DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre‐diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case‐control study nested within the EPIC‐Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case‐control pairs). We validated the top signals in 429 case‐control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p‐value_pooled = 4 × 10⁻¹⁷), cg03636183 in the F2RL3 gene (p‐value_pooled = 2 × 10 ^{− 13}), cg21566642 and cg05951221 in 2q37.1 (p‐value_pooled = 7 × 10⁻¹⁶ and 1 × 10⁻¹¹ respectively), cg06126421 in 6p21.33 (p‐value_pooled = 2 × 10⁻¹⁵) and cg23387569 in 12q14.1 (p‐value_pooled = 5 × 10⁻⁷). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p‐values_{heterogeneity} ≤ 1.8 x10 ^{− 7}), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p‐values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack‐years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.

In mammalian embryonic gonads, SOX9 is required for the determination of Sertoli cells that orchestrate testis morphogenesis. To identify genetic networks directly regulated by SOX9, we combined analysis of SOX9-bound chromatin regions from murine and bovine foetal testes with sequencing of RNA samples from mouse testes lacking Sox9. We found that SOX9 controls a conserved genetic programme that involves most of the sex-determining genes. In foetal testes, SOX9 modulates both transcription and directly or indirectly sex-specific differential splicing of its target genes through binding to genomic regions with sequence motifs that are conserved among mammals and that we called ‘Sertoli Cell Signature’ (SCS). The SCS is characterized by a precise organization of binding motifs for the Sertoli cell reprogramming factors SOX9, GATA4 and DMRT1. As SOX9 biological role in mammalian gonads is to determine Sertoli cells, we correlated this genomic signature with the presence of SOX9 on chromatin in foetal testes, therefore equating this signature to a genomic bar code of the fate of foetal Sertoli cells. Starting from the hypothesis that nuclear factors that bind to genomic regions with SCS could functionally interact with SOX9, we identified TRIM28 as a new SOX9 partner in foetal testes.

Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools. Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets. These results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools.

The disease- and mortality-related difference between biological age based on DNA methylation and chronological age (Δage) has been found to have approximately 40% heritability by assuming that the familial correlation is only explained by additive genetic factors. We calculated two different Δage measures for 132 middle-aged female twin pairs (66 monozygotic and 66 dizygotic twin pairs) and their 215 sisters using DNA methylation data measured by the Infinium HumanMethylation450 BeadChip arrays. For each Δage measure, and their combined measure, we estimated the familial correlation for MZ, DZ and sibling pairs using the multivariate normal model for pedigree analysis. We also pooled our estimates with those from a former study to estimate weighted average correlations. For both Δage measures, there was familial correlation that varied across different types of relatives. No evidence of a difference was found between the MZ and DZ pair correlations, or between the DZ and sibling pair correlations. The only difference was between the MZ and sibling pair correlations (p < .01), and there was marginal evidence that the MZ pair correlation was greater than twice the sibling pair correlation (p < .08). For weighted average correlation, there was evidence that the MZ pair correlation was greater than the DZ pair correlation (p < .03), and marginally greater than twice the sibling pair correlation (p < .08). The varied familial correlation of Δage is not explained by additive genetic factors alone, implying the existence of shared non-genetic factors explaining variation in Δage for middle-aged women.

Biological invasions are increasing globally in number and extent despite efforts to restrict their spread. Knowledge of incursion pathways is necessary to prevent new invasions and to design effective biosecurity protocols at source and recipient locations. This study uses genome‐wide single nucleotide polymorphisms (SNPs) to determine the origin of 115 incursive Aedes aegypti(yellow fever mosquito) detected at international ports in Australia and New Zealand. We also genotyped mosquitoes at three point mutations in the voltage‐sensitive sodium channel (Vssc) gene: V1016G, F1534C and S989P. These mutations confer knockdown resistance to synthetic pyrethroid insecticides, widely used for controlling invertebrate pests. We first delineated reference populations using Ae. aegypti sampled from 15 locations in Asia, South America, Australia and the Pacific Islands. Incursives were assigned to these populations using discriminant analysis of principal components (DAPC) and an assignment test with a support vector machine predictive model. Bali, Indonesia, was the most common origin of Ae. aegypti detected in Australia, while Ae. aegypti detected in New Zealand originated from Pacific Islands such as Fiji. Most incursives had the same allelic genotype across the three Vsscgene point mutations, which confers strong resistance to synthetic pyrethroids, the only insecticide class used in current, widely implemented aircraft disinsection protocols endorsed by the World Health Organization (WHO). Additionally, all internationally assigned Ae. aegypti had Vssc point mutations linked to pyrethroid resistance that are not found in Australian populations. These findings demonstrate that protocols for preventing introductions of invertebrates must consider insecticide resistance, and highlight the usefulness of genomic data sets for managing global biosecurity objectives.