Saguenay Youth Study : a multi-generational approach to studying virtual trajectories of the brain and cardio-metabolic health

This paper provides an overview of the Saguenay Youth Study (SYS) and its parental arm. The overarching goal of this effort is to develop trans-generational models of developmental cascades contributing to the emergence of common chronic disorders, such as depression, addictions, dementia and cardio-metabolic diseases. Over the past 10 years, we have acquired detailed brain and cardio-metabolic phenotypes, and genome-wide genotypes, in 1029 adolescents recruited in a population with a known genetic founder effect. At present, we are extending this dataset to acquire comparable phenotypes and genotypes in the biological parents of these individuals. After providing conceptual background for this work (transactions across time, systems and organs), we describe briefly the tools employed in the adolescent arm of this cohort and highlight some of the initial accomplishments. We then outline in detail the phenotyping protocol used to acquire comparable data in the parents

Prenatal exposure to maternal cigarette smoking, amygdala volume, and fat intake in adolescence

Context : Prenatal exposure to maternal cigarette smoking is a well-established risk factor for obesity, but the underlying mechanisms are not known. Preference for fatty foods, regulated in part by the brain reward system, may contribute to the development of obesity. Objective : To examine whether prenatal exposure to maternal cigarette smoking is associated with enhanced fat intake and risk for obesity, and whether these associations may be related to subtle structural variations in brain regions involved in reward processing. Design : Cross-sectional study of a population-based cohort. Setting : The Saguenay Youth Study, Quebec, Canada. Participants : A total of 378 adolescents (aged 13 to 19 years; Tanner stage 4 and 5 of sexual maturation), half of whom were exposed prenatally to maternal cigarette smoking (mean [SD], 11.1 [6.8] cigarettes/d). Main Outcome Measures : Fat intake was assessed with a 24-hour food recall (percentage of energy intake consumed as fat). Body adiposity was measured with anthropometry and multifrequency bioimpedance. Volumes of key brain structures involved in reward processing, namely the amygdala, nucleus accumbens, and orbitofrontal cortex, were measured with magnetic resonance imaging. Results : Exposed vs nonexposed subjects exhibited a higher total body fat (by approximately 1.7 kg; P = .009) and fat intake (by 2.7%; P = .001). They also exhibited a lower volume of the amygdala (by 95 mm3; P < .001) but not of the other 2 brain structures. Consistent with its possible role in limiting fat intake, amygdala volume correlated inversely with fat intake (r = −0.15; P = .006). Conclusions : Prenatal exposure to maternal cigarette smoking may promote obesity by enhancing dietary preference for fat, and this effect may be mediated in part through subtle structural variations in the amygdala

Orbitofrontal cortex and drug use during adolescence : role of prenatal exposure to maternal smoking and BDNF genotype

Context : Prenatal exposure to maternal cigarette smoking (PEMCS) may affect brain development and behavior in adolescent offspring. Objective : To evaluate the involvement of the orbitofrontal cortex (OFC) in mediating the relationship between PEMCS and substance use. Design : Cross-sectional analyses from the Saguenay Youth Study aimed at evaluating the effects of PEMCS on brain development and behavior among adolescents. Nonexposed adolescents were matched with adolescents exposed prenatally to cigarette smoking by maternal educational level. Participants and Setting : A French Canadian founder population of the Saguenay–Lac-Saint-Jean region of Quebec, Canada.The behavioral data set included 597 adolescents (275 sibships; 12-18 years of age), half of whom were exposed in utero to maternal cigarette smoking. Analysis of cortical thickness and genotyping were performed using available data from 314 adolescents. Main Outcome Measures : The likelihood of substance use was assessed with the Diagnostic Interview Schedule for Children Predictive Scales. The number of different drugs tried by each adolescent was assessed using another questionnaire. Thickness of the OFC was estimated from T1-weighted magnetic resonance images using FreeSurfer software. Results : Prenatal exposure to maternal cigarette smoking is associated with an increased likelihood of substance use. Among exposed adolescents, the likelihood of drug experimentation correlates with the degree of OFC thinning. In nonexposed adolescents, the thickness of the OFC increases as a function of the number of drugs tried. The latter effect is moderated by a brain-derived neurotrophic factor (BDNF) genotype (Val66Met). Conclusions : We speculate that PEMCS interferes with the development of the OFC and, in turn, increases the likelihood of drug use among adolescents. In contrast, we suggest that, among nonexposed adolescents, drug experimentation influences the OFC thickness via processes akin to experience-induced plasticity

Communications Biophysics

Contains reports on four research projects.National Institutes of Health (Grant 5 P01 NS13126-02)National Institutes of Health (Grant 5 K04 NS00113-03)National Institutes of Health (Grant 2 ROI NS11153-02A1)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 RO1 NS10916-03)National Institutes of Health (Fellowship 1 F32 NS05327)National Institutes of Health (Grant 5 ROI NS12846-02)National Institutes of Health (Fellowship 1 F32 NS05266)Edith E. Sturgis FoundationNational Institutes of Health (Grant 1 R01 NS11680-01)National Institutes of Health (Grant 2 RO1 NS11080-04)National Institutes of Health (Grant 5 T32 GIM107301-03)National Institutes of Health (Grant 5 TOI GM01555-10

Communications Biophysics

Contains reports on nine research projects split into four sections.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 5 KO4 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Institutes of Health (Training Grant 1 T32 NS07099)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 ROI NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 RO1 NS14092)Edith E. Sturgis FoundationHealth Sciences FundNational Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Fellowship 5 F32 NS05327)National Institutes of Health (Grant 2 ROI NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

Communications Biophysics

Contains reports on nine research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Institutes of Health (Grant 5 ROl NS11153-03)National Institutes of Health (Fellowship 1 T32 NS07099-01)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 ROl NS10916)National Institutes of Health (Grant 5 ROl NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 RO1 NS14092)Health Sciences FundNational Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 2 RO1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

Communications Biophysics

Contains reports on eight research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Science Foundation (Grant BNS80-06369)National Institutes of Health (Grant 5 ROl NS11153)National Institutes of Health (Fellowship 1 F32 NS06544)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 R01 NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 R01 NS14092)National Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 5 ROl1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

Communications Biophysics

Contains reports on seven research projects split into three sections.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 1 RO1 NS18682)National Institutes of Health (Training Grant 5 T32 NS07047)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 1 F33 NS07202-01)National Institutes of Health (Grant 5 RO1 NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Institutes of Health (Grant 1 RO1 NS16917)National Institutes of Health (Grant 1 RO1 NS14092-05)National Science Foundation (Grant BNS 77 21751)National Institutes of Health (Grant 5 R01 NS11080)National Institutes of Health (Grant GM-21189

Genome modeling system: A knowledge management platform for genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms

Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images

Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI) provide excellent contrast between soft tissues. Given its three-dimensional nature, MRI represents an ideal imaging modality for the analysis of craniofacial structure in living individuals. Here we describe how T1-weighted MR images, acquired to examine brain anatomy, can also be used to analyze facial features. Using a sample of typically developing adolescents from the Saguenay Youth Study (N = 597; 292 male, 305 female, ages: 12 to 18 years), we quantified inter-individual variations in craniofacial structure in two ways. First, we adapted existing nonlinear registration-based morphological techniques to generate iteratively a group-wise population average of craniofacial features. The nonlinear transformations were used to map the craniofacial structure of each individual to the population average. Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features. Second, we employed a landmark-based approach to quantify variations in face surfaces. This approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line, cheekbones, and eyes) on a surface representation of the MRI-based group average; (b) warping the landmarks to the individual faces using the inverse nonlinear transformation estimated for each person; and (3) using a principal components analysis (PCA) of the warped landmarks to identify facial features (i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As with the voxel-wise analysis of the deformation fields, we examined the effects of sex and age on the PCA-derived spatial relationships between facial features. Both methods demonstrated significant sexual dimorphism in craniofacial structure in areas such as the chin, mandible, lips, and nose