Sequencing for the cream of the crop

Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.
Nature Biotechnology (Impact Factor: 41.51). 02/2011; 29(2):138-9. DOI: 10.1038/nbt.1756
Source: PubMed
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    • "This might be particularly true for polyploid crops in which assignment of genic SNPs to specific homologous loci can often be more reliable using haplotype-oriented sequencing data than on array platforms. Furthermore, because they can theoretically access novel allelic variants in genetically diverse association panels, NGS-based genotyping methods (see Davey et al. 2011 for a comprehensive review) have a potential advantage over array-based SNP screening methods that only survey known alleles in a given panel of SNPs (Chia and Ware 2011). Use of SNPs discovered by genomic resequencing for GWAS was shown by Huang et al. (2010) to be highly effective for uncovering genes involved in important agronomic traits in rice. "
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    ABSTRACT: Many important crop species have genomes originating from ancestral or recent polyploidisation events. Multiple homoeologous gene copies, chromosomal rearrangements and amplification of repetitive DNA within large and complex crop genomes can considerably complicate genome analysis and gene discovery by conventional, forward genetics approaches. On the other hand, ongoing technological advances in molecular genetics and genomics today offer unprecedented opportunities to analyse and access even more recalcitrant genomes. In this review, we describe next-generation sequencing and data analysis techniques that vastly improve our ability to dissect and mine genomes for causal genes underlying key traits and allelic variation of interest to breeders. We focus primarily on wheat and oilseed rape, two leading examples of major polyploid crop genomes whose size or complexity present different, significant challenges. In both cases, the latest DNA sequencing technologies, applied using quite different approaches, have enabled considerable progress towards unravelling the respective genomes. Our ability to discover the extent and distribution of genetic diversity in crop gene pools, and its relationship to yield and quality-related traits, is swiftly gathering momentum as DNA sequencing and the bioinformatic tools to deal with growing quantities of genomic data continue to develop. In the coming decade, genomic and transcriptomic sequencing, discovery and high-throughput screening of single nucleotide polymorphisms, presence-absence variations and other structural chromosomal variants in diverse germplasm collections will give detailed insight into the origins, domestication and available trait-relevant variation of polyploid crops, in the process facilitating novel approaches and possibilities for genomics-assisted breeding.
    Theoretical and Applied Genetics 09/2012; 126(1). DOI:10.1007/s00122-012-1964-x · 3.79 Impact Factor
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    ABSTRACT: Changes in climate and urbanisation rapidly affecting human livelihood are particularly threatening to developing nations in tropical regions. Food production crises have focused the global development agenda on agricultural research, a proven approach for increasing crop yield. A few crops benefit from private investment, but improvement of most crops will rely on limited public funding that must be deployed strategically, pushing forward both proven approaches and new ideas. Why not invest in beans? More than 300 million people rely on this crop, considered to be the most important grain legume for human consumption. Yet the yield of beans, especially in poor regions or marginal soils, is reduced by abiotic stresses such as phosphorus deficiency, aluminum toxicity and especially drought. Is it possible to assemble resources, including genetic diversity in beans, breeding expertise, genomic information and tools, and physiological insight to generate rapid progress in developing new lines of beans more tolerant to abiotic stress? A workshop to address this question was held in November 2010 at the International Center for Tropical Agriculture (CIAT) in Colombia. The resulting 'call to action' is presented in this issue which also includes research papers focused on tolerance of beans to stress.
    Functional Plant Biology 01/2011; 38(12):iii-vi. DOI:10.1071/FPv38n12_FO · 3.15 Impact Factor
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    ABSTRACT: The genome sequence of apple (Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range (r(2) = 0.33 and 0.19 at 100 kb and 1,000 kb respectively), contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits.
    PLoS ONE 05/2012; 7(5):e36674. DOI:10.1371/journal.pone.0036674 · 3.23 Impact Factor
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