Evidence map›Paper›PMID 18382088›Full record

ArticleHuman heredity2008

Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data.

Frank Dudbridge

Open access · bronzeAbstract read
In one paragraph

Article in Human heredity, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 282 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
282citing papers in PubMed, 9 pooled it
46.3field-weighted citation impact, top 1% 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

282 citing papers in PubMed, 9 syntheses or guidelines pooled it, 610 citations in OpenAlex.

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  15. Genetic Variations inJournal of obesity · 2024
    Article
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  18. UBASH3A Interacts with PTPN22 to RegulateInternational journal of molecular sciences · 2023
    Article
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222 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

1 author at 1 institution in 1 country.

Frank DudbridgeMRC Biostatistics Unit, Cambridge, UK. frank.dudbridge@mrc-bsu.cam.ac.uk
MRC Biostatistics Unit · GB

Funding

Medical Research Council MC_U105260799Medical Research Council U.1052.00.003.00002.01
6 · The paper itself

Abstract

Missing data occur in genetic association studies for several reasons including missing family members and uncertain haplotype phase. Maximum likelihood is a commonly used approach to accommodate missing data, but it can be difficult to apply to family-based association studies, because of possible loss of robustness to confounding by population stratification. Here a novel likelihood for nuclear families is proposed, in which distinct sets of association parameters are used to model the parental genotypes and the offspring genotypes. This approach is robust to population structure when the data are complete, and has only minor loss of robustness when there are missing data. It also allows a novel conditioning step that gives valid analysis for multiple offspring in the presence of linkage. Unrelated subjects are included by regarding them as the children of two missing parents. Simulations and theory indicate similar operating characteristics to TRANSMIT, but with no bias with missing data in the presence of linkage. In comparison with FBAT and PCPH, the proposed model is slightly less robust to population structure but has greater power to detect strong effects. In comparison to APL and MITDT, the model is more robust to stratification and can accommodate sibships of any size. The methods are implemented for binary and continuous traits in software, UNPHASED, available from the author.

Indexed as

Genetic TechniquesModels, GeneticModels, StatisticalNuclear FamilyGenotypeHumansLikelihood FunctionsProbabilitySoftware

Identifiers

PMID18382088
PMCPMC2386559
OpenAlexW2029220352

What OpenQuestion holds

Textmetadata
LicenceTDM
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.