Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with increased risk of cancer. However, the amount of heritable risk explained by SNPs is limited, leaving most of cancer heritability unexplained. Tumor sequencing projects have shown that causal mutations are enriched in genic regions. We hypothesized that SNPs located in protein coding genes and nearby regulatory regions could explain a significant proportion of the heritable risk of cancer.To perform gene-level heritability analysis, we developed a new method, called Bayesian Gene HERitability Analysis (BAGHERA), to estimate the heritability explained by all genotyped SNPs and by those located in genic regions using GWAS summary statistics. BAGHERA was specifically designed for low heritability traits such as cancer and provides robust heritability estimates under different genetic architectures. BAGHERA-based analysis of 38 cancers reported in the UK Biobank showed that SNPs explain at least 10% of the heritable risk for 14 of them, including late onset malignancies. We then identified 1,146 genes, called cancer heritability genes (CHGs), explaining a significant proportion of cancer heritability. CHGs were involved in hallmark processes controlling the transformation from normal to cancerous cells. Importantly, 60 of them also harbored somatic driver mutations, and 27 are tumor suppressors. Our results suggest that germline and somatic mutation information could be exploited to identify subgroups of individuals at higher risk of cancer in the broader population and could prove useful to establish strategies for early detection and cancer surveillance. SignificanceThis study describes a new statistical method to identify genes associated with cancer heritability in the broader population, creating a map of the heritable cancer genome with gene-level resolution.Research.

The Sc2.0 project is building a eukaryotic synthetic genome from scratch, incorporating thousands of designer features. A major milestone has been achieved with the assembly of all individual Sc2.0 chromosomes. Here, we describe the consolidation of multiple synthetic chromosomes using endoreduplication intercross to generate a strain with 6.5 synthetic chromosomes. Genome-wide chromosome conformation capture and long-read direct RNA sequencing were performed on this strain to evaluate the effects of designer modifications, such as loxPsym site insertion, tRNA relocation, and intron deletion, on 3D chromosome organization and transcript isoform profiles. To precisely map "bugs", we developed a method, CRISPR Directed Biallelic URA3-assisted Genome Scan, or CRISPR D-BUGS, exploiting directed mitotic recombination in heterozygous diploids. Using this method, we first fine-mapped a synII defect resulting from two loxPsym sites in the 3′ UTR of SHM1. This approach was also used to map a combinatorial bug associated with synIII and synX, revealing a highly unexpected genetic interaction that links transcriptional regulation, inositol metabolism and tRNASerCGA abundance. "Starvation" for tRNASerCGA leads to insufficient levels of the key positive inositol biosynthesis regulator, Swi3, which contains tandem UCG codons. Finally, to further expedite consolidation, we employed a new method, chromosome swapping, to incorporate the largest chromosome (synIV), thereby consolidating more than half of the Sc2.0 genome in a single strain.

IL‐17 mediates immune protection from fungi and bacteria, as well as it promotes autoimmune pathologies. However, the regulation of the signal transduction from the IL‐17 receptor (IL‐17R) remained elusive. We developed a novel mass spectrometry‐based approach to identify components of the IL‐17R complex followed by analysis of their roles using reverse genetics. Besides the identification of linear ubiquitin chain assembly complex (LUBAC) as an important signal transducing component of IL‐17R, we established that IL‐17 signaling is regulated by a robust negative feedback loop mediated by TBK1 and IKKε. These kinases terminate IL‐17 signaling by phosphorylating the adaptor ACT1 leading to the release of the essential ubiquitin ligase TRAF6 from the complex. NEMO recruits both kinases to the IL‐17R complex, documenting that NEMO has an unprecedented negative function in IL‐17 signaling, distinct from its role in NF‐κB activation. Our study provides a comprehensive view of the molecular events of the IL‐17 signal transduction and its regulation.

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Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.