Evidence map›Paper›PMID 16424672›Full record

ArticleHuman heredity2005

Multilocus linkage analysis of affected sib pairs.

Iuliana Ionita, Shaw-Hwa Lo

Abstract read
In one paragraph

Article in Human heredity, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Iuliana IonitaCourant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA. ionita@cs.nyu.edu
Shaw-Hwa Lo

Funding

New Approach Towards Mapping of Complex Human DisordersR01GM070789 · NIGMS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI LO, SHAW-HWA · 2005 to 2009
$1.1M
NIGMS NIH HHS R01 GM070789NIGMS NIH HHS R01 GM070789-01NIGMS NIH HHS R01 GM070789-01A1
6 · The paper itself

Abstract

objectiveThe conventional affected sib pair methods evaluate the linkage information at a locus by considering only marginal information. We describe a multilocus linkage method that uses both the marginal information and information derived from the possible interactions among several disease loci, thereby increasing the significance of loci with modest effects.

methodsOur method is based on a statistic that quantifies the linkage information contained in a set of markers. By a marker selection-reduction process, we screen a set of polymorphisms and select a few that seem linked to disease.

resultsWe test our approach on genome scan data for inflammatory bowel disease (InfBD) and on simulated data. On real data we detect 6 of the 8 known InfBD loci; on simulated data we obtain improvements in power of up to 40% compared to a conventional single-locus method.

conclusionOur extensive simulations and the results on real data show that our method is in general more powerful than single-locus methods in detecting disease loci responsible for complex traits. A further advantage of our approach is that it can be extended to make use of both the linkage and the linkage disequilibrium between disease loci and nearby markers.

Indexed as

Models, GeneticSequence Analysis, DNAGenetic LinkageGenetic MarkersGenome, HumanHumansInflammatory Bowel DiseasesQuantitative Trait LociGenetic Markers

Identifiers

PMID16424672
PMCPMC2269733

What OpenQuestion holds

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Registered trials

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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.