Additional file 5: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S5. Distribution of cases/deaths and sub-hazard ratios from competing risk analysis for each Cambridge Prognostic Group (CPG) in the Singapore cohort (n = 2550). (DOCX 15 kb

Additional file 6: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S6. Distribution of cases and deaths from prostate cancer and hazard ratios for each Cambridge Prognostic Group (CPG) in the PCBaSe radical prostatectomy cohort (n = 20,586). (DOCX 15 kb

Additional file 2: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S2. Distribution of the PCBaSe study cohort (n = 72,337) by age, serum PSA at presentation, biopsy Grade Group (GG) and clinical stage (PSA in ng/ml). (DOCX 16 kb

Additional file 4: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S4. Distribution of the Singapore study cohort (n = 2550) by age, PSA at presentation, biopsy Grade Group (GG) and clinical stage (PSA in ng/ml). (DOCX 15 kb

Additional file 10: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S10. Cross tabulation of the CPG and three-strata NICE criteria to show the sub-distributions of the cases between the two models in the PCBaSe cohort (n = 72,337). (DOCX 15 kb

Additional file 8: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S8. Competing risk regression analysis of the Cambridge Prognostic Group (CPG) by treatment type. A. Radical prostatectomy cohort (n = 20,586), B. radical radiotherapy cohort (n = 11,872) and C. conservative management cohort (n = 14,950). Intergroup comparisons are shown. (DOCX 17 kb

Additional file 1: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S1. Use of treatments according to Cambridge Prognostic Group (CPG) in the PCBaSe cohort. (DOCX 15 kb

Additional file 9: of The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study

Table S9. Comparative 10-year prostate cancer mortality rate per 1000 men stratified by treatment type and CPG category in the PCBaSe cohort (n = 72,337) (DOCX 15 kb

Rare and low-frequency coding variants alter human adult heigh

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways