Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/88484
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dc.contributor.authorFerreira, M.-
dc.contributor.authorHottenga, J.-
dc.contributor.authorWarrington, N.-
dc.contributor.authorMedland, S.-
dc.contributor.authorWillemsen, G.-
dc.contributor.authorLawrence, R.-
dc.contributor.authorGordon, S.-
dc.contributor.authorde Geus, E.-
dc.contributor.authorHenders, A.-
dc.contributor.authorSmit, J.-
dc.contributor.authorCampbell, M.-
dc.contributor.authorWallace, L.-
dc.contributor.authorEvans, D.-
dc.contributor.authorWright, M.-
dc.contributor.authorNyholt, D.-
dc.contributor.authorJames, A.-
dc.contributor.authorBeilby, J.-
dc.contributor.authorPenninx, B.-
dc.contributor.authorPalmer, L.-
dc.contributor.authorFrazer, I.-
dc.contributor.authoret al.-
dc.date.issued2009-
dc.identifier.citationAmerican Journal of Human Genetics, 2009; 85(5):745-749-
dc.identifier.issn0002-9297-
dc.identifier.issn1537-6605-
dc.identifier.urihttp://hdl.handle.net/2440/88484-
dc.description.abstractBlood cells participate in vital physiological processes, and their numbers are tightly regulated so that homeostasis is maintained. Disruption of key regulatory mechanisms underlies many blood-related Mendelian diseases but also contributes to more common disorders, including atherosclerosis. We searched for quantitative trait loci (QTL) for hematology traits through a whole-genome association study, because these could provide new insights into both hemopoeitic and disease mechanisms. We tested 1.8 million variants for association with 13 hematology traits measured in 6015 individuals from the Australian and Dutch populations. These traits included hemoglobin composition, platelet counts, and red blood cell and white blood cell indices. We identified three regions of strong association that, to our knowledge, have not been previously reported in the literature. The first was located in an intergenic region of chromosome 9q31 near LPAR1, explaining 1.5% of the variation in monocyte counts (best SNP rs7023923, p = 8.9 × 10−14). The second locus was located on chromosome 6p21 and associated with mean cell erythrocyte volume (rs12661667, p = 1.2 × 10−9, 0.7% variance explained) in a region that spanned five genes, including CCND3, a member of the D-cyclin gene family that is involved in hematopoietic stem cell expansion. The third region was also associated with erythrocyte volume and was located in an intergenic region on chromosome 6q24 (rs592423, p = 5.3 × 10−9, 0.6% variance explained). All three loci replicated in an independent panel of 1543 individuals (p values = 0.001, 9.9 × 10−5, and 7 × 10−5, respectively). The identification of these QTL provides new opportunities for furthering our understanding of the mechanisms regulating hemopoietic cell fate.-
dc.description.statementofresponsibilityManuel A.R. Ferreira ... Lyle J. Palmer ... et al.-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2009 by The American Society of Human Genetics. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.ajhg.2009.10.005-
dc.subjectMonocytes-
dc.subjectChromosomes, Human, Pair 6-
dc.subjectChromosomes, Human, Pair 9-
dc.subjectHumans-
dc.subjectLeukocyte Count-
dc.subjectPlatelet Count-
dc.subjectErythrocyte Indices-
dc.subjectCohort Studies-
dc.subjectChromosome Mapping-
dc.subjectGenetics, Population-
dc.subjectAge Factors-
dc.subjectBase Sequence-
dc.subjectGene Frequency-
dc.subjectGenotype-
dc.subjectHaplotypes-
dc.subjectLinkage Disequilibrium-
dc.subjectPhenotype-
dc.subjectPolymorphism, Single Nucleotide-
dc.subjectAlleles-
dc.subjectQuantitative Trait Loci-
dc.subjectGenome, Human-
dc.subjectComputer Simulation-
dc.subjectAustralia-
dc.subjectNetherlands-
dc.subjectFemale-
dc.subjectMale-
dc.subjectGenome-Wide Association Study-
dc.titleSequence variants in three loci influence monocyte counts and erythrocyte volume-
dc.typeJournal article-
dc.identifier.doi10.1016/j.ajhg.2009.10.005-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/241944-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/339462-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389927-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389875-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389891-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389892-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389938-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/443036-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/442915-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/442981-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/496739-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/552485-
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/552498-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0212016-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0343921-
pubs.publication-statusPublished-
dc.identifier.orcidPalmer, L. [0000-0002-1628-3055]-
Appears in Collections:Aurora harvest 7
Translational Health Science publications

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