Evidence map›Paper›PMID 23077511›Full record

ArticlePloS one2012

Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.

Yoshitaka Nagamine, Ricardo Pong-Wong, Pau Navarro, Veronique Vitart, Caroline Hayward, Igor Rudan, Harry Campbell, James Wilson, Sarah Wild, Andrew A Hicks and 4 more

Abstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing 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

66 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Empowering genome-wide association studies via a visualizable test based on the regional association score.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Tree-based QTL mapping with expected local genetic relatedness matrices.bioRxiv : the preprint server for biology · 2023
    Article
  13. Article
  14. Article
  15. Article
  16. Article
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  18. Detection of genomic regions underlying milk production traits in Valle del Belice dairy sheep using regional heritability mapping.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2021
    Article
  19. Article
  20. Article

6 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

14 authors.

Yoshitaka NagamineNational Institute of Livestock and Grassland Science, Tsukuba, Japan.
Ricardo Pong-Wong
Pau Navarro
Veronique Vitart
Caroline Hayward
Igor Rudan
Harry Campbell
James Wilson
Sarah Wild
Andrew A Hicks
Peter P Pramstaller
Nicholas Hastie
Alan F Wright
Chris S Haley

Funding

Biotechnology and Biological Sciences Research Council BB/J002844/1Biotechnology and Biological Sciences Research Council BBS/E/R/00001604Chief Scientist Office CZB/4/710Medical Research Council MC_PC_U127561128Medical Research Council MC_PC_U127592696Medical Research Council MC_U127592696
6 · The paper itself

Abstract

The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may contain a number of different alleles, we have developed an analytical approach termed Regional Genomic Relationship Mapping that, like linkage-based family methods, integrates variance contributed by founder gametes within a pedigree. This approach takes advantage of very distant (and unrecorded) relationships, and this greatly increases the power of the method, compared with traditional pedigree-based linkage analyses. By integrating variance contributed by founder gametes in the population, our approach provides an estimate of the Regional Heritability attributable to a small genomic region (e.g. 100 SNP window covering ca. 1 Mb of DNA in a 300000 SNP GWAS) and has the power to detect regions containing multiple alleles that individually contribute too little variance to be detectable by GWAS as well as regions with single common GWAS-detectable SNPs. We use genome-wide SNP array data to obtain both a genome-wide relationship matrix and regional relationship ("identity by state" or IBS) matrices for sequential regions across the genome. We then estimate a heritability for each region sequentially in our genome-wide scan. We demonstrate by simulation and with real data that, when compared to traditional ("individual SNP") GWAS, our method uncovers new loci that explain additional trait variation. We analysed data from three Southern European populations and from Orkney for exemplar traits - serum uric acid concentration and height. We show that regional heritability estimates are correlated with results from genome-wide association analysis but can capture more of the genetic variance segregating in the population and identify additional trait loci.

Indexed as

Chromosome MappingGenome, HumanGenetic LinkageGenome-Wide Association StudyHumansPolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID23077511
PMCPMC3471913

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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.