G3: Genes, Genomes, Genetics

The importance of the Gallus gallus (chicken) as a model organism and agricultural animal merits a continuation of sequence assembly improvement efforts. We present a new version of the chicken genome assembly (Gallus_gallus-5.0; GCA_000002315.3), built from combined long single molecule sequencing technology, finished BACs, and improved physical maps. In overall assembled bases, we see a gain of 183 Mb, including 16.4 Mb in placed chromosomes with a corresponding gain in the percentage of intact repeat elements characterized. Of the 1.21 Gb genome, we include three previously missing autosomes, GGA30, 31, and 33, and improve sequence contig length 10-fold over the previous Gallus_gallus-4.0. Despite the significant base representation improvements made, 138 Mb of sequence is not yet located to chromosomes. When annotated for gene content, Gallus_gallus-5.0 shows an increase of 4679 annotated genes (2768 noncoding and 1911 protein-coding) over those in Gallus_gallus-4.0. We also revisited the question of what genes are missing in the avian lineage, as assessed by the highest quality avian genome assembly to date, and found that a large fraction of the original set of missing genes are still absent in sequenced bird species. Finally, our new data support a detailed map of MHC-B, encompassing two segments: one with a highly stable gene copy number and another in which the gene copy number is highly variable. The chicken model has been a critical resource for many other fields of study, and this new reference assembly will substantially further these efforts.

Maximization of genetic gain in forest tree breeding programs is contingent on the accuracy of the predicted breeding values and precision of the estimated genetic parameters. We investigated the effect of the combined use of contemporary pedigree information and genomic relatedness estimates on the accuracy of predicted breeding values and precision of estimated genetic parameters, as well as rankings of selection candidates, using single-step genomic evaluation (HBLUP). In this study, two traits with diverse heritabilities [tree height (HT) and wood density (WD)] were assessed at various levels of family genotyping efforts (0, 25, 50, 75, and 100%) from a population of white spruce (Picea glauca) consisting of 1694 trees from 214 open-pollinated families, representing 43 provenances in Québec, Canada. The results revealed that HBLUP bivariate analysis is effective in reducing the known bias in heritability estimates of open-pollinated populations, as it exposes hidden relatedness, potential pedigree errors, and inbreeding. The addition of genomic information in the analysis considerably improved the accuracy in breeding value estimates by accounting for both Mendelian sampling and historical coancestry that were not captured by the contemporary pedigree alone. Increasing family genotyping efforts were associated with continuous improvement in model fit, precision of genetic parameters, and breeding value accuracy. Yet, improvements were observed even at minimal genotyping effort, indicating that even modest genotyping effort is effective in improving genetic evaluation. The combined utilization of both pedigree and genomic information may be a cost-effective approach to increase the accuracy of breeding values in forest tree breeding programs where shallow pedigrees and large testing populations are the norm.

The aquatic planorbid snail Biomphalaria glabrata is one of the most intensively-studied mollusks due to its role in the transmission of schistosomiasis. Its 916 Mb genome has recently been sequenced and annotated, but it remains poorly assembled. Here, we used targeted capture markers to map over 10,000 B. glabrata scaffolds in a linkage cross of 94 F1 offspring, generating 24 linkage groups (LGs). We added additional scaffolds to these LGs based on linkage disequilibrium (LD) analysis of targeted capture and whole-genome sequences of 96 unrelated snails. Our final linkage map consists of 18,613 scaffolds comprising 515 Mb, representing 56% of the genome and 75% of genic and nonrepetitive regions. There are 18 large (> 10 Mb) LGs, likely representing the expected 18 haploid chromosomes, and > 50% of the genome has been assigned to LGs of at least 17 Mb. Comparisons with other gastropod genomes reveal patterns of synteny and chromosomal rearrangements. Linkage relationships of key immune-relevant genes may help clarify snail–schistosome interactions. By focusing on linkage among genic and nonrepetitive regions, we have generated a useful resource for associating snail phenotypes with causal genes, even in the absence of a complete genome assembly. A similar approach could potentially improve numerous poorly-assembled genomes in other taxa. This map will facilitate future work on this host of a serious human parasite.

Advances in sequencing technology provide special opportunities for genotyping individuals with speed and thrift, but the lack of software to automate the calling of tens of thousands of genotypes over hundreds of individuals has hindered progress. Stacks is a software system that uses short-read sequence data to identify and genotype loci in a set of individuals either de novo or by comparison to a reference genome. From reduced representation Illumina sequence data, such as RAD-tags, Stacks can recover thousands of single nucleotide polymorphism (SNP) markers useful for the genetic analysis of crosses or populations. Stacks can generate markers for ultra-dense genetic linkage maps, facilitate the examination of population phylogeography, and help in reference genome assembly. We report here the algorithms implemented in Stacks and demonstrate their efficacy by constructing loci from simulated RAD-tags taken from the stickleback reference genome and by recapitulating and improving a genetic map of the zebrafish, Danio rerio.

Among the seven tetraploid cotton species, little is known about transmission genetics and genome organization in Gossypium mustelinum, the species most distant from the source of most cultivated cotton, G. hirsutum. In this research, an F2 population was developed from an interspecific cross between G. hirsutum and G. mustelinum (HM). A genetic linkage map was constructed mainly using simple sequence repeat (SSRs) and restriction fragment length polymorphism (RFLP) DNA markers. The arrangements of most genetic loci along the HM chromosomes were identical to those of other tetraploid cotton species. However, both major and minor structural rearrangements were also observed, for which we propose a parsimony-based model for structural divergence of tetraploid cottons from common ancestors. Sequences of mapped markers were used for alignment with the 26 scaffolds of the G. hirsutum draft genome, and showed high consistency. Quantitative trait locus (QTL) mapping of fiber elongation in advanced backcross populations derived from the same parents demonstrated the value of the HM map. The HM map will serve as a valuable resource for QTL mapping and introgression of G. mustelinum alleles into G. hirsutum, and help clarify evolutionary relationships between the tetraploid cotton genomes.

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.

Although the brown rat (Rattus norvegicus) is widely used as a model mammal throughout biological sciences, little is known about genetic variation in wild rat populations or the relationship of commonly used inbred strains to their wild relatives. We sampled wild brown rats from the species’ presumed ancestral range in NW China and from a derived population in the UK and estimated nucleotide diversity and population subdivision, based on the sequences of 30 autosomal protein-coding loci. Neutral genetic diversity was close to 0.2% in both populations, which is about five times lower than diversity at the orthologous sites in a population of wild house mice from the species’ putative ancestral range in India. We found significant population differentiation between UK and Chinese populations, as assessed by Fst and the program STRUCTURE. Based on synonymous diversity and divergence between the brown rat and house mouse, we estimate that the recent effective population size in brown rats is approximately 130,000 (approximate 95% confidence interval 85,000-184,000), about fivefold lower than wild house mice.

The objective of this study was to construct a single nucleotide polymorphism (SNP)-based genetic map at the cultivated tetraploid level to locate quantitative trait loci (QTL) contributing to economically important traits in potato (Solanum tuberosum L.). The 156 F1 progeny and parents of a cross (MSL603) between “Jacqueline Lee” and “MSG227-2” were genotyped using the Infinium 8303 Potato Array. Furthermore, the progeny and parents were evaluated for foliar late blight reaction to isolates of the US-8 genotype of Phytophthora infestans (Mont.) de Bary and vine maturity. Linkage analyses and QTL mapping were performed using a novel approach that incorporates allele dosage information. The resulting genetic maps contained 1972 SNP markers with an average density of 1.36 marker per cM. QTL mapping identified the major source of late blight resistance in “Jacqueline Lee.” The best SNP marker mapped ∼0.54 Mb from a resistance hotspot on the long arm of chromosome 9. For vine maturity, the major-effect QTL was located on chromosome 5 with allelic effects from both parents. A candidate SNP marker for this trait mapped ∼0.25 Mb from the StCDF1 gene, which is a candidate gene for the maturity trait. The identification of markers for P. infestans resistance will enable the introgression of multiple sources of resistance through marker-assisted selection. Moreover, the discovery of a QTL for late blight resistance not linked to the QTL for vine maturity provides the opportunity to use marker-assisted selection for resistance independent of the selection for vine maturity classifications.

The Cell Wall Integrity (CWI) pathway is the primary signaling cascade that controls the de novo synthesis of the fungal cell wall, and in Saccharomyces cerevisiae this event is highly dependent on the RLM1 transcription factor. Here, we investigated the function of RlmA in the fungal pathogen Aspergillus fumigatus. We show that the ΔrlmA strain exhibits an altered cell wall organization in addition to defects related to vegetative growth and tolerance to cell wall-perturbing agents. A genetic analysis indicated that rlmA is positioned downstream of the pkcA and mpkA genes in the CWI pathway. As a consequence, rlmA loss-of-function leads to the altered expression of genes encoding cell wall-related proteins. RlmA positively regulates the phosphorylation of MpkA and is induced at both protein and transcriptional levels during cell wall stress. The rlmA was also involved in tolerance to oxidative damage and transcriptional regulation of genes related to oxidative stress adaptation. Moreover, the ΔrlmA strain had attenuated virulence in a neutropenic murine model of invasive pulmonary aspergillosis. Our results suggest that RlmA functions as a transcription factor in the A. fumigatus CWI pathway, acting downstream of PkcA-MpkA signaling and contributing to the virulence of this fungus.

Prediction of phenotypes from genotypes is an important objective to fulfill the promises of genomics, precision medicine and agriculture. Although it’s now possible to account for the majority of genetic variation through model fitting, prediction of phenotypes remains a challenge, especially across populations that have diverged in the past. In this study, we designed simulation experiments to specifically investigate the role of genetic interactions in failure of polygenic prediction. We found that non-additive genetic interactions can significantly reduce the accuracy of polygenic prediction. Our study demonstrated the importance of considering genetic interactions in genetic prediction.