Canadian infants' nutrient intakes from complementary foods during the first year of life

<p>Abstract</p> <p>Background</p> <p>Complementary feeding is currently recommended after six months of age, when the nutrients in breast milk alone are no longer adequate to support growth. Few studies have examined macro- and micro-nutrient intakes from complementary foods (CF) only. Our purpose was to assess the sources and nutritional contribution of CF over the first year of life.</p> <p>Methods</p> <p>In July 2003, a cross-sectional survey was conducted on a nationally representative sample of mothers with infants aged three to 12 months. The survey was administered evenly across all regions of the country and included a four-day dietary record to assess infants' CF intakes in household (tablespoon) measures (breast milk and formula intakes excluded). Records from 2,663 infants were analyzed for nutrient and CF food intake according to 12 categories. Mean daily intakes for infants at each month of age from CF were pooled and compared to the Dietary Reference Intakes for the respective age range.</p> <p>Results</p> <p>At three months of age, 83% of infants were already consuming infant cereals. Fruits and vegetables were among the most common foods consumed by infants at all ages, while meats were least common at all ages except 12 months. Macro- and micro-nutrient intakes from CF generally increased with age. All mean nutrient intakes, except vitamin D and iron, met CF recommendations at seven to 12 months.</p> <p>Conclusions</p> <p>Complementary foods were introduced earlier than recommended. Although mean nutrient intakes from CF at six to 12 months appear to be adequate among Canadian infants, further attention to iron and vitamin D intakes and sources may be warranted.</p

Expression QTLs Mapping and Analysis: A Bayesian Perspective.

The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results