The goal of managing genetic diversity is to maintain both the genomic uniqueness and adaptability of a population. This entails both maintaining heterozygosity and avoiding deleterious alleles from drifting to higher frequencies. Thus, genomic management strategies should not systematically change frequencies at neutral loci, as has been observed in current genomic management. We simulated 50 replicates of a population managed with optimal contribution selection (OCS), where inbreeding was controlled using different relationship matrices, where Van Raden method I (GVR), runs-of-homozygosity (GROH), and identity by descent relationships (IBD) only used genomic information and a linkage analysis-based matrix (GFGLA) leveraged both genomic and pedigree information. Across 50 simulated replicates, GFGLA achieved the highest correlation with true IBD for both inbreeding and coancestry estimates and provided the most accurate rate of inbreeding (ΔF). GFGLA also delivered the greatest genetic gain per unit of inbreeding and was the only scheme to remain below the inbreeding target while minimizing frequency change at neutral loci. GVR using base population frequencies produced high genetic gain and controlled drift effectively but introduced systematic allele frequency changes towards the closest extreme frequency. Using current allele frequency resulted in high levels of inbreeding compared to the genetic gain achieved. GROH showed strong dependence on the minimum ROH length: short ROH improved ΔF estimation but increased allele frequency changes toward intermediate values, whereas long ROH underestimated inbreeding and reduced genetic gain. IBD-based matrices such as GFGLA offer the most balanced approach for sustainable population management in OCS schemes, combining accurate relationship estimates, effective inbreeding control, and neutrality at neutral loci. In contrast, GVR and GROH require calibration and cause systematic allele frequency changes.

Abstract Background Genomic relationship and inbreeding estimates are either based on genetic drift (e.g. the Genomic Relationship Matrix; GRM), homozygosity (e.g. Runs of Homozygosity; ROH), or Identity-By-Descent (IBD). A genomic IBD-based relationship matrix, G la , is obtained by linkage analysis which uses genomic data to distinguish paternal versus maternal inheritances of chromosomal segments to replace the 50/50 probabilities used to calculate pedigree-based relationships (A matrix). Our aim was to develop a fast approximate algorithm, FGla, to estimate the G la matrix in large complex pedigrees making use of dense marker genotypes, and to compare G la to A, GRM and ROH based inbreeding (F ROH ) in simulated and a large scale Norwegian Red Cattle (NRF) data set. Results Given pedigree data and ≥ 3 generations of 45 k marker genotypes, marker positions were detected that unambiguously identified maternal/paternal inheritance, and inheritances at intermediate positions were imputed by the Viterbi algorithm from the positions with known inheritance. Any remaining unknown inheritances were randomly sampled (paternal or maternal), and the sampling errors that this introduced were averaged out by the large number of marker loci used (correlation between replicated estimates: 0.9998). Also, calculations were limited to the relationship coefficients that were actually needed, assuming that relationships for a limited set of candidates were needed. The accuracy of estimated G la coefficients increased from 0.971 to 0.998, when genotyping increased from the actually genotyped NRF cattle towards all pedigreed animals. The accuracy of the GRM was 0.936, but required only genotyping of the animals whose relationships were needed. G la relationships were approximately unbiased in the Best Linear Unbiased Prediction (BLUP) sense. Hence, if G la based inbreeding management predicts an increase in relationships then an identical increase in true IBD relationships is expected. G la uses the same base population as A, namely that of the pedigree. Conclusions An approximate computationally efficient multipoint linkage analysis algorithm was developed to estimate unbiased IBD-based relationship and inbreeding coefficients. Its unbiasedness and precise definition of the base population makes it well suited for the genomic management of inbreeding and genomic optimal contribution selection. In addition, G la based optimal contribution selection is neutral with respect to allele frequency changes.

Accurate and large-scale phenotyping of feed intake and growth traits is crucial for genetic improvement and ensuring economic sustainability in fish breeding programs. Although standard methods for measuring growth traits such as body weight (BW) and average daily gain (ADG) are relatively high-throughput, conventional techniques for assessing individual feed intake (IFI) and feed efficiency (FE) remain inefficient, labor-intensive, time-consuming and costly. This limits the ability to conduct routine large-scale phenotyping. Here, we validate non-contact interactance near-infrared spectroscopy (iNIR) as a rapid, cost-effective and non-invasive phenotyping tool for predicting key production traits, including IFI, BW, ADG, residual feed intake (RFI), and feed conversion ratio (FCR), in Atlantic salmon. Using a cohort of 680 salmon smolts, we compared different supervised learning techniques, including Lasso, partial least squares regression (PLSR), Bayesian learning and convolutional neural network (CNN) for trait prediction. Lasso regression achieved the highest accuracy for IFI (r = 0.78 f 0.02), BW (r = 0.85 f 0.02), and ADG (r = 0.85 f 0.02), while PLSR performed best for RFI (r = 0.28 f 0.04) and FCR (r = 0.38 f 0.03). We then conducted genomic validation of the iNIR predictions to ensure their reliability for use in breeding programs. First, we estimated the genomic parameters for both the reference measurements and iNIR-predicted traits using the restricted maximum likelihood (REML) approach. Subsequently, we carried out genomic prediction through 20 random iterations of a 5-fold cross-validation scheme to validate the utility of iNIR-predicted phenotypes in the genomic prediction model. Our analyses revealed moderate to high heritability for iNIRpredicted traits, ranging from 0.31 to 0.50. Additionally, iNIR-predicted traits exhibited strong genetic correlations with reference measurements, ranging from 0.67 to 0.96, and demonstrated robust genomic prediction stability, ranging from 0.83 to 0.91. These encouraging estimates confirm that iNIR-predicted traits can serve as reliable proxies. Furthermore, incorporating iNIR-predicted traits into multi-trait genomic prediction models enhanced predictability by 14 to 20 % and stability by 4.9 to 8.5 %, compared to single-trait models for IFI, BW and ADG. While predictions for RFI and FCR were less accurate, likely due to their metabolic complexity, our results demonstrate that iNIR can be a viable high-throughput and cost-effective phenotyping tool for growth and feed intake, thereby advancing genomic selection in Atlantic Salmon. Although the phenomic predictions showed room for improvement, genomic validation highlighted the potential of using inexpensive and non-destructive iNIR proxy traits to improve genomic selection. In practice, genomic reference cohorts comprising a few thousand individuals can be genotyped and phenotyped for reference traits that are both expensive and difficult-tomeasure phenotypes (such as IFI) alongside rapid, inexpensive iNIR to generate accurate iNIR proxies. Subsequently, larger cohorts, including selection candidates or informant fish on the slaughter line, can be phenotyped with iNIR to improve multi-trait genomic predictions in selection candidates. However, independent studies replicating these findings are needed.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations that we aim to replicate, thereby highlighting a fundamental limitation of current classical quantitative genetic simulation models. Deep learning (DL) models have demonstrated promising results in capturing complex interactions essential for maintaining genetic variance; thus, we hypothesize that DL-based genetic simulation models may preserve more genetic variance than classical models, because the biological pathways underlying complex traits exhibit interactions that classical models ignore. The primary objective of this study was to introduce alternative DL-based genetic simulation models and compare them with classical genetic simulation models in terms of their retention of additive genetic variance under truncation selection in a simulated full-sib pig breeding scheme using real haplotypes as founders. After 20 generations of directional truncation selection, the classical models (A, ADAA, and ADAAADDD) retained between 55% and 64% of their initial additive genetic variance. In contrast, while the DL_simple model lost all its additive variance, the DL medium retained 92% to 98% of its additive variance, and the DL_complex model's initial additive variance increased by 296% to 314%. This paper introduces DL-based genetic simulation models and concludes that their ability to retain additive genetic variance depends on the models' architectural complexity. When sufficiently complex, DL-based models exhibit greater retention of additive genetic variance because they intrinsically capture epistatic interactions that are converted into additive variance, as selection progresses, thus, affirming the role of non-additive genetic effects in maintaining long-term genetic variation.

Sustainable breeding of native breeds is essential to preserve genetic diversity and cultural heritage. Several native Nordic horse breeds are at risk of extinction and lack genetic characterization. This study aimed to analyze genetic variation and kinship within and among native Nordic horse breeds using whole genome sequence data, and to compare results from using a Finnhorse genome assembly to that of the EquCab3.0 (Thoroughbred) reference genome. The breeds Dola horse, North Swedish horse and Coldblooded Trotter showed close genetic relationship for fixation index (0.04-0.10), and in principal component analysis. The other breeds showed stronger genetic differentiation, especially the Faroese horse with fixation index above 0.21 to all other breeds. This breed had the highest genomic inbreeding of 33% and a heterozygosity of 12%. The Swedish Ardennes showed the lowest inbreeding at 14% and a heterozygosity of 16%. The North Swedish horses had the highest historical Ne of 96, estimated 13 generations back in time, and the Faroese horse the lowest (23). The mean identity by descent varied from 17% for Swedish Ardennes to 40% for Faroese horses. The choice of reference genomes gave minor to moderate differences, suggesting that a closer related reference improves precision for fine mapping and understanding of genetic landscapes of Nordic breeds. Together, the different analyses showed low genetic diversity in all breeds, and the general pattern of relatedness largely agreed with the known breed history. The results underline the importance of maintaining genetic diversity for the survival of the breeds.

Pork is the most widely consumed meat globally, and the industry has achieved substantial genetic advancements for several traits using genomic selection. However, traditional linear genomic prediction models may be inadequate for predicting complex traits, such as feed efficiency, as they primarily capture additive genetic effects and overlook nonadditive effects, including dominance and epistasis. Deep learning (DL) has the potential to address this limitation due to its ability to model nonlinear patterns inherent in genomic data. The objectives of this study were to compare the predictive ability of DL models to the linear models for predicting feed efficiency (FE) trait in 2 boar populations, estimate the nonadditive genetic variance captured by DL, and assess its effect on predictive ability. Our results showed that the DL models using the averaged-prediction method had the highest predictive ability in the sire line test population (0.381 for multilayer perceptron [MLP] and 0.377 for convolutional neural network [CNN]), compared to 0.366 for linear models. DL models also showed higher abilities in the dam line test population, with MLP achieving a predictive ability of 0.364. Additionally, we showed that DL models captured nonadditive variance; however, this did not significantly improve predictive ability. In conclusion, DL models, particularly MLP, demonstrated the highest predictive ability for FE, improving performance by approximately 4.1% for the sire line and 2.8% for the dam line compared to the traditional linear models. Therefore, DL models are recommended for predicting phenotypes and for estimating total genetic effects, including nonadditive components. However, this comes at a significant increase of computational cost.

Meiotic crossover patterning shows huge variation within and between chromosomes, individuals, and species, yet the molecular and evolutionary causes and consequences of this variation remain poorly understood. A key step is to understand the genetic architecture of the crossover rate, positioning, and interference to determine if these factors are governed by common or distinct genetic processes. Here, we investigate individual variation in autosomal crossover count, crossover position (measured as both intra-chromosomal shuffling and distance to telomere), and crossover interference in a large breeding population of domestic pigs (N = 82,474 gametes). We show that all traits are heritable in females at the gamete (h2 = 0.07–0.11) and individual mean levels (h2 = 0.08–0.41). In females, crossover count, and interference are strongly associated with RNF212, but crossover positioning is associated with SYCP2, MEI4, and PRDM9. Our results show that crossover positioning and rate/interference are driven by distinct genetic processes in female pigs and have the capacity to evolve independently.

Accurate and repeated whole-body fat (WBF) measurements across production phases are important for optimizing feed utilization, reducing production waste, safeguarding fish health, ensuring product quality and improving overall salmon production. However, the chemical extraction (reference) methods for WBF recording, although precise and accurate, are costly, destructive and have limited applicability for repeated measurements on live fish. This study validates two digital phenotyping technologies: Dielectric spectroscopy (DS) and Near-infrared spectroscopy (NIR) against the reference Soxhlet chemical extraction method. DS is a fast and non-destructive commercial fat meter, whereas the specially designed NIR interactance allows deep penetration (up to 10 mm) through the fish skin into the body. Approximately 2800 fish belonging to 35 full sibs fish families were recorded for WBF at mean body weight (BW) of similar to 110, 300 and 750 g in parr, pre-smolt and post-smolt phases, respectively. The WBF percentage changed across the studied production phases: with parr having a mean of 11.3 % and coefficient of variation (CV = 11 %), pre-smolt decreasing slightly in both mean 10.9 - 11.1 % and CV (5.5 - 8.8 %) and post-smolt with an increase to 14.9 % and CV (8.5 %). Both methods performed well relative to the reference, with NIR (R-2 = 0.77 - 0.91) slightly outperforming the DS (R-2 = 0.61 - 0.71). Significantly high genetic estimates for NIR (h(2) = 0.57 +/- 0.04 - 0.62 +/- 0.06) and DS (h(2) = 0.38 +/- 0.07 - 0.56 +/- 0.10) signify the potential use of these digital technologies for improving WBF in future selective breeding. The low genetic correlations (r(g) = 0.22 +/- 0.13 - 0.33 +/- 0.14) across fresh and seawater phases raise questions about the generalizability of metabolic, lipid and feed efficiency research conducted in parr and pre-smolt phases in freshwater, to the post-smolt stage in seawater. The study highlighted the unexplored detail of the genetic regulation of WBF as fish undergoes production phases. Overall, these results will add to our understanding of genetic architecture for WBF and pave the way for digital phenotyping technologies to record complex but economically important traits and refine breeding strategies.

Runs of Homozygosity (ROH) are commonly used to quantify autozygosity/identity-by-descent (IBD) in an individual or population. However, the method's accuracy at the segment level in livestock populations has only been evaluated in a few studies. Thus, the aim of this study was to determine to what extent ROH are truly IBD and estimate the proportion of IBD segments that go undetected in a simulated livestock population. We simulated a population of randomly mating animals for 100 generations. The genome consisted of a single chromosome with a SNP density of either 46 or 92 SNPs per mega base (Mb). In addition, a set of founder markers tracing IBD was recorded. ROH were detected using four different parameter combinations. Using the two sets of markers, we calculated the true positive rate, power, and overall correlation between true (FIBD) and estimated inbreeding (FROH). Additionally, a new measure for within-ROH inbreeding (F|ROH) was introduced and calculated the level of homozygosity within a ROH compared to the general expectation in the genome. The results indicate that ROH longer than 2 Mb are a reliable indicator of IBD, with the F|ROH being over 0.9 for all ROH lengths and parameter combinations. True positive rates only exceeded 0.9 consistently for ROH over 9 Mb, indicating that many of the identified ROH may be associated with common ancestors more ancient than the base population. The power was mainly controlled by the parameter stringency, that is, allowing for shorter ROH increased the power. The ROH-based individual measure of inbreeding FROH was highly correlated to FIBD while also having regression coefficients close to 1 (i.e., a 1% variation in FROH corresponded to a 1% variation in FIBD). Using stringent ROH parameters resulted in underestimation of the rate of inbreeding in the population. Increasing marker density improved predictions, including a higher true positive rate, power, higher correlations, and less underestimation of inbreeding rates.

Improving feed efficiency (FE) is poised to become a primary focus of future aquaculture breeding programs, with ongoing research aiming to incorporate this trait to increase fish production, reduce feed costs and minimize environmental impact. Selection for improving FE requires a detailed understanding of its genetic parameters and association with growth traits under commercial conditions. However, scientific studies on FE at the individual level are missing in Atlantic salmon, likely due to the absence of accurate and efficient methods for measuring individual feed intake (IFI). Here we have used X-ray imaging on 700 salmon smolts, which have consumed feed containing radio-opaque beads, to phenotype the IFI at multiple time points. The study aims to I) train and validate a deep learning image based "YOLO-XBeads" model for accurate estimation of IFI from X-ray images, II) estimate the genetic parameters of IFI and different FE metrics such as feed conversion ratio (FCR), phenotypic residual feed intake (pRFI), genetic residual feed intake (gRFI), and growth traits such as Body weight (BW), average daily gain (ADG) and whole-body fat (WBF), and III) quantify the phenotypic and genetic relationships among these traits in Atlantic salmon. The YOLO-XBeads model performed well on X-ray images, with a high correlation to the true value (R2 = 0.99) and mean absolute percentage error of 2.60-5.94 %. The time efficiency (two orders of magnitude faster than available software) of our model significantly reduces the analysis time, labour and cost compared to conventional image software and manual human counting. We found significant heritability estimates for IFI (0.20 f 0.05-0.50 f 0.07), ADG (0.44 f 0.06-0.55 f 0.06), BW (0.50 f 0.06-0.55 f 0.06), pRFI (0.11 f 0.05-0.16 f 0.07), gRFI (0.06 f 0.03-0.18 f 0.06), FCR (0.09 f 0.04-0.23 f 0.06) and WBF (0.61 f 0.06), indicating potential for genetic improvement. Additionally, the strong genetic correlations (rg = 0.71 f 0.07-0.98 f 0.01) between different point estimates of the primary traits (IFI, ADG and BW) over time demonstrated the stability of these traits over the studied growth period. Averaging the FE and related traits over the whole growth period (50-300 g) of salmon showed variable rg between traits. The highest rg values (0.86 f 0.05-0.99 f 0.01) were observed among different FE metrics, indicating similar genetic regulation. Following this, a strong rg (0.95 f 0.02) was found between IFI and ADG, indicating that ADG can explain a significant proportion of the genetic variation in IFI. However, we found low but significant genetic variation for pRFI and gRFI, which shows heritable variation remains after accounting for the relationships of BW and ADG with the IFI. In addition, WBF showed moderate rg (0.52 f 0.09-0.64 f 0.07) with IFI, BW and ADG traits. Conversely, FE metrics showed weak rg with ADG (0.00 f 0.21-0.38 f 0.17) as well as with WBF (-0.03 f 0.18-- 0.21 f 0.19), indicating little or no indirect genetic improvement in FE through selection for growth or WBF in freshwater smolts. Overall, the FE and related traits were found to be measurable and heritable at the individual level in Atlantic salmon smolts, signifying the potential for improving economic gain and minimizing environmental impact. However, further studies are needed at the sea phase.

Meiotic crossovers are essential for proper chromosome segregation, and provide an important mechanism for adaptation through linking beneficial alleles and purging deleterious mutations. However, crossovers can also break apart beneficial alleles and are themselves a source of new mutations within the genome. The rate and distribution of crossovers shows huge variation both within and between chromosomes, individuals and species, yet the molecular and evolutionary causes and consequences of this variation remain poorly understood. A key step in understanding this variation is to understand the genetic architecture of how many crossovers occur, where they occur, and how they interfere, as this allows us to identify the degree to which these factors are governed by common or distinct genetic processes. Here, we investigate individual variation in crossover count, crossover interference (ν), and crossover positioning measured as both intra-chromosomal allelic shuffling and distance to telomere (Mb), in a large genotyped breeding population of domestic pigs. Using measures from 82,474 gametes from 4,704 mothers and 271 fathers, we show that crossover traits are heritable within each sex (h2 = 0.03 - 0.11), with the exception of male crossover interference. Crossover count and interference have a strongly shared genetic architecture in females, mostly driven by variants at RNF212. Female crossover positioning is mediated by variants at MEI4, PRDM9, and SYCP2. We also identify tentative associations at genomic regions corresponding to CTCF and REC114/REC8/CCNB1IP1 (crossover count), and ZCWPW1 and ZCWPW2 (crossover positioning). Our results show that crossover count and crossover positioning in female pigs have the capacity to evolve somewhat independently in our dataset.

Yuedonghei (YDH) is the only local pig breed with full black hair among the four well-known local pig breeds originated and distributed in Guangdong province, China, which caters to the consumers' preference of the local market of 127 million residents and thus brings a significantly above-average price. However, considerable genetic introgression (GI) has been reported for the YDH population, i.e., gene flow into YDH from other pig breeds, which is mainly due to the recent crossbreeding with several mainstream breeds for upgrading reasons. Therefore, this study aimed to evaluate the GI as well as the conservation status in the current YDH population and test the feasibility of advanced optimum contribution selection (aOCS) in alleviating GI in YDH. We first analysed the genetic diversity, ancestral structure, population structure, and phylogeny of 360 YDH relative to 782 publicly downloaded pigs of 42 Eurasian or American breeds and wild boars, based on single nucleotide polymorphism chip data. Then, we selected 304 initial YDH and stochastically simulated a practical conservation programme that spanned 10 discrete generations and implemented haplotype segment-based aOCS in every generation. The expected and observed heterozygosity of 360 YDH were 0.344 and 0.336. The linkage disequilibrium-based recent effective population size (Ne) was 32.89. Considerable GI amounting to 32.9% foreign ancestry was found in 28 lowly related YDH individuals using admixture analysis. In the simulated YDH conservation programme, the average native genomic contribution was increased from 50.4 to 71.4% while maintaining a Ne of 100 by controlling classic kinship and native kinship. Our study showed that segment-based aOCS that required only genomic data can be used to alleviate GI in the current YDH population and meanwhile increase its Ne, which provided strategic insights into the sustainable conservation of local genetic resources of livestock.

In recent years, genomic selection (GS) has accelerated genetic gain in dairy cattle breeds worldwide. Despite the evident genetic progress, several dairy populations have also encountered challenges such as heightened inbreeding rates and reduced effective population sizes. The challenge has been to find a balance between achieving substantial genetic gain while managing genetic diversity within the population, thereby mitigating the negative effects of inbreeding depression. This study aims to elucidate the impact of GS on pedigree and genomic rates of inbreeding (z\F) and coancestry (z\C) in Nordic Jersey (NJ) and Holstein (NH) cattle populations. Furthermore, key genetic metrics, including the generation interval (L), effective population size (Ne), and future effective population size (FNe) were assessed between 2 time periods, before and after GS, and across distinct animal cohorts in both breeds: females, bulls, and approved semen-producing bulls (AI-sires). Analysis of z\F and z\C revealed distinct trends across the studied periods and animal groups. Notably, there was a consistent increase in yearly z\F for most animal groups in both breeds. An exception was observed in NH AI-sires, which demonstrated a slight decrease in yearly z\F. Moreover, NJ displayed minimal changes in yearly z\C between the periods, whereas NH exhibited elevated z\C values across all animal groups. Particularly striking was the substantial increase in yearly z\C within the NH female population, surging from 0.02% to 0.39% between the periods. Implementation of GS resulted in a reduction of the generation interval across all animal cohorts in both NJ and NH breeds. However, the extent of reduction was more pronounced in males compared with females. This reduction in generation interval influenced generational changes in z\F and z\C. Bulls and AI-sires of both breeds exhibited reduced generational z\F between periods, in contrast to females that demonstrated an opposing pattern. Between the periods, NJ maintained a relatively stable Ne (29.4 before and 30.3 after GS), whereas NH experienced a notable decline from 54.3 to 42.8. Female groups in both breeds displayed a negative Ne trend, whereas males demonstrated either neutral or positive Ne developments. Regarding FNe, NJ exhibited positive FNe development with an increase from 40.7 to 57.2. The opposite was observed in NH, where FNe decreased from 198.8 to 42.7. In summary, it was evident that the genomic methods could detect differences between the populations and changes in z\F and z\C more efficiently than pedigree methods. Implementation of GS yielded positive outcomes within the NJ population regarding the rate of coancestry but the opposite was observed with NH. Moreover, analysis of z\C data hints at the potential to decrease future z\F through informed mating strategies. Conversely, NH faces more pressing concerns, even though z\F remains comparatively modest in contrast to what has been observed in other Holstein populations. These findings underscore the necessity of genomic control of inbreeding and coancestry with strategic changes in the Nordic breeding schemes for dairy to ensure longterm sustainability in the forthcoming years.

In the last decade, several studies aimed at dissecting the genetic architecture of local small ruminant breeds to discover which variations are involved in the process of adaptation to environmental conditions, a topic that has acquired priority due to climate change. Considering that traditional breeds are a reservoir of such important genetic variation, improving the current knowledge about their genetic diversity and origin is the first step forward in designing sound conservation guidelines. The genetic composition of North-Western European archetypical goat breeds is still poorly exploited. In this study we aimed to fill this gap investigating goat breeds across Ireland and Scandinavia, including also some other potential continental sources of introgression. The PCA and Admixture analyses suggest a well-defined cluster that includes Norwegian and Swedish breeds, while the crossbred Danish landrace is far apart, and there appears to be a close relationship between the Irish and Saanen goats. In addition, both graph representation of historical relationships among populations and f4-ratio statistics suggest a certain degree of gene flow between the Norse and Atlantic landraces. Furthermore, we identify signs of ancient admixture events of Scandinavian origin in the Irish and in the Icelandic goats. The time when these migrations, and consequently the introgression, of Scandinavian-like alleles occurred, can be traced back to the Viking colonisation of these two isles during the Viking Age (793-1066 CE). The demographic analysis indicates a complicated history of these traditional breeds with signatures of bottleneck, inbreeding and crossbreeding with the improved breeds. Despite these recent demographic changes and the historical genetic background shaped by centuries of human-mediated gene flow, most of them maintained their genetic identity, becoming an irreplaceable genetic resource as well as a cultural heritage.

BACKGROUND:Genomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden's method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2p(1-p) where p is the reference allele frequency at each locus, and VanRaden's method 2 (VR2) scales each locus with 2p(1-p), thereby giving greater weight to loci with a low minor allele frequency. We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship.RESULTS:We found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant.CONCLUSIONS:Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.

Meiotic recombination through chromosomal crossovers ensures proper segregation of homologous chromosomes during meiosis, while also breaking down linkage disequilibrium and shuffling alleles at loci located on the same chromosome. Rates of recombination can vary between species, but also between and within individuals, sex and chromosomes within species. Indeed, the Atlantic salmon genome is known to have clear sex differences in recombination with female biased heterochiasmy and markedly different landscapes of crossovers between males and females. In male meiosis, crossovers occur strictly in the telomeric regions, whereas in female meiosis crossovers tend to occur closer to the centromeres. However, little is known about the genetic control of these patterns and how this differs at the individual level. Here, we investigate genetic variation in individual measures of recombination in > 5000 large full-sib families of a Norwegian Atlantic salmon breeding population with high-density SNP genotypes. We show that females had 1.6 × higher crossover counts (CC) than males, with autosomal linkage maps spanning a total of 2174 cM in females and 1483 cM in males. However, because of the extreme telomeric bias of male crossovers, female recombination is much more important for generation of new haplotypes with 8 × higher intra-chromosomal genetic shuffling than males. CC was heritable in females (h2 = 0.11) and males (h2 = 0.10), and shuffling was also heritable in both sex but with a lower heritability in females (h2 = 0.06) than in males (h2 = 0.11). Inter-sex genetic correlations for both traits were close to zero, suggesting that rates and distribution of crossovers are genetically distinct traits in males and females, and that there is a potential for independent genetic change in both sexes in the Atlantic Salmon. Together, these findings give novel insights into the genetic architecture of recombination in salmonids and contribute to a better understanding of how rates and distribution of recombination may evolve in eukaryotes more broadly.

Meiotic recombination is an important evolutionary mechanism that breaks up linkages between loci and creates novel haplotypes for selection to act upon. Understanding the genetic control of variation in recombination rates is therefore of great interest in both natural and domestic breeding populations. In this study, we used pedigree information and medium-density (∼50K) genotyped data in a large cattle (Bos taurus) breeding population in Norway (Norwegian Red cattle) to investigate recombination rate variation between sexes and individual animals. Sex-specific linkage mapping showed higher rates in males than in females (total genetic length of autosomes = 2,492.9 cM in males and 2,308.9 cM in females). However, distribution of recombination along the genome showed little variation between males and females compared with that in other species. The heritability of autosomal crossover count was low but significant in both sexes (h2 = 0.04 and 0.09 in males and females, respectively). We identified 2 loci associated with variation in individual crossover counts in female, one close to the candidate gene CEP55 and one close to both MLH3 and NEK9. All 3 genes have been associated with recombination rates in other cattle breeds. Our study contributes to the understanding of how recombination rates are controlled and how they may vary between closely related breeds as well as between species.

Increased rates of inbreeding have been observed in several Holstein populations across the world since genomic selection (GS) was implemented into the breeding programs. The objective of this study was to test whether this is also the case in the Nordic Holstein population using pedigree data. Rates of inbreeding were estimated before, during and after the GS implementation. We could not detect any increase in inbreeding or coancestry rates in Nordic Holstein. Effective population size increased over the studied time periods from 68 to 104, despite fewer sires being used. This might be because more of sires of sons have been used to produce AI bulls compared to the time before implementation of GS.

Irish Wolfhound (IW) is a large dog breed developed several centuries ago with an average longevity of 7 years The genetics behind the short lifespan is poorly understood. Based on data from the Irish Wolfhound database (IWDB) on 118,095 pedigreed IW and data from the IW Longevity Study including 10,122 dogs with known longevity and 6,057 of these having a known cause of death (COD), longevity and the two most common COD were analysed. Heritability, maternal effects, and correlations were estimated accounting for sex, birthyear, country of origin and inbreeding coefficient. Both longevity (0.27) and COD (0.17-0.20) were moderately heritable. Longevity and COD showed low correlations, both genetic and residual correlations. We argue that this is likely due to short longevity being caused by early aging and COD being specific expressions of early aging. In addition, inbreeding was found to significantly reduce longevity. Implications for increasing longevity in IW are discussed.

We compared the effects of using three genomic relationship matrices and a pedigree based relationship matrix for optimum contribution selection (OCS) with a constraint on number of sires selected, on genetic gain, kinship and additive genetic variance. This was done by simulating a genomically selected small dairy cattle population. The cattle population underwent genomic selection for 11 generations. In total, 6,000 females and 2,500 males were genotyped per generation. We compared a pedigree-based matrix as well as VanRaden’s method 1 to construct genomic relationship matrices using either allele frequencies of the base population, of the current generation, or allele frequencies among all genotyped animals. The pedigree-based OCS resulted in higher kinship and more loss of genetic variance than using genomic OCS based on base population frequencies. Thus, genomic OCS with genomic relationship matrices using base population frequencies are preferable compared with pedigree-based OCS.