Genetic Epidemiology of Longitudinal Change in Bone Mineral Density in Mexican Americans: The San Antonio Family Osteoporosis Study

Motivation: Bone mineral density (BMD), the principal determinant of bone strength and a risk factor for osteoporosis, is the net result of two processes: (i) the acquisition of peak BMD during young adulthood, and (ii) the subsequent rate of bone loss with age. While the genetics of peak BMD has been extensively studied, the specific genetic polymorphisms influencing peak BMD and the genetic contribution to bone loss are largely unknown. We investigated the extent to which genes influence 5-year change in BMD and searched for specific chromosomal regions influencing peak BMD and change in BMD in 1047 Mexican Americans from 34 large, multigenerational families. Methods: BMD measurements of the hip, spine, and forearm were collected at baseline and follow-up (3-8 years later, mean = 5.6 years) by dual-energy x-ray absoptiometry, from which annual BMD change was calculated. Pedigree-based maximum likelihood methods modeling the variance decomposition of longitudinal and cross-sectional measurements of BMD were used to estimate heritability (h²) and perform genome-wide linkage analysis (using a 7.6 cM genetic map) for BMD change and peak BMD. The effects of several environmental covariates, notably sex, age, weight, change in weight, and menopause, were simultaneously modeled.Results: We determined that change in BMD varied over time and could be categorized into two heritable (h² = 31% to 44%) phases: early adult bone loss in participants 45 years of age. A quantitative trait locus (QTL) influencing early bone loss was observed on chromosome 1q (LOD = 3.6) in the cohort 45 years. By comparing cross-sectional genetic analyses at baseline and follow-up, we identified QTLs on chromosomes 6q and 13q with consistent effects on peak BMD of the hip and showed that QTLs influencing peak BMD did not overlap with QTLs influencing bone loss.Public health significance: This work demonstrated the importance of genes in the etiology of osteoporosis, a growing public health problem. Understanding the genetic determinants of bone strength could lead to new biological targets for the treatment of osteoporosis, and/or the identification of persons at risk who would benefit from preventative interventions

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes

<p>Abstract</p> <p>Background</p> <p>Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures × time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set.</p> <p>Results</p> <p>We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost.</p> <p>Conclusion</p> <p>Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.</p

Creosote Treatment Effect On Hardwood Glulam Beam Properties

Flexure tests were conducted to determine the effect of creosote treatment on the performance of Combination A northern red oak, yellow poplar, and red maple glued-laminated (glulam) beams. This testing was conducted in accordance with ASTM D198-84 (ASTM 1987a), and the beams were fabricated in accordance with AITC 119-85 (AITC 1986), ANSI/AITC 190.1-83(AITC 1983b), and AITC 200-83 (AITC 1983a). Shear tests were also conducted on samples taken from the beams to determine the glueline shear strength and percent wood failure (WF).There was no significant difference (P < 0.05) between the modulus of rupture (MOR) of creosote-treated and untreated northern red oak beams. However, the MORs of the creosote-treated red maple and yellow poplar beams were significantly (P < 0.05) higher than those for untreated beams. There was no significant difference (P < 0.05) between the treated and untreated apparent modulus of elasticity (MOE) of each species. Therefore, the post-fabrication creosote treatment process from 145.92 to 215.76 kg/m3 (9.11 to 13.47 pcf) average weight retention did not adversely affect the strength (MOR) or stiffness (MOE) of northern red oak, red maple, and yellow poplar Combination A glulam beams.Glueline shear strengths for treated and untreated specimens of each species met or exceeded minimum performance criteria in AITC 200-83. Creosote treatment significantly (P < 0.05) increased glueline shear strength of red maple, but had no effect on the shear strength of red oak and yellow poplar specimens. Mean percentage wood failure of treated shear specimens was significantly (P < 0.05) greater than of untreated specimens in each species. Mean percentage wood failures of red oak and yellow poplar gluelines exceeded AITC 200-83 performance criteria; percentage wood failure of untreated (48%) and treated (59%) red maple shear specimens did not meet AITC 200-83 performance criteria

Periodontal Status and Quality of Life: Impact of Fear of Pain and Dental Fear

Background. Oral health-related quality of life (OHRQoL) is impacted by periodontal disease and orofacial pain. There is a limited research examining the impact of avoidance of care or physiological arousal related to the fear of pain response on periodontal-related OHRQoL. Methods. Data are from the Center for Oral Health Research in Appalachia family-based study focusing on 1,339 adults. Measures included a modified Periodontal Screening and Recording Index across sextants of dentition, dental fear survey, Fear of Pain Questionnaire-9, and Oral Health Impact Profile-14. Structural equation modeling was used to estimate the effects of periodontal disease screening indicators on OHRQoL including the mediating role of dental fear while accounting for fear of pain. Results. A significant total effect was found for the mandibular anterior sextant, components of dental anxiety/fear, and indicators of OHRQoL (pain and discomfort, , ; psychosocial impact, , ). The maxillary anterior region was significantly associated with pain discomfort (, ) and functionality (, ). Conclusions. Findings provide a granular perspective of periodontal disease indicators and OHRQoL. Dental avoidance/anticipatory fear and physiological arousal mediate OHRQoL in individuals who have indicators of periodontal disease in sextants that may be visible and susceptible to higher pain and psychosocial impact

SNPs associated with testosterone levels influence human facial morphology

Many factors influence human facial morphology, including genetics, age, nutrition, biomechanical forces, and endocrine factors. Moreover, facial features clearly differ between males and females, and these differences are driven primarily by the influence of sex hormones during growth and development. Specific genetic variants are known to influence circulating sex hormone levels in humans, which we hypothesize, in turn, affect facial features. In this study, we investigated the effects of testosterone-related genetic variants on facial morphology. We tested 32 genetic variants across 22 candidate genes related to levels of testosterone, sex hormone-binding globulin (SHGB) and dehydroepiandrosterone sulfate (DHEAS) in three cohorts of healthy individuals for which 3D facial surface images were available (Pittsburgh 3DFN, Penn State and ALSPAC cohorts; total n = 7418). Facial shape was described using a recently developed extension of the dense-surface correspondence approach, in which the 3D facial surface was partitioned into a set of 63 hierarchically organized modules. Each variant was tested against each of the facial surface modules in a multivariate genetic association-testing framework and meta-analyzed. Additionally, the association between these candidate SNPs and five facial ratios was investigated in the Pittsburgh 3DFN cohort. Two significant associations involving intronic variants of SHBG were found: both rs12150660 (p = 1.07E-07) and rs1799941 (p = 6.15E-06) showed an effect on mandible shape. Rs8023580 (an intronic variant of NR2F2-AS1) showed an association with the total and upper facial width to height ratios (p = 9.61E-04 and p = 7.35E-04, respectively). These results indicate that testosterone-related genetic variants affect normal-range facial morphology, and in particular, facial features known to exhibit strong sexual dimorphism in humans

Health knowledge among the millennial generation

The Millennial Generation, also known as Generation Y, is the demographic cohort following Generation X, and is generally regarded to be composed of those individuals born between 1980 and 2000. They are the first to grow up in an environment where health-related information is widely available by internet, TV and other electronic media, yet we know very little about the scope of their health knowledge. This study was undertaken to quantify two domains of clinically relevant health knowledge: factual content and ability to solve health related questions (application) in nine clinically related medical areas. Study subjects correctly answered, on average, 75% of health application questions but only 54% of health content questions. Since students were better able to correctly answer questions dealing with applications compared to those on factual content contemporary US high school students may not use traditional hierarchical learning models in acquisition of their health knowledge

Effects of Smoking and Genotype on the PSR Index of Periodontal Disease in Adults Aged 18–49

Studies have found both genetic and environmental influences on chronic periodontitis. The purpose of this study was to examine the relationships among previously identified genetic variants, smoking status, and two periodontal disease-related phenotypes (PSR1 and PSR2) in 625 Caucasian adults (aged 18–49 years). The PSR Index was used to classify participants as affected or unaffected under the PSR1 and PSR2 phenotype definitions. Using logistic regression, we found that the form of the relationship varied by single nucleotide polymorphism (SNP): For rs10457525 and rs12630931, the effects of smoking and genotype on risk were additive; whereas for rs10457526 and rs733048, smoking was not independently associated with affected status once genotype was taken into consideration. In contrast, smoking moderated the relationships of rs3870371 and rs733048 with affected status such that former and never smokers with select genotypes were at increased genetic risk. Thus, for several groups, knowledge of genotype may refine the risk prediction over that which can be determined by knowledge of smoking status alone. Future studies should replicate these findings. These findings provide the foundation for the exploration of novel pathways by which periodontitis may occur

Sentiment analysis with genetically evolved Gaussian kernels

Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a prede- fined kernels with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for the evolution of Gaussian Process kernels that are more precise for sentiment analysis. We use use a very flexible representation of kernels combined with a multi-objective approach that considers si- multaneously two quality metrics and the computational time required to evaluate those kernels. Our results show that the algorithm can outper- form Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered

A Preliminary Genome-Wide Association Study of Pain-Related Fear: Implications for Orofacial Pain

Background. Acute and chronic orofacial pain can significantly impact overall health and functioning. Associations between fear of pain and the experience of orofacial pain are well-documented, and environmental, behavioral, and cognitive components of fear of pain have been elucidated. Little is known, however, regarding the specific genes contributing to fear of pain. Methods. A genome-wide association study (GWAS; ) was performed to identify plausible genes that may predispose individuals to various levels of fear of pain. The total score and three subscales (fear of minor, severe, and medical/dental pain) of the Fear of Pain Questionnaire-9 (FPQ-9) were modeled in a variance components modeling framework to test for genetic association with 8.5 M genetic variants across the genome, while adjusting for sex, age, education, and income. Results. Three genetic loci were significantly associated with fear of minor pain (8q24.13, 8p21.2, and 6q26; for all) near the genes TMEM65, NEFM, NEFL, AGPAT4, and PARK2. Other suggestive loci were found for the fear of pain total score and each of the FPQ-9 subscales. Conclusions. Multiple genes were identified as possible candidates contributing to fear of pain. The findings may have implications for understanding and treating chronic orofacial pain

Novel caries loci in children and adults implicated by genome-wide analysis of families

Background: Dental caries is a common chronic disease among children and adults alike, posing a substantial health burden. Caries is affected by multiple genetic and environmental factors, and prior studies have found that a substantial proportion of caries susceptibility is genetically inherited. Methods: To identify such genetic factors, we conducted a genome-wide linkage scan in 464 extended families with 2616 individuals from Iowa, Pennsylvania and West Virginia for three dental caries phenotypes: (1) PRIM: dichotomized as zero versus one or more affected primary teeth, (2) QTOT1: age-adjusted quantitative caries measure for both primary and permanent dentitions including pre-cavitated lesions, and (3) QTOT2: age-adjusted quantitative caries excluding pre-cavitated lesions. Genotyping was conducted for approximately 600,000 SNPs on an Illumina platform, pruned to 127,511 uncorrelated SNPs for the analyses reported here. Results: Multipoint non-parametric linkage analyses generated peak LOD scores exceeding 2.0 for eight genomic regions, but no LOD scores above 3.0 were observed. The maximum LOD score for each of the three traits was 2.90 at 1q25.3 for PRIM, 2.38 at 6q25.3 for QTOT1, and 2.76 at 5q23.3 for QTOT2. Some overlap in linkage regions was observed among the phenotypes. Genes with a potential role in dental caries in the eight chromosomal regions include CACNA1E, LAMC2, ALMS1, STAMBP, GXYLT2, SLC12A2, MEGF10, TMEM181, ARID1B, and, as well as genes in several immune gene families. Our results are also concordant with previous findings from association analyses on chromosomes 11 and 19. Conclusions: These multipoint linkage results provide evidence in favor of novel chromosomal regions, while also supporting earlier association findings for these data. Understanding the genetic etiology of dental caries will allow designing personalized treatment plans based on an individual’s genetic risk of disease