Insulin Resistance, Hyperinsulinemia, and LH: Relative Roles in Peripubertal Obesity-Associated Hyperandrogenemia.

Context: Peripubertal obesity is associated with variable hyperandrogenemia, but precise mechanisms remain unclear. Objective: To assess insulin resistance, hyperinsulinemia, and LH roles in peripubertal obesity-associated hyperandrogenemia. Design: Cross-sectional analysis. Setting: Academic clinical research unit. Participants: Eleven obese (body mass index for age ≥95%) peripubertal girls. Intervention: Blood samples were taken during a mixed-meal tolerance test (1900 to 2100), overnight (2100 to 0700), while fasting (0700 to 0900), and during an 80 mU/m2/min hyperinsulinemic-euglycemic clamp (0900 to 1100). Main Outcome Measures: The dependent variable was morning free testosterone level; independent variables were insulin sensitivity index (ISI), estimated 24-hour insulin, and estimated 24-hour LH levels. Results: All participants demonstrated insulin resistance and hyperinsulinemia. ISI, but not estimated 24-hour insulin level, correlated positively with morning free testosterone level when correcting for estimated 24-hour LH level and Tanner stage (rs = 0.68, P = 0.046). The correlation between estimated 24-hour LH and free testosterone levels approached significance after adjusting for estimated 24-hour insulin level and Tanner stage (rs = 0.63, P = 0.067). Estimated 24-hour insulin level did not correlate with free testosterone level after adjusting for estimated 24-hour LH level and Tanner stage (rs = 0.47, P = 0.20). Conclusion: In insulin-resistant obese girls with hyperinsulinemia, free testosterone levels correlated positively with insulin sensitivity and, likely, circulating LH concentrations but not with circulating insulin levels. In the setting of relatively uniform hyperinsulinemia, variable steroidogenic-cell insulin sensitivity may correlate with metabolic insulin sensitivity and contribute to variable free testosterone concentrations.

Childhood obesity and its impact on the development of adolescent PCOS.

Obesity exacerbates the reproductive and metabolic manifestations of polycystic ovary syndrome (PCOS). The symptoms of PCOS often begin in adolescence, and the rising prevalence of peripubertal obesity has prompted concern that the prevalence and severity of adolescent PCOS is increasing in parallel. Recent data have disclosed a high prevalence of hyperandrogenemia among peripubertal adolescents with obesity, suggesting that such girls are indeed at risk for developing PCOS. Obesity may impact the risk of PCOS via insulin resistance and compensatory hyperinsulinemia, which augments ovarian/adrenal androgen production and suppresses sex hormone-binding globulin (SHBG), thereby increasing androgen bioavailability. Altered luteinizing hormone (LH) secretion plays an important role in the pathophysiology of PCOS, and although obesity is generally associated with relative reductions of LH, higher LH appears to be the best predictor of increased free testosterone among peripubertal girls with obesity. Other potential mechanisms of obesity-associated hyperandrogenemia include enhanced androgen production in an expanded fat mass and potential effects of abnormal adipokine/cytokine levels. Adolescents with PCOS are at risk for comorbidities such as metabolic syndrome and impaired glucose tolerance, and concomitant obesity compounds these risks. For all of these reasons, weight loss represents an important therapeutic target in obese adolescents with PCOS.

Migration and melanoma incidence rates among Washington state counties.

The purpose of this study was to look for a possible explanation for the variation in the incidence rate of melanoma among counties in Washington state. We used data from the Washington State Cancer Registry (WSCR), the Cancer Center at PeaceHealth St. Joseph Hospital in Whatcom County, and the Surveillance, Epidemiology, and End Results registry to get information on melanoma incidence. Demographic and migration records were obtained from the US Census Bureau, the Washington State Department of Licensing (DOL), and the US Internal Revenue Service (IRS). A number of different analytic techniques were used to address our research question, including a multiple regression analysis, time trend comparisons, and an analysis of birthplace data of melanoma patients in Whatcom county. We found a significant association between migration rate from the Southwest (SW) USA and melanoma incidence (P<0.001). Plots of time trend show that melanoma rates track with migration from the SW. In Whatcom county, almost half of all residents were born outside of Washington state, but they accounted for about 70% of all melanoma cases. Our analyses suggest that migration from the SW is an important factor in explaining the variation in melanoma rate among counties in Washington.

Maximum likelihood estimation of individual inbreeding coefficients and null allele frequencies.

In this paper, we developed and compared several expectation-maximization (EM) algorithms to find maximum likelihood estimates of individual inbreeding coefficients using molecular marker information. The first method estimates the inbreeding coefficient for a single individual and assumes that allele frequencies are known without error. The second method jointly estimates inbreeding coefficients and allele frequencies for a set of individuals that have been genotyped at several loci. The third method generalizes the second method to include the case in which null alleles may be present. In particular, it is able to jointly estimate individual inbreeding coefficients and allele frequencies, including the frequencies of null alleles, and accounts for missing data. We compared our methods with several other estimation procedures using simulated data and found that our methods perform well. The maximum likelihood estimators consistently gave among the lowest root-mean-square-error (RMSE) of all the estimators that were compared. Our estimator that accounts for null alleles performed particularly well and was able to tease apart the effects of null alleles, randomly missing genotypes and differing degrees of inbreeding among members of the datasets we analysed. To illustrate the performance of our estimators, we analysed previously published datasets on mice (Mus musculus) and white-tailed deer (Odocoileus virginianus).

A screen for regulators of survival of motor neuron protein levels.

The motor neuron disease spinal muscular atrophy (SMA) results from mutations that lead to low levels of the ubiquitously expressed protein survival of motor neuron (SMN). An ever-increasing collection of data suggests that therapeutics that elevate SMN may be effective in treating SMA. We executed an image-based screen of annotated chemical libraries and discovered several classes of compounds that were able to increase cellular SMN. Among the most important was the RTK-PI3K-AKT-GSK-3 signaling cascade. Chemical inhibitors of glycogen synthase kinase 3 (GSK-3) and short hairpin RNAs (shRNAs) directed against this target elevated SMN levels primarily by stabilizing the protein. It was particularly notable that GSK-3 chemical inhibitors were also effective in motor neurons, not only in elevating SMN levels, but also in blocking the death that was produced when SMN was acutely reduced by an SMN-specific shRNA. Thus, we have established a screen capable of detecting drug-like compounds that correct the main phenotypic change underlying SMA.

Comparison of marker-based pairwise relatedness estimators on a pedigreed plant population.

Several estimators have been proposed that use molecular marker data to infer the degree of relatedness for pairs of individuals. The objective of this study was to evaluate the performance of seven estimators when applied to marker data of a set of 33 key individuals from a large complex apple pedigree. The evaluation considered different scenarios of allele frequencies and different numbers of marker loci. The method of moments estimators were Similarity, Queller-Goodknight, Lynch-Ritland and Wang. The maximum likelihood estimators were Thompson, Anderson-Weir and Jacquard. The pedigree-based coancestry coefficients were taken as the point of reference in calculating correlations and root mean square error (RMSE). The marker data comprised 86 multi-allelic SSR markers on 17 linkage groups, covering 11 Morgans. Additionally, we simulated 10 datasets conditional on the real pedigree to support the results on the real dataset. None of the estimators outperformed the others. Knowledge of allele frequencies appeared to be the most influential, i.e., the highest correlations and lowest RMSE were found when frequencies from the founder population were available. When equal allele frequencies were used, all estimators resulted in very similar, but on average lower, correlations. The use of allele frequencies estimated from the set of 33 individuals gave, on average, the poorest results. The maximum likelihood estimators and the Lynch-Ritland estimator were the most sensitive to allele frequencies. The results from the simulation study fully supported the trends in results of the real dataset. This study indicated that high correlations (up to 0.90) and small RMSE (below 0.03), may be obtained when population allelic frequencies are available. In this scenario, the performances of the various estimators were similar, but seemed to favor the maximum likelihood estimators. In the absence of reliable allele frequencies the method of moments estimators were shown to be more robust. The number of marker loci influenced the average performance of the estimators; however, the ranking was not affected. Correlations up to 0.80 were obtained when two markers per chromosome and appropriate allele frequencies were available. Adding more markers to the current dataset may lead to marginal improvements.

Linkage disequilibrium in wild mice.

Crosses between laboratory strains of mice provide a powerful way of detecting quantitative trait loci for complex traits related to human disease. Hundreds of these loci have been detected, but only a small number of the underlying causative genes have been identified. The main difficulty is the extensive linkage disequilibrium (LD) in intercross progeny and the slow process of fine-scale mapping by traditional methods. Recently, new approaches have been introduced, such as association studies with inbred lines and multigenerational crosses. These approaches are very useful for interval reduction, but generally do not provide single-gene resolution because of strong LD extending over one to several megabases. Here, we investigate the genetic structure of a natural population of mice in Arizona to determine its suitability for fine-scale LD mapping and association studies. There are three main findings: (1) Arizona mice have a high level of genetic variation, which includes a large fraction of the sequence variation present in classical strains of laboratory mice; (2) they show clear evidence of local inbreeding but appear to lack stable population structure across the study area; and (3) LD decays with distance at a rate similar to human populations, which is considerably more rapid than in laboratory populations of mice. Strong associations in Arizona mice are limited primarily to markers less than 100 kb apart, which provides the possibility of fine-scale association mapping at the level of one or a few genes. Although other considerations, such as sample size requirements and marker discovery, are serious issues in the implementation of association studies, the genetic variation and LD results indicate that wild mice could provide a useful tool for identifying genes that cause variation in complex traits.

A maximum-likelihood method for the estimation of pairwise relatedness in structured populations.

A maximum-likelihood estimator for pairwise relatedness is presented for the situation in which the individuals under consideration come from a large outbred subpopulation of the population for which allele frequencies are known. We demonstrate via simulations that a variety of commonly used estimators that do not take this kind of misspecification of allele frequencies into account will systematically overestimate the degree of relatedness between two individuals from a subpopulation. A maximum-likelihood estimator that includes F(ST) as a parameter is introduced with the goal of producing the relatedness estimates that would have been obtained if the subpopulation allele frequencies had been known. This estimator is shown to work quite well, even when the value of F(ST) is misspecified. Bootstrap confidence intervals are also examined and shown to exhibit close to nominal coverage when F(ST) is correctly specified.

One-stage design is empirically more powerful than two-stage design for family-based genome-wide association studies.

Finding a genetic marker associated with a trait is a classic problem in human genetics. Recently, two-stage approaches have gained popularity in marker-trait association studies, in part because researchers hope to reduce the multiple testing problem by testing fewer markers in the final stage. We compared one two-stage family-based approach to an analogous single-stage method, calculating the empirical type I error rates and power for both methods using fully simulated data sets modeled on nuclear families with rheumatoid arthritis, and data sets of real single-nucleotide polymorphism genotypes from Centre d'Etude du Polymorphisme Humain pedigrees with simulated traits. In these analyses performed in the absence of population stratification, the single-stage method was consistently more powerful than the two-stage method for a given type I error rate. To explore the sources of this difference, we performed a case study comparing the individual steps of two-stage designs, the two-stage design itself, and the analogous one-stage design.

Genetic relatedness analysis: modern data and new challenges.

Individuals who belong to the same family or the same population are related because of their shared ancestry. Population and quantitative genetics theory is built with parameters that describe relatedness, and the estimation of these parameters from genetic markers enables progress in fields as disparate as plant breeding, human disease gene mapping and forensic science. The large number of multiallelic microsatellite loci and biallelic SNPs that are now available have markedly increased the precision with which relationships can be estimated, although they have also revealed unexpected levels of genomic heterogeneity of relationship measures.

Three major haplotypes of the beta2 adrenergic receptor define psychological profile, blood pressure, and the risk for development of a common musculoskeletal pain disorder.

Adrenergic receptor beta(2) (ADRB2) is a primary target for epinephrine. It plays a critical role in mediating physiological and psychological responses to environmental stressors. Thus, functional genetic variants of ADRB2 will be associated with a complex array of psychological and physiological phenotypes. These genetic variants should also interact with environmental factors such as physical or emotional stress to produce a phenotype vulnerable to pathological states. In this study, we determined whether common genetic variants of ADRB2 contribute to the development of a common chronic pain condition that is associated with increased levels of psychological distress and low blood pressure, factors which are strongly influenced by the adrenergic system. We genotyped 202 female subjects and examined the relationships between three major ADRB2 haplotypes and psychological factors, resting blood pressure, and the risk of developing a chronic musculoskeletal pain condition-Temporomandibular Joint Disorder (TMD). We propose that the first haplotype codes for lower levels of ADRB2 expression, the second haplotype codes for higher ADRB2 expression, and the third haplotype codes for higher receptor expression and rapid agonist-induced internalization. Individuals who carried one haplotype coding for high and one coding for low ADRB2 expression displayed the highest positive psychological traits, had higher levels of resting arterial pressure, and were about 10 times less likely to develop TMD. Thus, our data suggest that either positive or negative imbalances in ADRB2 function increase the vulnerability to chronic pain conditions such as TMD through different etiological pathways that imply the need for tailored treatment options.

It was one of my brothers.

When DNA evidence is used to implicate a suspect, it may be of interest to know whether it is likely that the suspect's near relatives also share the suspect's DNA profile. In this study we discuss methods for evaluating the probability that at least one of a set of the suspect's full or half-siblings shares the suspect's DNA profile. We present three such methods: exact calculation, estimation via Monte Carlo simulations, and estimation by means of sandwiching the probability between an upper and a lower bound. We show that, under many circumstances, this upper bound itself provides an extremely quick and accurate estimate of the probability that at least one of the relatives matches the suspect's profile.

Measures of human population structure show heterogeneity among genomic regions.

Estimates of genetic population structure (F(ST)) were constructed from all autosomes in two large SNP data sets. The Perlegen data set contains genotypes on approximately 1 million SNPs segregating in all three samples of Americans of African, Asian, and European descent; and the Phase I HapMap data set contains genotypes on approximately 0.6 million SNPs segregating in all four samples from specific Caucasian, Chinese, Japanese, and Yoruba populations. Substantial heterogeneity of F(ST) values was found between segments within chromosomes, although there was similarity between the two data sets. There was also substantial heterogeneity among population-specific F(ST) values, with the relative sizes of these values often changing along each chromosome. Population-structure estimates are often used as indicators of natural selection, but the analyses presented here show that individual-marker estimates are too variable to be useful. There is inherent variation in these statistics because of variation in genealogy even among neutral loci, and values at pairs of loci are correlated to an extent that reflects the linkage disequilibrium between them. Furthermore, it may be that the best indications of selection will come from population-specific F(ST) values rather than the usually reported population-average values.

Regional brain responses to serotonin in major depressive disorder.

BACKGROUND: Positron Emission Tomography (PET) studies have reported altered resting regional brain glucose metabolism in mood disorders. This study examines the relationship of such changes to serotonin system abnormalities associated with depression. METHODS: Thirteen male medication free subjects who were inpatients with a DSM-IIIR major depressive disorder and seven healthy male subjects underwent an [18F]-fluorodeoxyglucose (18FDG) PET scan on consecutive days. Three hours prior to 18FDG subjects received single blind placebo or fenfluramine. Comparisons of voxel level regional glucose metabolic rate responses (rCMRglu) between groups in the two states were performed with SPM99. RESULTS: Unlike healthy male subjects who have significant increases in rCMRglu in prefrontal and parietal cortical regions after receiving fenfluramine, depressed male subjects have no significant increases in rCMRglu. CONCLUSIONS: Blunted increases in rCMRglu in response to fenfluramine in prefrontal and parietal cortex are consistent with our previous pilot study and the indoleamine hypothesis of depression. Differences in specific brain regions affected between this study and previous studies may be attributable to gender differences.