Evidence map›Paper›PMID 23667285›Full record

ArticleJournal of applied statistics2013

A note on statistical method for genotype calling of high-throughput SNP arrays.

Jiaqi Yang, Wei Zhang, Baolin Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied statistics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Jiaqi YangDivision of Biostatistics, School of Public Health, University of Minnesota.
Wei Zhang
Baolin Wu

Funding

Dominantly Inherited Alzheimer NetworkUF1AG032438 · NIA · WASHINGTON UNIVERSITY · PI BATEMAN, RANDALL J · 2014 to 2017
$21.6M
Statistical Model Building for High Dimensional Biomedical DataR01GM083345 · NIGMS · UNIVERSITY OF MINNESOTA · PI WU, BAOLIN · 2008 to 2011
$1.0M
Statistical methods for large-scale significance and prediction analysis with appR01CA134848 · NCI · UNIVERSITY OF MINNESOTA · PI WU, BAOLIN · 2009 to 2010
$291k
NCI NIH HHS R01 CA134848NIA NIH HHS UF1 AG032438NIGMS NIH HHS R01 GM083345
6 · The paper itself

Abstract

We study the genotype calling algorithms for the high-throughput single-nucleotide polymorphism (SNP) arrays. Building upon the novel SNP-RMA preprocessing approach and the state-of-the-art CRLMM approach for genotype calling, we propose a simple modification to better model and combine the information across multiple SNPs with empirical Bayes modeling, which could often significantly improve the genotype calling of CRLMM. Through applications to the HapMap Trio data set and a non-HapMap test set of high quality SNP chips, we illustrate the competitive performance of the proposed method.

Indexed as

empirical Bayesgenotype calling algorithmmixture modelSNP arrays

Identifiers

PMID23667285
PMCPMC3647617

What OpenQuestion holds

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Registered trials

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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.