Evidence map›Paper›PMID 26680660›Full record

ArticleGenetics2016

Genetic Variant Selection: Learning Across Traits and Sites.

Laurel Stell, Chiara Sabatti

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Laurel StellDepartment of Health Research and Policy, Stanford University, Stanford, California 94305 lstell@stanford.edu.
Chiara SabattiDepartment of Health Research and Policy, Stanford University, Stanford, California 94305 Department of Statistics, Stanford University, Stanford, California 94305.

Funding

1/2 Genomic Strategies to Identify High-impact Psychiatric Risk VariantsU01MH105578 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FREIMER, NELSON B., GESCHWIND, DANIEL H · 2014 to 2018
$8.5M
Genomic and Metabolomic Profiling of Finnish Familial Dyslipidemia FamiliesR01HL113315 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FREIMER, NELSON B., PALOTIE, AARNO · 2012 to 2016
$3.4M
Genome Sequencing in Extended Bipolar PedigreesR01MH095454 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FREIMER, NELSON B. · 2012 to 2014
$2.4M
New Statistical Methods for High Resolution Mapping of Multiple PhenotypesR01HG006695 · NHGRI · STANFORD UNIVERSITY · PI SABATTI, CHIARA · 2013 to 2015
$1.0M
NHGRI NIH HHS HG006695NHGRI NIH HHS R01 HG006695NHLBI NIH HHS HL113315NHLBI NIH HHS R01 HL113315NIMH NIH HHS MH095454NIMH NIH HHS MH105578NIMH NIH HHS R01 MH095454NIMH NIH HHS U01 MH105578
6 · The paper itself

Abstract

We consider resequencing studies of associated loci and the problem of prioritizing sequence variants for functional follow-up. Working within the multivariate linear regression framework helps us to account for the joint effects of multiple genes; and adopting a Bayesian approach leads to posterior probabilities that coherently incorporate all information about the variants' function. We describe two novel prior distributions that facilitate learning the role of each variable site by borrowing evidence across phenotypes and across mutations in the same gene. We illustrate their potential advantages with simulations and reanalyzing a data set of sequencing variants.

Indexed as

Genetic VariationModels, GeneticSelection, GeneticAlgorithmsBayes TheoremChromosome MappingComputer SimulationGenotypeHumansPhenotypeQuantitative Trait, HeritableQuantitative Trait LociReproducibility of ResultsBayesian variant selectionfine mappingpleiotropyrare variants

Identifiers

PMID26680660
PMCPMC4788227

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.