Biomedical subjects

J Ziegle

Publications and source records attributed to J Ziegle.

4 recordsLinked to original sources

PubMed2003

The second generation of the International Equine Gene Mapping Workshop half-sibling linkage map.

A low-density, male-based linkage map was constructed as one of the objectives of the International Equine Gene Mapping Workshop. Here we report the second generation map based on testing 503 half-sibling offspring from 13 sire families for 344 informative markers using the CRIMAP program. The multipoint linkage analysis localized 310 markers (90%) with 257 markers being linearly ordered. The map included 34 linkage groups representing all 31 autosomes and spanning 2262 cM with an average interval between loci of 10.1 cM. This map is a milestone in that it is the first map with linkage groups assigned to each of the 31 automosomes and a single linkage group to all but three chromosomes.

Animals↗

PubMed1999

Report of the International Equine Gene Mapping Workshop: male linkage map.

The goal of the First International Equine Gene Mapping Workshop, held in 1995, was the construction of a low density, male linkage map for the horse. For this purpose, the International Horse Reference Family Panel (IHRFP) was established, consisting of 12 paternal half-sib families with 448 half-sib offspring provided by 10 laboratories. Blood samples were collected and DNA extracted in each laboratory and sent to the Lexington laboratory (KY, USA) for dispatch in aliquots to 14 typing laboratories. In total, 161 markers (144 microsatellites, seven blood groups and 10 proteins) were tested for all families for which the sire was heterozygous. Genealogies and typing data were sent for analysis to the INRA laboratory (Jouy-en-Josas, France) according to a specific format and entered into a database with input verification and output processes. Linkage analysis was performed with the CRIMAP program. Significant linkage was detected for 124 loci, of which 95 were unambiguously ordered using a multipoint analysis with an average spacing of 14.2 CM. These loci were distributed among 29 linkage groups. A more comprehensive analysis including synteny group data and FISH data suggested that 26 autosomes out of 31 are covered. The complete map spans 936 CM.

Animals↗

PubMed1991

Genetic typing using automated electrophoresis and fluorescence detection.

Multi-color fluorescence detection systems offer unique advantages when compared to single label detection methods for DNA typing, genetic disease testing, population fingerprinting, and DNA mapping. Internal controls are easily used and identified by different color dye labels. Multiple independent samples or multiple analyses of the same sample are run in each lane of a gel. Precision of size assignment and quantification are improved. Here, we will review a variety of methods used to analyze DNA and present the advantages of the multi-color fluorescence dye approach. An automated and quantitative DNA typing assay for human identification is shown. This method is an improvement over previous manual techniques and uses multi-color fluorescence labeling, electrophoresis and real-time detection methodology.

Analog-Digital Conversion↗

PubMed1991

Automated genetic analysis.

Automation of several new, non-traditional techniques for genetic analysis has now become possible. A new system is described that performs gel electrophoretic analysis of DNA including VNTRs, gene segments, and restriction enzyme digests. The instrument detects emitted fluorescence from labeled DNA segments in real-time as they electrophore through a gel matrix past a scanning laser beam. Molecular length determination and band quantification is accomplished by comparison to an in-lane standard. Since DNA segments can be labeled and detected with any of four different dyes, the simultaneous analysis of similar length segments from different reactions within a single lane is possible. PCR products are analyzed for research in the areas of human identification and genetic disease. These examples illustrate how automation will play key role in this new era of genetic analysis.

Automation↗