Browsing by Subject "Endophenotypes"

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Open Access
Gene by stress genome-wide interaction analysis and path analysis identify EBF1 as a cardiovascular and metabolic risk gene.
(European journal of human genetics : EJHG, 2015-06) Singh, Abanish; Babyak, Michael A; Nolan, Daniel K; Brummett, Beverly H; Jiang, Rong; Siegler, Ilene C; Kraus, William E; Shah, Svati H; Williams, Redford B; Hauser, Elizabeth R
We performed gene-environment interaction genome-wide association analysis (G × E GWAS) to identify SNPs whose effects on metabolic traits are modified by chronic psychosocial stress in the Multi-Ethnic Study of Atherosclerosis (MESA). In Whites, the G × E GWAS for hip circumference identified five SNPs within the Early B-cell Factor 1 (EBF1) gene, all of which were in strong linkage disequilibrium. The gene-by-stress interaction (SNP × STRESS) term P-values were genome-wide significant (Ps = 7.14E-09 to 2.33E-08, uncorrected; Ps = 1.99E-07 to 5.18E-07, corrected for genomic control). The SNP-only (without interaction) model P-values (Ps = 0.011-0.022) were not significant at the conventional genome-wide significance level. Further analysis of related phenotypes identified gene-by-stress interaction effects for waist circumference, body mass index (BMI), fasting glucose, type II diabetes status, and common carotid intimal-medial thickness (CCIMT), supporting a proposed model of gene-by-stress interaction that connects cardiovascular disease (CVD) risk factor endophenotypes such as central obesity and increased blood glucose or diabetes to CVD itself. Structural equation path analysis suggested that the path from chronic psychosocial stress to CCIMT via hip circumference and fasting glucose was larger (estimate = 0.26, P = 0.033, 95% CI = 0.02-0.49) in the EBF1 rs4704963 CT/CC genotypes group than the same path in the TT group (estimate = 0.004, P = 0.34, 95% CI = -0.004-0.012). We replicated the association of the EBF1 SNPs and hip circumference in the Framingham Offspring Cohort (gene-by-stress term P-values = 0.007-0.012) as well as identified similar path relationships. This observed and replicated interaction between psychosocial stress and variation in the EBF1 gene may provide a biological hypothesis for the complex relationship between psychosocial stress, central obesity, diabetes, and cardiovascular disease.
Open Access
General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks.
(NeuroImage, 2019-04) Elliott, Maxwell L; Knodt, Annchen R; Cooke, Megan; Kim, M Justin; Melzer, Tracy R; Keenan, Ross; Ireland, David; Ramrakha, Sandhya; Poulton, Richie; Caspi, Avshalom; Moffitt, Terrie E; Hariri, Ahmad R
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.
Open Access
Heritability estimates of endophenotypes of long and health life: the Long Life Family Study.
(J Gerontol A Biol Sci Med Sci, 2010-12) Matteini, Amy M; Fallin, M Daniele; Kammerer, Candace M; Schupf, Nicole; Yashin, Anatoli I; Christensen, Kaare; Arbeev, Konstantin G; Barr, Graham; Mayeux, Richard; Newman, Anne B; Walston, Jeremy D
BACKGROUND: Identification of gene variants that contribute to exceptional survival may provide critical biologic information that informs optimal health across the life span. METHODS: As part of phenotype development efforts for the Long Life Family Study, endophenotypes that represent exceptional survival were identified and heritability estimates were calculated. Principal components (PCs) analysis was carried out using 28 physiologic measurements from five trait domains (cardiovascular, cognition, physical function, pulmonary, and metabolic). RESULTS: The five most dominant PCs accounted for 50% of underlying trait variance. The first PC (PC1), which consisted primarily of poor pulmonary and physical function, represented 14.3% of the total variance and had an estimated heritability of 39%. PC2 consisted of measures of good metabolic and cardiovascular function with an estimated heritability of 27%. PC3 was made up of cognitive measures (h(2) = 36%). PC4 and PC5 contained measures of blood pressure and cholesterol, respectively (h(2) = 25% and 16%). CONCLUSIONS: These PCs analysis-derived endophenotypes may be used in genetic association studies to help identify underlying genetic mechanisms that drive exceptional survival in this and other populations.