Evidence map›Paper›PMID 26634345›Full record

ArticleBMC bioinformatics2015

M(3)-S: a genotype calling method incorporating information from samples with known genotypes.

Gengxin Li, Hongyu Zhao

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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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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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Gengxin LiDepartment of Mathematics and Statistics, Wright State University, 3640 Colonel Glenn Hwy, Dayton, 45435, USA. gengxin.li@wright.edu.
Hongyu ZhaoDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06520, USA. hongyu.zhao@yale.edu.

Funding

STATISTICAL METHODS TO MAP GENES FOR COMPLEX TRAITSR01GM059507 · NIGMS · YALE UNIVERSITY · PI ZHAO, HONGYU · 1999 to 2013
$3.6M
Lost-of-function variants in the 1000 genomes data set and implications to GWASU01HG005718 · NHGRI · YALE UNIVERSITY · PI ZHAO, HONGYU · 2010 to 2011
$533k
NHGRI NIH HHS U01 HG005718NIGMS NIH HHS R01 GM059507NIGMS NIH HHS R01 GM59507
6 · The paper itself

Abstract

backgroundA key challenge in analyzing high throughput Single Nucleotide Polymorphism (SNP) arrays is the accurate inference of genotypes for SNPs with low minor allele frequencies. A number of calling algorithms have been developed to infer genotypes for common SNPs, but they are limited in their performance in calling rare SNPs. The existing algorithms can be broadly classified into three categories, including: population-based methods, SNP-based methods, and a hybrid of the two approaches. Despite the relatively better performance of the hybrid approach, it is still challenging to analyze rare SNPs.

resultsWe propose to utilize information from samples with known genotypes to develop a two stage genotyping procedure, namely M(3)-S, for rare SNP calling. This new approach can improve genotyping accuracy through clearly defining the boundaries of genotype clusters from samples with known genotypes, and enlarge the call rate by combining the simulated data based on the inferred genotype clusters information with the study population.

conclusionsApplications to real data demonstrates that this new approach M(3)-S outperforms existing methods in calling rare SNPs.

Indexed as

AlgorithmsGene FrequencyGenome-Wide Association StudyGenotypeGenotyping TechniquesHumansPolymorphism, Single Nucleotide

Identifiers

PMID26634345
PMCPMC4669649

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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.