Evidence map›Paper›PMID 25519386›Full record

ArticleBMC proceedings2014

Adjusting for population stratification and relatedness with sequencing data.

Yiwei Zhang, Wei Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC proceedings, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Polygenic Index for Sleep Duration and Brain Changes over Time.Medical sciences (Basel, Switzerland) · 2026
    Article
  2. Article
  3. 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.

Yiwei ZhangDivision of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, USA.
Wei PanDivision of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, USA.

Funding

GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Powerful Inference and Prediction for Genetic AssociationR01HL105397 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2011 to 2018
$2.9M
New Machine Learning Tools for Biomedical DataR01GM081535 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2007 to 2014
$2.2M
Statistical Methods for Genomic and Proteomic DataR01HL065462 · NHLBI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI PAN, WEI · 2002 to 2012
$1.5M
NHLBI NIH HHS R01 HL065462NHLBI NIH HHS R01 HL105397NIGMS NIH HHS R01 GM031575NIGMS NIH HHS R01 GM081535
6 · The paper itself

Abstract

To avoid inflated type I error and reduced power in genetic association studies, it is necessary to adjust properly for population stratification and known/unknown subject relatedness. It would be interesting to compare the performance of a principal component-based approach with a linear mixed model. Furthermore, with the availability of genome-wide sequencing data, the question of whether it is preferable to use common variants or rare variants for such an adjustment remains largely unknown. In this paper, we use the Genetic Analysis Workshop 18 data to empirically investigate these issues. We consider both a quantitative trait and a binary trait.

Identifiers

PMID25519386
PMCPMC4143729

What OpenQuestion holds

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Registered trials

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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.