A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

Bethesda categorization of thyroid nodule cytology and prediction of thyroid cancer type and prognosis

Background: Since its inception, the Bethesda System for Reporting Thyroid Cytopathology (TBS) has been widely adopted. Each category conveys a risk of malignancy and recommended next steps, though it is unclear if each category also predicts the type and extent of malignancy. If so, this would greatly expand the utility of the TBS by providing prognostic information in addition to baseline cancer risk. Methods: All patients prospectively enrolled into the authors' thyroid nodule database from 1995 to 2013 with histologically proven malignancy were analyzed. The primary ultrasound-guided fine-needle aspiration cytology (AUS, atypia of unknown significance; FN, follicular neoplasm; SUSP, suspicious; M, malignant) was correlated with the type of thyroid cancer and histological features known to impact prognosis and recurrence, including lymph node metastasis (LNM), lymphovascular invasion, and extrathyroidal extension (ETE). Primary cytology was separately correlated with higher risk malignancy. Results: A total of 1291 malignancies were identified, with primary cytology AUS in 130 cases, FN in 241 cases, SUSP in 411 cases, and M in 509 cases. AUS, SUSP, and M cytology were progressively associated with an increasing risk of high-risk disease (p < 0.001), LNM (p < 0.001), ETE (p < 0.001), and margin positivity (p < 0.001). Notably, 71% of malignancies with AUS cytology were follicular variants of papillary thyroid cancer compared with 63% with SUSP cytology and only 20% with M cytology. In contrast, high-risk malignancies were diagnosed in only 4% with AUS cytology, but 9% and 27% with SUSP and M cytology, respectively. FN conveyed a significantly increased risk of follicular thyroid carcinoma compared with all other types (28% vs. 2%; p < 0.001). A composite endpoint of recurrence, distant metastases, and death similarly increased as cytology progressed from AUS to SUSP to M (p < 0.001). Conclusion: In addition to predicting cancer prevalence, the TBS also imparts important prognostic information about cancer type, variant, and risk of recurrence. These data extend the utility of TBS classification by fostering an improved understanding of the risk posed by any confirmed malignancy

Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: A multi-ethnic meta-analysis of 45,891 individuals

Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10−8- 1.2 ×10−43). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10−4). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10−3, n = 22,044), increased triglycerides (p = 2.6×10−14, n = 93,440), increased waist-to-hip ratio (p = 1.8×10−5, n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10−3, n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL- cholesterol concentrations (p = 4.5×10−13, n = 96,748) and decreased BMI (p = 1.4×10−4, n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance