Classifying aneuploidy in genotype intensity data using deep learning.

Aneuploidy is the loss or gain of one or more chromosomes. Although it is a rare phenomenon in liveborn individuals, it is observed in livestock breeding populations. These breeding populations are often routinely genotyped and the genotype intensity data from single nucleotide polymorphism (SNP) arrays can be exploited to identify aneuploidy cases. This identification is a time-consuming and costly task, because it is often performed by visual inspection of the data per chromosome, usually done in plots of the intensity data by an expert. Therefore, we wanted to explore the feasibility of automated image classification to replace (part of) the visual detection procedure for any diploid species. The aim of this study was to develop a deep learning Convolutional Neural Network (CNN) classification model based on chromosome level plots of SNP array intensity data that can classify the images into disomic, monosomic and trisomic cases. A multispecies dataset enriched for aneuploidy cases was collected containing genotype intensity data of 3321 disomic, 1759 monosomic and 164 trisomic chromosomes. The final CNN model had an accuracy of 99.9%, overall precision was 1, recall was 0.98 and the F1 score was 0.99 for classifying images from intensity data. The high precision assures that cases detected are most likely true cases, however, some trisomy cases may be missed (the recall of the class trisomic was 0.94). This supervised CNN model performed much better than an unsupervised k-means clustering, which reached an accuracy of 0.73 and had especially difficult to classify trisomic cases correctly. The developed CNN classification model provides high accuracy to classify aneuploidy cases based on images of plotted X and Y genotype intensity values. The classification model can be used as a tool for routine screening in large diploid populations that are genotyped to get a better understanding of the incidence and inheritance, and in addition, avoid anomalies in breeding candidates.

A low-tech, cost-effective and efficient method for safeguarding genetic diversity by direct cryopreservation of poultry embryonic reproductive cells.

Chickens are an important resource for smallholder farmers who raise locally adapted, genetically distinct breeds for eggs and meat. The development of efficient reproductive technologies to conserve and regenerate chicken breeds safeguards existing biodiversity and secures poultry genetic resources for climate resilience, biosecurity, and future food production. The majority of the over 1600 breeds of chicken are raised in low and lower to middle income countries under resource-limited, small-scale production systems, which necessitates a low-tech, cost-effective means of conserving diversity is needed. Here, we validate a simple biobanking technique using cryopreserved embryonic chicken gonads. The gonads are quickly isolated, visually sexed, pooled by sex, and cryopreserved. Subsequently, the stored material is thawed and dissociated before injection into sterile host chicken embryos. By using pooled GFP and RFP-labelled donor gonadal cells and Sire Dam Surrogate mating, we demonstrate that chicks deriving entirely from male and female donor germ cells are hatched. This technology will enable ongoing efforts to conserve chicken genetic diversity for both commercial and smallholder farmers, and to preserve existing genetic resources at poultry research facilities.

Avian Primordial Germ Cells Are Bipotent for Male or Female Gametogenesis.

In birds, males are the homogametic sex (ZZ) and females are the heterogametic sex (ZW). Here, we investigate the role of chromosomal sex and germ cell competition on avian germ cell differentiation. We recently developed genetically sterile layer cockerels and hens for use as surrogate hosts for primordial germ cell (PGC) transplantation. Using in vitro propagated and cryopreserved PGCs from a pedigree Silkie broiler breed, we now demonstrate that sterile surrogate layer hosts injected with same sex PGCs have normal fertility and produced pure breed Silkie broiler offspring when directly mated to each other in Sire Dam Surrogate mating. We found that female sterile hosts carrying chromosomally male (ZZ) PGCs formed functional oocytes and eggs, which gave rise to 100% male offspring after fertilization. Unexpectedly, we also observed that chromosomally female (ZW) PGCs carried by male sterile hosts formed functional spermatozoa and produced viable offspring. These findings demonstrate that avian PGCs are not sexually restricted for functional gamete formation and provide new insights for the cryopreservation of poultry and other bird species using diploid stage germ cells.

Direct allele introgression into pure chicken breeds using Sire Dam Surrogate (SDS) mating.

Poultry is the most abundant livestock species with over 60 billion chickens raised globally per year. The majority of chicken are produced from commercial flocks, however many indigenous chicken breeds play an important role in rural economies as they are well adapted to local environmental and scavenging conditions. The ability to make precise genetic changes in chicken will permit the validation of genetic variants responsible for climate adaptation and disease resilience, and the transfer of beneficial alleles between breeds. Here, we generate a novel inducibly sterile surrogate host chicken. Introducing donor genome edited primordial germ cells into the sterile male and female host embryos produces adult chicken carrying only exogenous germ cells. Subsequent direct mating of the surrogate hosts, Sire Dam Surrogate (SDS) mating, recreates the donor chicken breed carrying the edited allele in a single generation. We demonstrate the introgression and validation of two feather trait alleles, Dominant white and Frizzle into two pure chicken breeds using the SDS surrogate hosts.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers.

BACKGROUND: Deep neural networks (DNN) are a particular case of artificial neural networks (ANN) composed by multiple hidden layers, and have recently gained attention in genome-enabled prediction of complex traits. Yet, few studies in genome-enabled prediction have assessed the performance of DNN compared to traditional regression models. Strikingly, no clear superiority of DNN has been reported so far, and results seem highly dependent on the species and traits of application. Nevertheless, the relatively small datasets used in previous studies, most with fewer than 5000 observations may have precluded the full potential of DNN. Therefore, the objective of this study was to investigate the impact of the dataset sample size on the performance of DNN compared to Bayesian regression models for genome-enable prediction of body weight in broilers by sub-sampling 63,526 observations of the training set. RESULTS: Predictive performance of DNN improved as sample size increased, reaching a plateau at about 0.32 of prediction correlation when 60% of the entire training set size was used (i.e., 39,510 observations). Interestingly, DNN showed superior prediction correlation using up to 3% of training set, but poorer prediction correlation after that compared to Bayesian Ridge Regression (BRR) and Bayes Cπ. Regardless of the amount of data used to train the predictive machines, DNN displayed the lowest mean square error of prediction compared to all other approaches. The predictive bias was lower for DNN compared to Bayesian models, across all dataset sizes, with estimates close to one with larger sample sizes. CONCLUSIONS: DNN had worse prediction correlation compared to BRR and Bayes Cπ, but improved mean square error of prediction and bias relative to both Bayesian models for genome-enabled prediction of body weight in broilers. Such findings, highlights advantages and disadvantages between predictive approaches depending on the criterion used for comparison. Furthermore, the inclusion of more data per se is not a guarantee for the DNN to outperform the Bayesian regression methods commonly used for genome-enabled prediction. Nonetheless, further analysis is necessary to detect scenarios where DNN can clearly outperform Bayesian benchmark models.

Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken.

BACKGROUND: As more and more genotypes become available, accuracy of genomic evaluations can potentially increase. However, the impact of genotype data on accuracy depends on the structure of the genotyped cohort. For populations such as dairy cattle, the greatest benefit has come from genotyping sires with high accuracy, whereas the benefit due to adding genotypes from cows was smaller. In broiler chicken breeding programs, males have less progeny than dairy bulls, females have more progeny than dairy cows, and most production traits are recorded for both sexes. Consequently, genotyping both sexes in broiler chickens may be more advantageous than in dairy cattle. METHODS: We studied the contribution of genotypes from males and females using a real dataset with genotypes on 15 723 broiler chickens. Genomic evaluations used three training sets that included only males (4648), only females (8100), and both sexes (12 748). Realized accuracies of genomic estimated breeding values (GEBV) were used to evaluate the benefit of including genotypes for different training populations on genomic predictions of young genotyped chickens. RESULTS: Using genotypes on males, the average increase in accuracy of GEBV over pedigree-based EBV for males and females was 12 and 1 percentage points, respectively. Using female genotypes, this increase was 1 and 18 percentage points, respectively. Using genotypes of both sexes increased accuracies by 19 points for males and 20 points for females. For two traits with similar heritabilities and amounts of information, realized accuracies from cross-validation were lower for the trait that was under strong selection. CONCLUSIONS: Overall, genotyping males and females improves predictions of all young genotyped chickens, regardless of sex. Therefore, when males and females both contribute to genetic progress of the population, genotyping both sexes may be the best option.

The genetic architecture of climatic adaptation of tropical cattle.

Adaptation of global food systems to climate change is essential to feed the world. Tropical cattle production, a mainstay of profitability for farmers in the developing world, is dominated by heat, lack of water, poor quality feedstuffs, parasites, and tropical diseases. In these systems European cattle suffer significant stock loss, and the cross breeding of taurine x indicine cattle is unpredictable due to the dilution of adaptation to heat and tropical diseases. We explored the genetic architecture of ten traits of tropical cattle production using genome wide association studies of 4,662 animals varying from 0% to 100% indicine. We show that nine of the ten have genetic architectures that include genes of major effect, and in one case, a single location that accounted for more than 71% of the genetic variation. One genetic region in particular had effects on parasite resistance, yearling weight, body condition score, coat colour and penile sheath score. This region, extending 20 Mb on BTA5, appeared to be under genetic selection possibly through maintenance of haplotypes by breeders. We found that the amount of genetic variation and the genetic correlations between traits did not depend upon the degree of indicine content in the animals. Climate change is expected to expand some conditions of the tropics to more temperate environments, which may impact negatively on global livestock health and production. Our results point to several important genes that have large effects on adaptation that could be introduced into more temperate cattle without detrimental effects on productivity.

Genomic regions associated with fertility traits in male and female cattle: advances from microsatellites to high-density chips and beyond.

A current challenge in genetic improvement of cattle is to identify genomic selection strategies that could work across breeds. Breed differences, scarcity of data, and lack of quantitative trait loci (QTL) validation contribute to this challenge. We conducted a review of the literature to identify QTL, markers, and candidate genes that are associated with fertility across breeds to arrive at an integrated view of bovine fertility genomics and to guide the direction of future studies. This review considers both male and female fertility traits as these are economically relevant for all breeds and production systems. Regions associated with fertility traits were found in each of the 30 bovine chromosomes, confirming the complexity of these polygenic traits. Across breeds, regions on chromosomes 1, 5, 14, and 16 were associated with female reproductive traits. The X chromosome was associated with male reproductive traits in both dairy and beef bulls. It has recently been proposed that a Y chromosome anomaly may be involved in infertility in cows. Knowledge of these QTL may assist discovery of causative mutations and has the potential to improve the accuracy of genomic selection, especially across breeds of cattle.

The IGF1 pathway genes and their association with age of puberty in cattle.

Insulin-like growth factor I (somatomedin C) (IGF1) influences gonadotrophin-releasing hormone (GnRH) neurons during puberty, and GnRH release guides pubertal development. Therefore, genes of the IGF1 pathway are biological candidates for the identification of single-nucleotide polymorphisms (SNPs) affecting age of puberty. In a genome-wide association study, genotyped heifers were Tropical Composite (TCOMP, n = 866) or Brahman (BRAH, n = 843), with observation of age at first corpus luteum defining puberty. We examined SNPs in or near genes of the IGF1 pathway and report seven genes associated with age at puberty in cattle: IGF1R, IGFBP2, IGFBP4, PERK (HUGO symbol EIF2AK3), PIK3R1, GSK3B and IRS1. SNPs in the IGF1 receptor (IGF1R) showed the most promising associations: two SNPs were associated with puberty in TCOMP (P < 0.05) and one in BRAH (P = 0.00009). This last SNP explained 2% of the genetic variation (R(2) = 2.04%) for age of puberty in BRAH. Hence, IGF1R was examined further. Additional SNPs were genotyped, and haplotypes were analysed. To test more SNPs in this gene, four new SNPs from dbSNP were selected and genotyped. Single SNP and haploytpe analysis revealed associations with age of puberty in both breeds. There were two haplotypes of 12 IGF1R SNPs associated with puberty in BRAH (P < 0.05) and one in TCOMP (P < 0.05). One haplotype of two SNPs was associated (P < 0.01) with puberty in BRAH, but not in TCOMP. In conclusion, the IGF1 pathway appeared more relevant for age of puberty in Brahman cattle, and IGF1R showed higher significance when compared with other genes from the pathway.

Candidate genes associated with testicular development, sperm quality, and hormone levels of inhibin, luteinizing hormone, and insulin-like growth factor 1 in Brahman bulls.

Bull fertility is an important target for genetic improvement, and early prediction using genetic markers is therefore a goal for livestock breeding. We performed genome-wide association studies to identify genes associated with fertility traits measured in young bulls. Data from 1118 Brahman bulls were collected for six traits: blood hormone levels of inhibin (IN) at 4 mo, luteinizing hormone (LH) following a gonadotropin-releasing hormone challenge at 4 mo, and insulin-like growth factor 1 (IGF1) at 6 mo, scrotal circumference (SC) at 12 mo, ability to produce sperm (Sperm) at 18 mo, and percentage of normal sperm (PNS) at 24 mo. All the bulls were genotyped with the BovineSNP50 chip. Sires and dams of the bull population (n = 304) were genotyped with the high-density chip (∼800 000 polymorphisms) to allow for imputation, thereby contributing detail on genome regions of interest. Polymorphism associations were discovered for all traits, except for Sperm. Chromosome 2 harbored polymorphisms associated with IN. For LH, associated polymorphisms were located in five different chromosomes. A region of chromosome 14 contained polymorphisms associated with IGF1 and SC. Regions of the X chromosome showed associations with SC and PNS. Associated polymorphisms yielded candidate genes in chromosomes 2, 14, and X. These findings will contribute to the development of genetic markers to help select cattle with improved fertility and will lead to better annotation of gene function in the context of reproductive biology.

Estimating the effect of SNP genotype on quantitative traits from pooled DNA samples.

BACKGROUND: Studies to detect associations between DNA markers and traits of interest in humans and livestock benefit from increasing the number of individuals genotyped. Performing association studies on pooled DNA samples can provide greater power for a given cost. For quantitative traits, the effect of an SNP is measured in the units of the trait and here we propose and demonstrate a method to estimate SNP effects on quantitative traits from pooled DNA data. METHODS: To obtain estimates of SNP effects from pooled DNA samples, we used logistic regression of estimated allele frequencies in pools on phenotype. The method was tested on a simulated dataset, and a beef cattle dataset using a model that included principal components from a genomic correlation matrix derived from the allele frequencies estimated from the pooled samples. The performance of the obtained estimates was evaluated by comparison with estimates obtained using regression of phenotype on genotype from individual samples of DNA. RESULTS: For the simulated data, the estimates of SNP effects from pooled DNA are similar but asymptotically different to those from individual DNA data. Error in estimating allele frequencies had a large effect on the accuracy of estimated SNP effects. For the beef cattle dataset, the principal components of the genomic correlation matrix from pooled DNA were consistent with known breed groups, and could be used to account for population stratification. Correctly modeling the contemporary group structure was essential to achieve estimates similar to those from individual DNA data, and pooling DNA from individuals within groups was superior to pooling DNA across groups. For a fixed number of assays, pooled DNA samples produced results that were more correlated with results from individual genotyping data than were results from one random individual assayed from each pool. CONCLUSIONS: Use of logistic regression of allele frequency on phenotype makes it possible to estimate SNP effects on quantitative traits from pooled DNA samples. With pooled DNA samples, genotyping costs are reduced, and in cases where trait records are abundant this approach is promising to obtain SNP associations for marker-assisted selection.

Association weight matrix for the genetic dissection of puberty in beef cattle.

We describe a systems biology approach for the genetic dissection of complex traits based on applying gene network theory to the results from genome-wide associations. The associations of single-nucleotide polymorphisms (SNP) that were individually associated with a primary phenotype of interest, age at puberty in our study, were explored across 22 related traits. Genomic regions were surveyed for genes harboring the selected SNP. As a result, an association weight matrix (AWM) was constructed with as many rows as genes and as many columns as traits. Each {i, j} cell value in the AWM corresponds to the z-score normalized additive effect of the ith gene (via its neighboring SNP) on the jth trait. Columnwise, the AWM recovered the genetic correlations estimated via pedigree-based restricted maximum-likelihood methods. Rowwise, a combination of hierarchical clustering, gene network, and pathway analyses identified genetic drivers that would have been missed by standard genome-wide association studies. Finally, the promoter regions of the AWM-predicted targets of three key transcription factors (TFs), estrogen-related receptor gamma (ESRRG), Pal3 motif, bound by a PPAR-gamma homodimer, IR3 sites (PPARG), and Prophet of Pit 1, PROP paired-like homeobox 1 (PROP1), were surveyed to identify binding sites corresponding to those TFs. Applied to our case, the AWM results recapitulate the known biology of puberty, captured experimentally validated binding sites, and identified candidate genes and gene-gene interactions for further investigation.

A primary assembly of a bovine haplotype block map based on a 15,036-single-nucleotide polymorphism panel genotyped in holstein-friesian cattle.

Analysis of data on 1000 Holstein-Friesian bulls genotyped for 15,036 single-nucleotide polymorphisms (SNPs) has enabled genomewide identification of haplotype blocks and tag SNPs. A final subset of 9195 SNPs in Hardy-Weinberg equilibrium and mapped on autosomes on the bovine sequence assembly (release Btau 3.1) was used in this study. The average intermarker spacing was 251.8 kb. The average minor allele frequency (MAF) was 0.29 (0.05-0.5). Following recent precedents in human HapMap studies, a haplotype block was defined where 95% of combinations of SNPs within a region are in very high linkage disequilibrium. A total of 727 haplotype blocks consisting of > or =3 SNPs were identified. The average block length was 69.7 +/- 7.7 kb, which is approximately 5-10 times larger than in humans. These blocks comprised a total of 2964 SNPs and covered 50,638 kb of the sequence map, which constitutes 2.18% of the length of all autosomes. A set of tag SNPs, which will be useful for further fine-mapping studies, has been identified. Overall, the results suggest that as many as 75,000-100,000 tag SNPs would be needed to track all important haplotype blocks in the bovine genome. This would require approximately 250,000 SNPs in the discovery phase.

A first-generation metric linkage disequilibrium map of bovine chromosome 6.

We constructed a metric linkage disequilibrium (LD) map of bovine chromosome 6 (BTA6) on the basis of data from 220 SNPs genotyped on 433 Australian dairy bulls. This metric LD map has distances in LD units (LDUs) that are analogous to centimorgans in linkage maps. The LD map of BTA6 has a total length of 8.9 LDUs. Within the LD map, regions of high LD (represented as blocks) and regions of low LD (steps) are observed, when plotted against the integrated map in kilobases. At the most stringent block definition, namely a set of loci with zero LDU increase over the span of these markers, BTA6 comprises 40 blocks, accounting for 41% of the chromosome. At a slightly lower stringency of block definition (a set of loci covering a maximum of 0.2 LDUs on the LD map), up to 81% of BTA6 is spanned by 46 blocks and with 13 steps that are likely to reflect recombination hot spots. The mean swept radius (the distance over which LD is likely to be useful for mapping) is 13.3 Mb, confirming extensive LD in Holstein-Friesian dairy cattle, which makes such populations ideal for whole-genome association studies.

Piggy-BACing the human genome II. A high-resolution, physically anchored, comparative map of the porcine autosomes.

Using the INRA-Minnesota porcine radiation hybrid panel, we have constructed a human-pig comparative map composed of 2274 loci, including 206 ESTs and 2068 BAC-end sequences, assigned to 34 linkage groups. The average spacing between comparative anchor loci is 1.15 Mb based on human genome sequence coordinates. A total of 51 conserved synteny groups that include 173 conserved segments were identified. This radiation hybrid map has the highest resolution of any porcine map to date and its integration with the porcine linkage map (reported here) will greatly facilitate the positional cloning of genes influencing complex traits of both agricultural and biomedical interest. Additionally, this map will provide a framework for anchoring contigs generated through BAC fingerprinting efforts and assist in the selection of a BAC minimal tiling path and assembly of the first sequence-ready map of the porcine genome.

An interactive bovine in silico SNP database (IBISS).

An interactive bovine in silico SNP (IBISS) database has been created through the clustering and aligning of bovine EST and mRNA sequences. Approximately 324,000 EST and mRNA sequences were clustered to produce 29,965 clusters (producing 48,679 consensus sequences) and 48,565 singletons. A SNP screening regime was placed on variations detected in the multiple sequence alignment files to determine which SNPs are more likely to be real rather than sequencing errors. A small subset of predicted SNPs was validated on a diverse set of bovine DNA samples using PCR amplification and sequencing. Fifty percent of the predicted SNPs in the "putative >1" category were polymorphic in the population sampled. The IBISS database represents more than just a SNP database; it is also a genomic database containing uniformly annotated predicted gene mRNA and protein sequences, gene structure, and genomic organization information.