Evidence map›Paper›PMID 15611197›Full record

ArticleGenetics2004

Association test algorithm between a qualitative phenotype and a haplotype or haplotype set using simultaneous estimation of haplotype frequencies, diplotype configurations and diplotype-based penetrances.

Toshikazu Ito, Eisuke Inoue, Naoyuki Kamatani

Open access · bronzeAbstract read
In one paragraph

Article in Genetics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

14 citing papers in PubMed, 32 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Toshikazu ItoDivision of Genomic Medicine, Department of Applied Biomedical Engineering and Science and Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan.
Eisuke Inoue
Naoyuki Kamatani
Japan Biological Informatics Consortium · JPTokyo Women's Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analysis of the association between haplotypes and phenotypes is becoming increasingly important. We have devised an expectation-maximization (EM)-based algorithm to test the association between a phenotype and a haplotype or a haplotype set and to estimate diplotype-based penetrance using individual genotype and phenotype data from cohort studies and clinical trials. The algorithm estimates, in addition to haplotype frequencies, penetrances for subjects with a given haplotype and those without it (dominant mode). Relative risk can thus also be estimated. In the dominant mode, the maximum likelihood under the assumption of no association between the phenotype and presence of the haplotype (L(0max)) and the maximum likelihood under the assumption of association (L(max)) were calculated. The statistic -2 log(L(0max)/L(max)) was used to test the association. The present algorithm along with the analyses in recessive and genotype modes was implemented in the computer program PENHAPLO. Results of analysis of simulated data indicated that the test had considerable power under certain conditions. Analyses of two real data sets from cohort studies, one concerning the MTHFR gene and the other the NAT2 gene, revealed significant associations between the presence of haplotypes and occurrence of side effects. Our algorithm may be especially useful for analyzing data concerning the association between genetic information and individual responses to drugs.

Indexed as

AlgorithmsData Interpretation, StatisticalHaplotypesPhenotypeComputer SimulationLinkage Disequilibrium

Identifiers

PMID15611197
PMCPMC1448736
OpenAlexW2171227058

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.