Evidence map›Paper›PMID 24667785›Full record

ArticleEuropean journal of human genetics : EJHG2015

Test of rare variant association based on affected sib-pairs.

Qiuying Sha, Shuanglin Zhang

Abstract read
In one paragraph

Article in European journal of human genetics : EJHG, 2015. 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.

Qiuying ShaDepartment of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
Shuanglin ZhangDepartment of Mathematical Sciences, Michigan Technological University, Houghton, MI, 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
QUANTITATIVE TRAIT LOCUS MAPPING IN HUMAN PEDIGREESR01MH059490 · NIMH · SOUTHWEST FOUNDATION FOR BIOMEDICAL RES · PI BLANGERO, JOHN · 1998 to 2000
$426k
Statistical Methods for Family-Based Association StudiesR03HG006155 · NHGRI · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI SHA, QIUYING · 2012 to 2013
$156k
NHGRI NIH HHS R03 HG006155NIGMS NIH HHS R01 GM031575NIMH NIH HHS NIH R01 MH059490NIMH NIH HHS R01 MH059490
6 · The paper itself

Abstract

With the development of sequencing techniques, there is increasing interest to detect associations between rare variants and complex traits. Quite a few statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. Statistical methods for detecting rare variant associations under family-based designs have not received as much attention as methods for unrelated individuals. Recent studies show that rare disease variants will be enriched in family data and thus family-based designs may improve power to detect rare variant associations. In this article, we propose a novel test to test association between the optimally weighted combination of variants and trait of interests for affected sib-pairs. The optimal weights are analytically derived and can be calculated from sampled genotypes and phenotypes. Based on the optimal weights, the proposed method is robust to the directions of the effects of causal variants and is less affected by neutral variants than existing methods are. Our simulation results show that, in all the cases, the proposed method is substantially more powerful than existing methods based on unrelated individuals and existing methods based on affected sib-pairs.

Indexed as

Models, GeneticAlgorithmsGenetic Association StudiesGenetic Diseases, InbornGenetic LinkageGenotypeHumansMutationRare Diseases

Identifiers

PMID24667785
PMCPMC4297896

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.