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David A Hoisington

Publications and source records attributed to David A Hoisington.

3 recordsLinked to original sources

Advances in cereal genomics and applications in crop breeding.

Recent advances in cereal genomics have made it possible to analyse the architecture of cereal genomes and their expressed components, leading to an increase in our knowledge of the genes that are linked to key agronomically important traits. These studies have used molecular genetic mapping of quantitative trait loci (QTL) of several complex traits that are important in breeding. The identification and molecular cloning of genes underlying QTLs offers the possibility to examine the naturally occurring allelic variation for respective complex traits. Novel alleles, identified by functional genomics or haplotype analysis, can enrich the genetic basis of cultivated crops to improve productivity. Advances made in cereal genomics research in recent years thus offer the opportunities to enhance the prediction of phenotypes from genotypes for cereal breeding.

Alleles↗

Laboratory Information Management Software for genotyping workflows: applications in high throughput crop genotyping.

BACKGROUND: With the advances in DNA sequencer-based technologies, it has become possible to automate several steps of the genotyping process leading to increased throughput. To efficiently handle the large amounts of genotypic data generated and help with quality control, there is a strong need for a software system that can help with the tracking of samples and capture and management of data at different steps of the process. Such systems, while serving to manage the workflow precisely, also encourage good laboratory practice by standardizing protocols, recording and annotating data from every step of the workflow. RESULTS: A laboratory information management system (LIMS) has been designed and implemented at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) that meets the requirements of a moderately high throughput molecular genotyping facility. The application is designed as modules and is simple to learn and use. The application leads the user through each step of the process from starting an experiment to the storing of output data from the genotype detection step with auto-binning of alleles; thus ensuring that every DNA sample is handled in an identical manner and all the necessary data are captured. The application keeps track of DNA samples and generated data. Data entry into the system is through the use of forms for file uploads. The LIMS provides functions to trace back to the electrophoresis gel files or sample source for any genotypic data and for repeating experiments. The LIMS is being presently used for the capture of high throughput SSR (simple-sequence repeat) genotyping data from the legume (chickpea, groundnut and pigeonpea) and cereal (sorghum and millets) crops of importance in the semi-arid tropics. CONCLUSION: A laboratory information management system is available that has been found useful in the management of microsatellite genotype data in a moderately high throughput genotyping laboratory. The application with source code is freely available for academic users and can be downloaded from http://www.icrisat.org/gt-bt/lims/lims.asp.

Algorithms↗

A new intervarietal linkage map and its application for quantitative trait locus analysis of "gigas" features in bread wheat.

A doubled-haploid (DH) population from an intervarietal cross between the Japanese cultivar 'Fukuho-komugi' and the Israeli wheat line 'Oligoculm' was produced by means of wheat x maize crosses. One hundred seven DH lines were genotyped to construct a simple sequence repeat (SSR) based linkage map with RFLP, RAPD, and inter-simple sequence repeat markers. Out of 570 loci genotyped, 330 were chosen based on their positions on the linkage map to create a "framework" map for quantitative trait locus (QTL) analysis. Among the 28 linkage groups identified, 25 were assigned to the 21 chromosomes of wheat. The total map length was 3948 cM, including the three unassigned linkage groups (88 cM), and the mean interval between loci was 12.0 cM. Loci with segregation distortion were clustered on chromosomes 1A, 4B, 4D, 5A, 6A, 6B, and 6D. After vernalization, the DH lines were evaluated for spike number per plant (SN) and spike length (SL) in a greenhouse under 24-h daylength to assess the "gigas" features (extremely large spikes and leaves) of 'Oligoculm'. The DH lines were also autumn-sown in the field in two seasons (1990-1991 and 1997-1998) for SN and SL evaluation. QTL analysis was performed by composite interval mapping (CIM) with the framework map to detect QTLs for SN and SL. A major QTL on 1AS, which was stable in both greenhouse and field conditions, was found to control SN. This QTL was close to the glume pubescence locus (Hg) and explained up to 62.9% of the total phenotypic variation. The 'Oligoculm' allele restricted spike number. The SSR locus Xpsp2999 was the closest locus to this QTL and is considered to be a possible marker for restricted tillering derived from 'Oligoculm'. Eight QTLs were detected for SL. The largest QTL detected on 2DS was common to the greenhouse and field environments. It explained up to 33.3% of the total phenotypic variation. The second largest QTL on 1AS was common to the greenhouse and the 1997-1998 season. The position of this QTL was close to that for the SN detected on 1AS. The association between SN and SL is discussed.

Chromosome Mapping↗