Marcel Kucharik

While rare, highly penetrant variants in APP, PSEN1 and PSEN2 are associated with early-onset Alzheimer’s disease, especially its familial form, the disease in general is shaped by a complex interplay of various genetic factors. Both common and rare variants across numerous loci, which were identified mainly during large genome-wide association studies, contribute to disease risk, phenotypic heterogeneity and progression in both early- and late-onset forms. This review synthesizes current understanding of the genetic architecture of Alzheimer's disease in the era of next-generation sequencing, with particular emphasis on variant classes beyond single nucleotide polymorphisms. Structural variants, including copy number variations, tandem repeat expansions and transposable element insertions, are considered as potential contributors to the ‘missing heritability’ in Alzheimer's disease. In addition, we discuss somatic mosaicism that represents another layer of its complexity. We highlight how whole-genome sequencing, combined with advanced bioinformatic pipelines and reference resources, enables systematic detection of different variant types. We also outline the translational potential of genetic discoveries, including polygenic risk and hazard score models for patient stratification, and underscore the importance of integrative genomic and multi-omic approaches in advancing precision medicine in Alzheimer's disease.

Non-invasive prenatal testing (NIPT) has revolutionized prenatal diagnostics by providing a safer alternative to invasive techniques such as amniocentesis and chorionic villus sampling. NIPT detects chromosomal abnormalities through the analysis of cell-free fetal DNA (cffDNA) in maternal plasma. One of the critical factors influencing accuracy of NIPT is the fetal fraction (FF) – the proportion of fetal cell-free DNA relative to total cell-free DNA in maternal plasma. This study investigates the potential of using telomere length measurements as a novel biomarker for fetal fraction prediction in NIPT. Telomere-derived fragments, which differ between maternal and fetal DNA, may serve as a measure of FF due to the distinct telomere length. Specifically, deviations from the expected shorter telomere lengths of maternal DNA toward longer lengths could be more pronounced at higher FF levels. Various models incorporating telomere content and features selected by Ordinary Least Squares (OLS) were evaluated to enhance fetal fraction prediction. Our results showed that telomere content also works as an independent predictor (with Pearson correlation 0.23), yielding a small improvement in prediction precision when combined with traditional models.

Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving the clinicians from the tedious search in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using wider characterization of affected genomic elements. Such tools utilize features that are additional to ACMG criteria, thus, they have the potential to significantly improve and/or provide supportive evidence for accurate CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/ .

Non-invasive prenatal testing (NIPT) has become a routine practice in screening for common aneuploidies of chromosomes 21, 18, and 13 and gonosomes X and Y in fetuses worldwide since 2015 and has even expanded to include smaller subchromosomal events. In fact, the fetal fraction represents only a small proportion of cell-free DNA on a predominant background of maternal DNA. Unlike fetal findings that have to be confirmed using invasive testing, it has been well documented that NIPT provides information on maternal mosaicism, occult malignancies, and hidden health conditions due to copy number variations (CNVs) with diagnostic resolution. Although large duplications or deletions associated with certain medical conditions or syndromes are usually well recognized and easy to interpret, very little is known about small, relatively common copy number variations on the order of a few hundred kilobases and their potential impact on human health. We analyzed data from 6422 NIPT patient samples with a CNV detection resolution of 200 kb for the maternal genome and identified 942 distinct CNVs; 328 occurred repeatedly. We defined them as multiple occurring variants (MOVs). We scrutinized the most common ones, compared them with frequencies in the gnomAD SVs v2.1, dbVar, and DGV population databases, and analyzed them with an emphasis on genomic content and potential association with specific phenotypes.

Copy number variations (CNVs) represent a type of structural variant involving alterations in the number of copies of specific regions of DNA that can either be deleted or duplicated. CNVs contribute substantially to normal population variability, however, abnormal CNVs cause numerous genetic disorders. At present, several methods for CNV detection are applied, ranging from the conventional cytogenetic analysis, through microarray-based methods (aCGH), to next-generation sequencing (NGS). In this paper, we present GenomeScreen, an NGS-based CNV detection method for low-coverage, whole-genome sequencing. We determined the theoretical limits of its accuracy and obtained confirmation in an extensive in silico study and in real patient samples with known genotypes. In theory, at least 6 M uniquely mapped reads are required to detect a CNV with the length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in silico analysis required at least 8 M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has mean resolution of 200 kb. GenomeScreen and aCGH both detected 59 deviations, while GenomeScreen furthermore detected 134 other (usually) smaller variations. When compared to aCGH, overall performance of the proposed GenemoScreen tool is comparable or superior in terms of accuracy, turn-around time, and cost-effectiveness, thus providing reasonable benefits, particularly in a prenatal diagnosis setting.

Introduction Current and future applications of genomic data may raise ethical and privacy concerns. Processing and storing genomic data introduces a risk of abuse by a potential adversary since the human genome contains information about sensitive personal traits. For this reason, we developed a privacy preserving method, called Varlock, for secure storage and dissemination of sequenced genomic data. Materials and methods The Varlock uses a set of population allele frequencies to mask personal alleles detected in genomic reads. Each detected allele is replaced by a randomly selected population allele concerning its frequency. Masked alleles are preserved in an encrypted confidential file that can be shared, in whole or in part, using public-key cryptography. Results Our method masked personal variants and introduced new variants called on an individual’s genome, while alternative alleles with lower population frequency were masked and introduced more often. We performed joint PCA analysis of personal and masked VCFs, showing that the VCFs between the two groups can not be trivially mapped. Moreover, the method is reversible; therefore, personal alleles can be unmasked in specific genomic regions on demand. Conclusion Our method masks personal alleles within mapped reads while preserving valuable non-sensitive properties of sequenced DNA fragments for further research. Accordingly, masked reads can be stored publicly, since they are deprived of sensitive personal information. Personal alleles may be restored in arbitrary genomic regions for interested parties: patients, medical units, and researchers.

Copy number variations (CNVs) are a type of structural variants involving alterations in the number of copies of specific regions of DNA, which can either be deleted or duplicated. CNVs contribute substantially to normal population variability; however, abnormal CNVs cause numerous genetic disorders. Nowadays, several methods for CNV detection are used, from the conventional cytogenetic analysis through microarray-based methods (aCGH) to next-generation sequencing (NGS). We present GenomeScreen – NGS-based CNV detection method for lowcoverage whole-genome sequencing. We determined the theoretical limits of its accuracy and confirmed it with extensive in-silico study and real patient samples with known genotypes. Theoretically, at least 6M uniquely mapped reads are required to detect CNV with a length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in-silico analysis showed the requirement of at least 8M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has a 200 kb mean resolution. GenomeScreen and aCGH both detected 59 deviations, GenomeScreen furthermore detected 134 other (usually) smaller variations. The performance of the proposed GenemoScreen tool is comparable or superior to the aCGH regarding accuracy, turnaround time, and cost-effectiveness, presenting a reasonable benefit particularly in a prenatal diagnosis setting.

Detection of copy number variants as an integral part of noninvasive prenatal testing is increasingly used in clinical practice worldwide. We performed validation on plasma samples from 34 pregnant women with known aberrations using cell-free DNA sequencing to evaluate the sensitivity for copy number variants (CNV) detection using an in-house CNV fraction-based detection algorithm. The sensitivity for CNVs smaller than 3 megabases (Mb), larger than 3Mb, and overall was 78.57%, 100%, and 90.6%, respectively. Regarding the fetal fraction, detection sensitivity in the group with a fetal fraction of less than 10% was 57.14%, whereas there was 100% sensitivity in the group with fetal fraction exceeding 10%. The assay is also capable of indicating whether the origin of an aberration is exclusively fetal or fetomaternal/maternal. This validation demonstrated that a CNV fraction-based algorithm was applicable and feasible in clinical settings as a supplement to testing for common trisomies 21, 18, and 13.

Introduction: Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of structural variants is a challenging problem due to highly variable numbers of gene, regulatory or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. Materials and Methods: We designed a classifier method based on the annotations of CNVs from several publicly available databases. The attributes take into account gene elements, regulatory elements affected by the CNV, as well as other CNVs with known clinical significance that overlap the candidate CNV. We also describe the process of model selection and the construction of training, validation, and test set. Results: The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants and can be improved by imposing probability thresholds to eliminate low confidence predictions. Discussion: Method has shown considerable performance in predicting the clinical impact of CNVs and therefore has a great potential to guide users to more precise conclusions. The CNV annotation and pathogenicity prediction can be fully automated, relieving users of tedious interpretation processes. Availability and Implementation: The results can be reproduced by following instructions at {{https://github.com/tsladecek/isv}}.

Noninvasive prenatal testing (NIPT) is one of the most common prenatal screening tests used worldwide. Trisomy Test^® belongs to NIPT tests based on low-coverage whole-genome sequencing. In our prospective study, 7279 samples of pregnant women collected during approximately two years were analyzed. In this cohort, 117 positive cases for trisomies 21, 18, and 13 were reported. An in-house designed bioinformatic pipeline and proprietary biostatistical approach was used for the detection of trisomies. The pooled sensitivity and specificity of our test reached 99.12% and 99.94%, respectively. The proportion of repeatedly uninformative results after repeated blood draws was 1.11%. Based on the presented results, we can confirm that the Trisomy Test^® is fully comparable with other commercial NIPT tests available worldwide.