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Aedes aegypti, the major vector of dengue, yellow fever, chikungunya, and Zika viruses, remains of great medical and public health concern. There is little doubt that the ancestral home of the species is Africa. This mosquito invaded the New World 400‐500 years ago and later, Asia. However, little is known about the genetic structure and history of Ae. aegypti across Africa, as well as the possible origin(s) of the New World invasion. Here, we use ~17,000 genome‐wide single nucleotide polymorphisms (SNPs) to characterize a heretofore undocumented complex picture of this mosquito across its ancestral range in Africa. We find signatures of human‐assisted migrations, connectivity across long distances in sylvan populations, and of local admixture between domestic and sylvan populations. Finally, through a phylogenetic analysis combined with the genetic structure analyses, we suggest West Africa and especially Angola as the source of the New World's invasion, a scenario that fits well with the historic record of 16th‐century slave trade between Africa and Americas. We have detected distinct genetic structuring of populations of the major vector of human diseases Aedes aegypti in its native range, Africa. Both isolation by distance and long‐range anthropogenic migration are observed. Strong evidence indicates Angola populations gave rise to the species outside Africa.

European flat oyster (Ostrea edulis) is an ecologically and economically important marine bivalve, that has been severely affected by the intracellular parasite Bonamia ostreae. In this study, a flat oyster SNP array (~14,000 SNPs) was used to validate previously reported outlier loci for divergent selection associated with B. ostreae exposure in the Northeast Atlantic Area. A total of 134 wild and hatchery individuals from the North Sea, collected in naïve (NV) and long‐term affected (LTA) areas, were analysed. Genetic diversity and differentiation were related to the sampling origin (wild vs. hatchery) when using neutral markers, and to bonamiosis status (NV vs. LTA) when using outlier loci for divergent selection. Two genetic clusters appeared intermingled in all sampling locations when using outlier loci, and their frequency was associated with their bonamiosis status. When both clusters were compared, outlier data sets showed high genetic divergence (FST > 0.25) unlike neutral loci (FST not ≠ 0). Moreover, the cluster associated with LTA samples showed much higher genetic diversity and significant heterozygote excess with outlier loci, but not with neutral data. Most outliers mapped on chromosome 8 (OE‐C8) of the flat oyster genome, supporting a main genomic region underlying resilience to bonamiosis. Furthermore, differentially expressed genes previously reported between NV and LTA strains showed higher mapping density on OE‐C8. A range of relevant immune functions were specifically enriched among genes annotated on OE‐C8, providing hypotheses for resilience mechanisms to an intracellular parasite. The results suggest that marker‐assisted selection could be applied to breed resilient strains of O. edulis to bonamiosis, if lower parasite load and/or higher viability of the LTA genetic cluster following B. ostreae infection is demonstrated.

Metastable and somatically heritable patterns of DNA methylation provide an important level of genomic regulation. In this article, we review methods for analyzing these genome-wide epigenetic patterns and offer a perspective on the ever-expanding literature, which we hope will be useful for investigators who are new to this area. The historical aspects that we cover will be helpful in interpreting this literature and we hope that our discussion of the newest analytical methods will stimulate future progress. We emphasize that no single approach can provide a complete view of the overall methylome, and that combinations of several modalities applied to the same sample set will give the clearest picture. Given the unexpected epigenomic patterns and new biological principles, as well as new disease markers, that have been uncovered in recent studies, it is likely that important discoveries will continue to be made using genome-wide DNA methylation profiling.

Background To date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array. Results Using whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of ~5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor (Affymetrix, now Thermo Fisher Scientific) a set of ~2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of ~670 thousand SNPs (MNEc670k), was designed for genotype imputation. Conclusions Here, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.

Height is an important trait related to plant architecture and yield potential in bread wheat ( L.). We previously identified a major quantitative trait locus flanked by simple sequence repeat markers and that reduced height by 8.0-10.4%. Here , designated as , was confirmed using recombinant inbred lines (RILs) derived from a Jingdong 8/Aikang 58 cross. The target sequences of and were used as queries to BLAST against International Wheat Genome Sequence Consortium database and hit a super scaffold of approximately 208 Mb. Based on gene annotation of the scaffold, three gene-specific markers were developed to genotype the RILs, and was narrowed to a 1.85 cM interval between and . In addition, three single nucleotide polymorphism (SNP) markers linked to were identified from SNP chip-based screening in combination with bulked segregant analysis. The allelic efficacy of was validated in 242 elite wheat varieties using and markers. These showed a significant association between genotypes and plant height. reduced plant height by an average of 6.0-7.9 cm across environments and were significantly associated with an increased TGW of 2.0-3.4 g. The findings indicate that is a common dwarfing gene in wheat breeding, and and can be used for marker-assisted selection.

Blue mussels from the genus Mytilus are an abundant component of the benthic community, found in the high latitude habitats. These foundation species are relevant to the aquaculture industry, with over 2 million tonnes produced globally each year. Mussels withstand a wide range of environmental conditions and species from the Mytilus edulis complex readily hybridize in regions where their distributions overlap. Significant effort has been made to investigate the consequences of environmental stress on mussel physiology, reproductive isolation, and local adaptation. Yet our understanding on the genomic mechanisms underlying such processes remains limited. In this study, we developed a multi species medium‐density 60 K SNP‐array including four species of the Mytilus genus. SNPs included in the platform were called from 138 mussels from 23 globally distributed mussel populations, sequenced using a whole‐genome low coverage approach. The array contains polymorphic SNPs which capture the genetic diversity present in mussel populations thriving across a gradient of environmental conditions (~59 K SNPs) and a set of published and validated SNPs informative for species identification and for diagnosis of transmissible cancer (610 SNPs). The array will allow the consistent genotyping of individuals, facilitating the investigation of ecological and evolutionary processes in these taxa. The applications of this array extend to shellfish aquaculture, contributing to the optimization of this industry via genomic selection of blue mussels, parentage assignment, inbreeding assessment and traceability. Further applications such as genome wide association studies (GWAS) for key production traits and those related to environmental resilience are especially relevant to safeguard aquaculture production under climate change.

Background Genomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F 1 hybrids to develop genomic prediction models for eight phenotypic traits - basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA). Results Heritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, the GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the true proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in F 1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore either GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions. Conclusions The size and composition of the training set and number of SNPs used are the two most important factors for model prediction, compared to the statistical methods and the genomic location of SNPs. Furthermore, training the prediction model based on pure parental species only provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in Eucalyptus breeding programs by the selection of interspecific hybrids.

Chip-based high-throughput genotyping has facilitated genome-wide studies of genetic diversity. Many studies have utilized these large data sets to make inferences about the demographic history of human populations using measures of genetic differentiation such as FST or principal component analyses. However, the single nucleotide polymorphism (SNP) chip data suffer from ascertainment biases caused by the SNP discovery process in which a small number of individuals from selected populations are used as discovery panels. In this study, we investigate the effect of the ascertainment bias on inferences regarding genetic differentiation among populations in one of the common genome-wide genotyping platforms. We generate SNP genotyping data for individuals that previously have been subject to partial genome-wide Sanger sequencing and compare inferences based on genotyping data to inferences based on direct sequencing. In addition, we also analyze publicly available genome-wide data. We demonstrate that the ascertainment biases will distort measures of human diversity and possibly change conclusions drawn from these measures in some times unexpected ways. We also show that details of the genotyping calling algorithms can have a surprisingly large effect on population genetic inferences. We not only present a correction of the spectrum for the widely used Affymetrix SNP chips but also show that such corrections are difficult to generalize among studies.

Preharvest sprouting (PHS) significantly reduces grain yield and quality. Identification of genetic loci for PHS resistance will facilitate breeding sprouting-resistant wheat cultivars. In this study, we constructed a genetic map comprising 1,702 non-redundant markers in a recombinant inbred line (RIL) population derived from cross Yangxiaomai/Zhongyou9507 using the wheat 15K single-nucleotide polymorphism (SNP) assay. Four quantitative trait loci (QTL) for germination index (GI), a major indicator of PHS, were identified, explaining 4.6–18.5% of the phenotypic variances. Resistance alleles of Qphs.caas-3AL, Qphs.caas-3DL , and Qphs.caas-7BL were from Yangxiaomai, and Zhongyou9507 contributed a resistance allele in Qphs.caas-4AL . No epistatic effects were detected among the QTL, and combined resistance alleles significantly increased PHS resistance. Sequencing and linkage mapping showed that Qphs.caas-3AL and Qphs.caas-3DL corresponded to grain color genes Tamyb10-A and Tamyb10-D , respectively, whereas Qphs.caas-4AL and Qphs.caas-7BL were probably new QTL for PHS. We further developed cost-effective, high-throughput kompetitive allele-specific PCR (KASP) markers tightly linked to Qphs.caas-4AL and Qphs.caas-7BL and validated their association with GI in a test panel of cultivars. The resistance alleles at the Qphs.caas-4AL and Qphs.caas-7BL loci were present in 72.2 and 16.5% cultivars, respectively, suggesting that the former might be subjected to positive selection in wheat breeding. The findings provide not only genetic resources for PHS resistance but also breeding tools for marker-assisted selection.

Background Investments in genetic selection have played a major role in the New Zealand sheep industry competitiveness. Selection may erode genetic diversity, which is a crucial factor for the success of breeding programs. Better understanding of linkage disequilibrium (LD) and ancestral effective population size (Ne) through quantifying this diversity and comparison between populations allows for more informed decisions with regards to selective breeding taking population genetic diversity into account. The estimation of N e can be determined via genetic markers and requires knowledge of genetic distances between these markers. Single nucleotide polymorphisms (SNP) data from a sample of 12,597 New Zealand crossbred and purebred sheep genotyped with the Illumina Ovine SNP50 BeadChip was used to perform a genome-wide scan of LD and N e . Three methods to estimate genetic distances were investigated: 1) M1: a ratio fixed across the whole genome of one Megabase per centiMorgan; 2) M2: the ratios of genetic distance (using M3, below) over physical distance fixed for each chromosome; and, 3) M3: a genetic map of inter-SNP distances estimated using CRIMAP software (v2.503). Results The estimates obtained with M2 and M3 showed much less variability between autosomes than those with M1, which tended to give lower N e results and higher LD decay. The results suggest that N e has decreased since the development of sheep breeds in Europe and this reduction in Ne has been accelerated in the last three decades. The N e estimated for five generations in the past ranged from 71 to 237 for Texel and Romney breeds, respectively. A low level of genetic kinship and inbreeding was estimated in those breeds suggesting avoidance of mating close relatives. Conclusions M3 was considered the most accurate method to create genetic maps for the estimation of LD and Ne. The findings of this study highlight the history of genetic selection in New Zealand crossbred and purebred sheep and these results will be very useful to understand genetic diversity of the population with respect to genetic selection. In addition, it will help geneticists to identify genomic regions which have been preferentially selected within a variety of breeds and populations.