Heritability of testosterone levels in 12-year-old twins and its relation to pubertal development

The aim of this study was to estimate the heritability of variation in testosterone levels in 12-year-old children, and to explore the overlap in genetic and environmental influences on circulating testosterone levels and androgen dependent pubertal development. Midday salivary testosterone samples were collected on two consecutive days in a sample of 183 unselected twin pairs. Androgen induced pubertal development was assessed using self report Tanner scales of pubic hair development (boys and girls) and genital development (boys). A significant contribution of genetic effects to the variance in testosterone levels was found. Heritability was approximately 50% in both boys and girls. The remaining proportion of the variance in testosterone levels could be explained by non-shared environmental influences. The relatively high correlation between testosterone levels of opposite sex dizygotic twins suggests that sex differences in genes influencing variation in testosterone levels have not yet developed in pre- and early puberty. Variance in pubertal development was explained by a large genetic component, moderate shared environmental influences, and a small non-shared environmental effect. Testosterone levels correlated moderately (r = .31) with pubertal development; the covariance between testosterone levels and pubertal development was entirely accounted for by genetic influences

Genetic and environmental covariation between autistic traits and behavioral problems

Objective: To examine the overlap between autistic traits and other behavioral problems in a general population sample, and explore the extent to which this overlap is due to genetic or environmental factors. Method: Youth Self Report (YSR) data were collected in a general population sample of 424 twin pairs at 18 years of age, and their non twin siblings. In 197 of these twin families, self-report ratings on the Autism-spectrum Quotient (AQ) were collected. Results: Stepwise backward regression analyses revealed that of all 8 YSR syndrome scales, the Withdrawn Behavior (WB) and Social Problems (SOC) scale were the most important predictors of AQ scores, and together with sex, explained 23% of the variance in AQ scores. Genetic structural equation modeling showed that the overlap between AQ and WB and SOC was mainly due to genetic effects. About half of the genetic variance in AQ scores was specific to the AQ, with the remaining half shared with genetic variance in WB and SOC. Conclusions: Endorsement of autistic traits in a general population sample is associated with social and withdrawn behavioral problems and these problems partly share a common genetic etiology with autistic traits. However, most of the variance in AQ scores remains unexplained by YSR scores, and half of the genetic variance in AQ is unshared with WB and SOC. These results indicate that autistic traits have specific characteristics that are substantially genetically independent from other common but related behavioral domains such as social problems and withdrawn behavior

Integrative DNA methylome analysis of pan-cancer biomarkers in cancer discordant monozygotic twin-pairs

BACKGROUND: A key focus in cancer research is the discovery of biomarkers that accurately diagnose early lesions in non-invasive tissues. Several studies have identified malignancy-associated DNA methylation changes in blood, yet no general cancer biomarker has been identified to date. Here, we explore the potential of blood DNA methylation as a biomarker of pan-cancer (cancer of multiple different origins) in 41 female cancer discordant monozygotic (MZ) twin-pairs sampled before or after diagnosis using the Illumina HumanMethylation450 BeadChip. RESULTS: We analysed epigenome-wide DNA methylation profiles in 41 cancer discordant MZ twin-pairs with affected individuals diagnosed with tumours at different single primary sites: the breast, cervix, colon, endometrium, thyroid gland, skin (melanoma), ovary, and pancreas. No significant global differences in whole blood DNA methylation profiles were observed. Epigenome-wide analyses identified one novel pan-cancer differentially methylated position at false discovery rate (FDR) threshold of 10 % (cg02444695, P = 1.8 × 10(-7)) in an intergenic region 70 kb upstream of the SASH1 tumour suppressor gene, and three suggestive signals in COL11A2, AXL, and LINC00340. Replication of the four top-ranked signals in an independent sample of nine cancer-discordant MZ twin-pairs showed a similar direction of association at COL11A2, AXL, and LINC00340, and significantly greater methylation discordance at AXL compared to 480 healthy concordant MZ twin-pairs. The effects at cg02444695 (near SASH1), COL11A2, and LINC00340 were the most promising in biomarker potential because the DNA methylation differences were found to pre-exist in samples obtained prior to diagnosis and were limited to a 5-year period before diagnosis. Gene expression follow-up at the top-ranked signals in 283 healthy individuals showed correlation between blood methylation and gene expression in lymphoblastoid cell lines at PRL, and in the skin tissue at AXL. A significant enrichment of differential DNA methylation was observed in enhancer regions (P = 0.03). CONCLUSIONS: We identified DNA methylation signatures in blood associated with pan-cancer, at or near SASH1, COL11A2, AXL, and LINC00340. Three of these signals were present up to 5 years prior to cancer diagnosis, highlighting the potential clinical utility of whole blood DNA methylation analysis in cancer surveillance

The use of vector bootstrapping to improve variable selection precision in Lasso models

The Lasso is a shrinkage regression method that is widely used for variable selection in statistical genetics. Commonly, K-fold cross-validation is used to fit a Lasso model. This is sometimes followed by using bootstrap confidence intervals to improve precision in the resulting variable selections. Nesting cross-validation within bootstrapping could provide further improvements in precision, but this has not been investigated systematically. We performed simulation studies of Lasso variable selection precision (VSP) with and without nesting cross-validation within bootstrapping. Data were simulated to represent genomic data under a polygenic model as well as under a model with effect sizes representative of typical GWAS results. We compared these approaches to each other as well as to software defaults for the Lasso. Nested cross-validation had the most precise variable selection at small effect sizes. At larger effect sizes, there was no advantage to nesting. We illustrated the nested approach with empirical data comprising SNPs and SNP-SNP interactions from the most significant SNPs in a GWAS of borderline personality symptoms. In the empirical example, we found that the default Lasso selected low-reliability SNPs and interactions which were excluded by bootstrapping

Scale construction and evaluation in practice:A review of factor analysis versus item response theory applications

In scale construction and evaluation, factor analysis (FA) and item response theory (IRT) are two methods frequently used to determine whether a set of items reliably measures a latent variable. In a review of 41 published studies we examined which methodology – FA or IRT – was used, and what researchers’ motivations were for applying either method. Characteristics of the studies were compared to gain more insight into the practice of scale analysis. Findings indicate that FA is applied far more often than IRT. Many times it is unclear whether the data justify the chosen method because model assumptions are neglected. We recommended that researchers (a) use substantive knowledge about the items to their advantage by more frequently employing confirmatory techniques, as well as adding item content and interpretability of factors to the criteria in model evaluation; and (b) investigate model assumptions and report corresponding findings. To this end, we recommend more collaboration between substantive researchers and statisticians/psychometricians

A frailty model for (interval) censored family survival data, applied to the age at onset of non-physical problems

Family survival data can be used to estimate the degree of genetic and environmental contributions to the age at onset of a disease or of a specific event in life. The data can be modeled with a correlated frailty model in which the frailty variable accounts for the degree of kinship within the family. The heritability (degree of heredity) of the age at a specific event in life (or the onset of a disease) is usually defined as the proportion of variance of the survival age that is associated with genetic effects. If the survival age is (interval) censored, heritability as usually defined cannot be estimated. Instead, it is defined as the proportion of variance of the frailty associated with genetic effects. In this paper we describe a correlated frailty model to estimate the heritability and the degree of environmental effects on the age at which individuals contact a social worker for the first time and to test whether there is a difference between the survival functions of this age for twins and non-twins. © 2009 The Author(s)

Genetic Covariance Structure of Reading, Intelligence and Memory in Children

This study investigates the genetic relationship among reading performance, IQ, verbal and visuospatial working memory (WM) and short-term memory (STM) in a sample of 112, 9-year-old twin pairs and their older siblings. The relationship between reading performance and the other traits was explained by a common genetic factor for reading performance, IQ, WM and STM and a genetic factor that only influenced reading performance and verbal memory. Genetic variation explained 83% of the variation in reading performance; most of this genetic variance was explained by variation in IQ and memory performance. We hypothesize, based on these results, that children with reading problems possibly can be divided into three groups: (1) children low in IQ and with reading problems; (2) children with average IQ but a STM deficit and with reading problems; (3) children with low IQ and STM deficits; this group may experience more reading problems than the other two