Background

Sexual health education in the United Kingdom is of variable quality, typically focusing on the biological aspects of sex rather than on communication, relationships, and sexual pleasure. The Internet offers a unique opportunity to provide sexual health education to young people, since they can be difficult to engage but frequently use the Internet as a health information resource.

Objectives

To explore through qualitative research young people’s views on what elements of a sexual health website would be appealing and engaging, and their views on the content, design, and interactive features of the Sexunzipped intervention website.

Methods

We recruited 67 young people aged 16–22 years in London, UK. We held 21 focus groups and 6 one-to-one interviews to establish sexual health priorities, views on website look and feel, and what features of a sexual heath website would attract and engage them. Two researchers facilitated the focus groups, using a semistructured topic guide to lead the discussions and asking open questions to elicit a range of views. The discussions and interviews were audio recorded and detailed notes were made on key topics from the audio recording. Young people’s views influenced design templates for the content and interactive features of Sexunzipped.

Results

Young people particularly wanted straightforward information on sexual pleasure, sexually transmitted infections and pregnancy, how to communicate with partners, how to develop skills in giving pleasure, and emotions involved in sex and relationships. Focus group participants wanted social interaction with other young people online and wanted to see themselves reflected in some way such as through images or videos.

Conclusions

While it is challenging to meet all of young people’s technological and design requirements, consultation with the target audience is valuable and necessary in developing an online sexual health intervention. Young people are willing to talk about sensitive issues, enjoy the discussions, and can offer key insights that influence intervention development.

Summary

Chronic activation of the hypothalamic-pituitary adrenal (HPA) system is a risk factor for a variety of physical and mental disorders, and yet the complexity of the system has made it difficult to define the role of genetic and environmental factors in producing long term individual differences in HPA activity. Cortisol levels in hair have been suggested as a marker of total HPA activation over a period of several months. This study takes advantage of a pedigreed nonhuman primate colony to investigate genetic and environmental influences on hair cortisol levels before and after an environmental change. A sample of 226 adult female vervet monkeys (age 3–18) living in multigenerational, matrilineal social groups at the Vervet Research Colony were sampled in a stable low stress baseline environment and 6 months after the entire colony was moved to a new facility with more frequent handling and group disturbances (higher stress environment). Variance components analysis using the extended colony pedigree was applied to determine heritability of hair cortisol levels in the two environments. Bivariate genetic correlation assessed degree of overlap in genes influencing hair cortisol levels in the low and higher stress environments. The results showed that levels of cortisol in hair of female vervets increased significantly from the baseline to the post-move environment. Hair cortisol levels were heritable in both environments (h2 = 0.31), and there was a high genetic correlation across environments (rhoG = 0.79), indicating substantial overlap in the genes affecting HPA activity in low and higher stress environments. This is the first study to demonstrate that the level of cortisol in hair is a heritable trait. It shows the utility of hair cortisol as a marker for HPA activation, and a useful tool for identifying genetic influences on long term individual differences in HPA activity. The results provide support for an additive model of the effects of genes and environment on this measure of long term HPA activity.

Considerable attention has been paid to identifying genetic influences and gene-environment interactions that increase vulnerability to environmental stressors, with promising but inconsistent results. A nonhuman primate model is presented here that allows assessment of genetic influences on response to a stressful life event for a behavioral trait with relevance for psychopathology. Genetic and environmental influences on free choice novelty seeking behavior were assessed in a pedigreed colony of vervet monkeys before and after relocation from a low stress to a higher stress environment. Heritability of Novelty Seeking scores, and genetic correlations within and between environments, were conducted using variance components analysis. The results showed that Novelty Seeking was markedly inhibited in the higher stress environment, with effects persisting across a two year period for adults but not for juveniles. There were significant genetic contributions to Novelty Seeking scores in each year (h2 = .35–.43), with high genetic correlations within each environment (rhoG > .80), and a lower genetic correlation (rhoG = .35, ns) between environments. There were also significant genetic contributions to individual change scores from before to after the move (h2 = .48) These results indicate that genetic regulation of novelty seeking was modified by the level of environmental stress, and they support a role for gene-environment interactions in a behavioral trait with relevance for mental health.

Genotyping of rare variants on a large scale is now possible using next-generation sequencing. The sample selection is a crucial step in designing the genetic study of a complex disease and knowledge of the efficiency and limitations of the population-based and family-based designs can help making the appropriate choice.

The 9 contributions to the Group 5 of the Genetic Analysis Workshop 17 evaluated the population-based and family-based designs by comparing the results obtained with various methods applied on the mini-exome simulations. These simulations consisted of 200 replicates comprising unrelated individuals and 8 extended pedigrees with genotypes and various phenotypes. The methods tested for association with a population-based and/or a family-based design, tested for linkage with a family-based design or estimated heritability.

In this paper, we summarize the strength and weaknesses of both designs. While a population-based design seems more suitable to detect the effect of multiple rare variants, a family-based design can potentially enrich the sample in very rare variants, for which the effect would be concealed at the population level. However, as of today, the main limitation is still the expensive cost of next-generation sequencing.

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.

Genome-wide gene expression studies may provide substantial insight into gene activities and biological pathways differing between tissues and individuals. We investigated such gene expression variation by analyzing expression profiles in brain tissues derived from eight different brain regions and from blood in 12 monkeys from a biomedically important non-human primate model, the vervet (Chlorocebus aethiops sabaeus). We characterized brain regional differences in gene expression, focusing on transcripts for which inter-individual variation of expression in brain correlates well with variation in blood from the same individuals. Using stringent criteria, we identified 29 transcripts whose expression is measurable, stable, replicable, variable between individuals, relevant to brain function and heritable. Polymorphisms identified in probe regions could, in a minority of transcripts, confound the interpretation of the observed inter-individual variation. The high heritability of levels of these transcripts in a large vervet pedigree validated our approach of focusing on transcripts that showed higher inter-individual compared with intra-individual variation. These selected transcripts are candidate expression Quantitative Trait Loci, differentially regulating transcript levels in the brain among individuals. Given the high degree of conservation of tissue expression profiles between vervets and humans, our findings may facilitate the understanding of regional and individual transcriptional variation and its genetic mechanisms in humans. The approach employed here—utilizing higher quality tissue and more precise dissection of brain regions than is usually possible in humans—may therefore provide a powerful means to investigate variation in gene expression relevant to complex brain related traits, including human neuropsychiatric diseases.

This paper aims to illustrate what discourse analysis is and how it can contribute to our understanding of family practice. Firstly, we describe what ‘discourse analysis’ is, mapping the discourse analysis terrain by discussing four studies relevant to primary care to illustrate different methodological approaches and key concepts. We then address the practicalities of how to actually do discourse analysis, providing readers with a worked example using one particular approach. Thirdly, we touch on some common debates about discursive research. We conclude by advocating that researchers and practitioners take up the challenge of understanding, utilizing and extending the field of discourse studies within family practice.

Background

The regulation of gene expression is an emerging area of investigation. Increased knowledge can deepen our understanding of the genetic contributions to variations in complex traits. The purpose of this study is to explore the feasibility of detecting regulatory elements of gene expression with multivariate analyses.

Methods

Peripheral blood lymphocyte expression levels of 30 genes on chromosome 5 and a single gene, DEAD, on chromosome 22 were analyzed in single-point variance-component linkage analyses in multiplex families to identify putative regulatory regions. To explore the possibility of regulatory regions having individual relationships with the expression levels of a single gene, we utilized stepwise regression. To explore the possibility of pleiotropy of a single regulatory locus for multiple genes, bivariate linkage analysis was applied.

Results

Twenty-one loci were linked to five expression levels. The two most significant were for the known region on chromosome 22 (LOD = 4.62). On chromosome 5 a LOD of 4.57 was found for the gene leukocyte-derived arginine aminopeptidase (LRAP) with a single-nucleotide polymorphism (SNP) within 5 Mb. Both genes showed evidence of linkage to multiple SNPs. When 194 family members were treated as independent, stepwise regression identified fewer single-nucleotide polymorphisms with significant predictive values (p < 0.05), providing evidence for multiple regulatory regions of unequal effect. However, when corrections for non-independence were applied these results could no longer be detected.

Conclusion

The complex nature of gene regulation can be explored by linkage analysis with single-nucleotide polymorphisms followed by multivariate methods to explore co-regulation.

The purpose of these analyses was to determine if incorporating or adjusting for covariates in genetic analyses helped or hindered in genetic analyses, specifically heritability and linkage analyses. To study this question, two types of covariate models were used in the simulated Genetic Analysis Workshop 14 dataset in which the true gene locations are known. All four populations of one replicate were combined for the analyses. The first model included typical covariates of sex and cohort (population) and the second included the typical covariates and also those related endophenotypes that are thought to be associated with the trait (phenotypes A, B, C, D, E, F, G, H, I, J, K, and L). A final best fit model produced in the heritability analyses was used for linkage. Linkage for disease genes D1, D3, and D4 were localized using models with and without the covariates. The use of inclusion of covariates did not appear to have any consistent advantage or disadvantage for the different phenotypes in regards to gene localization or false positive rate.