Evidence map›Paper›PMID 19393044›Full record

ArticleBMC bioinformatics2009

SNP HiTLink: a high-throughput linkage analysis system employing dense SNP data.

Yoko Fukuda, Yasuo Nakahara, Hidetoshi Date, Yuji Takahashi, Jun Goto, Akinori Miyashita, Ryozo Kuwano, Hiroki Adachi, Eiji Nakamura, Shoji Tsuji

Abstract read
In one paragraph

Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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  6. Partial duplication ofAnnals of clinical and translational neurology · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yoko FukudaDepartment of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp
Yasuo Nakahara
Hidetoshi Date
Yuji Takahashi
Jun Goto
Akinori Miyashita
Ryozo Kuwano
Hiroki Adachi
Eiji Nakamura
Shoji Tsuji

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring this recent decade, microarray-based single nucleotide polymorphism (SNP) data are becoming more widely used as markers for linkage analysis in the identification of loci for disease-associated genes. Although microarray-based SNP analyses have markedly reduced genotyping time and cost compared with microsatellite-based analyses, applying these enormous data to linkage analysis programs is a time-consuming step, thus, necessitating a high-throughput platform.

resultsWe have developed SNP HiTLink (SNP High Throughput Linkage analysis system). In this system, SNP chip data of the Affymetrix Mapping 100 k/500 k array set and Genome-Wide Human SNP array 5.0/6.0 can be directly imported and passed to parametric or model-free linkage analysis programs; MLINK, Superlink, Merlin and Allegro. Various marker-selecting functions are implemented to avoid the effect of typing-error data, markers in linkage equilibrium or to select informative data.

conclusionThe results using the 100 k SNP dataset were comparable or even superior to those obtained from analyses using microsatellite markers in terms of LOD scores obtained. General personal computers are sufficient to execute the process, as runtime for whole-genome analysis was less than a few hours. This system can be widely applied to linkage analysis using microarray-based SNP data and with which one can expect high-throughput and reliable linkage analysis.

Indexed as

Genetic LinkagePolymorphism, Single NucleotideComputational BiologyGenome-Wide Association StudyGenotypeHumans

Identifiers

PMID19393044
PMCPMC2680848

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.