Evidence map›Paper›PMID 21923410›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2011

Haplotype reconstruction in large pedigrees with untyped individuals through IBD inference.

Xin Li, Jing Li

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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.

Xin LiDepartment of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Jing Li

Funding

X-LINKED EXTENSION OF THE REVISED HASEMAN-ELSTON ALGORITHM FOR LINKAGE ANALYSISP41RR003655 · NCRR · LOUISIANA STATE UNIV HSC NEW ORLEANS · PI ELSTON, ROBERT C. · 1987 to 2011
$11.5M
Multi-point and multi-locus analysis of genomic association dataR01LM008991 · NLM · CASE WESTERN RESERVE UNIVERSITY · PI LI, JING · 2006 to 2010
$3.1M
NCRR NIH HHS RR03655NLM NIH HHS LM008991NLM NIH HHS R01 LM008991
6 · The paper itself

Abstract

unlabelledHaplotypes, as they specify the linkage patterns between dispersed genetic variations, provide important information for understanding the genetics of human traits. However, haplotypes are not directly obtainable from current genotyping platforms, which pushes extensive investigations of computational methods to recover such information. Two major computational challenges arising in current family-based disease studies are large family sizes and many ungenotyped family members. Traditional haplotyping methods can neither handle large families nor families with missing members. In this article, we propose a method that addresses these issues by integrating multiple novel techniques. The method consists of three major components: pairwise identical-by-descent (IBD) inference, global IBD reconstruction, and haplotype restoring. By reconstructing the global IBD of a family from pairwise IBD and then restoring the haplotypes based on the inferred IBD, this method can scale to large pedigrees, and more importantly it can handle families with missing members. Compared with existing approaches, this method demonstrates much higher power to recover haplotype information, especially in families with many untyped individuals. AVAILABILITY: http://sites.google.com/site/xinlishomepage/pedibd.

Indexed as

Computer SimulationHaplotypesModels, GeneticPedigreeAlgorithmsGene FrequencyHeredityHumansLinkage DisequilibriumMarkov Chains

Identifiers

PMID21923410
PMCPMC3216110

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.