Investigating inbreeding in the turkey (Meleagris gallopavo) genome.

The detrimental effects of increased homozygosity due to inbreeding have prompted the development of methods to reduce inbreeding. The detection of runs of homozygosity (ROH), or contiguous stretches of homozygous marker genotypes, can be used to describe and quantify the level of inbreeding in an individual. The estimation of inbreeding coefficients can be calculated based on pedigree information, ROH, or the genomic relationship matrix. The aim of this study was to detect and describe ROH in the turkey genome and compare estimates of pedigree-based inbreeding coefficients (FPED) with genomic-based inbreeding coefficients estimated from ROH (FROH) and the genomic relationship matrix (FGRM). A total of 2,616,890 pedigree records were available. Of these records, 6,371 genotyped animals from three purebred turkey (Meleagris gallopavo) lines between 2013 and 2019 were available, and these were obtained using a dense single nucleotide polymorphism array (56,452 SNPs). The overall mean length of detected ROH was 2.87 ± 0.29 Mb with a mean number of 84.87 ± 8.79 ROH per animal. Short ROH with lengths of 1 to 2 Mb long were the most abundant throughout the genome. Mean ROH coverage differed greatly between chromosomes and lines. Considering inbreeding coefficient means across all lines, genomic derived inbreeding coefficients (FROH = 0.27; FGRM = 0.32) were higher than coefficients estimated from pedigree records (FPED = 0.14). Correlations between FROH and FPED, FROH and FGRM, and FPED and FGRM ranged between 0.19 to 0.31, 0.68 to 0.73, and 0.17 to 0.30, respectively. Additionally, correlations between FROH from different lengths and FPED substantially increased with ROH length from -0.06 to 0.33. Results of the current research, including the distribution of ROH throughout the genome and ROH-derived inbreeding estimates, can provide a more comprehensive description of inbreeding in the turkey genome. This knowledge can be used to evaluate genetic diversity, a requirement for genetic improvement, and develop methods to minimize inbreeding in turkey breeding programs.

Preimplantation Genetic Testing for Aneuploidy Improves Live Birth Rates with In Vitro Produced Bovine Embryos: A Blind Retrospective Study.

Approximately one million in vitro produced (IVP) cattle embryos are transferred worldwide each year as a way to improve the rates of genetic gain. The most advanced programmes also apply genomic selection at the embryonic stage by SNP genotyping and the calculation of genomic estimated breeding values (GEBVs). However, a high proportion of cattle embryos fail to establish a pregnancy. Here, we demonstrate that further interrogation of the SNP data collected for GEBVs can effectively remove aneuploid embryos from the pool, improving live births per embryo transfer (ET). Using three preimplantation genetic testing for aneuploidy (PGT-A) approaches, we assessed 1713 cattle blastocysts in a blind, retrospective analysis. Our findings indicate aneuploid embryos have a 5.8% chance of establishing a pregnancy and a 5.0% chance of given rise to a live birth. This compares to 59.6% and 46.7% for euploid embryos (p < 0.0001). PGT-A improved overall pregnancy and live birth rates by 7.5% and 5.8%, respectively (p < 0.0001). More detailed analyses revealed donor, chromosome, stage, grade, and sex-specific rates of error. Notably, we discovered a significantly higher incidence of aneuploidy in XY embryos and, as in humans, detected a preponderance of maternal meiosis I errors. Our data strongly support the use of PGT-A in cattle IVP programmes.

Identification of unique ROH regions with unfavorable effects on production and fertility traits in Canadian Holsteins.

BACKGROUND: The advent of genomic information and the reduction in the cost of genotyping have led to the use of genomic information to estimate genomic inbreeding as an alternative to pedigree inbreeding. Using genomic measures, effects of genomic inbreeding on production and fertility traits have been observed. However, there have been limited studies on the specific genomic regions causing the observed negative association with the trait of interest. Our aim was to identify unique run of homozygosity (ROH) genotypes present within a given genomic window that display negative associations with production and fertility traits and to quantify the effects of these identified ROH genotypes. METHODS: In total, 50,575 genotypes based on a 50K single nucleotide polymorphism (SNP) array and 259,871 pedigree records were available. Of these 50,575 genotypes, 46,430 cows with phenotypic records for production and fertility traits and having a first calving date between 2008 and 2018 were available. Unique ROH genotypes identified using a sliding-window approach were fitted into an animal mixed model as fixed effects to determine their effect on production and fertility traits. RESULTS: In total, 133 and 34 unique ROH genotypes with unfavorable effects were identified for production and fertility traits, respectively, at a 1% genome-wise false discovery rate. Most of these ROH regions were located on bovine chromosomes 8, 13, 14 and 19 for both production and fertility traits. For production traits, the average of all the unfavorably identified unique ROH genotypes effects were estimated to decrease milk yield by 247.30 kg, fat yield by 11.46 kg and protein yield by 8.11 kg. Similarly, for fertility traits, an average 4.81-day extension in first service to conception, a 0.16 increase in number of services, and a - 0.07 incidence in 56-day non-return rate were observed. Furthermore, a ROH region located on bovine chromosome 19 was identified that, when homozygous, had a negative effect on production traits. Signatures of selection proximate to this region have implicated GH1 as a potential candidate gene, which encodes the growth hormone that binds the growth hormone receptor. This observed negative effect could be a consequence of unfavorable alleles in linkage disequilibrium with favorable alleles. CONCLUSIONS: ROH genotypes with unfavorable effects on production and fertility traits were identified within and across multiple traits on most chromosomes. These identified ROH genotypes could be included in mate selection programs to minimize their frequency in future generations.

GALLO: An R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci.

BACKGROUND: The development of high-throughput sequencing and genotyping methodologies has enabled the identification of thousands of genomic regions associated with several complex traits. The integration of multiple sources of biological information is a crucial step required to better understand patterns regulating the development of these traits. FINDINGS: Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package developed for the accurate annotation of genes and quantitative trait loci (QTLs) located in regions identified in common genomic analyses performed in livestock, such as genome-wide association studies and transcriptomics using RNA sequencing. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies), and QTL enrichment in different livestock species such as cattle, pigs, sheep, and chickens. CONCLUSIONS: Consequently, GALLO is a useful package for annotation, identification of hidden patterns across datasets, and data mining previously reported associations, as well as the efficient examination of the genetic architecture of complex traits in livestock.

Symposium review: The genomic architecture of inbreeding: How homozygosity affects health and performance.

Inbreeding depression is a growing concern in livestock because it can detrimentally affect animal fitness, health, and production levels. Genomic information can be used to more effectively capture variance in Mendelian sampling, thereby enabling more accurate estimation of inbreeding, but further progress is still required. The calculation of inbreeding for herd management purposes is largely still done using pedigree information only, although inbreeding coefficients calculated in this manner have been shown to be less accurate than genomic inbreeding measures. Continuous stretches of homozygous genotypes, so called runs of homozygosity, have been shown to provide a better estimate of autozygosity at the genomic level than conventional measures based on inbreeding coefficients calculated through conventional pedigree information or even genomic relationship matrices. For improved and targeted management of genomic inbreeding at the population level, the development of methods that incorporate genomic information in mate selection programs may provide a more precise tool for reducing the detrimental effects of inbreeding in dairy herds. Additionally, a better understanding of the genomic architecture of inbreeding and incorporating that knowledge into breeding programs could significantly refine current practices. Opportunities to maintain high levels of genetic progress in traits of interest while managing homozygosity and sustaining acceptable levels of heterozygosity in highly selected dairy populations exist and should be examined more closely for continued sustainability of both the dairy cattle population as well as the dairy industry. The inclusion of precise genomic measures of inbreeding, such as runs of homozygosity, inbreeding, and mating programs, may provide a path forward. In this symposium review article, we describe traditional measures of inbreeding and the recent developments made toward more precise measures of homozygosity using genomic information. The effects of homozygosity resulting from inbreeding on phenotypes, the identification and mapping of detrimental homozygosity haplotypes, management of inbreeding with genomic data, and areas in need of further research are discussed.

Signatures of selection and environmental adaptation across the goat genome post-domestication.

BACKGROUND: Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds. RESULTS: Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments. CONCLUSIONS: These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide.

Patterns of homozygosity in insular and continental goat breeds.

BACKGROUND: Genetic isolation of breeds may result in a significant loss of diversity and have consequences on health and performance. In this study, we examined the effect of geographic isolation on caprine genetic diversity patterns by genotyping 480 individuals from 25 European and African breeds with the Goat SNP50 BeadChip and comparing patterns of homozygosity of insular and nearby continental breeds. RESULTS: Among the breeds analysed, number and total length of ROH varied considerably and depending on breeds, ROH could cover a substantial fraction of the genome (up to 1.6 Gb in Icelandic goats). When compared with their continental counterparts, goats from Iceland, Madagascar, La Palma and Ireland (Bilberry and Arran) displayed a significant increase in ROH coverage, ROH number and FROH values (P value < 0.05). Goats from Mediterranean islands represent a more complex case because certain populations displayed a significantly increased level of homozygosity (e.g. Girgentana) and others did not (e.g. Corse and Sarda). Correlations of number and total length of ROH for insular goat populations with the distance between islands and the nearest continental locations revealed an effect of extremely long distances on the patterns of homozygosity. CONCLUSIONS: These results indicate that the effects of insularization on the patterns of homozygosity are variable. Goats raised in Madagascar, Iceland, Ireland (Bilberry and Arran) and La Palma, show high levels of homozygosity, whereas those bred in Mediterranean islands display patterns of homozygosity that are similar to those found in continental populations. These results indicate that the diversity of insular goat populations is modulated by multiple factors such as geographic distribution, population size, demographic history, trading and breed management.

Genome-wide patterns of homozygosity provide clues about the population history and adaptation of goats.

BACKGROUND: Patterns of homozygosity can be influenced by several factors, such as demography, recombination, and selection. Using the goat SNP50 BeadChip, we genotyped 3171 goats belonging to 117 populations with a worldwide distribution. Our objectives were to characterize the number and length of runs of homozygosity (ROH) and to detect ROH hotspots in order to gain new insights into the consequences of neutral and selection processes on the genome-wide homozygosity patterns of goats. RESULTS: The proportion of the goat genome covered by ROH is, in general, less than 15% with an inverse relationship between ROH length and frequency i.e. short ROH (< 3 Mb) are the most frequent ones. Our data also indicate that ~ 60% of the breeds display low FROH coefficients (< 0.10), while ~ 30 and ~ 10% of the goat populations show moderate (0.10 < FROH < 0.20) or high (> 0.20) FROH values. For populations from Asia, the average number of ROH is smaller and their coverage is lower in goats from the Near East than in goats from Central Asia, which is consistent with the role of the Fertile Crescent as the primary centre of goat domestication. We also observed that local breeds with small population sizes tend to have a larger fraction of the genome covered by ROH compared to breeds with tens or hundreds of thousands of individuals. Five regions on three goat chromosomes i.e. 11, 12 and 18, contain ROH hotspots that overlap with signatures of selection. CONCLUSIONS: Patterns of homozygosity (average number of ROH of 77 and genome coverage of 248 Mb; FROH < 0.15) are similar in goats from different geographic areas. The increased homozygosity in local breeds is the consequence of their small population size and geographic isolation as well as of founder effects and recent inbreeding. The existence of three ROH hotspots that co-localize with signatures of selection demonstrates that selection has also played an important role in increasing the homozygosity of specific regions in the goat genome. Finally, most of the goat breeds analysed in this work display low levels of homozygosity, which is favourable for their genetic management and viability.

SNPConvert: SNP Array Standardization and Integration in Livestock Species.

One of the main advantages of single nucleotide polymorphism (SNP) array technology is providing genotype calls for a specific number of SNP markers at a relatively low cost. Since its first application in animal genetics, the number of available SNP arrays for each species has been constantly increasing. However, conversely to that observed in whole genome sequence data analysis, SNP array data does not have a common set of file formats or coding conventions for allele calling. Therefore, the standardization and integration of SNP array data from multiple sources have become an obstacle, especially for users with basic or no programming skills. Here, we describe the difficulties related to handling SNP array data, focusing on file formats, SNP allele coding, and mapping. We also present SNPConvert suite, a multi-platform, open-source, and user-friendly set of tools to overcome these issues. This tool, which can be integrated with open-source and open-access tools already available, is a first step towards an integrated system to standardize and integrate any type of raw SNP array data. The tool is available at: https://github. com/nicolazzie/SNPConvert.git.

Use of canonical discriminant analysis to study signatures of selection in cattle.

BACKGROUND: Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of "signatures of selection" is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach. METHODS: A total of 4094 bulls from five breeds with different production aptitudes (two dairy breeds: Italian Holstein and Italian Brown Swiss; two beef breeds: Piemontese and Marchigiana; and one dual purpose breed: Italian Simmental) were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an FST-based approach. RESULTS: Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including MSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the FST-based approach. CONCLUSIONS: The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the FST index.

Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories.

BACKGROUND: Domestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history. METHODS: In this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations. RESULTS: Genome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected. CONCLUSIONS: This study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.

Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy.

Increased inbreeding is an inevitable consequence of selection in livestock populations. The analysis of high-density single nucleotide polymorphisms (SNPs) facilitates the identification of long and uninterrupted runs of homozygosity (ROH) that can be used to identify chromosomal regions that are identical by descent. In this work, the distribution of ROH of different lengths in five Italian cattle breeds is described. A total of 4095 bulls from five cattle breeds (2093 Italian Holstein, 749 Italian Brown, 364 Piedmontese, 410 Marchigiana and 479 Italian Simmental) were genotyped at 54K SNP loci. ROH were identified and used to estimate molecular inbreeding coefficients (FROH ), which were compared with inbreeding coefficients estimated from pedigree information (FPED ) and using the genomic relationship matrix (FGRM ). The average number of ROH per animal ranged from 54 ± 7.2 in Piedmontese to 94.6 ± 11.6 in Italian Brown. The highest number of short ROH (related to ancient consanguinity) was found in Piedmontese, followed by Simmental. The Italian Brown and Holstein had a higher proportion of longer ROH distributed across the whole genome, revealing recent inbreeding. The FPED were moderately correlated with FROH > 1 Mb (0.662, 0.700 and 0.669 in Italian Brown, Italian Holstein and Italian Simmental respectively) but poorly correlated with FGRM (0.134, 0.128 and 0.448 for Italian Brown, Italian Holstein and Italian Simmental respectively). The inclusion of ROH > 8 Mb in the inbreeding calculation improved the correlation of FROH with FPED and FGRM . ROH are a direct measure of autozygosity at the DNA level and can overcome approximations and errors resulting from incomplete pedigree data. In populations with high linkage disequilibrium (LD) and recent inbreeding (e.g. Italian Holstein and Italian Brown), a medium-density marker panel, such as the one used here, may provide a good estimate of inbreeding. However, in populations with low LD and ancient inbreeding, marker density would have to be increased to identify short ROH that are identical by descent more precisely.

Use of locally weighted scatterplot smoothing (LOWESS) regression to study selection signatures in Piedmontese and Italian Brown cattle breeds.

Selection is the major force affecting local levels of genetic variation in species. The availability of dense marker maps offers new opportunities for a detailed understanding of genetic diversity distribution across the animal genome. Over the last 50 years, cattle breeds have been subjected to intense artificial selection. Consequently, regions controlling traits of economic importance are expected to exhibit selection signatures. The fixation index (Fst ) is an estimate of population differentiation, based on genetic polymorphism data, and it is calculated using the relationship between inbreeding and heterozygosity. In the present study, locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach were used to investigate selection signatures in two cattle breeds with different production aptitudes (dairy and beef). Fst was calculated for 42 514 SNP marker loci distributed across the genome in 749 Italian Brown and 364 Piedmontese bulls. The statistical significance of Fst values was assessed using a control chart. The LOWESS technique was efficient in removing noise from the raw data and was able to highlight selection signatures in chromosomes known to harbour genes affecting dairy and beef traits. Examples include the peaks detected for BTA2 in the region where the myostatin gene is located and for BTA6 in the region harbouring the ABCG2 locus. Moreover, several loci not previously reported in cattle studies were detected.

Use of partial least squares regression to impute SNP genotypes in Italian cattle breeds.

BACKGROUND: The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. METHODS: Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. RESULTS: In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. CONCLUSIONS: Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available.