Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome.

Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (r <sub>BMI </sub> = 0.11, P <sub>BMI </sub> = 2.0 × 10 <sup>-51</sup> and r <sub>TG </sub> = 0.13, P <sub>TG </sub> = 1.1 × 10 <sup>-68</sup> ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones

Asparaginase-Associated Pancreatitis in Acute Lymphoblastic Leukemia : Results From the NOPHO ALL2008 Treatment of Patients 1-45 Years of Age

PURPOSE Asparaginase-associated pancreatitis (AAP) is common in patients with acute lymphoblastic leukemia (ALL), but risk differences across age groups both in relation to first-time AAP and after asparaginase re-exposure have not been explored. PATIENTS AND METHODS We prospectively registered AAP (n = 168) during treatment of 2,448 consecutive ALL patients aged 1.0-45.9 years diagnosed from July 2008 to October 2018 and treated according to the Nordic Society of Pediatric Hematology and Oncology (NOPHO) ALL2008 protocol. RESULTS Compared with patients aged 1.0-9.9 years, adjusted AAP hazard ratios (HRa) were associated with higher age with almost identical HRa (1.6; 95% CI, 1.1 to 2.3; P = .02) for adolescents (10.0-17.9 years) and adults (18.0-45.9 years). The day 280 cumulative incidences of AAP were 7.0% for children (1.0-9.9 years: 95% CI, 5.4 to 8.6), 10.1% for adolescents (10.0 to 17.9 years: 95% CI, 7.0 to 13.3), and 11.0% for adults (18.0-45.9 years: 95% CI, 7.1 to 14.9; P = .03). Adolescents had increased odds of both acute (odds ratio [OR], 5.2; 95% CI, 2.1 to 13.2; P = .0005) and persisting complications (OR, 6.7; 95% CI, 2.4 to 18.4; P = .0002) compared with children (1.0-9.9 years), whereas adults had increased odds of only persisting complications (OR, 4.1; 95% CI, 1.4 to 11.8; P = .01). Fifteen of 34 asparaginase-rechallenged patients developed a second AAP. Asparaginase was truncated in 17/21 patients with AAP who subsequently developed leukemic relapse, but neither AAP nor the asparaginase truncation was associated with increased risk of relapse. CONCLUSION Older children and adults had similar AAP risk, whereas morbidity was most pronounced among adolescents. Asparaginase re-exposure should be considered only for patients with an anticipated high risk of leukemic relapse, because multiple studies strongly indicate that reduction of asparaginase treatment intensity increases the risk of relapse. (C) 2019 by American Society of Clinical OncologyPeer reviewe

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries