ANO7 is associated with aggressive prostate cancer.

Prostate cancer is one of the most common and heritable human cancers. Our aim was to find germline biomarkers that can predict disease outcome. We previously detected predisposing signals at 2q37, the location of the prostate specific ANO7 gene. To investigate, in detail, the associations between the ANO7 gene and PrCa risk and disease aggressiveness, ANO7 was sequenced in castration resistant tumors together with samples from unselected PrCa patients and unaffected males. Two pathogenic variants were discovered and genotyped in 1769 patients and 1711 unaffected males. Expression of ANO7 vs. PrCa aggressiveness was investigated. Different databases along with Swedish and Norwegian cohorts were used for validation. Case-control and aggressive vs. nonaggressive association analyses were performed against risk and/or cancer aggressiveness. The ANO7 mRNA level and patient survival were analyzed using expression data from databases. Variant rs77559646 showed both risk (OR 1.40; p = 0.009, 95% CI 1.09-1.78) and association with aggressive PrCa (Genotype test p = 0.04). It was found to be an eQTL for ANO7 (Linear model p-values for Finnish patients p = 0.009; Camcap prostate tumor p = 2.53E-06; Stockholm prostate tumor cohort p = 1.53E-13). rs148609049 was not associated with risk, but was related to shorter survival (HR 1.56; 95% CI 1.03-2.36). High ANO7 expression was independently linked to poor survival (HR 18.4; 95% CI 1.43-237). ANO7 genotypes correlate with expression and biochemical relapse, suggesting that ANO7 is a potential PrCa susceptibility gene and that its elevated expression correlates with disease severity and outcome.

Kuura-An automated workflow for analyzing WES and WGS data.

The advent of high-throughput sequencing technologies has revolutionized the field of genomic sciences by cutting down the cost and time associated with standard sequencing methods. This advancement has not only provided the research community with an abundance of data but has also presented the challenge of analyzing it. The paramount challenge in analyzing the copious amount of data is in using the optimal resources in terms of available tools. To address this research gap, we propose "Kuura-An automated workflow for analyzing WES and WGS data", which is optimized for both whole exome and whole genome sequencing data. This workflow is based on the nextflow pipeline scripting language and uses docker to manage and deploy the workflow. The workflow consists of four analysis stages-quality control, mapping to reference genome & quality score recalibration, variant calling & variant recalibration and variant consensus & annotation. An important feature of the DNA-seq workflow is that it uses the combination of multiple variant callers (GATK Haplotypecaller, DeepVariant, VarScan2, Freebayes and Strelka2), generating a list of high-confidence variants in a consensus call file. The workflow is flexible as it integrates the fragmented tools and can be easily extended by adding or updating tools or amending the parameters list. The use of a single parameters file enhances reproducibility of the results. The ease of deployment and usage of the workflow further increases computational reproducibility providing researchers with a standardized tool for the variant calling step in different projects. The source code, instructions for installation and use of the tool are publicly available at our github repository https://github.com/dhanaprakashj/kuura_pipeline.