An Integrated Genetic and Cytogenetic Map for Zhikong Scallop,Chlamys farreri,Based on Microsatellite Markers

The reliability of genome analysis and proficiency of genetic manipulation requires knowledge of the correspondence between the genetic and cytogenetic maps. In the present study, we integrated cytogenetic and microsatellite-based linkage maps for Zhikong scallop, Chlamys farreri. Thirty-eight marker-anchored BAC clones standing for the 19 linkage groups were used to be FISH probes. Of 38 BAC clones, 30 were successfully located on single chromosome by FISH and used to integrate the genetic and cytogenetic map. Among the 19 linkage groups, 12 linkage groups were physically anchored by 2 markers, 6 linkage groups were anchored by 1 marker, and one linkage group was not anchored any makers by FISH. In addition, using two-color FISH, six linkage groups were distinguished by different chromosomal location; linkage groups LG6 and LG16 were placed on chromosome 10, LG8 and LG18 on chromosome 14. As a result, 18 of 19 linkage groups were localized to 17 pairs of chromosomes of C. farreri. We first integrated genetic and cytogenetic map for C. farreri. These 30 chromosome specific BAC clones in the cytogenetic map could be used to identify chromosomes of C. farreri. The integrated map will greatly facilitate molecular genetic studies that will be helpful for breeding applications in C. farreri and the upcoming genome projects of this species.     

Editor's choice: High-Resolution Linkage and Quantitative Trait Locus Mapping Aided by Genome Survey Sequencing: Building Up An Integrative Genomic Framework for a Bivalve Mollusc

Genetic linkage maps are indispensable tools in genetic and genomic studies. Recent development of genotyping-by-sequencing (GBS) methods holds great promise for constructing high-resolution linkage maps in organisms lacking extensive genomic resources. In the present study, linkage mapping was conducted for a bivalve mollusc (Chlamys farreri) using a newly developed GBS method—2b-restriction site-associated DNA (2b-RAD). Genome survey sequencing was performed to generate a preliminary reference genome that was utilized to facilitate linkage and quantitative trait locus (QTL) mapping in C. farreri. A high-resolution linkage map was constructed with a marker density (3806) that has, to our knowledge, never been achieved in any other molluscs. The linkage map covered nearly the whole genome (99.5%) with a resolution of 0.41 cM. QTL mapping and association analysis congruously revealed two growth-related QTLs and one potential sex-determination region. An important candidate QTL gene named PROP1, which functions in the regulation of growth hormone production in vertebrates, was identified from the growth-related QTL region detected on the linkage group LG3. We demonstrate that this linkage map can serve as an important platform for improving genome assembly and unifying multiple genomic resources. Our study, therefore, exemplifies how to build up an integrative genomic framework in a non-model organism.     

A Genomic Resource for the Development,Improvement, and Exploitation of Sorghum for Bioenergy

With high productivity and stress tolerance, numerous grass genera of the Andropogoneae have emerged as candidates for bioenergy production. To optimize these candidates, research examining the genetic architecture of yield, carbon partitioning, and composition is required to advance breeding objectives. Significant progress has been made developing genetic and genomic resources for Andropogoneae, and advances in comparative and computational genomics have enabled research examining the genetic basis of photosynthesis, carbon partitioning, composition, and sink strength. To provide a pivotal resource aimed at developing a comparative understanding of key bioenergy traits in the Andropogoneae, we have established and characterized an association panel of 390 racially, geographically, and phenotypically diverse Sorghum bicolor accessions with 232,303 genetic markers. Sorghum bicolor was selected because of its genomic simplicity, phenotypic diversity, significant genomic tools, and its agricultural productivity and resilience. We have demonstrated the value of sorghum as a functional model for candidate gene discovery for bioenergy Andropogoneae by performing genome-wide association analysis for two contrasting phenotypes representing key components of structural and non-structural carbohydrates. We identified potential genes, including a cellulase enzyme and a vacuolar transporter, associated with increased non-structural carbohydrates that could lead to bioenergy sorghum improvement. Although our analysis identified genes with potentially clear functions, other candidates did not have assigned functions, suggesting novel molecular mechanisms for carbon partitioning traits. These results, combined with our characterization of phenotypic and genetic diversity and the public accessibility of each accession and genomic data, demonstrate the value of this resource and provide a foundation for future improvement of sorghum and related grasses for bioenergy production.     

Large scale SNP unearthing and genetic architecture analysis in sea-captured and cultured populations of Cynoglossus semilaevis

Knowledge on population structure and genetic diversity is a focal point for association mapping studies and genomic selection. Genotyping by sequencing (GBS) represents an innovative method for large scale SNP detection and genotyping of genetic resources. Here we used the GBS approach for the genome-wide identification of SNPs in a collection of Cynoglossus semilaevis and for the assessment of the level of genetic diversity in C. semilaevis genotypes. GBS analysis generated a total of 55.12 Gb high-quality sequence data, with an average of 0.63 Gb per sample. The total number of SNP markers was 563, 109. In order to explore the genetic diversity of C. semilaevis and to select a minimal core set representing most of the total genetic variation with minimum redundancy, C. semilaevis sequences were analyzed using high quality SNPs. Based on hierarchical clustering, it was possible to divide the collection into 2 clusters. The marine fishing populations were clustered and clearly separated from the cultured populations, and the cultured populations from Hebei was also distinct from the other two local populations. These analyses showed that genotypes were clustered based on species-related features. Differential significant SNPs were also captured and validated by GBS and SNaPshot, with linkage disequilibrium and haplotype analysis, seven SNPs have been confirmed to have obvious differentiation in two populations, which may be used as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations. And SNP markers and information on population structure developed in this study will undoubtedly support genome-wide association mapping studies and marker-assisted selection programs. These differential SNPs could be also employed as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations in future.     

Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction

Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors.     

Genetic Positioning of Centromeres through Half-Tetrad Analysis in Gynogenetic Diploid Families of the Zhikong Scallop (Chlamys farreri)

Centromere mapping is a powerful tool for improving linkage maps, investigating crossover events, and understanding chiasma interference during meiosis. Ninety microsatellite markers selected across all linkage groups (LGs) from a previous Chlamys farreri genetic map were studied in three artificially induced meiogynogenetic families for centromere mapping by half-tetrad analysis. Inheritance analyses showed that all 90 microsatellite loci conformed to Mendelian inheritance in the control crosses, while 4.4 % of the microsatellite loci showed segregation departures from an expected 1:1 ratio of two homozygote classes in meiogynogenetic progeny. The second division segregation frequency (y) of the microsatellites ranged from 0.033 to 0.778 with a mean of 0.332, confirming the occurrence of partial chiasma interference in this species. Heterogeneity of y is observed in one of 42 cases in which markers were typed in more than one family, suggesting variation in gene–centromere recombination among families. Centromere location was mostly in accordance with the C. farreri karyotype, but differences in marker order between linkage and centromere maps occurred. Overall, this study makes the genetic linkage map a more complete and informative tool for genomic studies and it will also facilitate future research of the structure and function of the scallop centromeres.     

Genomic prediction of hybrid crops allows disentangling dominance and epistasis

We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted “residual genetic” line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme—e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.     

Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers

Background

Although the X chromosome is the second largest bovine chromosome, markers on the X chromosome are not used for genomic prediction in some countries and populations. In this study, we presented a method for computing genomic relationships using X chromosome markers, investigated the accuracy of imputation from a low density (7K) to the 54K SNP (single nucleotide polymorphism) panel, and compared the accuracy of genomic prediction with and without using X chromosome markers.

Methods

The impact of considering X chromosome markers on prediction accuracy was assessed using data from Nordic Holstein bulls and different sets of SNPs: (a) the 54K SNPs for reference and test animals, (b) SNPs imputed from the 7K to the 54K SNP panel for test animals, (c) SNPs imputed from the 7K to the 54K panel for half of the reference animals, and (d) the 7K SNP panel for all animals. Beagle and Findhap were used for imputation. GBLUP (genomic best linear unbiased prediction) models with or without X chromosome markers and with or without a residual polygenic effect were used to predict genomic breeding values for 15 traits.

Results

Averaged over the two imputation datasets, correlation coefficients between imputed and true genotypes for autosomal markers, pseudo-autosomal markers, and X-specific markers were 0.971, 0.831 and 0.935 when using Findhap, and 0.983, 0.856 and 0.937 when using Beagle. Estimated reliabilities of genomic predictions based on the imputed datasets using Findhap or Beagle were very close to those using the real 54K data. Genomic prediction using all markers gave slightly higher reliabilities than predictions without X chromosome markers. Based on our data which included only bulls, using a G matrix that accounted for sex-linked relationships did not improve prediction, compared with a G matrix that did not account for sex-linked relationships. A model that included a polygenic effect did not recover the loss of prediction accuracy from exclusion of X chromosome markers.

Conclusions

The results from this study suggest that markers on the X chromosome contribute to accuracy of genomic predictions and should be used for routine genomic evaluation.     

Genomic DNA Enrichment Using Sequence Capture Microarrays: a Novel Approach to Discover Sequence Nucleotide Polymorphisms (SNP) in Brassica napus L

Targeted genomic selection methodologies, or sequence capture, allow for DNA enrichment and large-scale resequencing and characterization of natural genetic variation in species with complex genomes, such as rapeseed canola (Brassica napus L., AACC, 2n=38). The main goal of this project was to combine sequence capture with next generation sequencing (NGS) to discover single nucleotide polymorphisms (SNPs) in specific areas of the B. napus genome historically associated (via quantitative trait loci –QTL– analysis) to traits of agronomical and nutritional importance. A 2.1 million feature sequence capture platform was designed to interrogate DNA sequence variation across 47 specific genomic regions, representing 51.2 Mb of the Brassica A and C genomes, in ten diverse rapeseed genotypes. All ten genotypes were sequenced using the 454 Life Sciences chemistry and to assess the effect of increased sequence depth, two genotypes were also sequenced using Illumina HiSeq chemistry. As a result, 589,367 potentially useful SNPs were identified. Analysis of sequence coverage indicated a four-fold increased representation of target regions, with 57% of the filtered SNPs falling within these regions. Sixty percent of discovered SNPs corresponded to transitions while 40% were transversions. Interestingly, fifty eight percent of the SNPs were found in genic regions while 42% were found in intergenic regions. Further, a high percentage of genic SNPs was found in exons (65% and 64% for the A and C genomes, respectively). Two different genotyping assays were used to validate the discovered SNPs. Validation rates ranged from 61.5% to 84% of tested SNPs, underpinning the effectiveness of this SNP discovery approach. Most importantly, the discovered SNPs were associated with agronomically important regions of the B. napus genome generating a novel data resource for research and breeding this crop species.     

Genomic Encyclopedia of Type Strains of the Genus Bifidobacterium

Bifidobacteria represent one of the dominant microbial groups that are present in the gut of various animals, being particularly prevalent during the suckling stage of life of humans and other mammals. However, the overall genome structure of this group of microorganisms remains largely unexplored. Here, we sequenced the genomes of 42 representative (sub)species across the Bifidobacterium genus and used this information to explore the overall genetic picture of this bacterial group. Furthermore, the genomic data described here were used to reconstruct the evolutionary development of the Bifidobacterium genus. This reconstruction suggests that its evolution was substantially influenced by genetic adaptations to obtain access to glycans, thereby representing a common and potent evolutionary force in shaping bifidobacterial genomes.     

The Effects of Demography and Long-Term Selection on the Accuracy of Genomic Prediction with Sequence Data

The use of dense SNPs to predict the genetic value of an individual for a complex trait is often referred to as “genomic selection” in livestock and crops, but is also relevant to human genetics to predict, for example, complex genetic disease risk. The accuracy of prediction depends on the strength of linkage disequilibrium (LD) between SNPs and causal mutations. If sequence data were used instead of dense SNPs, accuracy should increase because causal mutations are present, but demographic history and long-term negative selection also influence accuracy. We therefore evaluated genomic prediction, using simulated sequence in two contrasting populations: one reducing from an ancestrally large effective population size (N_e) to a small one, with high LD common in domestic livestock, while the second had a large constant-sized N_e with low LD similar to that in some human or outbred plant populations. There were two scenarios in each population; causal variants were either neutral or under long-term negative selection. For large N_e, sequence data led to a 22% increase in accuracy relative to ∼600K SNP chip data with a Bayesian analysis and a more modest advantage with a BLUP analysis. This advantage increased when causal variants were influenced by negative selection, and accuracy persisted when 10 generations separated reference and validation populations. However, in the reducing N_e population, there was little advantage for sequence even with negative selection. This study demonstrates the joint influence of demography and selection on accuracy of prediction and improves our understanding of how best to exploit sequence for genomic prediction.     

Exploring a Tomato Landraces Collection for Fruit-Related Traits by the Aid of a High-Throughput Genomic Platform

During its evolution and domestication Solanum lycopersicum has undergone various genetic ‘bottlenecks’ and extreme inbreeding of limited genotypes. In Europe the tomato found a secondary centre for diversification, which resulted in a wide array of fruit shape variation given rise to a range of landraces that have been cultivated for centuries. Landraces represent a reservoir of genetic diversity especially for traits such as abiotic stress resistance and high fruit quality. Information about the variation present among tomato landrace populations is still limited. A collection of 123 genotypes from different geographical areas was established with the aim of capturing a wide diversity. Eighteen morphological traits were evaluated, mainly related to the fruit. About 45% of morphological variation was attributed to fruit shape, as estimated by the principal component analysis, and the dendrogram of relatedness divided the population in subgroups mainly on the basis of fruit weight and locule number. Genotyping was carried out using the tomato array platform SolCAP able to interrogate 7,720 SNPs. In the whole collection 87.1% markers were polymorphic but they decreased to 44–54% when considering groups of genotypes with different origin. The neighbour-joining tree analysis clustered the 123 genotypes into two main branches. The STRUCTURE analysis with K = 3 also divided the population on the basis of fruit size. A genomic-wide association strategy revealed 36 novel markers associated to the variation of 15 traits. The markers were mapped on the tomato chromosomes together with 98 candidate genes for the traits analyzed. Six regions were evidenced in which candidate genes co-localized with 19 associated SNPs. In addition, 17 associated SNPs were localized in genomic regions lacking candidate genes. The identification of these markers demonstrated that novel variability was captured in our germoplasm collection. They might also provide a viable indirect selection tool in future practical breeding programs.     

Genetic polymorphisms of lipoprotein lipase gene and their associations with growth traits in Xiangxi cattle

Lipoprotein lipase (LPL), involved in the metabolism and transport of lipids, regulate energy balance, fat deposition and growth traits. The objective of this study was to investigate the single nucleotide polymorphisms (SNPs) of LPL gene and to determine their associations between these polymorphisms and growth traits in Xiangxi cattle breed. In this study, six novel SNPs (C355157T, T355169C, T355186G, A355210G, T355348A and T355420C) and one reported SNP (A355427T, has been recorded in dbSNP, ID rs110590698) were detected using polymerase chain reaction and DNA sequencing method. Genotyping and genetic diversity analysis were performed in 240 Xiangxi cattle on the basis of sequence alignment, which indicated that five SNPs (C355157T, 355186G, T355348A, T355420C, A355427T) were in abundant genetic diversity, and the other two SNPs (T355169C and TA355210G) were in low genetic diversity. Linkage disequilibrium analysis showed that 18 different haplotypes were identified in these animals. Moreover, the results of the association between LPL gene polymorphisms and growth traits indicated that the individuals with H1H1 haplotype combination had higher BW and HG than those with other haplotype combinations (P?<?0.05). The animals with CC genotype maintain higher mean values for BW than those with the CT and TT genotypes (P?<?0.05) at T355420C locus. The animals with the AA genotype have lower mean values for WH, BL, HG and BW than those with the AT and TT genotypes at A355427T locus (P?<?0.05). The results suggested that the SNPs of the LPL gene might be useful genetic markers for growth traits in the bovine reproduction and breeding.     

Single nucleotide polymorphisms in Cryptomeria japonica: their discovery and validation for genome mapping and diversity studies

In order to develop a large set of single-nucleotide polymorphisms (SNPs) in Cryptomeria japonica, for a wide range of applications, we adopted a systematic EST (expressed sequence tags) re-sequencing approach. We examined a group of four genotypes comprising parents of a mapping population as well as representatives of two main lines from natural populations. We sequenced 5,170 gene fragments, representing analysis of over 1.3?Mb of DNA sequences in C. japonica. This analysis leads to the discovery of 13,413 SNPs in 3,744 amplicons, with an average of one SNP for every 101.0?bp (one SNP for every 78.3?bp in introns and for every 106.7?bp in exon regions). Nucleotide diversity in C. japonica (???=?0.0045) was found to be similar to values recorded in highly polymorphic forest tree species such as pine. We also validated the use of the SNPs as molecular markers for genetic diversity studies using the high throughput SNP genotyping platform GoldenGate. From 1,536 candidate SNP sites tested, 1,164 (75.8?%) were confirmed to be polymorphic. We anticipate that the genome-wide SNP markers reported here will be useful for evaluating the species?? range-wide genetic structure and in marker-assisted selection used as part of the C. japonica tree improvement program.     

A set of EST-SNPs for map saturation and cultivar identification in melon

Background

There are few genomic tools available in melon (Cucumis melo L.), a member of the Cucurbitaceae, despite its importance as a crop. Among these tools, genetic maps have been constructed mainly using marker types such as simple sequence repeats (SSR), restriction fragment length polymorphisms (RFLP) and amplified fragment length polymorphisms (AFLP) in different mapping populations. There is a growing need for saturating the genetic map with single nucleotide polymorphisms (SNP), more amenable for high throughput analysis, especially if these markers are located in gene coding regions, to provide functional markers. Expressed sequence tags (ESTs) from melon are available in public databases, and resequencing ESTs or validating SNPs detected in silico are excellent ways to discover SNPs.

Results

EST-based SNPs were discovered after resequencing ESTs between the parental lines of the PI 161375 (SC) × 'Piel de sapo' (PS) genetic map or using in silico SNP information from EST databases. In total 200 EST-based SNPs were mapped in the melon genetic map using a bin-mapping strategy, increasing the map density to 2.35 cM/marker. A subset of 45 SNPs was used to study variation in a panel of 48 melon accessions covering a wide range of the genetic diversity of the species. SNP analysis correctly reflected the genetic relationships compared with other marker systems, being able to distinguish all the accessions and cultivars.

Conclusion

This is the first example of a genetic map in a cucurbit species that includes a major set of SNP markers discovered using ESTs. The PI 161375 × 'Piel de sapo' melon genetic map has around 700 markers, of which more than 500 are gene-based markers (SNP, RFLP and SSR). This genetic map will be a central tool for the construction of the melon physical map, the step prior to sequencing the complete genome. Using the set of SNP markers, it was possible to define the genetic relationships within a collection of forty-eight melon accessions as efficiently as with SSR markers, and these markers may also be useful for cultivar identification in Occidental melon varieties.