Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers.

Polygenic scores (PGS) enable the prediction of genetic predisposition for a wide range of traits and diseases by calculating the weighted sum of allele dosages for genetic variants associated with the trait or disease in question. Present approaches for calculating PGS from genotypes are often inefficient and labor-intensive, limiting transferability into clinical applications. Here, we present 'Imputation Server PGS', an extension of the Michigan Imputation Server designed to automate a standardized calculation of polygenic scores based on imputed genotypes. This extends the widely used Michigan Imputation Server with new functionality, bringing the simplicity and efficiency of modern imputation to the PGS field. The service currently supports over 4489 published polygenic scores from publicly available repositories and provides extensive quality control, including ancestry estimation to report population stratification. An interactive report empowers users to screen and compare thousands of scores in a fast and intuitive way. Imputation Server PGS provides a user-friendly web service, facilitating the application of polygenic scores to a wide range of genetic studies and is freely available at https://imputationserver.sph.umich.edu.

mtDNA-Server 2: advancing mitochondrial DNA analysis through highly parallelized data processing and interactive analytics.

Over the past decade, mtDNA-Server established itself as one of the most widely used variant calling web-services for human mitochondrial genomes. The service accepts sequencing data in BAM format and returns an annotated variant analysis report for both homoplasmic and heteroplasmic variants. In this work we present mtDNA-Server 2, which includes several new features highly requested by the community. Most importantly, it includes (a) the integration of a novel variant calling mode that accurately call insertions, deletions and single nucleotide variants at once, (b) the integration of additional quality control and input validation modules, (c) a method to estimate the required coverage to minimize false positives and (d) an interactive analytics dashboard. Furthermore, we migrated the complete analysis workflow to the Nextflow workflow manager for improved parallelization, reproducibility and local execution. Recognizing the importance of insertions and deletions as well as offering novel quality control, validation and reporting features, mtDNA-Server 2 provides researchers and clinicians a new state-of-the-art analysis platform for interpreting mitochondrial genomes. mtDNA-Server 2 is available via mitoverse, our analysis platform that offers a centralized place for mtDNA analysis in the cloud. The web-service, source code and its documentation are freely accessible at https://mitoverse.i-med.ac.at.

Interactive exploration of adverse events and multimorbidity in CKD.

BACKGROUND AND HYPOTHESIS: Persons with chronic kidney disease (CKD) are at increased risk of adverse events, early mortality, and multimorbidity. A detailed overview of adverse event types and rates from a large CKD cohort under regular nephrological care is missing. We generated an interactive tool to enable exploration of adverse events and their combinations in the prospective, observational German CKD (GCKD) study. METHODS: The GCKD study enrolled 5217 participants under regular nephrological care with an estimated glomerular filtration rate of 30-60 or >60 mL/min/1.73m2 and an overt proteinuria. Cardio-, cerebro- and peripheral vascular, kidney, infection, and cancer events, as well as deaths were adjudicated following a standard operation procedure. We summarized these time-to-event data points for exploration in interactive graphs within an R shiny app. Multivariable adjusted Cox models for time to first event were fitted. Cumulative incidence functions, Kaplan-Meier curves and intersection plots were used to display main adverse events and their combinations by sex and CKD etiology. RESULTS: Over a median of 6.5 years, 10 271 events occurred in total and 680 participants (13.0%) died while 2947 participants (56.5%) experienced any event. The new publicly available interactive platform enables readers to scrutinize adverse events and their combinations as well as mortality trends as a gateway to better understand multimorbidity in CKD: incident rates per 1000 patient-years varied by event type, CKD etiology, and baseline characteristics. Incidence rates for the most frequent events and their recurrence were 113.6 (cardiovascular), 75.0 (kidney), and 66.0 (infection). Participants with diabetic kidney disease and men were more prone to experiencing events. CONCLUSION: This comprehensive explorative tool to visualize adverse events (https://gckd.diz.uk-erlangen.de/), their combination, mortality, and multimorbidity among persons with CKD may manifest as a valuable resource for patient care, identification of high-risk groups, health services, and public health policy planning.

Sex and statin-related genetic associations at the PCSK9 gene locus: results of genome-wide association meta-analysis.

BACKGROUND: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player of lipid metabolism with higher plasma levels in women throughout their life. Statin treatment affects PCSK9 levels also showing evidence of sex-differential effects. It remains unclear whether these differences can be explained by genetics. METHODS: We performed genome-wide association meta-analyses (GWAS) of PCSK9 levels stratified for sex and statin treatment in six independent studies of Europeans (8936 women/11,080 men respectively 14,825 statin-free/5191 statin-treated individuals). Loci associated in one of the strata were tested for statin- and sex-interactions considering all independent signals per locus. Independent variants at the PCSK9 gene locus were then used in a stratified Mendelian Randomization analysis (cis-MR) of PCSK9 effects on low-density lipoprotein cholesterol (LDL-C) levels to detect differences of causal effects between the subgroups. RESULTS: We identified 11 loci associated with PCSK9 in at least one stratified subgroup (p < 1.0 × 10-6), including the PCSK9 gene locus and five other lipid loci: APOB, TM6SF2, FADS1/FADS2, JMJD1C, and HP/HPR. The interaction analysis revealed eight loci with sex- and/or statin-interactions. At the PCSK9 gene locus, there were four independent signals, one with a significant sex-interaction showing stronger effects in men (rs693668). Regarding statin treatment, there were two significant interactions in PCSK9 missense mutations: rs11591147 had stronger effects in statin-free individuals, and rs11583680 had stronger effects in statin-treated individuals. Besides replicating known loci, we detected two novel genome-wide significant associations: one for statin-treated individuals at 6q11.1 (within KHDRBS2) and one for males at 12q24.22 (near KSR2/NOS1), both with significant interactions. In the MR of PCSK9 on LDL-C, we observed significant causal estimates within all subgroups, but significantly stronger causal effects in statin-free subjects compared to statin-treated individuals. CONCLUSIONS: We performed the first double-stratified GWAS of PCSK9 levels and identified multiple biologically plausible loci with genetic interaction effects. Our results indicate that the observed sexual dimorphism of PCSK9 and its statin-related interactions have a genetic basis. Significant differences in the causal relationship between PCSK9 and LDL-C suggest sex-specific dosages of PCSK9 inhibitors.

The protein "proprotein convertase subtilisin/kexin type 9" (PCSK9) regulates the levels of low-density lipoprotein cholesterol (LDL-C) in blood, and thus, contributes to the risk of cardio-vascular diseases. Women tend to have higher PCSK9 plasma levels throughout their life, although the difference is smaller in patients under LDL-C lowering medication (e.g., statins). We investigated the interplay of genetics, statin-treatment and sex, using combined data from six European studies. We detected 11 genetic regions associated with PCSK9 levels, of which one was specific for women (at SLCO1B3, a statin-transporter gene), and three were specific for men (e.g., ALOX5, encoding a protein linked to chronic inflammatory diseases such as atherosclerosis). We also tested if statin use changed the genetic effect and found five genes only associated with PCSK9 levels in untreated participants. Variants in the gene encoding PCSK9 were most strongly associated and had heterogeneous effects in dependence on statin treatment and sex: On one hand, there were genetic variants with stronger effects in men than women. Those variants are also linked to sex-differential gene expression of PCSK9. On the other hand, there were also variants with treatment-depending effects, linked to protein structure and functionality of PCSK9. This indicates that the observed sexual and treatment-related effects on PCSK9 levels have a genetic basis. In addition, we compared the causal effects of PCSK9 on LDL-C levels between men and women and found a different response to statin treatment. This highlights the need for sex-sensitive dosages of lipid-lowering medication.

Apolipoprotein A-IV concentrations and cancer in a large cohort of chronic kidney disease patients: results from the GCKD study.

BACKGROUND: Chronic kidney disease (CKD) is highly connected to inflammation and oxidative stress. Both favour the development of cancer in CKD patients. Serum apolipoprotein A-IV (apoA-IV) concentrations are influenced by kidney function and are an early marker of kidney impairment. Besides others, it has antioxidant and anti-inflammatory properties. Proteomic studies and small case-control studies identified low apoA-IV as a biomarker for various forms of cancer; however, prospective studies are lacking. We therefore investigated whether serum apoA-IV is associated with cancer in the German Chronic Kidney Disease (GCKD) study. METHODS: These analyses include 5039 Caucasian patients from the prospective GCKD cohort study followed for 6.5 years. Main inclusion criteria were an eGFR of 30-60 mL/min/1.73m2 or an eGFR > 60 mL/min/1.73m2 in the presence of overt proteinuria. RESULTS: Mean apoA-IV concentrations of the entire cohort were 28.9 ± 9.8 mg/dL (median 27.6 mg/dL). 615 patients had a history of cancer before the enrolment into the study. ApoA-IV concentrations above the median were associated with a lower odds for a history of cancer (OR = 0.79, p = 0.02 when adjusted age, sex, smoking, diabetes, BMI, albuminuria, statin intake, and eGFRcreatinine). During follow-up 368 patients developed an incident cancer event and those with apoA-IV above the median had a lower risk (HR = 0.72, 95%CI 0.57-0.90, P = 0.004). Finally, 62 patients died from such an incident cancer event and each 10 mg/dL higher apoA-IV concentrations were associated with a lower risk for fatal cancer (HR = 0.62, 95%CI 0.44-0.88, P = 0.007). CONCLUSIONS: Our data indicate an association of high apoA-IV concentrations with reduced frequencies of a history of cancer as well as incident fatal and non-fatal cancer events in a large cohort of patients with CKD.

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.

An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD.

Introduction: Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV). Methods: An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed. Results: The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan. Conclusion: Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.

Nuclear and mitochondrial genetic variants associated with mitochondrial DNA copy number.

Mitochondrial DNA copy number (mtDNA-CN) is a biomarker for mitochondrial dysfunction associated with several diseases. Previous genome-wide association studies (GWAS) have been performed to unravel underlying mechanisms of mtDNA-CN regulation. However, the identified gene regions explain only a small fraction of mtDNA-CN variability. Most of this data has been estimated from microarrays based on various pipelines. In the present study we aimed to (1) identify genetic loci for qPCR-measured mtDNA-CN from three studies (16,130 participants) using GWAS, (2) identify potential systematic differences between our qPCR derived mtDNA-CN measurements compared to the published microarray intensity-based estimates, and (3) disentangle the nuclear from mitochondrial regulation of the mtDNA-CN phenotype. We identified two genome-wide significant autosomal loci associated with qPCR-measured mtDNA-CN: at HBS1L (rs4895440, p = 3.39 × 10-13) and GSDMA (rs56030650, p = 4.85 × 10-08) genes. Moreover, 113/115 of the previously published SNPs identified by microarray-based analyses were significantly equivalent with our findings. In our study, the mitochondrial genome itself contributed only marginally to mtDNA-CN regulation as we only detected a single rare mitochondrial variant associated with mtDNA-CN. Furthermore, we incorporated mitochondrial haplogroups into our analyses to explore their potential impact on mtDNA-CN. However, our findings indicate that they do not exert any significant influence on our results.

Meta-GWAS on PCSK9 concentrations reveals associations of novel loci outside the PCSK9 locus in White populations.

BACKGROUND AND AIMS: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key regulator of lipid homeostasis. A few earlier genome-wide association studies (GWAS) investigated genetic variants associated with circulating PCSK9 concentrations. However, uncertainty remains about some of the genetic loci discovered beyond the PCSK9 locus. By conducting the largest PCSK9 meta-analysis of GWAS (meta-GWAS) so far, we aimed to identify novel loci and validate the previously reported loci that regulate PCSK9 concentrations. METHODS: We performed GWAS for PCSK9 concentrations in two large cohorts (GCKD (n = 4,963) and KORA F3 (n = 2,895)). These were meta-analyzed with previously published data encompassing together 20,579 individuals. We further conducted a second meta-analysis in statin-naïve individuals (n = 15,390). A genetic risk score (GRS) was constructed on PCSK9-increasing SNPs and assessed its impact on the risk for coronary artery disease (CAD) in 394,943 statin-naïve participants (17,077 with events) of the UK Biobank by performing CAD-free survival analysis. RESULTS: Nine loci were genome-wide significantly associated with PCSK9 concentrations. These included the previously described PCSK9, APOB, KCNA1/KCNA5, and TM6SF2/SUGP1 loci. All imputed SNPs in the PCSK9 locus account for ∼15% of variance of PCSK9 concentrations. We further identified FADS2 as a novel locus that was also found in statin-naïve participants. All imputed SNPs within the FADS2 locus explain ∼1.2% of variance of PCSK9 concentrations. Additionally, four further loci (a region on chromosome 5, SDK1, SPATA16 and HPR) were genome-wide significant in either the main model or the statin-naïve subset. The linear increase in a PCSK9 genetic risk score was associated with 1.41-fold (95%CI 1.16-1.72, p < 0.001) higher risk for incident CAD. CONCLUSIONS: We identified five novel loci (FADS2, SPATA16, SDK1, HPR and a region on chromosome 5) for PCSK9 concentrations that would require further research. Additionally, we confirm the genome-wide significant loci that were previously detected.

Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease.

The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software ( https://github.com/immunogenomics/HLA_analyses_tutorial ). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.

Filling the gap: Genetic risk assessment in hypercholesterolemia using LDL-C and LPA genetic scores.

Routine genetic testing in hypercholesterolemia patients reveals a causative monogenic variant in less than 50% of affected individuals. Incomplete genetic characterization is partly due to polygenic factors influencing low-density-lipoprotein-cholesterol (LDL-C). Additionally, functional variants in the LPA gene affect lipoprotein(a)-associated cholesterol concentrations but are difficult to determine due to the complex structure of the LPA gene. In this study we examined whether complementing standard sequencing with the analysis of genetic scores associated with LDL-C and Lp(a) concentrations improves the diagnostic output in hypercholesterolemia patients. 1.020 individuals including 252 clinically diagnosed hypercholesterolemia patients from the FH Register Austria were analyzed by massive-parallel-sequencing of candidate genes combined with array genotyping, identifying nine novel variants in LDLR. For each individual, validated genetic scores associated with elevated LDL-C and Lp(a) were calculated based on imputed genotypes. Integrating these scores especially the score for Lp(a) increased the proportion of individuals with a clearly defined disease etiology to 68.8% compared to 46.6% in standard genetic testing. The study highlights the major role of Lp(a) in disease etiology in clinically diagnosed hypercholesterolemia patients, of which parts are misclassified. Screening for monogenic causes of hypercholesterolemia and genetic scores for LDL-C and Lp(a) permits more precise diagnosis, allowing individualized treatment.

South Asian medical cohorts reveal strong founder effects and high rates of homozygosity.

The benefits of large-scale genetic studies for healthcare of the populations studied are well documented, but these genetic studies have traditionally ignored people from some parts of the world, such as South Asia. Here we describe whole genome sequence (WGS) data from 4806 individuals recruited from the healthcare delivery systems of Pakistan, India and Bangladesh, combined with WGS from 927 individuals from isolated South Asian populations. We characterize population structure in South Asia and describe a genotyping array (SARGAM) and imputation reference panel that are optimized for South Asian genomes. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of rare homozygotes that reach 100 times that seen in outbred populations. Founder effects increase the power to associate functional variants with disease processes and make South Asia a uniquely powerful place for population-scale genetic studies.

Immune response after two doses of the BNT162b2 COVID-19 vaccine and risk of SARS-CoV-2 breakthrough infection in Tyrol, Austria: an open-label, observational phase 4 trial.

BACKGROUND: Correlates of protection could help to assess the extent to which a person is protected from SARS-CoV-2 infection after vaccination (so-called breakthrough infection). We aimed to clarify associations of antibody and T-cell responses after vaccination against COVID-19 with risk of a SARS-CoV-2 breakthrough infection and whether measurement of these responses enhances risk prediction. METHODS: We did an open-label, phase 4 trial in two community centres in the Schwaz district of the Federal State of Tyrol, Austria, before the emergence of the omicron (B.1.1.529) variant of SARS-CoV-2. We included individuals (aged ≥16 years) a mean of 35 days (range 27-43) after they had received a second dose of the BNT162b2 (Pfizer-BioNTech) COVID-19 vaccine. We quantified associations between immunological parameters and breakthrough infection and assessed whether information on these parameters improves risk discrimination. The study is registered with the European Union Drug Regulating Authorities Clinical Trials Database, 2021-002030-16. FINDINGS: 2760 individuals (1682 [60·9%] female, 1078 [39·1%] male, mean age 47·4 years [SD 14·5]) were enrolled into this study between May 15 and May 21, 2021, 712 (25·8%) of whom had a previous SARS-CoV-2 infection. Over a median follow-up of 5·9 months, 68 (2·5%) participants had a breakthrough infection. In models adjusted for age, sex, and previous infection, hazard ratios for breakthrough infection for having twice the immunological parameter level at baseline were 0·72 (95% CI 0·60-0·86) for anti-spike IgG, 0·80 (0·70-0·92) for neutralising antibodies in a surrogate virus neutralisation assay, 0·84 (0·58-1·21) for T-cell response after stimulation with a CD4 peptide pool, and 0·77 (0·54-1·08) for T-cell response after stimulation with a combined CD4 and CD8 peptide pool. For neutralising antibodies measured in a nested case-control sample using a pseudotyped virus neutralisation assay, the corresponding odds ratio was 0·78 (0·62-1·00). Among participants with previous infection, the corresponding hazard ratio was 0·73 (0·61-0·88) for anti-nucleocapsid Ig. Addition of anti-spike IgG information to a model containing information on age and sex improved the C-index by 0·085 (0·027-0·143). INTERPRETATION: In contrast to T-cell response, higher levels of binding and neutralising antibodies were associated with a reduced risk of breakthrough SARS-CoV-2 infection. The assessment of anti-spike IgG enhances the prediction of incident breakthrough SARS-CoV-2 infection and could therefore be a suitable correlate of protection in practice. Our phase 4 trial measured both humoral and cellular immunity and had a 6-month follow-up period; however, the longer-term protection against emerging variants of SARS-CoV-2 remains unclear. FUNDING: None.

Haplogrep 3 - an interactive haplogroup classification and analysis platform.

Over the last decade, Haplogrep has become a standard tool for haplogroup classification in the field of human mitochondrial DNA and is widely used by medical, forensic, and evolutionary researchers. Haplogrep scales well for thousands of samples, supports many file formats and provides an intuitive graphical web interface. Nevertheless, the currently available version has limitations when applying it to large biobank-scale data. In this paper, we present a major upgrade to the software by adding (a) haplogroup summary statistics and variant annotations from various publicly available genome databases, (b) an interface to connect new phylogenetic trees, (c) a new state-of-the-art web framework managing large scale data, (d) algorithmic adaptions to improve FASTA classification using BWA-specific alignment rules and (e) a pre-classification quality control step for VCF samples. These improvements will give researchers the opportunity to classify thousands of samples as usual but providing additional ways to investigate the dataset directly in the browser. The web service and its documentation can be accessed freely without any registration at https://haplogrep.i-med.ac.at.

A polygenic and family risk score are both independently associated with risk of type 2 diabetes in a population-based study.

The availability of polygenic scores for type 2 diabetes (T2D) raises the question, whether assessing family history might become redundant. However, family history not only involves shared genetics, but also shared environment. It was the aim of this study to assess the independent and combined effects of one family risk score (FamRS) and a polygenic score (PGS) on prevalent and incident T2D risk in a population-based study from Germany (n = 3071). The study was conducted in 2004/2005 with up to 12 years of follow-up. The FamRS takes into account not only the number of diseased first grade relatives, but also age at onset of the relatives and age of participants. 256 prevalent and additional 163 incident T2D cases were recorded. Prevalence of T2D increased sharply for those within the top quantile of the PGS distribution resulting in an OR of 19.16 (p < 2 × 10-16) for the top 20% compared to the remainder of the population, independent of age, sex, BMI, physical activity and FamRS. On the other hand, having a very strong family risk compared to average was still associated with an OR of 2.78 (p = 0.001), independent of the aforementioned factors and the PGS. The PGS and FamRS were only slightly correlated (r2Spearman = 0.018). The combined contribution of both factors varied with varying age-groups, though, with decreasing influence of the PGS with increasing age. To conclude, both, genetic information and family history are relevant for the prediction of T2D risk and might be used for identification of high risk groups to personalize prevention measures.

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.

Ultra-rapid rollout vaccination with BNT162b2 to reduce SARS-CoV-2 infections in the general population.

This study aimed to determine the impact of ultra-rapid rollout vaccination on incidence of SARS-CoV-2 infection. Vaccination with BNT162b2 was provided to 66.9% of eligible residents of the Schwaz district in Tyrol, Austria, within six days per dose (first dose: 11-16 March 2021, second dose: 8-13 April 2021). Of 11,955 individuals enrolled at nine vaccination centers (median age 44.6 years; 51.3% female), 71 had incident SARS-CoV-2 over a six-month follow-up. Incidence rates per 100,000 person-weeks were 92.3 (95% confidence interval [CI]: 70.8-120.2) at weeks 1-5 and 6.4 (3.9-10.4) at ≥6 weeks after dose 1. In these two periods, effectiveness of the vaccination campaign to reduce incident SARS-CoV-2 was 58.6% (50.8%-65.2%) and 91.1% (89.6%-92.3%) in study participants and 28.3% (23.1%-33.0%) and 64.0% (61.7%-66.1%) in the Schwaz district, compared with districts with slower vaccination rollout. Therefore, the vaccination campaign in the Schwaz district illustrates the impact of accelerated vaccination rollout in controlling the pandemic.

The kringle IV type 2 domain variant 4925G>A causes the elusive association signal of the LPA pentanucleotide repeat.

Lipoprotein(a) [Lp(a)] concentrations are regulated by the LPA gene mainly via the large kringle IV-type 2 (KIV-2) copy number variation and multiple causal variants. Early studies suggested an effect of long pentanucleotide repeat (PNR) alleles (10 and 11 repeats, PNR10 and PNR11) in the LPA promoter on gene transcription and found an association with lower Lp(a). Subsequent in vitro studies showed no effects on mRNA transcription, but the association with strongly decreased Lp(a) remained consistent. We investigated the isolated and combined effect of PNR10, PNR11, and the frequent splice site variant KIV-2 4925G>A on Lp(a) concentrations in the Cooperative Health Research in the Region of Augsburg F4 study by multiple quantile regression in single-SNP and joint models. Data on Lp(a), apolipoprotein(a) Western blot isoforms, and variant genotypes were available for 2,858 individuals. We found a considerable linkage disequilibrium between KIV-2 4925G>A and the alleles PNR10 and PNR11. In single-variant analysis adjusted for age, sex, and the shorter apo(a) isoform, we determined that both PNR alleles were associated with a highly significant Lp(a) decrease (PNR10: ß = -14.43 mg/dl, 95% CI: -15.84, -13.02, P = 3.33e-84; PNR11: ß = -17.21 mg/dl, 95% CI: -20.19, -14.23, P = 4.01e-29). However, a joint model, adjusting the PNR alleles additionally for 4925G>A, abolished the effect on Lp(a) (PNR10: ß = +0.44 mg/dl, 95% CI: -1.73, 2.60, P = 0.69; PNR11: ß = -1.52 mg/dl, 95% CI: -6.05, 3.00, P = 0.51). Collectively, we conclude that the previously reported Lp(a) decrease observed in pentanucleotide alleles PNR10 or PNR11 carriers results from a linkage disequilibrium with the frequent splicing mutation KIV-2 4925G>A.

Extent to which array genotyping and imputation with large reference panels approximate deep whole-genome sequencing.

Understanding the genetic basis of human diseases and traits is dependent on the identification and accurate genotyping of genetic variants. Deep whole-genome sequencing (WGS), the gold standard technology for SNP and indel identification and genotyping, remains very expensive for most large studies. Here, we quantify the extent to which array genotyping followed by genotype imputation can approximate WGS in studies of individuals of African, Hispanic/Latino, and European ancestry in the US and of Finnish ancestry in Finland (a population isolate). For each study, we performed genotype imputation by using the genetic variants present on the Illumina Core, OmniExpress, MEGA, and Omni 2.5M arrays with the 1000G, HRC, and TOPMed imputation reference panels. Using the Omni 2.5M array and the TOPMed panel, ≥90% of bi-allelic single-nucleotide variants (SNVs) are well imputed (r2 > 0.8) down to minor-allele frequencies (MAFs) of 0.14% in African, 0.11% in Hispanic/Latino, 0.35% in European, and 0.85% in Finnish ancestries. There was little difference in TOPMed-based imputation quality among the arrays with >700k variants. Individual-level imputation quality varied widely between and within the three US studies. Imputation quality also varied across genomic regions, producing regions where even common (MAF > 5%) variants were consistently not well imputed across ancestries. The extent to which array genotyping and imputation can approximate WGS therefore depends on reference panel, genotype array, sample ancestry, and genomic location. Imputation quality by variant or genomic region can be queried with our new tool, RsqBrowser, now deployed on the Michigan Imputation Server.