Real-time national GPS networks: Opportunities for atmospheric sensing

Real-time national Global Positioning System (GPS) networks are being established in a number of countries for atmospheric sensing. UCAR, in collaboration with participating universities, is developing one of these networks in the United States. The network, named "SuomiNet" to honor meteorological satellite pioneer Verner Suomi, is funded by the U.S. National Science Foundation. SuomiNet will exploit the recently-shown ability of ground-based GPS receivers to make thousands of accurate upper and lower atmospheric measurements per day. Phase delays induced in GPS signals by the ionosphere and neutral atmosphere can be measured with high precision simultaneously along up to a dozen GPS ray paths in the field of view. These delays can be converted into total electron content (TEC), and integrated water vapor (if surface pressure data or estimates are available), along each GPS ray path. The resulting continuous, accurate, all-weather, real-time upper and lower atmospheric data create a variety of opportunities for atmospheric research. In this letter we describe SuomiNet, its applications, and the opportunity to coordinate national real-time GPS networks to create, a global network with larger scientific and operational potential. Copy right© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences

Correction to: Real-time national GPS networks: opportunities for atmospheric sensing (Earth, Planets and Space, (2000), 52, 11, (901-905), 10.1186/BF03352303)

Portions of this letter come from the content published in the paper by Ware et al. [1]

Power calculations using exact data simulation: A useful tool for genetic study designs.

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes

Reconsidering the Heritability of Intelligence in Adulthood: Taking Assortative Mating and Cultural Transmission into Account

Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors. To investigate the effects of assortative mating on the estimates of the variance components of intelligence, we employed an extended twin-family design. Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence

Gene-Environment Interaction in Adults’ IQ Scores: Measures of Past and Present Environment

Gene-environment interaction was studied in a sample of young (mean age 26 years, N = 385) and older (mean age 49 years, N = 370) adult males and females. Full scale IQ scores (FSIQ) were analyzed using biometric models in which additive genetic (A), common environmental (C), and unique environmental (E) effects were allowed to depend on environmental measures. Moderators under study were parental and partner educational level, as well as urbanization level and mean real estate price of the participants’ residential area. Mean effects were observed for parental education, partner education and urbanization level. On average, FSIQ scores were roughly 5 points higher in participants with highly educated parents, compared to participants whose parents were less well educated. In older participants, IQ scores were about 2 points higher when their partners were highly educated. In younger males, higher urbanization levels were associated with slightly higher FSIQ scores. Our analyses also showed increased common environmental variation in older males whose parents were more highly educated, and increased unique environmental effects in older males living in more affluent areas. Contrary to studies in children, however, the variance attributable to additive genetic effects was stable across all levels of the moderators under study. Most results were replicated for VIQ and PIQ

Bayesian shrinkage mapping of quantitative trait loci in variance component models

<p>Abstract</p> <p>Background</p> <p>In this article, I propose a model-selection-free method to map multiple quantitative trait loci (QTL) in variance component model, which is useful in outbred populations. The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL. It is analogous to Xu's Bayesian shrinkage estimation method, but his method is based on allelic substitution model, while the new method is based on the variance component models.</p> <p>Results</p> <p>Extensive simulation experiments were conducted to investigate the performance of the proposed method. The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.</p> <p>Conclusions</p> <p>The newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.</p

Male × Female Interaction for a Pre-Copulatory Trait, but Not a Post-Copulatory Trait, among Cosmopolitan Populations of Drosophila melanogaster

Sexual coevolution occurs when changes in the phenotype of one sex select for changes in the other sex. We can identify the “footprint” of this coevolution by mating males and females from different populations and testing for a male-female genotype interaction for a trait associated with male (or female) performance. Here we mated male Drosophila melanogaster from five different continents with females from their own and different continents to test for a male-female interaction for mating speed, a pre-copulatory trait, and female reproductive investment, a post-copulatory trait. We found a strong male-female interaction for mating speed, consistent with previous studies using different populations, suggesting that the potential for sexual coevolution for this trait is present in this species. In contrast, we did not detect a male-female interaction for female reproductive investment. Although a male-female interaction for mating speed is compatible with the hypothesis of ongoing sexual coevolution, the nature of our experimental design is unable to exclude alternate explanations. Thus, the evolutionary mechanisms promoting male-female genotype interactions for pre-copulatory mating traits in D. melanogaster warrant further investigation

Heritability of cardiovascular risk factors in a Brazilian population: Baependi Heart Study

<p>Abstract</p> <p>Background</p> <p>The heritability of cardiovascular risk factors is expected to differ between populations because of the different distribution of environmental risk factors, as well as the genetic make-up of different human populations.</p> <p>Methods</p> <p>The purpose of this analysis was to evaluate genetic and environmental influences on cardiovascular risk factor traits, using a variance component approach, by estimating the heritability of these traits in a sample of 1,666 individuals in 81 families ascertained randomly from a highly admixed population of a city in a rural area in Brazil.</p> <p>Results</p> <p>Before adjustment for sex, age, age², and age × sex interaction, polygenic heritability of systolic (SBP) and diastolic (DBP) blood pressure were 15.0% and 16.4%, waist circumference 26.1%, triglycerides 25.7%, fasting glucose 32.8%, HDL-c 31.2%, total cholesterol 28.6%, LDL-c 26.3%, BMI 39.1%. Adjustment for covariates increased polygenic heritability estimates for all traits mainly systolic and diastolic blood pressure (25.9 and 26.2%, respectively), waist circumference (40.1%), and BMI (51.0%).</p> <p>Conclusion</p> <p>Heritability estimates for cardiovascular traits in the Brazilian population are high and not significantly different from other studied worldwide populations. Mapping efforts to identify genetic loci associated with variability of these traits are warranted.</p

Accounting for a Quantitative Trait Locus for Plasma Triglyceride Levels: Utilization of Variants in Multiple Genes

For decades, research efforts have tried to uncover the underlying genetic basis of human susceptibility to a variety of diseases. Linkage studies have resulted in highly replicated findings and helped identify quantitative trait loci (QTL) for many complex traits; however identification of specific alleles accounting for linkage remains elusive. The purpose of this study was to determine whether with a sufficient number of variants a linkage signal can be fully explained.We used comprehensive fine-mapping using a dense set of single nucleotide polymorphisms (SNPs) across the entire quantitative trait locus (QTL) on human chromosome 7q36 linked to plasma triglyceride levels. Analyses included measured genotype and combined linkage association analyses.Screening this linkage region, we found an over representation of nominally significant associations in five genes (MLL3, DPP6, PAXIP1, HTR5A, INSIG1). However, no single genetic variant was sufficient to account for the linkage. On the other hand, multiple variants capturing the variation in these five genes did account for the linkage at this locus. Permutation analyses suggested that this reduction in LOD score was unlikely to have occurred by chance (p = 0.008).With recent findings, it has become clear that most complex traits are influenced by a large number of genetic variants each contributing only a small percentage to the overall phenotype. We found that with a sufficient number of variants, the linkage can be fully explained. The results from this analysis suggest that perhaps the failure to identify causal variants for linkage peaks may be due to multiple variants under the linkage peak with small individual effect, rather than a single variant of large effect