Evidence map›Paper›PMID 27047537›Full record

ArticleFrontiers in genetics2016

Using Incomplete Trios to Boost Confidence in Family Based Association Studies.

Varsha Dhankani, David L Gibbs, Theo Knijnenburg, Roger Kramer, Joseph Vockley, John Niederhuber, Ilya Shmulevich, Brady Bernard

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2016. 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, top 99% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

8 authors at 3 institutions in 1 country.

Varsha DhankaniInstitute for Systems Biology Seattle, WA, USA.
David L GibbsInstitute for Systems Biology Seattle, WA, USA.
Theo KnijnenburgInstitute for Systems Biology Seattle, WA, USA.
Roger KramerInstitute for Systems Biology Seattle, WA, USA.
Joseph VockleyInova Translational Medicine InstituteFalls Church, VA, USA; School of Medicine, Virginia Commonwealth UniversityRichmond, VA, USA.
John NiederhuberInova Translational Medicine InstituteFalls Church, VA, USA; School of Medicine, John Hopkins UniversityBaltimore, MD, USA.
Ilya ShmulevichInstitute for Systems Biology Seattle, WA, USA.
Brady BernardInstitute for Systems Biology Seattle, WA, USA.
Institute for Systems Biology · USJohns Hopkins University · USVirginia Commonwealth University · US

Funding

AN NCRR CENTER FOR HIGH THROUGHPUT SNP GENOTYPING AND ANALYSIS: GAIN NETWORKU54RR020278 · NCRR · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIEL, STACEY · 2004 to 2008
$14.4M
NCRR NIH HHS U54 RR020278
6 · The paper itself

Abstract

Most currently available family based association tests are designed to account only for nuclear families with complete genotypes for parents as well as offspring. Due to the availability of increasingly less expensive generation of whole genome sequencing information, genetic studies are able to collect data for more families and from large family cohorts with the goal of improving statistical power. However, due to missing genotypes, many families are not included in the family based association tests, negating the benefits of large scale sequencing data. Here, we present the CIFBAT method to use incomplete families in Family Based Association Test (FBAT) to evaluate robustness against missing data. CIFBAT uses quantile intervals of the FBAT statistic by randomly choosing valid completions of incomplete family genotypes based on Mendelian inheritance rules. By considering all valid completions equally likely and computing quantile intervals over many randomized iterations, CIFBAT avoids assumption of a homogeneous population structure or any particular missingness pattern in the data. Using simulated data, we show that the quantile intervals computed by CIFBAT are useful in validating robustness of the FBAT statistic against missing data and in identifying genomic markers with higher precision. We also propose a novel set of candidate genomic markers for uterine related abnormalities from analysis of familial whole genome sequences, and provide validation for a previously established set of candidate markers for Type 1 diabetes. We have provided a software package that incorporates TDT, robustTDT, FBAT, and CIFBAT. The data format proposed for the software uses half the memory space that the standard FBAT format (PED) files use, making it efficient for large scale genome wide association studies.

Indexed as

family based association testsmemory efficient data formatmissing genotypespopulation stratificationquantile intervalsrandomized imputationwhole genome analysis

Identifiers

PMID27047537
PMCPMC4796035
OpenAlexW2318821778

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.