Chemotherapy-induced myelosuppression, including thrombocytopenia, is a recurrent problem during cancer treatments that may require dose alterations or cessations that could affect the anti-tumor effect of the treatment. To identify genetic markers associated with treatment-induced thrombocytopenia, we whole-exome sequenced 215 non-small cell lung cancer patients homogeneously treated with gemcitabine/carboplatin. The decrease in platelets (defined as nadir/baseline) was used to assess treatment-induced thrombocytopenia. Association between germline genetic variants and thrombocytopenia was analyzed at single-nucleotide variant (SNV) (based on the optimal false discovery rate, the severity of predicted consequence, and effect), gene, and pathway levels. These analyses identified 130 SNVs and 25 genes associated with thrombocytopenia (P-value<0.002). 23 SNVs were validated in an independent genome-wide association study (GWAS). The top associations include rs34491125 in JMJD1C (P-value=9.07×10-5), the validated variants rs10491684 in DOCK8 (P-value=1.95×10-4), rs6118 in SERPINA5 (P-value=5.83×10-4) and rs5877 in SERPINC1 (P-value=1.07×10-3), and the genes CAPZA2 (P-value=4.03×10-4) and SERPINC1 (P-value=1.55×10-3). The SNVs in the topscoring pathway "Factors involved in megakaryocyte development and platelet production" (P-value=3.34×10-4) were used to construct weighted genetic risk scores (wGRS) and logistic regression models that predict thrombocytopenia. The wGRS predict which patients are at high or low toxicity risk levels, for CTCAE (OR=22.35, P-value=1.55×10-8), and decrease (OR=66.82, P-value=5.92×10-9). The logistic regression models predict CTCAE grades 3-4 (receiver operator characteristics (ROC) area under the curve (AUC) = 0.79), and large decrease (ROC AUC=0.86). We identified and validated genetic variations within hematopoiesis-related pathways that provide a solid foundation for future studies using genetic markers for predicting chemotherapy-induced thrombocytopenia and personalizing treatments.

The curation of genetic variants from biomedical articles is required for various clinical and research purposes. Nowadays, establishment of variant databases that include overall information about variants is becoming quite popular. These databases have immense utility, serving as a user-friendly information storehouse of variants for information seekers. While manual curation is the gold standard method for curation of variants, it can turn out to be time-consuming on a large scale thus necessitating the need for automation. Curation of variants described in biomedical literature may not be straightforward mainly due to various nomenclature and expression issues. Though current trends in paper writing on variants is inclined to the standard nomenclature such that variants can easily be retrieved, we have a massive store of variants in the literature that are present as non-standard names and the online search engines that are predominantly used may not be capable of finding them. For effective curation of variants, knowledge about the overall process of curation, nature and types of difficulties in curation, and ways to tackle the difficulties during the task are crucial. Only by effective curation, can variants be correctly interpreted. This paper presents the process and difficulties of curation of genetic variants with possible solutions and suggestions from our work experience in the field including literature support. The paper also highlights aspects of interpretation of genetic variants and the importance of writing papers on variants following standard and retrievable methods.

Background: Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease. Methods: In this study, we generated high-resolution genomic interaction maps (≈750 bases) in aortic endothelial, smooth muscle cells and THP-1 (human leukemia monocytic cell line) macrophages stimulated with lipopolysaccharide using Hi-C coupled with sequence capture targeting 25 429 features, including variants associated with coronary artery disease. We also sequenced their transcriptomes and mapped putative enhancers using chromatin immunoprecipitation with an antibody against H3K27Ac. Results: The regions interacting with promoters showed strong enrichment for enhancer elements and validated several previously known interactions and enhancers. We detected interactions for 727 risk variants obtained by genome-wide association studies and identified novel, as well as established genes and functions associated with cardiovascular diseases. We were able to assign potential target genes for additional 398 genome-wide association studies variants using haplotype information, thereby identifying additional relevant genes and functions. Importantly, we discovered that a subset of risk variants interact with multiple promoters and their expression levels were strongly correlated. Conclusions: In summary, we present a catalog of candidate genes regulated by coronary artery disease–related variants and think that it will be an invaluable resource to further the investigation of cardiovascular pathologies and disease.