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1.  Understanding the Elusive Mechanism of Action of TCF7L2 in Metabolism 
Diabetes  2012;61(11):2657-2658.
doi:10.2337/db12-0891
PMCID: PMC3478546  PMID: 23093653
2.  GWAS of blood cell traits identifies novel associated loci and epistatic interactions in Caucasian and African-American children 
Human Molecular Genetics  2012;22(7):1457-1464.
Hematological traits are important clinical indicators, the genetic determinants of which have not been fully investigated. Common measures of hematological traits include red blood cell (RBC) count, hemoglobin concentration (HGB), hematocrit (HCT), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC), mean corpuscular volume (MCV), platelet count (PLT) and white blood cell (WBC) count. We carried out a genome-wide association study of the eight common hematological traits among 7943 African-American children and 6234 Caucasian children. In African Americans, we report five novel associations of HBE1 variants with HCT and MCHC, the alpha-globin gene cluster variants with RBC and MCHC, and a variant at the ARHGEF3 locus with PLT, as well as replication of four previously reported loci at genome-wide significance. In Caucasians, we report a novel association of variants at the COPZ1 locus with PLT as well as replication of four previously reported loci at genome-wide significance. Extended analysis of an association observed between MCH and the alpha-globin gene cluster variants demonstrated independent effects and epistatic interaction at the locus, impacting the risk of iron deficiency anemia in African Americans with specific genotype states. In summary, we extend the understanding of genetic variants underlying hematological traits based on analyses in African-American children.
doi:10.1093/hmg/dds534
PMCID: PMC3657475  PMID: 23263863
3.  Physical Activity And Physical Fitness 
The focus of the PhenX (Phenotypes and eXposures) Toolkit is to provide researchers whose expertise lies outside a particular area with key measures identified by experts for uniform use in large-scale genetic studies and other extensive epidemiologic efforts going forward. The current paper specifically addresses the PhenX Toolkit research domain of physical activity and physical fitness (PA/PF), which are often associated with health outcomes. A Working Group (WG) of content experts completed a 6-month consensus process in which they identified a set of 14 high-priority, low-burden, and scientifically supported measures. During this process the WG considered self-reported and objective measures which included the latest technology (e.g., accelerometers, pedometers, heart-rate monitors). They also sought the input of measurement experts and other members of the research community during their deliberations. A majority of the measures include protocols for children (or adolescents), adults, and older adults or are applicable to all ages.
Measures from the PA/PF domain and 20 other domains are publicly available and found at the PhenX Toolkit website, www.phenxtoolkit.org. The use of common measures and protocols across large studies enhances the capacity to combine or compare data across studies, benefitting both PA/PF experts and non-experts. Use of these common measures by the research community should increase statistical power and enhance the ability to answer scientific questions that might have previously gone unanswered.
doi:10.1016/j.amepre.2011.11.017
PMCID: PMC3331998  PMID: 22516489
4.  Developmental Origins of Genotype-Phenotype Correlations in Chronic Diseases of Old Age 
Aging and Disease  2012;3(5):385-403.
In recent years, genome wide association studies have revolutionized the understanding of the genetic architecture of complex disease, particularly in the context of disorders that present in old age, such as type 2 diabetes and cardiovascular disease. This new era is made all the more compelling by the fact that, through extensive validation efforts, there is now very strong consensus among human geneticists on what the key loci are that contribute to the pathogenesis of these traits. However, as these variants have been almost exclusively uncovered in an adult setting, there is the question of when these genetic variants start exerting their effects; indeed many may start setting up an individual’s predisposition to a disease of old age very early on in life. To this end, we review what breakthroughs have been made in elucidating which of these genetic factors are operating in childhood and conversely what discoveries have actually been made in the pediatric setting that have then been found subsequently to increase one’s risk of a late-onset disease. After all, it well known that complex traits like obesity, type 2 diabetes and inflammatory bowel disease are strongly determined by genetic factors, but the isolation of genes in these complex phenotypes in adults has been impeded by interaction with strong environmental factors. Distillation of the genetic component in these complex traits, which will at least partially have origins in childhood, should be easier to determine in a pediatric setting, where the relatively short period of a child’s lifetime limits the impact of environmental exposure.
PMCID: PMC3501394  PMID: 23185719
Disease; late-onset; childhood; genetic; association
5.  Correction: A Genome-Wide Association Study on Obesity and Obesity-Related Traits 
PLoS ONE  2012;7(2):10.1371/annotation/a34ee94e-3e6a-48bd-a19e-398a4bb88580.
doi:10.1371/annotation/a34ee94e-3e6a-48bd-a19e-398a4bb88580
PMCID: PMC3293772
6.  Common variants at 6q22 and 17q21 are associated with intracranial volume 
Nature genetics  2012;44(5):539-544.
During aging, intracranial volume remains unchanged and represents maximally attained brain size, while various interacting biological phenomena lead to brain volume loss. Consequently, intracranial volume and brain volume in late life reflect different genetic influences. Our genome-wide association study in 8,175 community-dwelling elderly did not reveal any genome-wide significant associations (p<5*10−8) for brain volume. In contrast, intracranial volume was significantly associated with two loci: rs4273712 (p=3.4*10−11), a known height locus on chromosome 6q22, and rs9915547, tagging the inversion on chromosome 17q21 (p=1.5*10−12). We replicated the associations of these loci with intracranial volume in a separate sample of 1,752 older persons (p=1.1*10−3 for 6q22 and p=1.2*10−3 for 17q21). Furthermore, we also found suggestive associations of the 17q21 locus with head circumference in 10,768 children (mean age 14.5 months). Our data identify two loci associated with head size, with the inversion on 17q21 also likely involved in attaining maximal brain size.
doi:10.1038/ng.2245
PMCID: PMC3618290  PMID: 22504418

Results 1-6 (6)