De novo mutations implicate novel genes in systemic lupus erythematosus.

The omnigenic model of complex disease stipulates that the majority of the heritability will be explained by the effects of common variation on genes in the periphery of core disease pathways. Rare variant associations, expected to explain far less of the heritability, may be enriched in core disease genes and thus will be instrumental in the understanding of complex disease pathogenesis and their potential therapeutic targets. Here, using complementary whole-exome sequencing, high-density imputation, and in vitro cellular assays, we identify candidate core genes in the pathogenesis of systemic lupus erythematosus (SLE). Using extreme-phenotype sampling, we sequenced the exomes of 30 SLE parent-affected-offspring trios and identified 14 genes with missense de novo mutations (DNM), none of which are within the >80 SLE susceptibility loci implicated through genome-wide association studies. In a follow-up cohort of 10, 995 individuals of matched European ancestry, we imputed genotype data to the density of the combined UK10K-1000 genomes Phase III reference panel across the 14 candidate genes. Gene-level analyses indicate three functional candidates: DNMT3A, PRKCD, and C1QTNF4. We identify a burden of rare variants across PRKCD associated with SLE risk (P = 0.0028), and across DNMT3A associated with two severe disease prognosis sub-phenotypes (P = 0.0005 and P = 0.0033). We further characterise the TNF-dependent functions of the third candidate gene C1QTNF4 on NF-κB activation and apoptosis, which are inhibited by the p.His198Gln DNM. Our results identify three novel genes in SLE susceptibility and support extreme-phenotype sampling and DNM gene discovery to aid the search for core disease genes implicated through rare variation.

Genomic, transcriptomic and epigenomic sequencing data of the B-cell leukemia cell line REH.

OBJECTIVES: The aim of this data paper is to describe a collection of 33 genomic, transcriptomic and epigenomic sequencing datasets of the B-cell acute lymphoblastic leukemia (ALL) cell line REH. REH is one of the most frequently used cell lines for functional studies of pediatric ALL, and these data provide a multi-faceted characterization of its molecular features. The datasets described herein, generated with short- and long-read sequencing technologies, can both provide insights into the complex aberrant karyotype of REH, and be used as reference datasets for sequencing data quality assessment or for methods development. DATA DESCRIPTION: This paper describes 33 datasets corresponding to 867 gigabases of raw sequencing data generated from the REH cell line. These datasets include five different approaches for whole genome sequencing (WGS) on four sequencing platforms, two RNA sequencing (RNA-seq) techniques on two different sequencing platforms, DNA methylation sequencing, and single-cell ATAC-sequencing.

A genome-wide association study of monozygotic twin-pairs suggests a locus related to variability of serum high-density lipoprotein cholesterol.

Genome-wide association analysis on monozygotic twin-pairs offers a route to discovery of gene environment interactions through testing for variability loci associated with sensitivity to individual environment/lifestyle. We present a genome-wide scan of loci associated with intra-pair differences in serum lipid and apolipoprotein levels. We report data for 1,720 monozygotic female twin-pairs from GenomEUtwin project with 2.5 million SNPs, imputed or genotyped, and measured serum lipid fractions for both twins. We found one locus associated with intra-pair differences in high-density lipoprotein cholesterol, rs2483058 in an intron of SRGAP2, where twins carrying the C allele are more sensitive to environmental factors(P=3.98 x 10-8). We followed up the association in further genotyped monozygotic twins (N= 1,261),which showed a moderate association for the variant (P= 0.200, same direction of an effect). In addition,we report a new association on the level of apolipoprotein A-ll (P= 4.03 x 1 o-8).

Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer.

BACKGROUND: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. METHODS: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). RESULTS: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. CONCLUSIONS: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

The SEQC2 epigenomics quality control (EpiQC) study.

BACKGROUND: Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS: Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS: The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

Screening for copy-number alterations and loss of heterozygosity in chronic lymphocytic leukemia--a comparative study of four differently designed, high resolution microarray platforms.

Screening for gene copy-number alterations (CNAs) has improved by applying genome-wide microarrays, where SNP arrays also allow analysis of loss of heterozygozity (LOH). We here analyzed 10 chronic lymphocytic leukemia (CLL) samples using four different high-resolution platforms: BAC arrays (32K), oligonucleotide arrays (185K, Agilent), and two SNP arrays (250K, Affymetrix and 317K, Illumina). Cross-platform comparison revealed 29 concordantly detected CNAs, including known recurrent alterations, which confirmed that all platforms are powerful tools when screening for large aberrations. However, detection of 32 additional regions present in 2-3 platforms illustrated a discrepancy in detection of small CNAs, which often involved reported copy-number variations. LOH analysis using dChip revealed concordance of mainly large regions, but showed numerous, small nonoverlapping regions and LOH escaping detection. Evaluation of baseline variation and copy-number ratio response showed the best performance for the Agilent platform and confirmed the robustness of BAC arrays. Accordingly, these platforms demonstrated a higher degree of platform-specific CNAs. The SNP arrays displayed higher technical variation, although this was compensated by high density of elements. Affymetrix detected a higher degree of CNAs compared to Illumina, while the latter showed a lower noise level and higher detection rate in the LOH analysis. Large-scale studies of genomic aberrations are now feasible, but new tools for LOH analysis are requested.

Evaluation of HapMap data in six populations of European descent.

We studied how well the European CEU samples used in the Haplotype Mapping Project (HapMap) represent five European populations by analyzing nuclear family samples from the Swedish, Finnish, Dutch, British and Australian (European ancestry) populations. The number of samples from each population (about 30 parent-offspring trios) was similar to that in the HapMap sample sets. A panel of 186 single nucleotide polymorphisms (SNPs) distributed over the 1.5 Mb region of the GRID2 gene on chromosome 4 was genotyped. The genotype data were compared pair-wise between the HapMap sample and the other population samples. Principal component analysis (PCA) was used to cluster the data from different populations with respect to allele frequencies and to define the markers responsible for observed variance. The only sample with detectable differences in allele frequencies was that from Kuusamo, Finland. This sample also separated from the others, including the other Finnish sample, in the PCA analysis. A set of tagSNPs was defined based on the HapMap data and applied to the samples. The tagSNPs were found to capture the genetic variation in the analyzed region at r(2)>0.8 at levels ranging from 95% in the Kuusamo sample to 87% in the Australian sample. To capture the maximal genetic variation in the region, the Kuusamo, HapMap and Australian samples required 58, 63 and 73 native tagSNPs, respectively. The HapMap CEU sample represents the European samples well for tagSNP selection, with some caution regarding estimation of allele frequencies in the Finnish Kuusamo sample, and a slight reduction in tagging efficiency in the Australian sample.

The relationship between the plasma concentration of irbesartan and the antihypertensive response is disclosed by an angiotensin II type 1 receptor polymorphism: results from the Swedish Irbesartan Left Ventricular Hypertrophy Investigation vs. Atenolol (SILVHIA) Trial.

BACKGROUND: The aim of this study was to investigate the effect of the plasma concentration of irbesartan, a specific angiotensin II type 1 receptor (AT1R) antagonist, and the blood pressure response in relation to AT1R gene polymorphisms. METHODS: Plasma irbesartan was analyzed in 42 patients with mild-to-moderate hypertension and left ventricular hypertrophy from the Swedish Irbesartan Left Ventricular Hypertrophy Investigation vs. Atenolol (SILVHIA) trial, who were treated with irbesartan as monotherapy for 12 weeks. Blood pressure and irbesartan concentration were measured at trough, i.e., 24 +/- 3 h after the last dose. Five AT1R gene polymorphisms were analyzed by minisequencing. RESULTS: Neither the plasma concentration of irbesartan, nor any of the AT1R polymorphisms were associated with the blood pressure response to irbesartan treatment. However, the interaction term between the plasma concentration of irbesartan and the AT1R C5245T polymorphism was related to the reduction in systolic blood pressure after 12 weeks of treatment (P = 0.025). Furthermore, the plasma concentration of irbesartan was related to the change in systolic blood pressure in individuals homozygous for the AT1R 5245 T allele (r = -0.56, P = 0.030), but not for other genotypes. CONCLUSIONS: There was an association between plasma concentrations of irbesartan and the blood pressure response for hypertensive patients with AT1R 5245 TT. Because of the small sample size, this study needs to be viewed as hypothesis generating. This is the first study, to our knowledge, indicating that the concentration-response relationship of an antihypertensive drug may be genotype dependent.

Data quality of whole genome bisulfite sequencing on Illumina platforms.

The powerful HiSeq X sequencers with their patterned flowcell technology and fast turnaround times are instrumental for many large-scale genomic and epigenomic studies. However, assessment of DNA methylation by sodium bisulfite treatment results in sequencing libraries of low diversity, which may impact data quality and yield. In this report we assess the quality of WGBS data generated on the HiSeq X system in comparison with data generated on the HiSeq 2500 system and the newly released NovaSeq system. We report a systematic issue with low basecall quality scores assigned to guanines in the second read of WGBS when using certain Real Time Analysis (RTA) software versions on the HiSeq X sequencer, reminiscent of an issue that was previously reported with certain HiSeq 2500 software versions. However, with the HD.3.4.0 /RTA 2.7.7 software upgrade for the HiSeq X system, we observed an overall improved quality and yield of the WGBS data generated, which in turn empowers cost-effective and high quality DNA methylation studies.

SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population.

Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set.

A quality assessment survey of SNP genotyping laboratories.

To survey the quality of SNP genotyping, a joint Nordic quality assessment (QA) round was organized between 11 laboratories in the Nordic and Baltic countries. The QA round involved blinded genotyping of 47 DNA samples for 18 or six randomly selected SNPs. The methods used by the participating laboratories included all major platforms for small- to medium-size SNP genotyping. The laboratories used their standard procedures for SNP assay design, genotyping, and quality control. Based on the joint results from all laboratories, a consensus genotype for each DNA sample and SNP was determined by the coordinator of the survey, and the results from each laboratory were compared to this genotype. The overall genotyping accuracy achieved in the survey was excellent. Six laboratories delivered genotype data that were in full agreement with the consensus genotype. The average accuracy per SNP varied from 99.1 to 100% between the laboratories, and it was frequently 100% for the majority of the assays for which SNP genotypes were reported. Lessons from the survey are that special attention should be given to the quality of the DNA samples prior to genotyping, and that a conservative approach for calling the genotypes should be used to achieve a high accuracy.

Quantitative evaluation by minisequencing and microarrays reveals accurate multiplexed SNP genotyping of whole genome amplified DNA.

Whole genome amplification (WGA) procedures such as primer extension preamplification (PEP) or multiple displacement amplification (MDA) have the potential to provide an unlimited source of DNA for large-scale genetic studies. We have performed a quantitative evaluation of PEP and MDA for genotyping single nucleotide polymorphisms (SNPs) using multiplex, four-color fluorescent minisequencing in a microarray format. Forty-five SNPs were genotyped and the WGA methods were evaluated with respect to genotyping success, signal-to-noise ratios, power of genotype discrimination, yield and imbalanced amplification of alleles in the MDA product. Both PEP and MDA products provided genotyping results with a high concordance to genomic DNA. For PEP products the power of genotype discrimination was lower than for MDA due to a 2-fold lower signal-to-noise ratio. MDA products were indistinguishable from genomic DNA in all aspects studied. To obtain faithful representation of the SNP alleles at least 0.3 ng DNA should be used per MDA reaction. We conclude that the use of WGA, and MDA in particular, is a highly promising procedure for producing DNA in sufficient amounts even for genome wide SNP mapping studies.

Hypertension and SNP genotyping in antihypertensive treatment.

Hypertension is prevalent, affecting approx 20--25% of the adult population in the Western world. Primary hypertension is a multifactorial, complex disorder where many genes and genetic variants are assumed to interact with environmental factors in order to produce the specific blood pressure level for a given individual. Family and twin studies show that between 30 and 60% of blood pressure variation is determined by genetic factors. Monogenic disorders of hypertension are rare and do not explain blood pressure variability in the population at large. Obvious candidate genes for the study of hypertension are those that encode components of a blood pressure regulating system targeted by an antihypertensive drug, or those that are involved in counter-regulatory systems. In this review, we give a brief pathophysiological background to hypertension and the rational behind utilizing SNP genotyping in the study of hypertension and the antihypertensive response to treatment. We also discuss some of the novel results of pharmacodynamic studies in antihypertensive treatment, an area in its infancy.

A microarray minisequencing system for pharmacogenetic profiling of antihypertensive drug response.

We aimed to develop a microarray genotyping system for multiplex analysis of a panel of single nucleotide polymorphisms (SNPs) in genes encoding proteins involved in blood pressure regulation, and to apply this system in a pilot study demonstrating its feasibility in the pharmacogenetics of hypertension. A panel of 74 SNPs in 25 genes involved in blood pressure regulation was selected from the SNP databases, and genotyped in DNA samples of 97 hypertensive patients. The patients had been randomized to double-blind treatment with either the angiotensin II type 1 receptor blocker irbesartan or the beta 1-adrenergic receptor blocker atenolol. Genotyping was performed using a microarray based DNA polymerase assisted 'minisequencing' single nucleotide primer extension assay with fluorescence detection. The observed genotypes were related to the blood pressure reduction using stepwise multiple regression analysis. The allele frequencies of the selected SNPs were determined in the Swedish population. The established microarray-based genotyping system was validated and allowed unequivocal multiplex genotyping of the panel of 74 SNPs in every patient. Almost 7200 SNP genotypes were generated in the study. Profiles of four or five SNP-genotypes that may be useful as predictors of blood pressure reduction after antihypertensive treatment were identified. Our results highlight the potential of microarray-based technology for SNP genotyping in pharmacogenetics.

Detecting imbalanced expression of SNP alleles by minisequencing on microarrays.

BACKGROUND: Each of the human genes or transcriptional units is likely to contain single nucleotide polymorphisms that may give rise to sequence variation between individuals and tissues on the level of RNA. Based on recent studies, differential expression of the two alleles of heterozygous coding single nucleotide polymorphisms (SNPs) may be frequent for human genes. Methods with high accuracy to be used in a high throughput setting are needed for systematic surveys of expressed sequence variation. In this study we evaluated two formats of multiplexed, microarray based minisequencing for quantitative detection of imbalanced expression of SNP alleles. We used a panel of ten SNPs located in five genes known to be expressed in two endothelial cell lines as our model system. RESULTS: The accuracy and sensitivity of quantitative detection of allelic imbalance was assessed for each SNP by constructing regression lines using a dilution series of mixed samples from individuals of different genotype. Accurate quantification of SNP alleles by both assay formats was evidenced for by R2 values > 0.95 for the majority of the regression lines. According to a two sample t-test, we were able to distinguish 1-9% of a minority SNP allele from a homozygous genotype, with larger variation between SNPs than between assay formats. Six of the SNPs, heterozygous in either of the two cell lines, were genotyped in RNA extracted from the endothelial cells. The coefficient of variation between the fluorescent signals from five parallel reactions was similar for cDNA and genomic DNA. The fluorescence signal intensity ratios measured in the cDNA samples were compared to those in genomic DNA to determine the relative expression levels of the two alleles of each SNP. Four of the six SNPs tested displayed a higher than 1.4-fold difference in allelic ratios between cDNA and genomic DNA. The results were verified by allele-specific oligonucleotide hybridisation and minisequencing in a microtiter plate format. CONCLUSIONS: We conclude that microarray based minisequencing is an accurate and accessible tool for multiplexed screening for imbalanced allelic expression in multiple samples and tissues in parallel.

Single nucleotide polymorphisms predict the change in left ventricular mass in response to antihypertensive treatment.

BACKGROUND: Our aim was to determine whether the change in left ventricular (LV) mass in response to antihypertensive treatment could be predicted by multivariate analysis of single nucleotide polymorphisms (SNPs) in candidate genes reflecting pathways likely to be involved in blood pressure control. METHODS: Patients with mild to moderate primary hypertension and LV hypertrophy were randomized in a double-blind fashion to treatment with either the angiotensin II type 1 receptor antagonist irbesartan (n = 48) or the beta1 adrenoreceptor blocker atenolol (n = 49). A microarray-based minisequencing system was used for genotyping 74 SNPs in 25 genes. These genotypes were related to the change in LV mass index by echocardiography, after 12 weeks treatment as monotherapy, using stepwise multiple regression analysis. RESULTS: The blood pressure reductions were similar and significant in both treatment groups. Two SNPs in two separate genes (the angiotensinogen T1198C polymorphism, corresponding to the M235T variant and the apolipoprotein B G10108A polymorphism) for those treated with irbesartan, and the adrenoreceptor alpha2A A1817G for those treated with atenolol, significantly predicted the change in LV mass. The predictive power of these SNPs was independent of the degree of blood pressure reduction. CONCLUSION: SNPs in the angiotensinogen, apolipoprotein B, and the alpha2 adrenoreceptor gene predicted the change in LV mass during antihypertensive therapy. These results illustrate the potential of using microarray-based technology for SNP genotyping in predicting individual drug responses.