Albert V. Smith

A Meta-analysis of Four Genome-Wide Association Studies of Survival to Age 90 Years or Older: The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium

BACKGROUND Genome-wide association studies (GWAS) may yield insights into longevity. METHODS We performed a meta-analysis of GWAS in Caucasians from four prospective cohort studies: the Age, Gene/Environment Susceptibility-Reykjavik Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Longevity was defined as survival to age 90 years or older (n = 1,836); the comparison group comprised cohort members who died between the ages of 55 and 80 years (n = 1,955). In a second discovery stage, additional genotyping was conducted in the Leiden Longevity Study cohort and the Danish 1905 cohort. RESULTS There were 273 single-nucleotide polymorphism (SNP) associations with p < .0001, but none reached the prespecified significance level of 5 x 10(-8). Of the most significant SNPs, 24 were independent signals, and 16 of these SNPs were successfully genotyped in the second discovery stage, with one association for rs9664222, reaching 6.77 x 10(-7) for the combined meta-analysis of CHARGE and the stage 2 cohorts. The SNP lies in a region near MINPP1 (chromosome 10), a well-conserved gene involved in regulation of cellular proliferation. The minor allele was associated with lower odds of survival past age 90 (odds ratio = 0.82). Associations of interest in a homologue of the longevity assurance gene (LASS3) and PAPPA2 were not strengthened in the second stage. CONCLUSION Survival studies of larger size or more extreme or specific phenotypes may support or refine these initial findings.

HapMap methylation-associated SNPs, markers of germline DNA methylation, positively correlate with regional levels of human meiotic recombination

Inter-individual and regional variability in recombination rates cannot be fully explained by the DNA sequence itself. Epigenetic mechanisms might be one additional factor affecting recombination. A biochemical approach to studying human germline methylation is difficult. We used the density of the 434,198 nonredundant methylation-associated SNPs (mSNPs) in the derived allele HapMap data set as a surrogate marker for germline DNA methylation. We validated our methodology by demonstrating that the mSNP density confirmed known patterns of genomic methylation, including the hypermutability of methylated cytosine and hypomethylation of CpG islands. Using this approach, we found a genome-wide positive correlation between germline methylation and regional recombination rate (500-kb windows: r = 0.622, P < 10(-15)). This remained significant with multiple correlations correcting for sequence features known to affect recombination, such as GC content and CpG dinucleotides (500-kb windows: r = 0.172, P < 10(-15)). Using the ENCODE data set for increased resolution, we found a positive correlation between germline DNA methylation and recombination rate (50-kb windows: r = 0.301, P = 0.002). This correlation was further strengthened when corrected for sequence features affecting recombination (50-kb windows: r = 0.445, P < 0.0001). In the Human Epigenome Project data set there was increased DNA methylation in regions within recombination hot spots in male germ cells (0.632 vs. 0.557, P = 0.007). The relationship we observed between germline DNA methylation and recombination could be explained in two ways that are not mutually exclusive: DNA methylation could indicate preferred sites for recombination, or methylation following recombination could inhibit further recombination, perhaps by being part of the enigmatic molecular pathway mediating crossover interference.

Association of Adiposity Genetic Variants With Menarche Timing in 92,105 Women of European Descent

Obesity is of global health concern. There are well-described inverse relationships between female pubertal timing and obesity. Recent genome-wide association studies of age at menarche identified several obesity-related variants. Using data from the ReproGen Consortium, we employed meta-analytical techniques to estimate the associations of 95 a priori and recently identified obesity-related (body mass index (weight (kg)/height (m)(2)), waist circumference, and waist:hip ratio) single-nucleotide polymorphisms (SNPs) with age at menarche in 92,116 women of European descent from 38 studies (1970-2010), in order to estimate associations between genetic variants associated with central or overall adiposity and pubertal timing in girls. Investigators in each study performed a separate analysis of associations between the selected SNPs and age at menarche (ages 9-17 years) using linear regression models and adjusting for birth year, site (as appropriate), and population stratification. Heterogeneity of effect-measure estimates was investigated using meta-regression. Six novel associations of body mass index loci with age at menarche were identified, and 11 adiposity loci previously reported to be associated with age at menarche were confirmed, but none of the central adiposity variants individually showed significant associations. These findings suggest complex genetic relationships between menarche and overall obesity, and to a lesser extent central obesity, in normal processes of growth and development.

The phenotype and genotype experiment object model (PaGE-OM): a robust data structure for information related to DNA variation

Torrents of genotype–phenotype data are being generated, all of which must be captured, processed, integrated, and exploited. To do this optimally requires the use of standard and interoperable “object models,” providing a description of how to partition the total spectrum of information being dealt with into elemental “objects” (such as “alleles,” “genotypes,” “phenotype values,” “methods”) with precisely stated logical interrelationships (such as “A objects are made up from one or more B objects”). We herein propose the Phenotype and Genotype Experiment Object Model (PaGE‐OM; www.pageom.org), which has been tested and implemented in conjunction with several major databases, and approved as a standard by the Object Management Group (OMG). PaGE‐OM is open‐source, ready for use by the wider community, and can be further developed as needs arise. It will help to improve information management, assist data integration, and simplify the task of informatics resource design and construction for genotype and phenotype data projects.Hum Mutat 30, 968–977, 2009.

Discussion on the meeting on 'Statistical modelling and analysis of genetic data'

David J. Balding .Imperial College School of Medicine, London/ I extendmy apologies to the authors that an unavoidable commitment arising unexpectedly in the 18 hours before the meeting robbed me of my final preparation, so that my comments at the meeting were poorly presented. I shall try to do a better job in this written version, and to leave enough space I shall omit my introductory comments about the role of statisticians in bioinformatics.

Abstract B90: Variation in circadian rhythm genes and risk of prostate cancer in the AGES-Reykjavik cohort

Background: Disruption of sleep duration or circadian rhythms has been associated with an increased risk of prostate cancer. Studies have suggested that circadian genes may be involved in regulating cancer-related pathways, such as cell proliferation, DNA damage response and apoptosis. As such, we hypothesized that genetic susceptibility to prostate cancer may be in part due to common variation in the core circadian rhythm genes. We assessed the association of genetic variation in core circadian rhythm genes with risk of prostate cancer and with urinary levels of melatonin among men in the prospective Age, Gene/Environment Susceptibility (AGES)-Reykjavik study. Methods: The study population was 1,352 men in the AGES-Reykjavik cohort with data from a previous genome wide association study. We examined 96 single-nucleotide polymorphisms (SNPs) across twelve genes chosen to represent common variation in genes that control the circadian rhythm (CLOCK, neuronal PAS domain protein 2 (NPAS2), aryl hydrocarbon receptor nuclear translocator-like (ARNTL), cryptochrome 1(CRY1), cryptochrome 2 (CRY2), period 1 (PER1), period 2 (PER2), period 3 (PER3), casein kinase 1-epsilon (CSNK1E)), two melatonin receptor genes (MTNR1A and MTNR1B), and the Timeless (TIM) gene. Melatonin levels were determined from first morning void urine samples using an ELISA assay. Prostate cancer (N = 138) was identified from hospital records using ICD codes; death from prostate cancer (N = 24) was determined from nationwide death records. We used unconditional logistic regression to evaluate the association between each SNP and overall and lethal prostate cancer. Linear regression was used to assess the association between each SNP and log-transformed melatonin levels. Finally, to test whether multimarker SNP-sets in the pathway were associated with prostate cancer or melatonin levels, we used a kernel machine test. Results: None of the 96 SNPs evaluated were significantly associated with prostate cancer overall. The variant allele in two SNPs in CRY1 (rs7297614 and rs1921126) and one in PER1 (rs2289591) were nominally (p-value <0.05 without adjusting for multiple testing) associated with an elevated risk of lethal prostate cancer, while SNPs in NPAS2 (rs3754674), ARNTL (rs969485) and PER2 (rs10462023) were nominally associated with a reduced risk of lethal disease. Among men without cancer, 4 SNPs in TIM and 6 in NPAS2 were nominally associated with lower levels of urinary melatonin; 2 SNPs in PER3 and 1 in CSNK1E were associated with higher levels of urinary melatonin. Pathway analyses showed that variability across the individual genes CRY1 and PER2 were nominally associated with lethal prostate cancer (p-value 0.01 and 0.05, respectively). For melatonin levels, the set of SNPs across all 12 genes and TIM was nominally associated with urinary melatonin levels (p-value 0.05 and 0.02, respectively). Conclusion: Variation in circadian genes, such as PER1, CRY1, and CRY2, has been previously associated with increased prostate cancer risk; variation in NPAS2 has been associated with reduced prostate cancer risk. Similar to previously reported literature, we observed several suggestive associations between variation in genes that regulate the circadian rhythm and lethal prostate cancer risk as well as urinary melatonin levels. Our findings provide further support for a role of circadian disruption in prostate cancer progression. Citation Format: Sarah Coseo Markt, Unnur A. Valdimarsdottir, Lara G. Sigurdardottir, Irene M. Shui, Jennifer R. Rider, Julie L. Kasperzyk, Steven W. Lockley, Charles A. Czeisler, Meir J. Stampfer, Thor Aspelund, Albert Vernon Smith, Vilmundur Gudnason, Lorelei A. Mucci. Variation in circadian rhythm genes and risk of prostate cancer in the AGES-Reykjavik cohort. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr B90.