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BMC Proceedings

, 3:S114

First Online: 15 December 2009DOI: 10.1186-1753-6561-3-S7-S114

Cite this article as: Kerner, B. & Muthén, B.O. BMC Proc 2009 3Suppl 7: S114. doi:10.1186-1753-6561-3-S7-S114

Abstract

Growth mixture modelling, a less explored method in genetic research, addresses unobserved heterogeneity in population samples. We applied this technique to longitudinal data of the Framingham Heart Study. We examined systolic blood pressure BP measures in 1060 males from 692 families and detected three subclasses, which varied significantly in their developmental trajectories over time. The first class consisted of 60 high-risk individuals with elevated BP early in life and a steep increase over time. The second group of 131 individuals displayed first normal BP, but showed a significant increase over time and reached high BP values late in their life time. The largest group of 869 individuals could be considered a normative group with normal BP on all exams. To identify genetic modulators for this phenotype, we tested 2,340 single-nucleotide polymorphisms on chromosome 8 for association with the class membership probabilities of our model. The probability of being in Class 1 was significantly associated with a very rare variant rs1445404 present in only four individuals from four different families located in the coding region of the gene EYA eyes absent homolog 1 in Drosophila p = 1.39 × 10. Mutations in EYA are known to cause brachio-oto-renal syndrome, as well as isolated renal malformations. Renal malformations could cause high BP early in life. This result awaits replication; however, it suggests that analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multi-factorial diseases.

List of abbreviations usedBICBayesian information criterion

BMIBody mass index

EMEstimation maximization

GMMGrowth mixture model

HTNRXTreatment for hypertension

QTQuantitative trait

MARMissing at random

SBPSystolic blood pressure

SNPSingle-nucleotide polymorphism.

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Autor: Berit Kerner - Bengt O Muthén

Fuente: https://link.springer.com/







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