Objective: Genetic components of energy homeostasis contributing to childhood obesity are poorly understood. Genome scans were performed to identify chromosomal regions contributing to physical activity and dietary intake traits in Hispanic children participating in the VIVA LA FAMILIA Study. Research Methods and Procedures: We report linkage findings on chromosome 18 for physical activity and dietary intake in 1030 siblings from 319 Hispanic families. Measurements entailed physical activity by accelerometry, dietary intake by two 24‐hour recalls, and genetic linkage analyses using SOLAR software. Results: Significant heritabilities were seen for physical activity and dietary intake, ranging from 0.46 to 0.69, except for vigorous activity (h2 = 0.18). Percentage time in sedentary activity mapped to markers D18S1102‐D18S64 on chromosome 18 [logarithm of the odds (LOD) score = 4.07], where melanocortin 4 receptor gene (MC4R) resides. Quantitative trait loci (QTLs) for total activity counts, percentage time in light or in moderate activity, and carbohydrate intake and percentage of energy intake from carbohydrates were detected in the same region (LOD = 2.28, 2.79, 2.2, 1.84, and 1.51, respectively). A novel loss of function mutation in MC4R (G55V) was detected in six obese relatives, but not in the rest of the cohort. Removal of these MC4R‐deficient subjects from the analysis reduced the LOD score for sedentary activity to 3.94. Discussion: Given its role in the regulation of food intake and energy expenditure, MC4R is a strong positional candidate gene for the QTL on chromosome 18 detected for physical activity and dietary intake in Hispanic children.

BackgroundThis investigation offers insights into system-wide pathological processes induced in response to cigarette smoke exposure by determining its influences at the gene expression level.MethodsWe obtained genome-wide quantitative transcriptional profiles from 1,240 individuals from the San Antonio Family Heart Study, including 297 current smokers. Using lymphocyte samples, we identified 20,413 transcripts with significantly detectable expression levels, including both known and predicted genes. Correlation between smoking and gene expression levels was determined using a regression model that allows for residual genetic effects.ResultsWith a conservative false-discovery rate of 5% we identified 323 unique genes (342 transcripts) whose expression levels were significantly correlated with smoking behavior. These genes showed significant over-representation within a range of functional categories that correspond well with known smoking-related pathologies, including immune response, cell death, cancer, natural killer cell signaling and xenobiotic metabolism.ConclusionsOur results indicate that not only individual genes but entire networks of gene interaction are influenced by cigarette smoking. This is the largest in vivo transcriptomic epidemiological study of smoking to date and reveals the significant and comprehensive influence of cigarette smoke, as an environmental variable, on the expression of genes. The central importance of this manuscript is to provide a summary of the relationships between gene expression and smoking in this exceptionally large cross-sectional data set.

Statistical genetic analysis of quantitative traits in large pedigrees is a formidable computational task due to the necessity of taking the non-independence among relatives into account. With the growing awareness that rare sequence variants may be important in human quantitative variation, heritability and association study designs involving large pedigrees will increase in frequency due to the greater chance of observing multiple copies of rare variants amongst related individuals. Therefore, it is important to have statistical genetic test procedures that utilize all available information for extracting evidence regarding genetic association. Optimal testing for marker/phenotype association involves the exact calculation of the likelihood ratio statistic which requires the repeated inversion of potentially large matrices. In a whole genome sequence association context, such computation may be prohibitive. Toward this end, we have developed a rapid and efficient eigensimplification of the likelihood that makes analysis of family data commensurate with the analysis of a comparable sample of unrelated individuals. Our theoretical results which are based on a spectral representation of the likelihood yield simple exact expressions for the expected likelihood ratio test statistic (ELRT) for pedigrees of arbitrary size and complexity. For heritability, the ELRT is: −∑lntrue[1+ĥ2false(λgi−1false)true], where ĥ2 and λgi are respectively the heritability and eigenvalues of the pedigree-derived genetic relationship kernel (GRK). For association analysis of sequence variants, the ELRT is given by ELRTtrue[hq2>0:unrelatedstrue]−true(ELRTtrue[ht2>0:pedigreestrue]−ELRTtrue[hr2>0:pedigreestrue]true), where ht2,hq2, and hr2 are the total, quantitative trait nucleotide, and residual heritabilities, respectively. Using these results, fast and accurate analytical power analyses are possible, eliminating the need for computer simulation. Additional benefits of eigensimplification include a simple method for calculation of the exact distribution of the ELRT under the null hypothesis which turns out to differ from that expected under the usual asymptotic theory. Further, when combined with the use of empirical GRKs—estimated over a large number of genetic markers— our theory reveals potential problems associated with non positive semi-definite kernels. These procedures are being added to our general statistical genetic computer package, SOLAR.

The immunogenicity of protein therapeutics is an important safety and efficacy concern during drug development and regulation. Strategies to identify individuals and subpopulations at risk for an undesirable immune response represent an important unmet need. The major histocompatibility complex (MHC)–associated peptide proteomics (MAPPs) assay directly identifies the presence of peptides derived from a specific protein therapeutic on a donor’s MHC class II (MHC-II) proteins. We applied this technique to address several questions related to the use of factor VIII (FVIII) replacement therapy in the treatment of hemophilia A (HA). Although >12 FVIII therapeutics are marketed, most fall into 3 categories: (i) human plasma-derived FVIII (pdFVIII), (ii) full-length (FL)–recombinant FVIII (rFVIII; FL-rFVIII), and (iii) B-domain–deleted rFVIII. Here, we investigated whether there are differences between the FVIII peptides found on the MHC-II proteins of the same individual when incubated with these 3 classes. Based on several observational studies and a prospective, randomized, clinical trial showing that the originally approved rFVIII products may be more immunogenic than the pdFVIII products containing von Willebrand factor (VWF) in molar excess, it has been hypothesized that the pdFVIII molecules yield/present fewer peptides (ie, potential T-cell epitopes). We have experimentally tested this hypothesis and found that dendritic cells from HA patients and healthy donors present fewer FVIII peptides when administered pdFVIII vs FL-rFVIII, despite both containing the same molar VWF excess. Our results support the hypothesis that synthesis of pdFVIII under physiological conditions could result in reduced heterogeneity and/or subtle differences in structure/conformation which, in turn, may result in reduced FVIII proteolytic processing relative to FL-rFVIII.

Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18-83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging.neurocognition | diffusion tensor imaging | fractional anisotropy | genetic correlation | gene x environment interaction P opulation projections suggest for the first time in human history there will be more individuals over the age of 65 than below the age of 14 by 2050 (1). This milestone reflects the dramatic increase of the average lifespan of people worldwide, rather than a reduction in the total number of children being born. Indeed, 25% of the US population is expected to be over the age of 60 midway through this century (1). The implications of our aging population are substantial, because aging is associated with decreased mental and physical ability coupled with increased health care utilization. Thus, there is considerable interest in delineating the biological mechanisms that influence age-related changes to facilitate successful aging (2), defined as avoidance of disease or disability, maintaining good physical and cognitive function, and engagement in social and productive activities. Because the brain appears to play a pivotal role in aging biology (3), one promising strategy is to define measures of brain structure and function that index concomitant aging outcomes (4). The observation that many measures of brain aging are heritable and can be localized to specific genomic regions (6) indicates that genetic factors play a crucial role in the brain's ability to either prosper or ...