The authors describe the best practices for a growing number of methods that use next-generation sequencing to rapidly discover and assess genetic markers across any genome, with applications from population genomics and quantitative trait locus mapping to marker-assisted selection.

Genome-wide genetic marker discovery and genotyping using next-generation sequencing.

Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline, tassel-gbs, designed for the efficient processing of raw GBS sequence data into SNP genotypes. The tassel-gbs pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8–16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals (e.g., for large breeding programs or genetic surveys), and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished “pseudo-reference” consisting of numerous contigs. We describe the tassel-gbs pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the tassel-gbs pipeline provide robust tools for studying genomic diversity.

/pdf/tassel-gbs-a-high-capacity-genotyping-by-sequencing-analysis-48u7fd4437.pdf

TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline

生活習慣病とgenome-wide association study

The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.

Genetical genomics : the added value from segregation

Gossypium hirsutum has proven difficult to sequence owing to its complex allotetraploid (AtDt) genome. Here we produce a draft genome using 181-fold paired-end sequences assisted by fivefold BAC-to-BAC sequences and a high-resolution genetic map. In our assembly 88.5% of the 2,173-Mb scaffolds, which cover 89.6%∼96.7% of the AtDt genome, are anchored and oriented to 26 pseudochromosomes. Comparison of this G. hirsutum AtDt genome with the already sequenced diploid Gossypium arboreum (AA) and Gossypium raimondii (DD) genomes revealed conserved gene order. Repeated sequences account for 67.2% of the AtDt genome, and transposable elements (TEs) originating from Dt seem more active than from At. Reduction in the AtDt genome size occurred after allopolyploidization. The A or At genome may have undergone positive selection for fiber traits. Concerted evolution of different regulatory mechanisms for Cellulose synthase (CesA) and 1-Aminocyclopropane-1-carboxylic acid oxidase1 and 3 (ACO1,3) may be important for enhanced fiber production in G. hirsutum.

/pdf/genome-sequence-of-cultivated-upland-cotton-gossypium-4wnt07e5c8.pdf

Genome sequence of cultivated Upland cotton ( Gossypium hirsutum TM-1) provides insights into genome evolution

Bar-coded multiplexed sequencing approaches based on new-generation sequencing technologies provide capacity to sequence a mapping population in a single sequencing run. However, such approaches usually generate low-coverage and error-prone sequences for each line in a population. Thus, it is a significant challenge to genotype individual lines in a population for linkage map construction based on low-coverage sequences without the availability of high-quality genotype data of the parental lines. In this paper, we report a method for constructing ultrahigh-density linkage maps composed of high-quality single-nucleotide polymorphisms (SNPs) based on low-coverage sequences of recombinant inbred lines. First, all potential SNPs were identified to obtain drafts of parental genotypes using a maximum parsimonious inference of recombination, making maximum use of SNP information found in the entire population. Second, high-quality SNPs were identified by filtering out low-quality ones by permutations involving resampling of windows of SNPs followed by Bayesian inference. Third, lines in the mapping population were genotyped using the high-quality SNPs assisted by a hidden Markov model. With 0.05x genome sequence per line, an ultrahigh-density linkage map composed of bins of high-quality SNPs using 238 recombinant inbred lines derived from a cross between two rice varieties was constructed. Using this map, a quantitative trait locus for grain width (GW5) was localized to its presumed genomic region in a bin of 200 kb, confirming the accuracy and quality of the map. This method is generally applicable in genetic map construction with low-coverage sequence data.

/pdf/parent-independent-genotyping-for-constructing-an-ultrahigh-1yn1lmqwox.pdf

Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing

Huge efforts have been invested in the last two decades to dissect the genetic bases of complex traits including yields of many crop plants, through quantitative trait locus (QTL) analyses. However, almost all the studies were based on linkage maps constructed using low-throughput molecular markers, e.g. restriction fragment length polymorphisms (RFLPs) and simple sequence repeats (SSRs), thus are mostly of low density and not able to provide precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, we constructed an ultra-high density genetic map based on high quality single nucleotide polymorphisms (SNPs) from low-coverage sequences of a recombinant inbred line (RIL) population of rice, generated using new sequencing technology. The quality of the map was assessed by validating the positions of several cloned genes including GS3 and GW5/qSW5, two major QTLs for grain length and grain width respectively, and OsC1, a qualitative trait locus for pigmentation. In all the cases the loci could be precisely resolved to the bins where the genes are located, indicating high quality and accuracy of the map. The SNP map was used to perform QTL analysis for yield and three yield-component traits, number of tillers per plant, number of grains per panicle and grain weight, using data from field trials conducted over years, in comparison to QTL mapping based on RFLPs/SSRs. The SNP map detected more QTLs especially for grain weight, with precise map locations, demonstrating advantages in detecting power and resolution relative to the RFLP/SSR map. Thus this study provided an example for ultra-high density map construction using sequencing technology. Moreover, the results obtained are helpful for understanding the genetic bases of the yield traits and for fine mapping and cloning of QTLs.

/pdf/gains-in-qtl-detection-using-an-ultra-high-density-snp-map-1pba4tvu4w.pdf

Gains in QTL Detection Using an Ultra-High Density SNP Map Based on Population Sequencing Relative to Traditional RFLP/SSR Markers

Breeding crops with the quality of broad-spectrum disease resistance using genetic resources is one of the principal goals of crop improvement. However, the molecular mechanism of broad-spectrum resistance remains largely unknown. Here, we show that GH3-2, encoding an indole-3-acetic acid (IAA)-amido synthetase, mediates a broad-spectrum resistance to bacterial Xanthomonas oryzae pv oryzae and Xanthomonas oryzae pv oryzicola and fungal Magnaporthe grisea in rice (Oryza sativa). IAA, the major form of auxin in rice, results in rice more vulnerable to the invasion of different types of pathogens, which is at least partly due to IAA-induced loosening of the cell wall, the natural protective barrier of plant cells to invaders. X. oryzae pv oryzae, X. oryzae pv oryzicola, and M. grisea secrete IAA, which, in turn, may induce rice to synthesize its own IAA at the infection site. IAA induces the production of expansins, the cell wall-loosening proteins, and makes rice vulnerable to pathogens. GH3-2 is likely contributing to a minor quantitative trait locus for broad-spectrum resistance. Activation of GH3-2 inactivates IAA by catalyzing the formation of an IAA-amino acid conjugate, which results in the suppression of expansin genes. Thus, GH3-2 mediates basal resistance by suppressing pathogen-induced IAA accumulation. It is expected that, regulated by a pathogen-induced strong promoter, GH3-2 alone may be used for breeding rice with a broad-spectrum disease resistance.

Manipulating Broad-Spectrum Disease Resistance by Suppressing Pathogen-Induced Auxin Accumulation in Rice

A high-density SNP genotyping array for rice biology and molecular breeding.

Summary The advances in genotyping technology provide an opportunity to use genomic tools in crop breeding. As compared to field selections performed in conventional breeding programmes, genomics-based genotype screen can potentially reduce number of breeding cycles and more precisely integrate target genes for particular traits into an ideal genetic background. We developed a whole-genome single nucleotide polymorphism (SNP) array, RICE6K, based on Infinium technology, using representative SNPs selected from more than four million SNPs identified from resequencing data of more than 500 rice landraces. RICE6K contains 5102 SNP and insertion-deletion (InDel) markers, about 4500 of which were of high quality in the tested rice lines producing highly repeatable results. Forty-five functional markers that are located inside 28 characterized genes of important traits can be detected using RICE6K. The SNP markers are evenly distributed on the 12 chromosomes of rice with the average density of 12 SNPs per 1 Mb and can provide information for polymorphisms between indica and japonica subspecies as well as varieties within indica and japonica groups. Application tests of RICE6K showed that the array is suitable for rice germplasm fingerprinting, genotyping bulked segregating pools, seed authenticity check and genetic background selection. These results suggest that RICE6K provides an efficient and reliable genotyping tool for rice genomic breeding.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbi.12113

Huihui Yu

Papers

Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing

Gains in QTL Detection Using an Ultra-High Density SNP Map Based on Population Sequencing Relative to Traditional RFLP/SSR Markers

Manipulating Broad-Spectrum Disease Resistance by Suppressing Pathogen-Induced Auxin Accumulation in Rice

A high-density SNP genotyping array for rice biology and molecular breeding.

A whole‐genome SNP array (RICE6K) for genomic breeding in rice