Evidence map›Paper›PMID 26715385›Full record

ArticleBMC genomics2015

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data.

Young Jin Kim, Juyoung Lee, Bong-Jo Kim, T2D-Genes Consortium, Taesung Park

Abstract read
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Young Jin KimInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, South Korea. anwltlarkr@gmail.com.
Juyoung LeeDivision of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, 363-951, South Korea. jylee_monte@hanmail.net.
Bong-Jo KimDivision of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, 363-951, South Korea. kbj6181@cdc.go.kr.
T2D-Genes Consortium
Taesung ParkInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, South Korea. tspark@stats.snu.ac.kr.

Funding

Pilot and Feasibility ProgramP30DK020595 · NIDDK · UNIVERSITY OF CHICAGO · PI Matthew J Brady · 2013 to 2026
$20.9M
Multiethnic Study of Type 2 Diabetes GenesU01DK085526 · NIDDK · BROAD INSTITUTE, INC. · PI ALTSHULER, DAVID, JABLONSKI, KATHLEEN ANN · 2009 to 2014
$15.7M
Einstein's Nathan Shock Center of Excellence in Basic Biology of AgingP30AG038072 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI VIJG, JAN · 2010 to 2024
$10.8M
Discovery of Functional Variants in Type 2 Diabetes Genes in Mexican AmericansU01DK085524 · NIDDK · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI BLANGERO, JOHN, DEFRONZO, RALPH A · 2009 to 2019
$5.9M
Identifying T2D Variants by DNA Sequencing in Multiethnic SamplesU01DK085584 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO, BOEHNKE, MICHAEL L · 2009 to 2013
$4.1M
Identifying variants causal for Type 2 Diabetes in Major human populationsU01DK085545 · NIDDK · UNIVERSITY OF OXFORD · PI CHAN, JULIANA CN, EBRAHIM, SHAH BRIAN · 2009 to 2013
$2.8M
Identification and Replication of Type 2 Diabetes Genes in Mexican AmericansU01DK085501 · NIDDK · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HANIS, CRAIG L · 2009 to 2013
$2.7M
LGP/LongenityR01AG046949 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI BARZILAI, NIR J · 2013 to 2017
$2.4M
NIA NIH HHS P30 AG038072NIA NIH HHS R01 AG046949NIDDK NIH HHS P30 DK020595NIDDK NIH HHS U01 DK085501NIDDK NIH HHS U01 DK085524NIDDK NIH HHS U01 DK085526NIDDK NIH HHS U01 DK085545NIDDK NIH HHS U01 DK085584
6 · The paper itself

Abstract

backgroundRare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants.

resultsIn this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data.

conclusionsOur study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants.

Indexed as

ExomeGenotypeHigh-Throughput Nucleotide SequencingOligonucleotide Array Sequence AnalysisPolymorphism, Single Nucleotide

Identifiers

PMID26715385
PMCPMC4696174

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