Evidence map›Paper›PMID 24739678›Full record

ReviewNature reviews. Genetics2014

Statistical power and significance testing in large-scale genetic studies.

Pak C Sham, Shaun M Purcell

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 292 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
292citing papers in PubMed, 14 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

292 citing papers in PubMed, 14 syntheses or guidelines pooled it.

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232 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

2 authors.

Pak C ShamCentre for Genomic Sciences, Jockey Club Building for Interdisciplinary Research; State Key Laboratory of Brain and Cognitive Sciences, and Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Shaun M Purcell1] Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York 10029-6574, USA. [2] Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.

Funding

Software for the analysis of large-scale genotyping and sequencing studiesR01HG005827 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI PURCELL, SHAUN M · 2010 to 2016
$2.1M
Leveraging identity-by-descent information in large-scale population sequencingR01MH099126 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PURCELL, SHAUN M · 2012 to 2014
$1.0M
NHGRI NIH HHS R01 HG005827NIMH NIH HHS R01 MH099126
6 · The paper itself

Abstract

Significance testing was developed as an objective method for summarizing statistical evidence for a hypothesis. It has been widely adopted in genetic studies, including genome-wide association studies and, more recently, exome sequencing studies. However, significance testing in both genome-wide and exome-wide studies must adopt stringent significance thresholds to allow multiple testing, and it is useful only when studies have adequate statistical power, which depends on the characteristics of the phenotype and the putative genetic variant, as well as the study design. Here, we review the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.

Indexed as

Case-Control StudiesData Interpretation, StatisticalGene FrequencyGenetic Predisposition to DiseaseGenetic TestingGenome-Wide Association StudyGenotypeHumansPolymorphism, Single NucleotideSequence Analysis, DNA

Identifiers

PMID24739678

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.