Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

Distribution of Huntington’s disease Haplogroups in Indian population

Huntington’s disease (HD), a rare neurodegenerative disorder, is inherited in an autosomal dominant manner, and caused by a pathological trinucleotide expansion at exon1 of the HTT locus. Previous studies have described the haplogroups at the HTT locus that can explain the differences in prevalence of HD. We have selected three informative SNPs (rs762855, rs3856973 and rs4690073) to study these haplogroups in an Indian sample. Our results show that the genotype frequencies are significantly different between cases and controls for these SNPs. More than 90% of both cases and controls belong to Haplogroup A which is the predominant European haplogroup.

PSXII-32 Testing of low-density SNP panel in wild and domestic reindeer populations (Rangifer tarandus)

A territory of northern Russia covering more than 4,194 square kilometers is the world’s largest dwelling and breeding area of wild and domestic reindeer. Unlocking its complete genome sequences should have promoted design and development of species-specific genotyping arrays, but this process is still in progress. However, evaluation of state of gene pool of reindeer populations cannot be paused to prevent dramatic losses in genetic diversity. Thus, we aimed to select SNPs to create low-density panel to perform genetic assessments without losing biological content. Our study was based on SNP genotypes of wild reindeer (n = 83), and domestic reindeer from the Nenets District (Nenets breed, n = 100), Murmansk Region (Nenets breed, n = 19), Republic of Sakha (Yakutia) (Evenk breed, n = 19) obtained using Illumina BovineHD BeadChip. The data processing performed in PLINK 1.9 revealed 4456 polymorphic SNPs. Using Delta method implemented in the TRES software, 368 informative SNPs were selected from the whole set of polymorphic SNPs for further analysis. To compare the resolution power of low-density SNP panel with a whole set of polymorphic SNPs, we estimated minor allele frequencies (MAF) and performed PCA analysis and Admixture clustering. In case of low-density panel, we observed obvious bias to higher MAF (≥ 0.30), and 4% of SNPs had MAF around 0,1. Among the whole set of polymorphic SNPs, 70% SNPs had MAF ≤ 0.2. PCA obtained with low-density panel and the whole SNP set provided similar pattern of genetic differentiation between studied groups. Wild animals were clearly separated by PC1 from their domestic relatives. Besides Evenk reindeer was distant from two Nenets groups. The Admixture clustering demonstrated the identical patterns for both SNPs sets. The next step of our study will be developing custom DNA array based on 368 selected informative SNPs. The research was funded by RSF № 16-16-10068-P.

59 Genome activation in intracytoplasmic sperm injection-derived horse embryos

During pre-implantation development, embryos go through a critical period of embryonic genome activation (EGA). The timing of EGA is species specific, but little is known in horse embryos. Here, we aimed to characterise EGA in equine embryos produced by intracytoplasmic sperm injection. Embryos were produced by intracytoplasmic sperm injection of oocytes from 3 mares. Two embryos from each mare at each of 8 developmental stages (MII, zygote, 2-cell, 4-cell, 8-cell, 16-cell, morula, and blastocyst) were individually analysed by RNA-seq. Differential expression was evaluated using binomial Wald tests with an absolute logFC (fold change) threshold of 1 in the DESEqn 2R package. We found that EGA occurred in a two-step fashion. Minor EGA took place during the 2-cell to 4-cell transition, and featured up-regulation of 751 genes and discrete down-regulation of 60 genes in 4-cell embryos compared with 2-cell embryos. Differentially upregulated genes were enriched in gene ontology terms related to transcriptional activator activity, homeobox domains, and nucleosome assembly. Major EGA occurred during the 4-cell to 8-cell transition and included the largest number of differentially expressed genes (n=2,023) between consecutive stages. This period also featured the first massive transcript downregulation (n=816). Upregulated genes were enriched in gene ontology terms related to ribosomal assembly, translation, and RNA modification. Additionally, we observed that the number of intronic sequences was significantly higher from the 4-cell stage onward, indicating active transcription in comparison to oocytes, zygotes, and 2-cell embryos. To evaluate the timing of paternal genome activation, we used whole-genome sequencing data from the parents (average genome coverage of 19×) to quantify allele-specific expression. The average number of informative SNPs in exons, i.e. SNPs with alternative homozygous genotypes from the sire (AA mare - BB sire), was 26 128 per mare, corresponding to 7696 genes. Parental-specific transcript abundance was determined for each embryo, with an average of 1,911±865 informative SNPs detected per sample. Paternal alleles were considered expressed when they reached 10% of the maternal count. Across development, paternal transcripts became appreciable at the 4-cell stage, with 14.15±7.60% of the informative SNPs exhibiting paternal expression, and increased thereafter until reaching a maximum of 96.34% at the blastocyst stage. Overall, this work demonstrates that EGA in horse embryos starts at the 4-cell stage and achieves its main activation at the 8-cell stage. Further analysis will be performed to detail paternal vs. maternal gene expression at the different embryonic stages.

Evaluation of Genomic Selection for Seven Economic Traits in Yellow Drum (Nibea albiflora)

Yellow drum (Nibea albiflora) is an important maricultural fish in China, and genetic improvement is necessary for this species. This research evaluated the application of genomic selection methods to predict the genetic values of seven economic traits for yellow drum. Using genome-wide single-nucleotide polymorphisms (SNPs), we estimated the genetic parameters for seven traits, including body length (BL), swimming bladder index (SBI), swimming bladder weight (SBW), body thickness (BT), body height (BH), body length/body height ratio (LHR), and gonad weight index (GWI). The heritability estimates ranged from 0.309 to 0.843. We evaluated the prediction performance of various statistical methods, and no one method provided the highest predictive ability for all traits. We then evaluated and compared the use of genome-wide association study (GWAS)–informative SNPs and random SNPs for prediction and found that GWAS-informative SNPs obviously increased. It only needed 5 and 100 informative SNPs for LHR and BT to achieve almost the same predictive abilities as using genome-wide SNPs, and for BL, SBI, SBW, BH, and GWI, about 1000 to 3000 informative SNPs were needed to achieve whole-genome level predictive abilities. It can be concluded from the test results that breeders can use fewer SNPs to save the breeding costs of genomic selection for some traits.