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Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes., (© 2024. The Author(s).)

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study's requirements., (© 2021. The Author(s).)

Inadequate sensitivity has been the primary limitation for implementing high-throughput sequencing for studies of tick-borne agents. Here we describe the development of TBDCapSeq, a sequencing assay that uses hybridization capture probes that cover the complete genomes of the eleven most common tick-borne agents found in the United States. The probes are used for solution-based capture and enrichment of pathogen nucleic acid followed by high-throughput sequencing. We evaluated the performance of TBDCapSeq to surveil samples that included human whole blood, mouse tissues, and field-collected ticks. For Borrelia burgdorferi and Babesia microti, the sensitivity of TBDCapSeq was comparable and occasionally exceeded the performance of agent-specific quantitative PCR and resulted in 25 to > 10,000-fold increase in pathogen reads when compared to standard unbiased sequencing. TBDCapSeq also enabled genome analyses directly within vertebrate and tick hosts. The implementation of TBDCapSeq could have major impact in studies of tick-borne pathogens by improving detection and facilitating genomic research that was previously unachievable with standard sequencing approaches.

Recent studies have used genome-wide single-nucleotide polymorphisms (SNPs) to investigate relationships among various Jewish populations and their non-Jewish historical neighbors, often focusing on small subsets of populations from a limited geographic range or relatively small samples within populations. Here, building on the significant progress that has emerged from genomic SNP studies in the placement of Jewish populations in relation to non-Jewish populations, we focus on population structure among Jewish populations. In particular, we examine Jewish population-genetic structure in samples that span much of the historical range of Jewish populations in Europe, the Middle East, North Africa, and South Asia. Combining 429 newly genotyped samples from 29 Jewish and 3 non-Jewish populations with previously reported genotypes on Jewish and non-Jewish populations, we investigate variation in 2789 individuals from 114 populations at 486,592 genome-wide autosomal SNPs. Using multidimensional scaling analysis, unsupervised model-based clustering, and population trees, we find that, genetically, most Jewish samples fall into four major clusters that largely represent four culturally defined groupings, namely the Ashkenazi, Mizrahi, North African, and Sephardi subdivisions of the Jewish population. We detect high-resolution population structure, including separation of the Ashkenazi and Sephardi groups and distinctions among populations within the Mizrahi and North African groups. Our results refine knowledge of Jewish population-genetic structure and contribute to a growing understanding of the distinctive genetic ancestry evident in closely related but historically separate Jewish communities.

Phasing is the process of inferring haplotypes from genotype data. Efficient algorithms and associated software for accurate phasing in pedigrees are needed, especially for populations lacking reference panels of sequenced individuals. We present a novel method for phasing genotypes from whole-genome sequence data in pedigrees, called PULSAR (Phasing Using Lineage Specific Alleles/Rare variants). The method is based on the property that alleles specific to a single founding chromosome within a pedigree are highly informative for identifying haplotypes that are shared identical by descent. Simulation studies are used to assess the performance of PULSAR with various pedigree sizes and structures, and the effect of genotyping errors and the presence of nonsequenced individuals is investigated. In pedigrees with complete sequencing and realistic genotyping error rates, PULSAR correctly phases >99.9% of heterozygous genotypes, excluding sites at which all individuals are heterozygous, and does so with a switch error rate frequently below 10 -4 . PULSAR is highly accurate, capable of genotype error correction and imputation, and computationally competitive with alternative phasing software applicable to pedigrees. Our method has the significant advantage of not requiring reference panels that are essential for other population-based phasing algorithms. A software implementation of PULSAR is freely available.

While genotyping studies are scavenging for suitable samples to analyze, large serum collections are currently left unused as they are assumed to provide insufficient amounts of DNA for array-based genotyping. Long-term stored serum is considered to be difficult to genotype since preanalytical treatments and storage effects on DNA yields are not well understood. Successful genotyping of such samples has the potential to activate large biobanks for future genome-wide association studies (GWAS). We aimed to evaluate genotyping of ultralow amounts of DNA from samples stored up to 45 years in the Janus Serum Bank with two commercially available platforms. 64 samples, with various preanalytical treatments, were genotyped on the Axiom Array from Thermo Fisher Scientific and a subset of 24 samples with slightly higher yield were genotyped on the HumanCoreExome array from Illumina. Our results showed that about 80% of the serum samples produced call rates with the Axiom arrays that would be satisfactory in GWAS. The mean DNA yield was 5.8 ng as measured with PicoGreen, 3-6% of recommended yield. The failed samples had on average lower input amounts of DNA. All serum samples genotyped on the HumanCoreExome with a standard and FFPE protocol produced GWAS satisfactory call rates, with mean 97.57% and 98.35% call rates, respectively. The mean yield was 10.65 ng, 6% of the recommendations. Successful array-based genotyping of ultralow DNA yields from serum samples stored up to 45 years is possible. These results demonstrate the potential to activate large serum biobank collections for future studies.

Hematopoietic stem cells (HSCs) are functionally and genetically diverse and this diversity decreases with age and disease. Numerous systems have been developed to quantify HSC diversity by genetic barcoding, but no framework has been established to empirically validate barcode sequences. Here we have developed an analytical framework, Selection of informative Amplicon Barcodes from Experimental Replicates (SABER), that identifies barcodes that are unique among a large set of experimental replicates. Amplicon barcodes were sequenced from the blood of 56 adult zebrafish divided into training and validation sets. Informative barcodes were identified and samples with a high fraction of informative barcodes were chosen by bootstrapping. There were 4.2 ± 1.8 barcoded HSC clones per sample in the training set and 3.5 ± 2.1 in the validation set (p = 0.3). SABER reproducibly quantifies functional HSCs and can accommodate a wide range of experimental group sizes. Future large-scale studies aiming to understand the mechanisms of HSC clonal evolution will benefit from this new approach to identifying informative amplicon barcodes.

This study compared imputation from lower-density commercial and customized panels to high-density panels and a combined panel (Illumina and Affymetrix) in Nelore beef cattle. Additionally, linkage disequilibrium and haplotype block conformation were estimated in individual high-density panels and compared with corresponding values in the combined panel after imputation. Overall, 814 animals were genotyped using BovineHD BeadChip (IllumHD), and 93 of these animals were also genotyped using the Axion Genome-Wide BOS 1 Array Plate (AffyHD). In general, customization considering linkage disequilibrium and minor allele frequency had the highest accuracies. The IllumHD panel had higher values of linkage disequilibrium for short distances between SNPs than AffyHD and the combined panel. The combined panel had an increased number of small haplotype blocks. The use of a combined panel is recommended due to its increased density and number of haplotype blocks, which in turn increase the probability of a marker being close to a quantitative trait locus of interest. Considering common SNPs between IllumHD and AffyHD for the customization of a low-density panel increases the imputation accuracy for IllumHD, AffyHD and the combined panel.

Short tandem repeat (STR) profiling has been routinely used in kinship testing since the introduction of commercial kits in the mid-1990s. While 15 to 23 STR loci normally give definitive results in simple kinship testing, additional loci are sometimes required to resolve complex cases. The SureID 23comp Human Identification Kit, recently released by Health Gene Technologies (China), multiplexes amelogenin and 22 autosomal STRs, 17 of which are non-CODIS STRs. This enables the profiling of 38-40 loci when used in conjunction with widely used commercial kits. In this study, the kit was evaluated for kinship applications as a supplementary STR kit following the minimum criteria for validation recommended by the European Network of Forensic Science Institutes (ENFSI) and the Scientific Working Group on DNA Analysis Methods (SWGDAM) using 500 samples. Performance was comparable with other commercial kits demonstrating: repeatability and reproducibility; precision (maximum s.d. 0.1048 nt); accuracy, all alleles were within ±0.41 nt compared to the actual sizes; heterozygous peak balances at all loci >68%; stutter ratios ranged from 3.8% to 16.15%; full profiles were generated with 125 pg DNA (95.12% of alleles at 62 pg),; and we found 100% concordance over 5 common STRs with the GlobalFiler kit.

The success of next-generation sequencing depends on the accuracy of variant calls. Few objective protocols exist for QC following variant calling from whole genome sequencing (WGS) data. After applying QC filtering based on Genome Analysis Tool Kit (GATK) best practices, we used genotype discordance of eight samples that were sequenced twice each to evaluate the proportion of potentially inaccurate variant calls. We designed a QC pipeline involving hard filters to improve replicate genotype concordance, which indicates improved accuracy of genotype calls. Our pipeline analyzes the efficacy of each filtering step. We initially applied this strategy to well-characterized variants from the ClinVar database, and subsequently to the full WGS dataset. The genome-wide biallelic pipeline removed 82.11% of discordant and 14.89% of concordant genotypes, and improved the concordance rate from 98.53% to 99.69%. The variant-level read depth filter most improved the genome-wide biallelic concordance rate. We also adapted this pipeline for triallelic sites, given the increasing proportion of multiallelic sites as sample sizes increase. For triallelic sites containing only SNVs, the concordance rate improved from 97.68% to 99.80%. Our QC pipeline removes many potentially false positive calls that pass in GATK, and may inform future WGS studies prior to variant effect analysis.

Quantitative reverse transcription PCR (RT-qPCR) is one of the most efficient, reliable and widely used techniques to quantify gene expression. In this study, we evaluated the performance of six southern corn rootworm, Diabrotica undecimpunctata howardi (Barber), housekeeping genes (HKG), β-actin (Actin), β-tubulin (Tubulin), elongation factor 1 alpha (EF1α), glyceraldehyde-3 phosphate dehydrogenase (GAPDH), 40 S ribosomal protein S9 (RpS9) and ubiquitin-conjugating protein (Ubi), under different experimental conditions such as developmental stage, exposure of neonate and adults to dsRNA, exposure of adults to different temperatures, different 3 rd instar larva tissues, and neonate starvation. The HKGs were analyzed with four algorithms, including geNorm, NormFinder, BestKeeper, and delta-CT. Although the six HKGs showed a relatively stable expression pattern among different treatments, some variability was observed. Among the six genes, EF1α exhibited the lowest Ct values for all treatments while Ubi exhibited the highest. Among life stages and across treatments, Ubi exhibited the least stable expression pattern. GAPDH, Actin, and EF1α were among the most stable HKGs in the majority of the treatments. This research provides HKG for accurate normalization of RT-qPCR data in the southern corn rootworm. Furthermore, this information can contribute to future genomic and functional genomic research in Diabrotica species.

High-throughput sequencing (HTS) provides the opportunity, once a diagnostic result is obtained, to extract additional information from a virus-containing sample. Hence, it offers advantages over established quantitative amplification technology, such as quantitative PCR, particularly in a public health environment. At this early stage of its clinical application, there have been limited studies comparing HTS performance to that of the more established quantitative PCR technology for direct detection of viruses. In this pilot-scale study, we tested HTS with a range of viruses and sample types routinely encountered in a public health virology laboratory. In comparison with quantitative PCR, our HTS method was able to sensitively (92%) detect all viruses in any sample type with the exception of certain tissues. Moreover, sufficient nucleotide sequence information was obtained to enable genotyping of strains detected, thus providing additional useful epidemiological information. While HTS sensitivity may not yet match that of PCR, the added value through enhanced epidemiological data has considerable potential to enable real-time surveillance of circulating strains so as to facilitate rapid and appropriate response to outbreaks and virus zoonotic spillover events.

Castor is a commercially important oilseed crop that provides raw materials for several industries. Currently, the availability of genomic resources for castor is very limited. In this study, genome-wide SNPs were discovered in castor via whole-genome sequencing of 14 diverse lines to an average of 34X coverage. A total of 2,179,759 putative SNPs were detected, and a genotyping array was designed with 6,000 high-quality SNPs representing 2,492 scaffolds of the draft castor genome (87.5% genome coverage). The array was validated by genotyping a panel of 314 inbred castor lines, which resulted in 5,025 scorable SNPs with a high call rate (98%) and reproducibility (100%). Using this array, a consensus linkage map consisting of 1,978 SNP loci was constructed with an average inter-marker distance of 0.55 cM. The genome-wide SNP data, the genotyping array and the dense linkage map are valuable genomic tools for promoting high-throughput genomic research and molecular breeding in castor.

Bio-geographic ancestry is an area of considerable interest in the medical genetics, anthropology and forensics. Although genome-wide panels are ideal as they provide dense genotyping data, small sets of ancestry informative marker provide a cost-effective way to investigate genetic ancestry and population structure. Here, we investigate the performance of a reduced marker set that combine different types of autosomal markers through haplotype analysis. In particular, recently described DIP-STR markers should offer the advantage of comprising both, low mutation rate Indels (DIPs), to study human history over longer time scale; and high mutation rate STRs, to trace relatively recent demographic events. In this study, we assessed the ability of an initial set of 23 DIP-STRs to distinguish major population groups using the HGDP-CEPH reference samples. The results obtained applying the STRUCTURE algorithm show that the discrimination capacity of the DIP-STRs is comparable to currently used small-scale ancestry informative markers by approaching seven major demographic groups. Yet, the DIP-STRs show an improved success rate in assigning individuals to populations of Europe and Middle East. These data show a remarkable ability of a preliminary set of 23 DIP-STR markers to infer major biogeographic origins. A novel set of DIP-STRs preselected to contain ancestry information should lead to further improvements.

High resolution melting analysis (HRMA) is a single-tube method, which can be carried out rapidly as an additional step following real-time quantitative PCR (qPCR). The method enables the differentiation of genetic variation (down to single nucleotide polymorphisms) in amplified DNA fragments without sequencing. HRMA has previously been adopted to determine variability in the amplified genes of a number of organisms. However, only one work to date has focused on pathogenic parasites-nematodes from the genus Trichinella. In this study, we employed a qPCR-HRMA assay specifically targeting two sequential gene fragments-cytochrome c oxidase subunit I (COI) and expansion segment V (ESV), in order to differentiate 37 single L1 muscle larvae samples of eight Trichinella species. We show that qPCR-HRMA based on the mitochondrial COI gene allows differentiation between the sequences of PCR products of the same length. This simple, rapid and reliable method can be used to identify at the species level single larvae of eight Trichinella taxa.

Hundreds of thousands of individuals have been genotyped in the past decades using genotyping arrays, representing both a valuable data resource for future biomedical research and a substantial investment in human genetic research. However, novel chip designs and their altered sets of single-nucleotide polymorphisms (SNPs) pose the question of how well established data resources, such as large samples of healthy controls genotyped on legacy arrays, can be combined with newer samples genotyped on those novel arrays using genotype imputation. We exemplarily investigated this question based on genotype data of 30 European and 30 African unrelated samples from the 1000 Genomes project and on markers present on two legacy SNP arrays, namely Affymetrix's Human SNP 6.0 and Illumina's 550k array, and three newer arrays, namely two Axiom arrays from Affymetrix and an OmniExpress array from Illumina. We cross-compared the imputation accuracy as well as efficacy and assessed genotype concordance among these arrays. Although the accuracy of genotype prediction was uniformly high across all arrays, the imputation efficacy, that is, the proportion of successfully imputed markers, differed considerably between array combinations in both sample sets, with legacy arrays showing a trend towards lower efficacy values compared with newer arrays when serving as imputation basis. We conclude that, given the substantial losses of markers covered by the legacy arrays, the re-genotyping of existing samples sets, in particular those of healthy population controls, would be a worthwhile endeavor to secure their continued use in the future.

We report our results of 1000 diagnostic WES cases based on 2819 sequenced samples from 54 countries with a wide phenotypic spectrum. Clinical information given by the requesting physicians was translated to HPO terms. WES processes were performed according to standardized settings. We identified the underlying pathogenic or likely pathogenic variants in 307 families (30.7%). In further 253 families (25.3%) a variant of unknown significance, possibly explaining the clinical symptoms of the index patient was identified. WES enabled timely diagnosing of genetic diseases, validation of causality of specific genetic disorders of PTPN23, KCTD3, SCN3A, PPOX, FRMPD4, and SCN1B, and setting dual diagnoses by detecting two causative variants in distinct genes in the same patient. We observed a better diagnostic yield in consanguineous families, in severe and in syndromic phenotypes. Our results suggest that WES has a better yield in patients that present with several symptoms, rather than an isolated abnormality. We also validate the clinical benefit of WES as an effective diagnostic tool, particularly in nonspecific or heterogeneous phenotypes. We recommend WES as a first-line diagnostic in all cases without a clear differential diagnosis, to facilitate personal medical care., Competing Interests: DT, AMBA, MERW, JK, KKK, AM, OP, MCdC, CB, KW, RS, JMGA, OB, SK, NN, MW, RAJ are employed at Centogene AG; AR has financial holdings in Centogene AG; WE, MTAR, AAR, WAT, AAlo, MAB, and MA are employees at King Abdulaziz Medical city; AAlh is employee at Prince Sultan Military Medical City; NAS is employee at Johns Hopkins Aramco hospital.

As an example of optimizing population-specific genotyping assays using a whole-genome sequence reference set, we detail the approach that followed to design the Axiom-NL array which is characterized by an improved imputation backbone based on the Genome of the Netherlands (GoNL) reference sequence and, compared with earlier arrays, a more comprehensive inclusion of SNPs on chromosomes X, Y, and the mitochondria. Common variants on the array were selected to be compatible with the Illumina Psych Array and the Affymetrix UK Biobank Axiom array. About 3.5% of the array (23 977 markers) represents SNPs from the GWAS catalog, including SNPs at FTO, APOE, Ion-channels, killer-cell immunoglobulin-like receptors, and HLA. Around 26 000 markers associated with common psychiatric disorders are included, as well as 6705 markers suggested to be associated with fertility and twinning. The platform can thus be used for risk profiling, detection of new variants, as well as ancestry determination. Results of coverage tests in 249 unrelated subjects with GoNL-based sequence data show that after imputation with 1000G as a reference, the median concordance between original and imputed genotypes is above 98%. The median imputation quality R 2 for MAF thresholds of 0.001, 0.01, 0.05, and >0.05 are 0.05, 0.28, 0.80, 0.99, respectively, for the 1000G imputed SNPs, with a similar quality for the autosomes and X chromosome, showing a good genome-wide coverage for association studies after imputation.

Although emerging sequencing technologies can characterize all genetic variants, the cost is still high. Illumina released the HumanOmni5M-4v1 (Omni5) genotype array with ~4.3M assayed SNPs, a much denser array compared with other available arrays. The Omni5 balances both cost and array density. In this article, we illustrate the power of Omni5 to detect genetic associations. The Omni5 includes variants with a wide range of minor allele frequencies down to <1%. The theoretical power calculation examples indicate the increased power of the Omni5 array compared with other arrays with lower density when evaluating associations with some known loci, although there are exceptions. We further evaluate the genetic associations between known loci and several quantitative traits in the Framingham Heart Study: femoral neck bone mineral density, lumbar spine bone mineral density and hippocampal volume. Finally, we search genome wide for novel associations using the Omni5 genotypes. We compare our association results from Affymetrix 500K+MIPS 50K arrays and two imputed data sets: (1) HapMap Phase II and (2) 1000 Genomes reference panel. We observed increased evidence for genotype-phenotype associations with smaller P-values for selected known loci using the Omni5 genotypes. With limited sample sizes, we identify novel variants with genome-wide significant P-values. Our observations support the notion that dense genotyping using the Omni5 can be powerful in detecting novel associated variants. Comparison with imputed data with higher density also suggests that imputation helps but cannot replace genotyping, especially when imputation quality is low.

The success of clinical genomics using next generation sequencing (NGS) requires the accurate and consistent identification of personal genome variants. Assorted variant calling methods have been developed, which show low concordance between their calls. Hence, a systematic comparison of the variant callers could give important guidance to NGS-based clinical genomics. Recently, a set of high-confident variant calls for one individual (NA12878) has been published by the Genome in a Bottle (GIAB) consortium, enabling performance benchmarking of different variant calling pipelines. Based on the gold standard reference variant calls from GIAB, we compared the performance of thirteen variant calling pipelines, testing combinations of three read aligners--BWA-MEM, Bowtie2, and Novoalign--and four variant callers--Genome Analysis Tool Kit HaplotypeCaller (GATK-HC), Samtools mpileup, Freebayes and Ion Proton Variant Caller (TVC), for twelve data sets for the NA12878 genome sequenced by different platforms including Illumina2000, Illumina2500, and Ion Proton, with various exome capture systems and exome coverage. We observed different biases toward specific types of SNP genotyping errors by the different variant callers. The results of our study provide useful guidelines for reliable variant identification from deep sequencing of personal genomes.

Genotype imputation has been the pillar of the success of genome-wide association studies (GWAS) for identifying common variants associated with common diseases. However, most GWAS have been run using only 60 HapMap samples as reference for imputation, meaning less frequent and rare variants not being comprehensively scrutinized. Next-generation arrays ensuring sufficient coverage together with new reference panels, as the 1000 Genomes panel, are emerging to facilitate imputation of low frequent single-nucleotide polymorphisms (minor allele frequency (MAF) <5%). In this study, we present a two-step imputation approach improving the quality of the 1000 Genomes imputation by genotyping only a subset of samples to create a local reference population on a dense array with many low-frequency markers. In this approach, the study sample, genotyped with a first generation array, is imputed first to the local reference sample genotyped on a dense array and hereafter to the 1000 Genomes reference panel. We show that mean imputation quality, measured by the r(2) using this approach, increases by 28% for variants with a MAF between 1 and 5% as compared with direct imputation to 1000 Genomes reference. Similarly, the concordance rate between calls of imputed and true genotypes was found to be significantly higher for heterozygotes (P<1e-15) and rare homozygote calls (P<1e-15) in this low frequency range. The two-step approach in our setting improves imputation quality compared with traditional direct imputation noteworthy in the low-frequency spectrum and is a cost-effective strategy in large epidemiological studies.