A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package

Oral etoposide as a single agent in childhood and young adult cancer in England: Still a poorly evaluated palliative treatment

Background Oral etoposide is commonly used in palliative treatment of childhood and young adult cancer without robust evidence. We describe a national, unselected cohort of young people in England treated with oral etoposide using routinely collected, population-level data. Methods Patients aged under 25 years at cancer diagnosis (1995–2017) with a treatment record of single-agent oral etoposide in the Systemic AntiCancer Dataset (SACT, 2012–2018) were identified, linked to national cancer registry data using NHS number and followed to 5 January 2019. Overall survival (OS) was estimated for all tumours combined and by tumour group. A Cox model was applied accounting for age, sex, tumour type, prior and subsequent chemotherapy. Results Total 115 patients were identified during the study period. Mean age was 11.8 years at cancer diagnosis and 15.5 years at treatment with oral etoposide. Median OS was 5.5 months from the start of etoposide; 13 patients survived beyond 2 years. Survival was shortest in patients with osteosarcoma (median survival 3.6 months) and longest in CNS embryonal tumours (15.5 months). Across the cohort, a median of one cycle (range one to nine) of etoposide was delivered. OS correlated significantly with tumour type and prior chemotherapy, but not with other variables. Conclusions This report is the largest series to date of oral etoposide use in childhood and young adult cancer. Most patients treated in this real world setting died quickly. Despite decades of use, there are still no robust data demonstrating a clear benefit of oral etoposide for survival

An Evolutionary Framework for Association Testing in Resequencing Studies

Sequencing technologies are becoming cheap enough to apply to large numbers of study participants and promise to provide new insights into human phenotypes by bringing to light rare and previously unknown genetic variants. We develop a new framework for the analysis of sequence data that incorporates all of the major features of previously proposed approaches, including those focused on allele counts and allele burden, but is both more general and more powerful. We harness population genetic theory to provide prior information on effect sizes and to create a pooling strategy for information from rare variants. Our method, EMMPAT (Evolutionary Mixed Model for Pooled Association Testing), generates a single test per gene (substantially reducing multiple testing concerns), facilitates graphical summaries, and improves the interpretation of results by allowing calculation of attributable variance. Simulations show that, relative to previously used approaches, our method increases the power to detect genes that affect phenotype when natural selection has kept alleles with large effect sizes rare. We demonstrate our approach on a population-based re-sequencing study of association between serum triglycerides and variation in ANGPTL4

What's in a name; Genetic structure in Solanum section Petota studied using population-genetic tools

Background - The taxonomy and systematic relationships among species of Solanum section Petota are complicated and the section seems overclassified. Many of the presumed (sub)species from South America are very similar and they are able to exchange genetic material. We applied a population genetic approach to evaluate support for subgroups within this material, using AFLP data. Our approach is based on the following assumptions: (i) accessions that may exchange genetic material can be analyzed as if they are part of one gene pool, and (ii) genetic differentiation among species is expected to be higher than within species. Results - A dataset of 566 South-American accessions (encompassing 89 species and subspecies) was analyzed in two steps. First, with the program STRUCTURE 2.2 in an 'unsupervised' procedure, individual accessions were assigned to inferred clusters based on genetic similarity. The results showed that the South American members of section Petota could be arranged in 16 clusters of various size and composition. Next, the accessions within the clusters were grouped by maximizing the partitioning of genetic diversity among subgroups (i.e., maximizing Fst values) for all available individuals of the accessions (2767 genotypes). This two-step approach produced an optimal partitioning into 44 groups. Some of the species clustered as genetically distinct groups, either on their own, or combined with one or more other species. However, accessions of other species were distributed over more than one cluster, and did not form genetically distinct units. Conclusions - We could not find any support for 43 species (almost half of our dataset). For 28 species some level of support could be found varying from good to weak. For 18 species no conclusions could be drawn as the number of accessions included in our dataset was too low. These molecular data should be combined with data from morphological surveys, with geographical distribution data, and with information from crossing experiments to identify natural units at the species level. However, the data do indicate which taxa or combinations of taxa are clearly supported by a distinct set of molecular marker data, leaving other taxa unsupported. Therefore, the approach taken provides a general method to evaluate the taxonomic system in any species complex for which molecular data are available

Search for astronomical neutrinos from blazar TXS 0506+056 in super-kamiokande

We report a search for astronomical neutrinos in the energy region from several GeV to TeV in the direction of the blazar TXS 0506+056 using the Super-Kamiokande detector following the detection of a 100 TeV neutrinos from the same location by the IceCube collaboration. Using Super-Kamiokande neutrino data across several data samples observed from 1996 April to 2018 February we have searched for both a total excess above known backgrounds across the entire period as well as localized excesses on smaller timescales in that interval. No significant excess nor significant variation in the observed event rate are found in the blazar direction. Upper limits are placed on the electron- and muon-neutrino fluxes at the 90% confidence level as 6.0 × 10−7 and 4.5 × 10−7–9.3 × 10−10 [erg cm−2 s−1], respectively