Seasonality of the Stress Response in House Sparrows (Passer domesticus).

Seasonal changes in plasma corticosterone (CORT) levels indicate that birds modify their stress response through the year. Although this has been well documented, the method by which birds achieve this seasonality is not well understood. In this study I used house sparrows to determine if changes in glucocorticoid receptor (GR) immunoreactivity in several stress-related brain nuclei showed seasonal variation. The house sparrowsshowed seasonal variation in their stress response with baseline CORT levels being highest during the breeding season and lowest during winter. There was also significant change in plasma CORT post-dexamethasone during breeding, but not during other times of the year. In spite of the seasonal changes in CORT regulation there was no seasonal variation in the GR-immunoreactivity of brain regions involved in the stress response, such as the hypothalamus, nucleus taeniae of the amygdala, or hippocampus. These findings add to the growing research to understand the stress response

Estimation of genetic model parameters: Variables correlated with a quantitative phenotype exhibiting major locus inheritance

A major locus that is detected through its effect on one phenotype (a primary trait) may also affect other quantitative phenotypes or qualitative disease endpoints (secondary traits). The pattern of effects for the mutant allele. The effects are directly estimable when “measured genotypes” or a tightly linked marker allow unambiguous assignment of major locus genotypes. When genotype assignments are ambiguous for a major locus detected through its effect on a quantitative primary trait, we propose estimators using genotypic probabilities. Making certain reasonable assumptions, we demonstrate asymptotic unbiasedness of these genotypic probability estimators of the genotypic means and variances for either the quantitative primary or secondary traits, of the covariances between quantitative primary and secondary traits, and of prevalences for the secondary qualitative traits. An important application of genotypic probability estimators is to define an effect of a major locus that cannot be detected upon analysis of the variable; for example, major locus effects may be defined for hypertension or blood pressure as secondary traits, but not detected as primary traits.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101835/1/1370060203_ftp.pd

An application of a model for a genotype-dependent relationship between a concomitant (age) and a quantitative trait(LDL cholesterol)in pedigree data

In most genetic studies in humans the variability in a quantitative trait is adjusted for variability in concomitants (age, sex, etc) using a single regression equation prior to analyses of pedigree data. To illustrate an alternative approach, a single locus genetic model was tested. This model incorporates genotypic effects on the level of the trait, the variability in the trait, and the relationship between a concomitant and the trait. In this study, the model was applied to measures of age and low-density lipoprotein (LDL) cholesterol in a large kindred with familial hypercholesterolemia. The application of this model to 322 individuals in four generations provided evidence that genotypic variation at a single locus influences LDL levels early in life, the rate of increase of LDL with age and the phenotypic variance. A model with genotype-dependent slope and variance fit the data signifcantly better than a model with slope and variance independent of genotype. The inclusion of age-specific genotypic differences contributed to identification of high-risk individuals, to statistical support for a major locus, and to evidence for genetic determination of the tracking of LDL levels. Models that incorporate genotype-specific concomitant effects have the potential to represent more realiscally the relationship between genotypic variability and quantitative phenotypic variation than models that assume that these effects do not exist.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/38495/1/1370010403_ftp.pd

A Copy Number Variant on Chromosome 20q13.3 Implicated in Thinness and Severe Obesity

Background/Objectives. To identify copy number variants (CNVs) which are associated with body mass index (BMI). Subjects/Methods. CNVs were identified using array comparative genomic hybridization (aCGH) on members of pedigrees ascertained through severely obese (BMI ≥ 35 kg/m2) sib pairs (86 pedigrees) and thin (BMI ≤ 23 kg/m2) probands (3 pedigrees). Association was inferred through pleiotropy of BMI with CNV log⁡2 intensity ratio. Results. A 77-kilobase CNV on chromosome 20q13.3, confirmed by real-time qPCR, exhibited deletions in the obese subjects and duplications in the thin subjects (P=2.2×10-6). Further support for the presence of a deletion derived from inference by likelihood analysis of null alleles for SNPs residing in the region. Conclusions. One or more of 7 genes residing in a chromosome 20q13.3 CNV region appears to influence BMI. The strongest candidate is ARFRP1, which affects glucose metabolism in mice