Arvutuslikud meetodid DNA koopiaarvu määramiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.DNA koopiaarvu variantideks või muutusteks nimetatakse selliseid erinevusi inimeste geneetilises materjalis, mille puhul mingi DNA lõigu koopiaarv on erinev oodatavast koopiaarvust kaks (üks koopia mingit kindlat DNA järjestust emalt päritud kromosoomil ja üks koopia isalt päritud kromosoomil). DNA koopiate vähenemist nimetatakse deletsiooniks ning vastavaid DNA variante nimetatakse deletsioonideks. DNA koopiate juurdetulemist nimetatakse duplitseerumiseks ning selliseid kahest suurema koopiaarvuga variante vastavalt duplikatsioonideks. Antud doktoritöös uuriti inimese DNA koopiaarvu variante, nende seotust erinevate haigustega ja nende tekkimise ja pärandumise eripärasid. Kasutades DNA mikrokiipe ehk geenikiipe uuriti esmalt kas ja millised DNA koopiaarvu muutused võivad olla seotud vaimse arengu mahajäämusega (VAM-ga). Uurides perekondasid, kus ühel või mitmel liikmel oli diagnoositud VAM, leiti mitmeid juba varem VAM-ga seostatud DNA koopiaarvu muutusi ning lisaks leiti ka mitmeid uusi DNA koopiaarvu variante, mille esinemine võib olla seotud VAM-e väljakujunemisega. Sarnane uuring viidi läbi ka korduva spontaanse raseduse katkemise probleemiga paaride ja naiste puhul. Võrreldes nende patsientide gruppi kuuluvate naiste DNA koopiaarvu muutusi ning nende sagedusi terveid emasid sisaldavate kontroll-grupi indiviidide omadega, leiti statistiliselt ja bioloogiliselt oluline erinevus muutunud koopiaarvuga DNA lõigus, mis sisaldab PDZD2 ja GOLPH3 geene ja kus esinevate duplikatsioonide „omamine“ suurendas naistel märkimisväärselt spontaanse raseduse katkemise ohtu. Doktoritöö viimases osas uuriti Tartu Ülikooli Eesti Geenivaramu ja rahvusvahelise HapMap projekti poolt kogutud tõsiste haigusteta inimestel esinevaid DNA koopiaarvu muutusi ja nende pärandumist perekondades. Selle uuringu üheks huvitavamaks tulemuseks oli deletsioonide alapärandumine vanematelt lastele ehk deletsioone kandvaid DNA regioone esines laste genoomides oluliselt vähem, kui normaalse Mendeliaalse (juhusliku) pärandumise korral oleks oodata võinud. Uurides duplikatsioonide regioone perekondades leiti aga, et kaks kolmandikku duplikatsioonides esinevatest DNA koopiatest ei olnud identsed (üksteise täpsed koopiad), vaid mõnevõrra erinevad, demonstreerides seniajani teadmata olnud alleelse varieeruvuse määra DNA duplikatsioonide regioonides.DNA copy number variation is a type of genetic variation in which case the number of copies of a particular region of a chromosome is altered from its normal state. In the non-repetitive portion of the human genome, the normal haploid copy number is one – one copy of each sequence per chromosome. Accordingly, the normal diploid copy number in humans is two – one copy inherited from both parents. A copy number variant (CNV) can result from either a loss of copies (most often called a deletion) or gain of copies (called a duplication or amplification). In this thesis we studied DNA copy number variation in human – how CNVs emerge and how they are inherited from parents to offspring. We also analysed CNVs in the context of few different diseases. By using DNA microarrays we first aimed to determine if CNVs are associated with mental retardation (MR). For this we studied not only index cases with MR but larger nuclear families, where we discovered several already MR-associated CNVs and also a few novel CNV regions that are possibly associated with predisposition to MR. Similar study was conducted in couples and females suffering from recurrent miscarriage. By comparing CNVs and their frequencies in the latter group to these of healthy mothers, we discovered a multi-copy duplication at 5p13.3 that disrupts PDZD2 and GOLPH3 genes and significantly increases maternal risk for pregnancy complications. In the last part of this thesis we studied how CNVs are inherited in Estonian nuclear families (22 trios and 12 families with multiple siblings) and in HapMap Yoruban trios. We determined that deletion-carrying chromosomal regions were observed in the offspring slightly less frequently than expected by random Mendelian inheritance. By analysing duplication-carrying chromosomal regions in these families, we discovered that in two-thirds of such regions the duplicated copies of the underlying DNA sequence were not exactly identical but somewhat different, allowing us to define alternative allelic copies within these copy number gain-carrying chromosomal regions and demonstrating extensive and to-date unmeasured allelic variability in multi-copy CNV regions of the human genome

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

Non-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no prior comprehensive, head-to-head accuracy comparison of the various tools has been published. Here, we compared the outcome accuracies obtained for clinically validated samples with five commonly used computational NIPT aneuploidy analysis tools (WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq) across various sequencing depths (coverage) and fetal DNA fractions. The sample set included cases of fetal trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). We determined that all of the compared tools were considerably affected by lower sequencing depths, such that increasing proportions of undetected trisomy cases (false negatives) were observed as the sequencing depth decreased. We summarised our benchmarking results and highlighted the advantages and disadvantages of each computational NIPT software. To conclude, trisomy detection for lower coverage NIPT samples (e.g. 2.5M reads per sample) is technically possible but can, with some NIPT tools, produce troubling rates of inaccurate trisomy detection, especially in low-FF samples.Author summaryNon-invasive prenatal testing analysis relies on computational algorithms that are used for inferring chromosomal aneuploidies, such as chromosome 21 triploidy in the case of Down syndrome. However, the performance of these algorithms has not been compared on the same clinically validated data. Here we conducted a head-to-head comparison of WGS-based NIPT aneuploidy detection tools. Our findings indicate that at and below 2.5M reads per sample, the least accurate algorithm would miss detection of almost a third of trisomy cases. Furthermore, we describe and quantify a previously undocumented aneuploidy risk uncertainty that is mainly relevant in cases of very low sequencing coverage (at and below 1.25M reads per sample) and could, in the worst-case scenario, lead to a false negative rate of 245 undetected trisomies per 1,000 trisomy cases. Our findings underscore the importance of the informed selection of NIPT software tools in combination with sequencing coverage, which directly impacts NIPT sequencing cost and accuracy

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

Non-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no prior comprehensive, head-to-head accuracy comparison of the various tools has been published. Here, we compared the outcome accuracies obtained for clinically validated samples with five commonly used computational NIPT aneuploidy analysis tools (WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq) across various sequencing depths (coverage) and fetal DNA fractions. The sample set included cases of fetal trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). We determined that all of the compared tools were considerably affected by lower sequencing depths, such that increasing proportions of undetected trisomy cases (false negatives) were observed as the sequencing depth decreased. We summarised our benchmarking results and highlighted the advantages and disadvantages of each computational NIPT software. To conclude, trisomy detection for lower coverage NIPT samples (e.g. 2.5M reads per sample) is technically possible but can, with some NIPT tools, produce troubling rates of inaccurate trisomy detection, especially in low-FF samples. Author summaryNon-invasive prenatal testing analysis relies on computational algorithms that are used for inferring chromosomal aneuploidies, such as chromosome 21 triploidy in the case of Down syndrome. However, the performance of these algorithms has not been compared on the same clinically validated data. Here we conducted a head-to-head comparison of WGS-based NIPT aneuploidy detection tools. Our findings indicate that at and below 2.5M reads per sample, the least accurate algorithm would miss detection of almost a third of trisomy cases. Furthermore, we describe and quantify a previously undocumented aneuploidy risk uncertainty that is mainly relevant in cases of very low sequencing coverage (at and below 1.25M reads per sample) and could, in the worst-case scenario, lead to a false negative rate of 245 undetected trisomies per 1,000 trisomy cases. Our findings underscore the importance of the informed selection of NIPT software tools in combination with sequencing coverage, which directly impacts NIPT sequencing cost and accuracy.Peer reviewe

Computational framework for targeted high-coverage sequencing based NIPT

Non-invasive prenatal testing (NIPT) enables accurate detection of fetal chromosomal trisomies. The majority of publicly available computational methods for sequencing-based NIPT analyses rely on low-coverage whole-genome sequencing (WGS) data and are not applicable for targeted high-coverage sequencing data from cell-free DNA samples. Here, we present a novel computational framework for a targeted high-coverage sequencing-based NIPT analysis. The developed framework uses a hidden Markov model (HMM) in conjunction with a supplemental machine learning model, such as decision tree (DT) or support vector machine (SVM), to detect fetal trisomy and parental origin of additional fetal chromosomes. These models were developed using simulated datasets covering a wide range of biologically relevant scenarios with various chromosomal quantities, parental origins of extra chromosomes, fetal DNA fractions, and sequencing read depths. Developed models were tested on simulated and experimental targeted sequencing datasets. Consequently, we determined the functional feasibility and limitations of each proposed approach and demonstrated that read count-based HMM achieved the best overall classification accuracy of 0.89 for detecting fetal euploidies and trisomies on simulated dataset. Furthermore, we show that by using the DT and SVM on the HMM classification results, it was possible to increase the final trisomy classification accuracy to 0.98 and 0.99, respectively. We demonstrate that read count and allelic ratio-based models can achieve a high accuracy (up to 0.98) for detecting fetal trisomy even if the fetal fraction is as low as 2%. Currently, existing commercial NIPT analysis requires at least 4% of fetal fraction, which can be possibly a challenge in case of early gestational age (35 kg/m2). More accurate detection can be achieved at higher sequencing depth using HMM in conjunction with supplemental models, which significantly improve the trisomy detection especially in borderline scenarios (e.g., very low fetal fraction) and enables to perform NIPT even earlier than 10 weeks of pregnancy.Peer reviewe

Detection of tmRNA molecules on microarrays at low temperatures using helper oligonucleotides

<p>Abstract</p> <p>Background</p> <p>The hybridization of synthetic <it>Streptococcus pneumoniae </it>tmRNA on a detection microarray is slow at 34°C resulting in low signal intensities.</p> <p>Results</p> <p>We demonstrate that adding specific DNA helper oligonucleotides (chaperones) to the hybridization buffer increases the signal strength at a given temperature and thus makes the specific detection of <it>Streptococcus pneumoniae </it>tmRNA more sensitive. No loss of specificity was observed at low temperatures compared to hybridization at 46°C. The effect of the chaperones can be explained by disruption of the strong secondary and tertiary structure of the target RNA by the selective hybridization of helper molecules. The amplification of the hybridization signal strength by chaperones is not necessarily local; we observed increased signal intensities in both local and distant regions of the target molecule.</p> <p>Conclusions</p> <p>The sensitivity of the detection of tmRNA at low temperature can be increased by chaperone oligonucleotides. Due to the complexity of RNA secondary and tertiary structures the effect of any individual chaperone is currently not predictable.</p

NIPTmer : rapid k-mer-based software package for detection of fetal aneuploidies

Non-invasive prenatal testing (NIPT) is a recent and rapidly evolving method for detecting genetic lesions, such as aneuploidies, of a fetus. However, there is a need for faster and cheaper laboratory and analysis methods to make NIPT more widely accessible. We have developed a novel software package for detection of fetal aneuploidies from next-generation low-coverage whole genome sequencing data. Our tool - NIPTmer - is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data, and applying linear regression model on the counts. Additionally, the filtering process used for k-mer list creation allows one to take into account the genetic variance in a specific sample, thus reducing the source of uncertainty. The processing time of one sample is less than 10 CPU-minutes on a high-end workstation. NIPTmer was validated on a cohort of 583 NIPT samples and it correctly predicted 37 non-mosaic fetal aneuploidies. NIPTmer has the potential to reduce significantly the time and complexity of NIPT post-sequencing analysis compared to mapping-based methods. For non-commercial users the software package is freely available at http://bioinfo.ut.ee/NIPTMer/.Peer reviewe

Creating basis for introducing non‐invasive prenatal testing in the Estonian public health setting

Objective The study aimed to validate a whole‐genome sequencing‐based NIPT laboratory method and our recently developed NIPTmer aneuploidy detection software with the potential to integrate the pipeline into prenatal clinical care in Estonia. Method In total, 424 maternal blood samples were included. Analysis pipeline involved cell‐free DNA extraction, library preparation and massively parallel sequencing on Illumina platform. Aneuploidies were determined with NIPTmer software, which is based on counting pre‐defined per‐chromosome sets of unique k‐mers from sequencing raw data. SeqFF was implemented to estimate cell‐free fetal DNA (cffDNA) fraction. Results NIPTmer identified correctly all samples of non‐mosaic trisomy 21 (T21, 15/15), T18 (9/9), T13 (4/4) and monosomy X (4/4) cases, with the 100% sensitivity. However, one mosaic T18 remained undetected. Six false‐positive (FP) results were observed (FP rate of 1.5%, 6/398), including three for T18 (specificity 99.3%) and three for T13 (specificity 99.3%). The level of cffDNA of <4% was estimated in eight samples, including one sample with T13 and T18. Despite low cffDNA level, these two samples were determined as aneuploid. Conclusion We believe that the developed NIPT method can successfully be used as a universal primary screening test in combination with ultrasound scan for the first trimester fetal examination

High-resolution population-specific recombination rates and their effect on phasing and genotype imputation

Previous research has shown that using population-specific reference panels has a significant effect on downstream population genomic analyses like haplotype phasing, genotype imputation, and association, especially in the context of population isolates. Here, we developed a high-resolution recombination rate mapping at 10 and 50 kb scale using high-coverage (20-30x) whole-genome sequenced data of 55 family trios from Finland and compared it to recombination rates of non-Finnish Europeans (NFE). We tested the downstream effects of the population-specific recombination rates in statistical phasing and genotype imputation in Finns as compared to the same analyses performed by using the NFE-based recombination rates. We found that Finnish recombination rates have a moderately high correlation (Spearman's rho = 0.67-0.79) with NFE, although on average (across all autosomal chromosomes), Finnish rates (2.268 +/- 0.4209 cM/Mb) are 12-14% lower than NFE (2.641 +/- 0.5032 cM/Mb). Finnish recombination map was found to have no significant effect in haplotype phasing accuracy (switch error rates similar to 2%) and average imputation concordance rates (97-98% for common, 92-96% for low frequency and 78-90% for rare variants). Our results suggest that haplotype phasing and genotype imputation mostly depend on population-specific contexts like appropriate reference panels and their sample size, but not on population-specific recombination maps. Even though recombination rate estimates had some differences between the Finnish and NFE populations, haplotyping and imputation had not been noticeably affected by the recombination map used. Therefore, the currently available HapMap recombination maps seem robust for population-specific phasing and imputation pipelines, even in the context of relatively isolated populations like Finland.Peer reviewe

Structural Genomic Variation as Risk Factor for Idiopathic Recurrent Miscarriage

Peer reviewe

A molecular tool for menstrual cycle phase dating of endometrial samples in endometriosis transcriptome studies

Correction: Volume: 101 Issue: 4 Pages: 868-868 DOI: 10.1093/biolre/ioz092Non peer reviewe