargus exists in largely isolated populations. Genetic diversity in all species is positively related to grassland extent in the surrounding landscape. Arable and forest extent are positively and negatively related to genetic diversity in Po. icarus, while the inverse is true in C. semiargus. Lower functional connectivity in C. semiargus is coupled with higher rates of inbreeding, suggesting more urgent need of conservation action aimed at restoring functional connectivity in this species, illustrating how genetic data can reveal threats that may go unnoticed with abundance data alone. We conclude that specialist butterfly genetic diversity relies upon preservation of well-connected semi-natural grasslands to maintain functional connectivity at longer distances.