Evidence map›Paper›PMID 23386037›Full record

ArticleEuropean journal of human genetics : EJHG2013

Family-based association tests for sequence data, and comparisons with population-based association tests.

Iuliana Ionita-Laza, Seunggeun Lee, Vladimir Makarov, Joseph D Buxbaum, Xihong Lin

Abstract readComparative Study
In one paragraph

Article in European journal of human genetics : EJHG, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 1 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

63 citing papers in PubMed, 1 synthesis or guideline pooled it.

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3 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

5 authors.

Iuliana Ionita-LazaDepartment of Biostatistics, Columbia University, New York, NY, USA.
Seunggeun Lee
Vladimir Makarov
Joseph D Buxbaum
Xihong Lin

Funding

Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI DOMINICI, FRANCESCA, LIN, XIHONG · 2008 to 2017
$6.8M
Novel Statistical methods for DNA Sequencing Data, and applications to Autism.R01MH095797 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA · 2012 to 2022
$3.1M
Statistical Methods for Correlated and High-Dimensional Biomedical DataR37CA076404 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG · 2007 to 2014
$2.5M
3/5-Elucidating the Genetic Architecture of Autism by Deep Genomic SequencingR01MH089025 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BUXBAUM, JOSEPH D. · 2009 to 2010
$1.4M
Statistical methods for rare variant effects in sequencing association studiesK99HL113164 · NHLBI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LEE, SEUNGGEUN SHAWN · 2012 to 2013
$175k
Statistical Methods to Assess the role of rare Variants in Complex Traits.R03HG005908 · NHGRI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA · 2010 to 2011
$161k
NCI NIH HHS P01 CA134294NCI NIH HHS P01CA134294NCI NIH HHS R35 CA197449NCI NIH HHS R37 CA076404NHGRI NIH HHS 1R03HG005908NHGRI NIH HHS R03 HG005908NHLBI NIH HHS K99 HL113164NIMH NIH HHS MH089025NIMH NIH HHS R01 MH089025NIMH NIH HHS R01 MH095797NIMH NIH HHS R01MH095797
6 · The paper itself

Abstract

Recent advances in high-throughput sequencing technologies make it increasingly more efficient to sequence large cohorts for many complex traits. We discuss here a class of sequence-based association tests for family-based designs that corresponds naturally to previously proposed population-based tests, including the classical Burden and variance-component tests. This framework allows for a direct comparison between the powers of sequence-based association tests with family- vs population-based designs. We show that for dichotomous traits using family-based controls results in similar power levels as the population-based design (although at an increased sequencing cost for the family-based design), while for continuous traits (in random samples, no ascertainment) the population-based design can be substantially more powerful. A possible disadvantage of population-based designs is that they can lead to increased false-positive rates in the presence of population stratification, while the family-based designs are robust to population stratification. We show also an application to a small exome-sequencing family-based study on autism spectrum disorders. The tests are implemented in publicly available software.

Indexed as

PedigreeSoftwareChild Development Disorders, PervasiveExomeGenetic Association StudiesHumansPopulationSensitivity and SpecificitySequence Analysis, DNA

Identifiers

PMID23386037
PMCPMC3778346

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.