Evidence map›Paper›PMID 25979475›Full record

ArticleBioinformatics (Oxford, England)2015

Likelihood-based complex trait association testing for arbitrary depth sequencing data.

Song Yan, Shuai Yuan, Zheng Xu, Baqun Zhang, Bo Zhang, Guolian Kang, Andrea Byrnes, Yun Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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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1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Song YanDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Shuai YuanDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Zheng XuDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Baqun ZhangDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Bo ZhangDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Guolian KangDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Andrea ByrnesDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Yun LiDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.

Funding

Design and Analysis of Sequencing-based Studies for Complex Human TraitsR01HG006292 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, YUN · 2011 to 2015
$1.8M
Imputation and Analysis of Rare Variants in Admixed PopulationsR01HG006703 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, YUN · 2012 to 2014
$931k
NHGRI NIH HHS R01-HG006292NHGRI NIH HHS R01-HG006703
6 · The paper itself

Abstract

unlabelledIn next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment; while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to ∼60% when the causal variant is of low frequency (MAF < 0.01). AVAILABILITY AND IMPLEMENTATION: UNC combo and the associated R files, including documentation, examples, are available at http://www.unc.edu/∼yunmli/UNCcombo/ CONTACT: yunli@med.unc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Genetic Association StudiesLikelihood FunctionsQuantitative Trait LociComputer SimulationGene FrequencyGenetic MarkersGenetic VariationGenotypeHigh-Throughput Nucleotide SequencingHumansPhenotypeSequence Analysis, DNAGenetic Markers

Identifiers

PMID25979475
PMCPMC4668777

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