Evidence map›Paper›PMID 25948564›Full record

ArticleGenetics2015

Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics.

Wenan Chen, Beth R Larrabee, Inna G Ovsyannikova, Richard B Kennedy, Iana H Haralambieva, Gregory A Poland, Daniel J Schaid

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers, 18 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
145citing papers in PubMed, 18 pooled it
–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

145 citing papers in PubMed, 18 syntheses or guidelines pooled it.

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85 more citing papers are in PubMed but not listed here.

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

7 authors.

Wenan ChenDivision of Biostatistics, Mayo Clinic, Rochester, Minnesota 55905.
Beth R LarrabeeDivision of Biostatistics, Mayo Clinic, Rochester, Minnesota 55905.
Inna G OvsyannikovaMayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, Minnesota 55905.
Richard B KennedyMayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, Minnesota 55905.
Iana H HaralambievaMayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, Minnesota 55905.
Gregory A PolandMayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, Minnesota 55905.
Daniel J SchaidDivision of Biostatistics, Mayo Clinic, Rochester, Minnesota 55905 schaid@mayo.edu.

Funding

Quantitative Methods for Genetic EpidemiologyR01GM065450 · NIGMS · MAYO CLINIC ROCHESTER · PI SCHAID, DANIEL J. · 2002 to 2020
$5.6M
NIAID NIH HHS HHSN272201000025CNIGMS NIH HHS GM065450NIGMS NIH HHS R01 GM065450PHS HHS HHSN272201000025C
6 · The paper itself

Abstract

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf.

Indexed as

Chromosome MappingModels, GeneticBayes TheoremComputer SimulationLinkage DisequilibriumPolymorphism, Single NucleotideSoftwareBayesian fine mappingcausal variantsmarginal test statistics

Identifiers

PMID25948564
PMCPMC4512539

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.