Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other finemapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to onefifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf. KEYWORDS Bayesian fine mapping; marginal test statistics; causal variants U NTIL recently, there have been .2000 genome-wide association studies (GWAS) published with different traits or disease status (Hindorff et al. 2014). Most of them reported only regions of association, represented by SNPs with the lowest P-values in each region. Only a few provide further information of likely underlying causal variants. A noted exception is refinement based on Bayesian methods (Maller et al. 2012). Fine mapping the causal variants from the verified association regions is an important step toward understanding the complex biological mechanisms linking the genetic code to various traits or phenotypes.Fine-mapping methods can be roughly divided into two groups. The first group was developed before the availability of high-density genotype data. These fine-mapping methods assume the causal variants are not genotyped in the data and aim to identify a region as close as possible to the causal variants (Morris et al. 2002; Durrant et al. 2004;Liang and Chiu 2005;Zollner and Pritchard 2005;Minichiello and Durbin 2006;Waldron et al. 2006). Because the causal variants are not observed in the data, these methods usually rely on various strong assumptions to model the relationship of the causal and the observed variants. Examples include models based on the coalescent theory (Morris et al. 2002;Zollner and Pritchard 2005;Minichiello and Durbin 2006) or statistical assumptions about the patterns of linkage disequilibrium (LD) (Liang and ...

A defining feature of adaptive immunity is the development of long-lived memory T cells to curtail infection. Recent studies have identified a unique stem-like T cell subset in exhausted CD8+ T cells in chronic infection1–3, but it remains unclear whether CD4+ T cell subsets with similar features exist in chronic inflammatory conditions. Among helper T cells, TH17 cells play prominent roles in autoimmunity and tissue inflammation and are characterized by inherent plasticity4–7, although the regulation of plasticity is poorly understood. Here we demonstrate that TH17 cells in autoimmune disease are functionally and metabolically heterogeneous and contain a subset with stemness-associated features but lower anabolic metabolism, and a reciprocal subset with higher metabolic activity that supports the transdifferentiation into TH1 cells. These two TH17 cell subsets are defined by selective expression of transcription factors TCF-1 and T-bet, and discrete CD27 expression levels. Moreover, we identify mTORC1 signaling as a central regulator to orchestrate TH17 cell fates by coordinating metabolic and transcriptional programs. TH17 cells with disrupted mTORC1 or anabolic metabolism fail to induce autoimmune neuroinflammation or develop into TH1-like cells, but instead upregulate TCF-1 expression and activity and acquire stemness-associated features. Single cell RNA-sequencing and experimental validation reveal heterogeneity in fate-mapped TH17 cells, and a developmental arrest in the TH1 transdifferentiation trajectory upon mTORC1 deletion or metabolic perturbation. Our results establish that the dichotomy of stemness and effector function underlies the heterogeneous TH17 responses and autoimmune pathogenesis, and point to previously unappreciated metabolic control of helper T cell plasticity.

Purpose Childhood cancer survivors are at increased risk of subsequent neoplasms (SNs), but the germline genetic contribution is largely unknown. We assessed the contribution of pathogenic/likely pathogenic (P/LP) mutations in cancer predisposition genes to their SN risk. Patients and Methods Whole-genome sequencing (30-fold) was performed on samples from childhood cancer survivors who were ≥ 5 years since initial cancer diagnosis and participants in the St Jude Lifetime Cohort Study, a retrospective hospital-based study with prospective clinical follow-up. Germline mutations in 60 genes known to be associated with autosomal dominant cancer predisposition syndromes with moderate to high penetrance were classified by their pathogenicity according to the American College of Medical Genetics and Genomics guidelines. Relative rates (RRs) and 95% CIs of SN occurrence by mutation status were estimated using multivariable piecewise exponential regression stratified by radiation exposure. Results Participants were 3,006 survivors (53% male; median age, 35.8 years [range, 7.1 to 69.8 years]; 56% received radiotherapy), 1,120 SNs were diagnosed among 439 survivors (14.6%), and 175 P/LP mutations were identified in 5.8% (95% CI, 5.0% to 6.7%) of survivors. Mutations were associated with significantly increased rates of breast cancer (RR, 13.9; 95% CI, 6.0 to 32.2) and sarcoma (RR, 10.6; 95% CI, 4.3 to 26.3) among irradiated survivors and with increased rates of developing any SN (RR, 4.7; 95% CI, 2.4 to 9.3), breast cancer (RR, 7.7; 95% CI, 2.4 to 24.4), nonmelanoma skin cancer (RR, 11.0; 95% CI, 2.9 to 41.4), and two or more histologically distinct SNs (RR, 18.6; 95% CI, 3.5 to 99.3) among nonirradiated survivors. Conclusion The findings support referral of all survivors for genetic counseling for potential clinical genetic testing, which should be prioritized for nonirradiated survivors with any SN and for those with breast cancer or sarcoma in the field of prior irradiation.

Read counting and unique molecular identifier (UMI) counting are the principal gene expression quantification schemes used in single-cell RNA-sequencing (scRNA-seq) analysis. By using multiple scRNA-seq datasets, we reveal distinct distribution differences between these schemes and conclude that the negative binomial model is a good approximation for UMI counts, even in heterogeneous populations. We further propose a novel differential expression analysis algorithm based on a negative binomial model with independent dispersions in each group (NBID). Our results show that this properly controls the FDR and achieves better power for UMI counts when compared to other recently developed packages for scRNA-seq analysis.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1438-9) contains supplementary material, which is available to authorized users.