Performing highly parallelized and reproducible GWAS analysis on biobank-scale data.

Genome-wide association studies (GWAS) are transforming genetic research and enable the detection of novel genotype-phenotype relationships. In the last two decades, over 60 000 genetic associations across thousands of traits have been discovered using a GWAS approach. Due to increasing sample sizes, researchers are increasingly faced with computational challenges. A reproducible, modular and extensible pipeline with a focus on parallelization is essential to simplify data analysis and to allow researchers to devote their time to other essential tasks. Here we present nf-gwas, a Nextflow pipeline to run biobank-scale GWAS analysis. The pipeline automatically performs numerous pre- and post-processing steps, integrates regression modeling from the REGENIE package and supports single-variant, gene-based and interaction testing. It includes an extensive reporting functionality that allows to inspect thousands of phenotypes and navigate interactive Manhattan plots directly in the web browser. The pipeline is tested using the unit-style testing framework nf-test, a crucial requirement in clinical and pharmaceutical settings. Furthermore, we validated the pipeline against published GWAS datasets and benchmarked the pipeline on high-performance computing and cloud infrastructures to provide cost estimations to end users. nf-gwas is a highly parallelized, scalable and well-tested Nextflow pipeline to perform GWAS analysis in a reproducible manner.

KLKB1 and CLSTN2 are associated with HDL-mediated cholesterol efflux capacity in a genome-wide association study.

BACKGROUND AND AIMS: HDL-mediated cholesterol efflux capacity (CEC) may protect from cardiovascular disease. Thus, we aimed to identify its genetic and non-genetic determinants. METHODS: We measured CEC to 2% apolipoprotein B-depleted serum using BODIPY-cholesterol and cAMP-stimulated J774A.1 macrophages using serum samples from 4,981 participants in the German Chronic Kidney Disease (GCKD) study. Variance of CEC explained by clinical and biochemical parameters in a multivariable linear regression model was calculated by proportional marginal variance decomposition. A genome-wide association study with 7,746,917 variants was performed based on an additive genetic model. The main model was adjusted for age, sex and principal components 1-10. Further models were selected for sensitivity analysis and to reduce residual variance by known CEC pathways. RESULTS: Variables that explained 1% and more of the variance of CEC were concentrations of triglycerides (12.9%), HDL-cholesterol (11.8%), LDL-cholesterol (3.0%), apolipoprotein A-IV (2.8%), PCSK9 (1.0%), and eGFR (1.0%). The KLKB1 (chr4) and APOE/C1 (chr19) loci were genome-wide significantly (p < 5x10-8) associated with CEC in our main model (p = 8.8x10-10 and p = 3.3x10-10, respectively). KLKB1 remained significantly associated after additional adjustment for either kidney parameters, HDL-cholesterol, triglycerides or apolipoprotein A-IV concentrations, while the APOE/C1 locus was not significantly associated anymore after adjustment for triglycerides. Adjustment for triglycerides also revealed an association with the CLSTN2 locus (chr3; p = 6.0x10-9). CONCLUSIONS: We identified HDL-cholesterol and triglycerides as the main determinants of CEC. Furthermore, we newly found a significant association of CEC with the KLKB1 and the CLSTN2 locus and confirmed the association with the APOE/C1 locus, likely mediated by triglycerides.

PCSK9 and Cardiovascular Disease in Individuals with Moderately Decreased Kidney Function.

BACKGROUND AND OBJECTIVES: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key regulator of lipid homeostasis. Studies investigating the association between PCSK9 and cardiovascular disease in large cohorts of patients with CKD are limited. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The association of PCSK9 concentrations with prevalent and incident cardiovascular disease was investigated in 5138 White participants of the German Chronic Kidney Disease study with a median follow-up of 6.5 years. Inclusion criteria were eGFR of 30-60 or >60 ml/min per 1.73 m2 in the presence of overt proteinuria (urine albumin-creatinine ratio >300 mg/g or equivalent). Prevalent cardiovascular disease was defined as a history of nonfatal myocardial infarction, coronary artery bypass grafting, percutaneous transluminal coronary angioplasty, carotid arteries interventions, and stroke. Incident major adverse cardiovascular disease events included death from cardiovascular causes, acute nonfatal myocardial infarction, and nonfatal stroke. RESULTS: Median PCSK9 concentration in the cohort was 285 ng/ml (interquartile range, 231-346 ng/ml). There was no association between PCSK9 concentrations and baseline eGFR and albuminuria. With each 100-ng/ml increment of PCSK9, the odds for prevalent cardiovascular disease (n=1284) were 1.22-fold (95% confidence interval, 1.12 to 1.34; P<0.001) higher in a model with extended adjustment for major confounders. This association was stronger in nonstatin than statin users (P value for interaction =0.009). During follow-up, 474 individuals experienced a major adverse cardiovascular disease event, and participants in PCSK9 quartiles 2-4 had a 32%-47% higher risk compared with those in quartile 1 (P<0.05). Subgroup analysis revealed that this association was restricted to those participants who already had cardiovascular disease at baseline (all hazard ratios >1.75; P=0.01). In addition, PCSK9 showed a valuable gain in classification accuracy for both prevalent cardiovascular disease (net reclassification index =0.27; 95% confidence interval, 0.20 to 0.33) and incident major adverse cardiovascular disease events during follow-up (net reclassification index =0.10; 95% confidence interval, 0.01 to 0.21) when added to an extended adjustment model. CONCLUSIONS: Our findings reveal no relation of PCSK9 with baseline eGFR and albuminuria but a significant association between higher PCSK9 concentrations and risk of cardiovascular disease independent of traditional risk factors, including LDL cholesterol levels.Clinical Trial registry name and registration number: German Chronic Kidney Disease Study (GCKD), DRKS 00003971.

Lysis reagents, cell numbers, and calculation method influence high-throughput measurement of HDL-mediated cholesterol efflux capacity.

HDL-mediated cholesterol efflux capacity (CEC) may protect against cardiovascular disease. However, CEC assays are not standardized, hampering their application in large cohorts and comparison between studies. To improve standardization, we systematically investigated technical differences between existing protocols that influence assay performance that have not been previously addressed. CEC was measured in 96-well plates using J774A.1 macrophages labeled with BODIPY-cholesterol and incubated for 4 h with 2% apolipoprotein B-depleted human serum. The time zero method, which calculates CEC using control wells, and the per-well method, which calculates CEC based on the actual content of BODIPY-cholesterol in each well, were compared in 506 samples. We showed that the per-well method had a considerably lower sample rejection rate (4.74% vs. 13.44%) and intra-assay (4.48% vs. 5.28%) and interassay coefficients of variation (two controls: 7.85%, 9.86% vs. 13.58%, 15.29%) compared with the time zero method. Correction for plate-to-plate differences using four controls on each plate also improved assay performance of both methods. In addition, we observed that the lysis reagent used had a significant effect. Compared with cholic acid, lysis with sodium hydroxide results in higher (P = 0.0082) and Triton X-100 in lower (P = 0.0028) CEC values. Furthermore, large cell seeding errors (30% variation) greatly biased CEC for both referencing methods (P < 0.0001) as measured by a resazurin assay. In conclusion, lysis reagents, cell numbers, and assay setup greatly impact the quality and reliability of CEC quantification and should be considered when this method is newly established in a laboratory.

Frequent LPA KIV-2 Variants Lower Lipoprotein(a) Concentrations and Protect Against Coronary Artery Disease.

BACKGROUND: Lipoprotein(a) (Lp(a)) concentrations are a major independent risk factor for coronary artery disease (CAD) and are mainly determined by variation in LPA. Up to 70% of the LPA coding sequence is located in the hypervariable kringle IV type 2 (KIV-2) region. It is hardly accessible by conventional technologies, but may contain functional variants. OBJECTIVES: This study sought to investigate the new, very frequent splicing variant KIV-2 4733G>A on Lp(a) and CAD. METHODS: We genotyped 4733G>A in the GCKD (German Chronic Kidney Disease) study (n = 4,673) by allele-specific polymerase chain reaction, performed minigene assays, identified proxy single nucleotide polymorphisms and used them to characterize its effect on CAD by survival analysis in UK Biobank (n = 440,234). Frequencies in ethnic groups were assessed in the 1000 Genomes Project. RESULTS: The 4733G>A variant (38.2% carrier frequency) was found in most isoform sizes. It reduces allelic expression without abolishing protein production, lowers Lp(a) by 13.6 mg/dL (95% CI: 12.5-14.7; P < 0.0001) and is the strongest variance-explaining factor after the smaller isoform. Splicing of minigenes was modified. Compound heterozygosity (4.6% of the population) for 4733G>A and 4925G>A, another KIV-2 splicing mutation, reduces Lp(a) by 31.8 mg/dL and most importantly narrows the interquartile range by 9-fold (from 42.1 to 4.6 mg/dL) when compared to the wild type. In UK Biobank 4733G>A alone and compound heterozygosity with 4925G>A reduced HR for CAD by 9% (95% CI: 7%-11%) and 12% (95% CI: 7%-16%) (both P < 0.001). Frequencies in ethnicities differ notably. CONCLUSIONS: Functional variants in the previously inaccessible LPA KIV-2 region cooperate in determining Lp(a) variance and CAD risk. Even a moderate but lifelong genetic Lp(a) reduction translates to a noticeable CAD risk reduction.

A TLR7 agonist strengthens T and NK cell function during BRAF-targeted therapy in a preclinical melanoma model.

Therapeutic success of targeted therapy with BRAF inhibitors (BRAFi) for melanoma is limited by resistance development. Observations from preclinical mouse models and recent insights into the immunological effects caused by BRAFi give promise for future development of combination therapy for human melanoma. In our study, we used the transplantable D4M melanoma mouse model with the BRAFV600E mutation and concomitant PTEN loss in order to characterize alterations in tumor-infiltrating effector immune cells when tumors become resistant to BRAFi. We found that BRAFi-sensitive tumors displayed a pronounced inflammatory milieu characterized by high levels of cytokines and chemokines accompanied by an infiltration of T and NK cells. The tumor-infiltrating effector cells were activated and produced high levels of IFN-γ, TNF-α and granzyme B. When tumors became resistant and progressively grew, they reverted to a low immunogenic state similar to untreated tumors as reflected by low mRNA levels of proinflammatory cytokines and chemokines and fewer tumor-infiltrating T and NK cells. Moreover, these T and NK cells were functionally impaired in comparison to their counterparts in BRAFi-sensitive tumors. Their effector cell function could be restored by additional peritumoral treatment with the TLR7 agonist imiquimod, a clinically approved agent for nonmelanoma skin cancer. Indeed, resistance to BRAFi therapy was delayed and accompanied by high numbers of activated T and NK cells in tumors. Thus, combining BRAFi with an immune stimulating agent such as a TLR ligand could be a promising alternative approach for the treatment of melanoma.