For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
55
views
64
references
 Top references
cited by
19
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      32
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      Isolated populations and complex disease gene identification

      editorial
      Author(s): 1 , 2 , 1 , 3 , 1 , 2 , 3 ,
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Isolated populations can be useful for the identification of genes underlying common complex diseases.

          Abstract

          The utility of genetically isolated populations (population isolates) in the mapping and identification of genes is not only limited to the study of rare diseases; isolated populations also provide a useful resource for studies aimed at improved understanding of the biology underlying common diseases and their component traits. Well characterized human populations provide excellent study samples for many different genetic investigations, ranging from genome-wide association studies to the characterization of interactions between genes and the environment.

          Related collections

          Most cited references64

          • Record: found
          • Abstract: found
          • Article: not found

          Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.

          Kirk Lohmueller, Celeste Pearce, Malcolm C. Pike … (2003)
          Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.
          Article 0 comments Cited 310 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Multiple rare alleles contribute to low plasma levels of HDL cholesterol.

            Jonathan Cohen, Robert Kiss, Alexander Pertsemlidis … (2004)
            Heritable variation in complex traits is generally considered to be conferred by common DNA sequence polymorphisms. We tested whether rare DNA sequence variants collectively contribute to variation in plasma levels of high density lipoprotein cholesterol (HDL-C). We sequenced three candidate genes (ABCA1, APOA1, and LCAT) that cause Mendelian forms of low HDL-C levels in individuals from a population-based study. Nonsynonymous sequence variants were significantly more common (16% versus 2%) in individuals with low HDL-C ( 95th percentile). Similar findings were obtained in an independent population, and biochemical studies indicated that most sequence variants in the low HDL-C group were functionally important. Thus, rare alleles with major phenotypic effects contribute significantly to low plasma HDL-C levels in the general population.
            Article 0 comments Cited 257 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Many sequence variants affecting diversity of adult human height.

              Daniel Gudbjartsson, G Walters, Gudmar Thorleifsson … (2008)
              Adult human height is one of the classical complex human traits. We searched for sequence variants that affect height by scanning the genomes of 25,174 Icelanders, 2,876 Dutch, 1,770 European Americans and 1,148 African Americans. We then combined these results with previously published results from the Diabetes Genetics Initiative on 3,024 Scandinavians and tested a selected subset of SNPs in 5,517 Danes. We identified 27 regions of the genome with one or more sequence variants showing significant association with height. The estimated effects per allele of these variants ranged between 0.3 and 0.6 cm and, taken together, they explain around 3.7% of the population variation in height. The genes neighboring the identified loci cluster in biological processes related to skeletal development and mitosis. Association to three previously reported loci are replicated in our analyses, and the strongest association was with SNPs in the ZBTB38 gene.
              Article 0 comments Cited 229 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2008
                Publication date (Electronic): 26 August 2008
                Volume: 9
                Issue: 8
                Page: 109
                Affiliations
                [1 ]National Public Health Institute and FIMM, Institute for Molecular Medicine Finland, Helsinki 00300, Finland
                [2 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
                [3 ]Department of Medical Genetics, University of Helsinki, Helsinki 00014, Finland
                Article
                Publisher ID: gb-2008-9-8-109
                DOI: 10.1186/gb-2008-9-8-109
                PMC ID: 2575505
                PubMed ID: 18771588
                SO-VID: d0a6556e-32cb-4bab-aea3-0026670c5ead
                Copyright © Copyright © 2008 BioMed Central Ltd
                History
                Categories
                Subject: Opinion

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

                Comments

                Comment on this article

                scite_

                Similar content32

                See all similar

                Cited by47

                See all cited by

                Most referenced authors3,556

                See all reference authors