Evidence map›Paper›PMID 30940094›Full record

ArticleBMC bioinformatics2019

Haplin power analysis: a software module for power and sample size calculations in genetic association analyses of family triads and unrelated controls.

Miriam Gjerdevik, Astanand Jugessur, Øystein A Haaland, Julia Romanowska, Rolv T Lie, Heather J Cordell, Håkon K Gjessing

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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  7. Gene-environment interactions in human health.Nature reviews. Genetics · 2024
    Review
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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

7 authors.

Miriam GjerdevikDepartment of Global Public Health and Primary Care, University of Bergen, Bergen, Norway. miriam.gjerdevik@uib.no.ORCID http://orcid.org/0000-0002-2604-2132
Astanand JugessurDepartment of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
Øystein A HaalandDepartment of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
Julia RomanowskaDepartment of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
Rolv T LieDepartment of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
Heather J CordellInstitute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, UK.
Håkon K GjessingDepartment of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.

Funding

Bergens Forskningsstiftelse Grant 807191Norges Forskningsråd Grant 245464/F50Norges Forskningsråd Grant 262700Wellcome TrustWellcome Trust Grant 102858/Z/13/Z
6 · The paper itself

Abstract

backgroundLog-linear and multinomial modeling offer a flexible framework for genetic association analyses of offspring (child), parent-of-origin and maternal effects, based on genotype data from a variety of child-parent configurations. Although the calculation of statistical power or sample size is an important first step in the planning of any scientific study, there is currently a lack of software for genetic power calculations in family-based study designs. Here, we address this shortcoming through new implementations of power calculations in the R package Haplin, which is a flexible and robust software for genetic epidemiological analyses. Power calculations in Haplin can be performed analytically using the asymptotic variance-covariance structure of the parameter estimator, or else by a straightforward simulation approach. Haplin performs power calculations for child, parent-of-origin and maternal effects, as well as for gene-environment interactions. The power can be calculated for both single SNPs and haplotypes, either autosomal or X-linked. Moreover, Haplin enables power calculations for different child-parent configurations, including (but not limited to) case-parent triads, case-mother dyads, and case-parent triads in combination with unrelated control-parent triads.

resultsWe compared the asymptotic power approximations to the power of analysis attained with Haplin. For external validation, the results were further compared to the power of analysis attained by the EMIM software using data simulations from Haplin. Consistency observed between Haplin and EMIM across various genetic scenarios confirms the computational accuracy of the inference methods used in both programs. The results also demonstrate that power calculations in Haplin are applicable to genetic association studies using either log-linear or multinomial modeling approaches.

conclusionsHaplin provides a robust and reliable framework for power calculations in genetic association analyses for a wide range of genetic effects and etiologic scenarios, based on genotype data from a variety of child-parent configurations.

Indexed as

SoftwareChildGenetic Association StudiesGenotyping TechniquesHaplotypesHumansPolymorphism, Single NucleotideSample SizeEMIMGenome-wide association studies (GWAS)HaplinLog-linear and multinomial modelsSample size estimationStatistical power estimation

Identifiers

PMID30940094
PMCPMC6444579

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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.