Evidence map›Paper›PMID 15883841›Full record

ArticleHuman genetics2005

Identifying nineteenth century genealogical links from genotypes.

Jim Stankovich, Melanie Bahlo, Justin P Rubio, Christopher R Wilkinson, Russell Thomson, Annette Banks, Maree Ring, Simon J Foote, Terence P Speed

Abstract read
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In one paragraph

Article in Human genetics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

9 authors.

Jim StankovichDivision of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. stankovich@wehi.edu.au
Melanie Bahlo
Justin P Rubio
Christopher R Wilkinson
Russell Thomson
Annette Banks
Maree Ring
Simon J Foote
Terence P Speed

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have developed a likelihood method to identify moderately distant genealogical relationships from genomewide scan data. The aim is to compare the genotypes of many pairs of people and identify those pairs most likely to be related to one another. We have tested the algorithm using the genotypes of 170 Tasmanians with multiple sclerosis recruited into a haplotype association study. It is estimated from genealogical records that approximately 65% of Tasmania's current population of 470,000 are direct descendants of the 13,000 female founders living in this island state of Australia in the mid-nineteenth century. All cases and four to five relatives of each case have been genotyped with microsatellite markers at a genomewide average density of 4 cM. Previous genealogical research has identified 51 pairwise relationships linking 56 of the 170 cases. Testing the likelihood calculation on these known relative pairs, we have good power to identify relationships up to degree eight (e.g. third cousins once removed). Applying the algorithm to all other pairs of cases, we have identified a further 61 putative relative pairs, with an estimated false discovery rate of 10%. The power to identify genealogical links should increase when the new, denser sets of SNP markers are used. Except in populations where there is a searchable electronic database containing virtually all genealogical links in the past six generations, the algorithm should be a useful aid for genealogists working on gene-mapping projects, both linkage studies and association studies.

Indexed as

AlgorithmsLinkage DisequilibriumModels, GeneticPedigreePolymorphism, Single NucleotideAllelesFemaleGene FrequencyHaplotypesHumansMicrosatellite RepeatsMultiple SclerosisTasmania

Identifiers

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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.