An approach to high-throughput genotyping.

Hall, Jeff; LeDuc, Charles A.; Watson, Andy; Roter, Alan

doi:10.1101/gr.6.9.781

Cited by 39 publications

(31 citation statements)

References 26 publications

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“…Management of large quantities of the resultant genotype data creates a bottleneck for high-throughput genotyping (Perlin et al 1995;Hall et al 1996). Developing a welldesigned genotype DBMS helps to break down the bottleneck and thus speeds up genotyping.…”

Section: Discussionmentioning

confidence: 99%

“…For example, automatic fluorescent detection systems and related software for fragment size analyses and data tracking make the high-throughput genotyping possible (Idury and Cardon 1997). Although some authors (Perlin et al 1995;Hall et al 1996) have realized that management of large amounts of genotype data is a bottleneck, little effort has been made to develop efficient genotype database management systems (DBMS) to support such large data flow during highthroughput genotyping.…”

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confidence: 99%

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Toward High-Throughput Genotyping: Dynamic and Automatic Software for Manipulating Large-Scale Genotype Data Using Fluorescently Labeled Dinucleotide Markers

Li¹,

Deng²,

Lai³

et al. 2001

Genome Res.

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To efficiently manipulate large amounts of genotype data generated with fluorescently labeled dinucleotide markers, we developed a Microsoft Access database management system, named GenoDB. GenoDB offers several advantages. First, it accommodates the dynamic nature of the accumulations of genotype data during the genotyping process; some data need to be confirmed or replaced by repeat lab procedures. By using GenoDB, the raw genotype data can be imported easily and continuously and incorporated into the database during the genotyping process that may continue over an extended period of time in large projects. Second, almost all of the procedures are automatic, including autocomparison of the raw data read by different technicians from the same gel, autoadjustment among the allele fragment-size data from cross-runs or cross-platforms, autobinning of alleles, and autocompilation of genotype data for suitable programs to perform inheritance check in pedigrees. Third, GenoDB provides functions to track electrophoresis gel files to locate gel or sample sources for any resultant genotype data, which is extremely helpful for double-checking consistency of raw and final data and for directing repeat experiments. In addition, the user-friendly graphic interface of GenoDB renders processing of large amounts of data much less labor-intensive. Furthermore, GenoDB has built-in mechanisms to detect some genotyping errors and to assess the quality of genotype data that then are summarized in the statistic reports automatically generated by GenoDB. The GenoDB can easily handle >500,000 genotype data entries, a number more than sufficient for typical whole-genome linkage studies. The modules and programs we developed for the GenoDB can be extended to other database platforms, such as Microsoft SQL server, if the capability to handle still greater quantities of genotype data simultaneously is desired

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Toward High-Throughput Genotyping: Dynamic and Automatic Software for Manipulating Large-Scale Genotype Data Using Fluorescently Labeled Dinucleotide Markers

Li¹,

Deng²,

Lai³

et al. 2001

Genome Res.

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“…Genotyping was performed using the ABI 373A system for DNA electrophoresis with the Genescan/Genotyper software for allele sizing as described by Hall et al (1996). Although the DNA samples represent small nuclear families, the 96-well plates were not possible to arrange by family, thereby requiring consistent allele-calling in the absence of Mendelian transmission information.…”

Section: Test Samplesmentioning

confidence: 99%

“…This variability is shown in Figure 1, with the left panel illustrating low variability, and the right panel showing a range of greater variability in allele sizes. There are many possible causes of the allelic dispersion, including plus-A amplification, gel-to-gel variability, incorrect allele sizing, nonspecific primer sequences, and others (Callen et al 1993;Hauge and Litt 1993;Hall et al 1996). Careful attention to primer design and PCR conditions, arrangement of individual samples on plates according to family relationships, and strategic usage of reference genotypes can be helpful for allele binning and calling in the presence of such variation.…”

mentioning

confidence: 99%

“…To meet such requirements, considerable effort has been devoted to the development of high-throughput genotyping methods. Advances in robotic devices for DNA extraction, PCR multiplexing methods, fluorescent detection systems, software for fragment size analysis and data tracking, and the availability of >5000 microsatellite markers have contributed significantly to the ability to genotype large numbers of samples rapidly and accurately (e.g., Hall et al 1996;Ghosh et al 1997). Many of these advances have been predicated on the need for automation, as wellconstructed hardware and software can increase the rate of processing genotypes as well as provide a constant environment for identifying and correcting problems.…”

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confidence: 99%

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A Simple Method for Automated Allele Binning in Microsatellite Markers

Idury¹,

Cardon²

1997

Genome Res.

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High-throughput fluorescent genotyping requires a considerable amount of automation for accurate and efficient processing of genetic markers. Automated DNA sequencers and corresponding software products are commercially available that contribute substantially to increased throughput rates for large-scale genotyping projects. However, some conceptually simple tasks still require time-consuming manual intervention that imposes bottlenecks on throughput capacity. One of these tasks is the conversion of imprecise DNA fragment sizes determined by commercial software programs to the underlying discrete alleles that the sizes represent. Here we describe a simple method for assigning allele sizes into their appropriate allele “bins” using least-squares minimization procedures. The method requires no special treatment of family data on plates, internal/external size standards, or electropherogram data manipulation. Tests of the method using the ABI 373A automated DNA sequencer and accompanying Genescan/Genotyper software resulted in accurate automatic classification of all alleles in >80% of 208 markers analyzed, with the remaining 20% being appropriately identified as requiring additional attention to laboratory conditions. Specific characteristics of different markers, including differences in PCR product size and inexact repeat lengths (e.g., 1.9 bp for a dinucleotide repeat), are accommodated by the method and their properties discussed.

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A genome-wide search for schizophrenia susceptibility genes

Shaw¹,

Kelly²,

et al. 1998

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We completed a systematic genome-wide search for evidence of loci linked to schizophrenia using a collection of 70 pedigrees containing multiple affected individuals according to three phenotype classifications: schizophrenia only (48 pedigrees; 70 sib-pairs); schizophrenia plus schizoaffective disorder (70 pedigrees; 101 sib-pairs); and a broad category consisting of schizophrenia, schizoaffective disorder, paranoid or schizotypal personality disorder, psychosis not otherwise specified (NOS), delusional disorder, and brief reactive psychosis (70 pedigrees; 111 sib-pairs). All 70 families contained at least one individual affected with chronic schizophrenia according to DSM-III-R criteria. Three hundred and thirty-eight markers spanning the genome were typed in all pedigrees for an average resolution of 10.5 cM (range, 0-31 cM) and an average heterozygosity of 74.3% per marker. The data were analyzed using multipoint nonparametric allele-sharing and traditional two-point lod score analyses using dominant and recessive, affecteds-only models. Twelve chromosomes (1, 2, 4, 5, 8, 10, 11, 12, 13, 14, 16, and 22) had at least one region with a nominal P value <0.05, and two of these chromosomes had a nominal P value <0.01 (chromosomes 13 and 16), using allele-sharing tests in GENEHUNTER. Five chromosomes (1, 2, 4, 11, and 13) had at least one marker with a lod score >2.0, allowing for heterogeneity. These regions will be saturated with additional markers and investigated in a new, larger set of families to test for replication.

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An approach to high-throughput genotyping.

Cited by 39 publications

References 26 publications

Toward High-Throughput Genotyping: Dynamic and Automatic Software for Manipulating Large-Scale Genotype Data Using Fluorescently Labeled Dinucleotide Markers

Toward High-Throughput Genotyping: Dynamic and Automatic Software for Manipulating Large-Scale Genotype Data Using Fluorescently Labeled Dinucleotide Markers

A Simple Method for Automated Allele Binning in Microsatellite Markers

A genome-wide search for schizophrenia susceptibility genes

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