Genetics Analysis Workshop 16 Problem 2: the Framingham Heart Study data

Cupples, L. Adrienne; Heard‐Costa, Nancy L.; Lee, Monica; Atwood, Larry D.

doi:10.1186/1753-6561-3-s7-s3

Cited by 58 publications

(57 citation statements)

References 6 publications

(6 reference statements)

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“…Data were drawn from the Framingham Heart Study original (FHS) and offspring (FHSO) cohorts (Cupples, Heard‐Costa, Lee & Atwood, 2009), the Cardiovascular Health Study (CHS) (Fried et al., 1991), the Health and Retirement Study (HRS) (Juster & Suzman, 1995), and the NIA Late‐Onset Alzheimer Disease Family Study (LOADFS) (Lee, Cheng, Graff‐Radford, Foroud & Mayeux, 2008) for individuals who identified themselves as of Caucasian ancestry. The LOADFS was designed to ascertain dementias of Alzheimer type in the elderly and recruited cases and controls.…”

Section: Methodsmentioning

confidence: 99%

Apolipoprotein E region molecular signatures of Alzheimer's disease

et al. 2018

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SummaryAlthough the APOE region is the strongest genetic risk factor for Alzheimer's diseases (ADs), its pathogenic role remains poorly understood. Elucidating genetic predisposition to ADs, a subset of age‐related diseases characteristic for postreproductive period, is hampered by the undefined role of evolution in establishing molecular mechanisms of such diseases. This uncertainty is inevitable source of natural‐selection–free genetic heterogeneity in predisposition to ADs. We performed first large‐scale analysis of linkage disequilibrium (LD) structures characterized by 30 polymorphisms from five genes in the APOE 19q13.3 region (BCAM,NECTIN2,TOMM40,APOE, and APOC1) in 2,673 AD‐affected and 16,246 unaffected individuals from five cohorts. Consistent with the undefined role of evolution in age‐related diseases, we found that these structures, being highly heterogeneous, are significantly different in subjects with and without ADs. The pattern of the difference represents molecular signature of AD comprised of single nucleotide polymorphisms (SNPs) from all five genes in the APOE region. Significant differences in LD in subjects with and without ADs indicate SNPs from different genes likely involved in AD pathogenesis. Significant and highly heterogeneous molecular signatures of ADs provide unprecedented insight into complex polygenetic predisposition to ADs in the APOE region. These findings are more consistent with a complex haplotype than with a single genetic variant origin of ADs in this region.

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Section: Methodsmentioning

confidence: 99%

Apolipoprotein E region molecular signatures of Alzheimer's disease

et al. 2018

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“…Unlike other simulation approaches that evolve population chromosomes (Peng and Amos 2010;Yuan et al 2011), our approach uses directly an initial set of real haplotypes from a population with significant structure and/or LD. For this purpose we used data on 1917 male X chromosomes from the Framingham Heart Study (FHS) data (Cupples et al 2009), chosen so that there were no reported relationships among them. Availability of this good-sized naturally phased chromosome eliminated the need for statistical phasing.…”

Section: Data Simulationmentioning

confidence: 99%

Inferring Coancestry in Population Samples in the Presence of Linkage Disequilibrium

et al. 2012

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In both pedigree linkage studies and in population-based association studies there has been much interest in the use of modern dense genetic marker data to infer segments of gene identity by descent (ibd) among individuals not known to be related, to increase power and resolution in localizing genes affecting complex traits. In this article, we present a hidden Markov model (HMM) for ibd among a set of chromosomes and describe methods and software for inference of ibd among the four chromosomes of pairs of individuals, using either phased (haplotypic) or unphased (genotypic) data. The model allows for missing data and typing error, but does not model linkage disequilibrium (LD), because fitting an accurate LD model requires large samples from well-studied populations. However, LD remains a major confounding factor, since LD is itself a reflection of coancestry at the population level. To study the impact of LD, we have developed a novel simulation approach to generate realistic dense marker data for the same set of markers but at varying levels of LD. Using this approach, we present results of a study of the impact of LD on the sensitivity and specificity of our HMM model in estimating segments of ibd among sets of four chromosomes and between genotype pairs. We show that, despite not incorporating LD, our model has been quite successful in detecting segments as small as 10 6 bp (1 Mpb); we present also comparisons with fastIBD which uses an LD model in estimating ibd. EVEN in large populations, sampled individuals may share genome inherited from a common ancestor on the order of tens of generations ago, at a time depth of up to 2000 years, and the probabilities are increased in samples from small populations, structured populations, or in individuals ascertained for particular traits. Generally, such segments of genome that are shared identically by descent (ibd) in remote relatives are rare but not short (Donnelly 1983). For example, the probability that a pair of human individuals separated by 20 meioses share any of their autosomal genome is 0.001, but, if it exists, an ibd segment is of order 5 · 10 6 bp (5 Mbp). For closer relatives, separated by 12 meioses, the chance of sharing some segment of autosomal genome is 0.148, while the expected length of the segment is of the same order of magnitude (8 Mbp).Modern dense SNP marker data provide information to detect such ibd segments in unknown relatives and thus increase information for genetic linkage mapping (Leutenegger et al. 2003;Albrechtsen et al. 2009). Indeed, the first such method developed by Leutenegger et al. (2003) to detect segments of homozygosity by descent in individuals affected by Taybi-Linder syndrome has recently led to gene discovery (Edery et al. 2011). In population samples also, detection of unknown remote segments of ibd enables association tests either to use (Browning and Browning 2010) or to adjust for (Choi et al. 2009) this coancestry in association methods of gene mapping. Combining inheritance information within the pe...

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“…We analysed independent individuals from the KORA S3/F3 and KORA S4/F4 cohorts 30,36 (from the KORA study, http://www.helmholtz-muenchen.de/en/ kora), and from the Framingham Heart Study Offspring cohort (FHS-Off) 37 (Genetic Analysis Workshop 16 Framingham data, accession number phs000128.v1.p1 from the Database of Genotypes and Phenotypes (dbGaP), http://www.ncbi.nlm.nih.gov/gap). Data were retrieved and analysed in compliance with the Declaration of Helsinki.…”

Section: Materials and Methods Samplesmentioning

confidence: 99%

Controversial association results for INSIG2 on body mass index may be explained by interactions with age and with MC4R

et al. 2014

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Among the single-nucleotide polymorphisms (SNPs) previously reported to be associated with body mass index (BMI) and obesity, we focus on a common risk variant rs7566605 upstream of the insulin-induced gene 2 (INSIG2) gene and a rare protective variant rs2229616 on the melanocortin-4 receptor (MC4R) gene. INSIG2 is involved in adipogenesis and MC4R effects hormonal appetite control in response to the amount of adipose tissue. The influence of rs2229616 (MC4R) on BMI and obesity has been confirmed repeatedly and insight into the underlying mechanism provided. However, a main effect of rs7566605 (INSIG2) is under debate because of inconsistent replications of association. Interaction of rs7566605 with age may offer an explanation. SNP-age and SNP-SNP interaction models were tested on independent individuals from three population-based longitudinal cohorts, restricting the analysis to an observed age of 25-74 years. KORA S3/F3, KORA S4/F4 (Augsburg, Germany, 1994, 1999, and Framingham-Offspring data (Framingham, USA, 1971-2001 were analysed, with a total sample size of N ¼ 6926 in the joint analysis. The effect of interaction between rs7566605 and age on BMI and obesity status is significant and consistent across studies. This new evidence for rs7566605 (INSIG2) complements previous research. In addition, the interaction effect of rs7566605 with the MC4R variant rs2229616 on BMI was observed. This effect size was three times larger than that in a previously reported single-locus main effect of rs2229616. This leads to the conclusion that SNP-age or SNP-SNP interactions can mask genetic effects for complex diseases if left unaccounted for.

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Genetics Analysis Workshop 16 Problem 2: the Framingham Heart Study data

Cited by 58 publications

References 6 publications

Apolipoprotein E region molecular signatures of Alzheimer's disease

Apolipoprotein E region molecular signatures of Alzheimer's disease

Inferring Coancestry in Population Samples in the Presence of Linkage Disequilibrium

Controversial association results for INSIG2 on body mass index may be explained by interactions with age and with MC4R

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