Inferring combined CNV/SNP haplotypes from genotype data

Accurate Detection of Clinically Actionable Copy Number Variants in Diverse Hematological Neoplasms By Routine Targeted Sequencing: A Comparative Performance Study

Many species on Earth can carry up to two different versions of a given gene, with each of these ‘alleles’ having only a 50/50 chance of being transmitted to the next generation via sexual reproduction. Certain ‘selfish’ sequences, however, can hijack this process and increase their probability of being passed on to an offspring. Known as transmission distortion, this phenomenon may result in alleles spreading through the population even if they are detrimental to fertility. Transmission distortion has been detected in many species such as flies, mice and some plants. It can take place at various stages during reproduction; for example, the selfish alleles may become overrepresented among eggs or sperm. However, scientists need to study a large number of offspring or reproductive cells to be able to detect whether an allele is inherited more often than expected. This has made it difficult to determine whether transmission distortion also happens in humans, and research so far has resulted in conflicting conclusions. A recently published dataset of human sperm from 25 donors offered Carioscia, Weaver et al. the opportunity to examine this question. Every volunteer had produced between 969 and 3377 sperm cells, each with about 1% of their genome sequenced. Carioscia, Weaver et al. developed a computational method, which they named rhapsodi, that allowed them to ‘fill in the gaps’ and infer missing regions of the genome for each cell. To do so, they relied on the fact that sperm cells from a given individual are highly related to one another. With this more complete data at hand, it became possible to look for evidence of transmission distortion by searching for alleles that were overrepresented in sperm from a given donor. No selfish sequence could be detected in any of the 25 individuals, suggesting that human sperm may not be subject to pervasive transmission distortion. Signatures of selfish alleles detected in previous human studies may have therefore not resulted from this mechanism taking place at the sperm level. Instead, transmission distortion in humans could primarily target eggs or happen at later stages (for instance, if embryos carrying the selfish allele have better chances of survival). The ‘rhapsodi’ method developed by Carioscia, Weaver et al. should allow other scientists to work with datasets for which large portions of the genetic information is missing. It may therefore become easier for researchers to track selfish alleles which are difficult to detect, and to examine bigger, more diverse samples which also include individuals with known fertility challenges.

High-throughput sequencing (HTS) refers to the simultaneous sequencing of millions of fragments of DNA, which can be either assembled to reconstitute a genome, or aligned to an existing reference genome. The protocol can be extended to assay a wide variety of biological states of the cell, including DNA copy number, mRNA abundance and various properties of chromatin. HTS experiments allow for these biological states to be quantified as read counts at genome-wide scale with a single experiment. Though the experiments are expensive and often datasets are produced with limited sample size, information can be shared across thousands of genomic ranges in order to obtain robust models which control for technical biases. In this thesis, I present three statistical models for analyzing HTS read count data, aimed at answering concise biological questions. First, a hidden Markov model is developed for detecting copy number variants (CNVs) in individual samples while controlling for technical artifacts, such as variation in read counts due to local GC-content. Applied to a study of 248 male patients with X-linked intellectual disability, the model predicts 16 large CNVs, of which 10 candidate disease-causing CNVs were tested and all experimentally validated. The proposed software is then compared with state-of-the-art segmentation algorithms on normalized data, showing higher sensitivity while controlling the total rate of predicted CNVs. Second, improvements for parameter estimation are made for a statistical model of differential gene expression from RNA-Seq data. The improvements involve the use of empirical Bayes priors – priors estimated using the observations from all genes – in order to moderate otherwise noisy estimates of dispersion and fold changes for individual genes. The improved model shows increased sensitivity and more robust estimation of fold change in comparison with other differential expression software packages for RNA-Seq. Finally, a hierarchical Bayes model is used to associate transcription factor binding with chromatin and sequence features in regions of accessible chromatin. The hierarchical model incorporates three levels of parameters: one for individual experiments, one for experiments of the same cell type and one across all cell types. The model parameters are used to generate hypotheses regarding the DNA-binding behavior of a transcription factor, the glucocorticoid receptor. In summary, this thesis describes a set of statistical methods for HTS read count data which can be used across various biological domains. The methods form a framework for robust estimation of variables and hypothesis testing.%%%%Mit Hochdurchsatz-Sequenzierverfahren (HTS) bezeichnet man das gleichzeitige Sequenzieren von Millionen von DNA-Fragmenten, welche entweder zur Genomrekonstrution genutzt oder auf ein bestehendes Referenzgenom aligniert werden konnen. Das Protokoll kann erweitert werden, um verschiedene biologische Zustande der Zelle, wie z.B. die Anzahl an DNA-Kopien, mRNA-Abundanzen oder…

Cytogenic testing is routinely applied in most neurological centres for severe paediatric epilepsies. However, which characteristics of copy number variants (CNVs) confer most epilepsy risk and which epilepsy subtypes carry the most CNV burden, have not been explored on a genome-wide scale. Here, we present the largest CNV investigation in epilepsy to date with 10 712 European epilepsy cases and 6746 ancestry-matched controls. Patients with genetic generalized epilepsy, lesional focal epilepsy, non-acquired focal epilepsy, and developmental and epileptic encephalopathy were included. All samples were processed with the same technology and analysis pipeline. All investigated epilepsy types, including lesional focal epilepsy patients, showed an increase in CNV burden in at least one tested category compared to controls. However, we observed striking differences in CNV burden across epilepsy types and investigated CNV categories. Genetic generalized epilepsy patients have the highest CNV burden in all categories tested, followed by developmental and epileptic encephalopathy patients. Both epilepsy types also show association for deletions covering genes intolerant for truncating variants. Genome-wide CNV breakpoint association showed not only significant loci for genetic generalized and developmental and epileptic encephalopathy patients but also for lesional focal epilepsy patients. With a 34-fold risk for developing genetic generalized epilepsy, we show for the first time that the established epilepsy-associated 15q13.3 deletion represents the strongest risk CNV for genetic generalized epilepsy across the whole genome. Using the human interactome, we examined the largest connected component of the genes overlapped by CNVs in the four epilepsy types. We observed that genetic generalized epilepsy and non-acquired focal epilepsy formed disease modules. In summary, we show that in all common epilepsy types, 1.5-3% of patients carry epilepsy-associated CNVs. The characteristics of risk CNVs vary tremendously across and within epilepsy types. Thus, we advocate genome-wide genomic testing to identify all disease-associated types of CNVs.

Targeted Array CGH

Can Genomic Copy Number Variants Be a Part of Complex Genetic Traits Predisposing to Marrow Failure?.

Genomic instability, the hallmark of cancer, presents with a variety of mutation types, most commonly single nucleotide variations (SNVs) and copy number variations (CNVs), which traditionally have required different methods for identification. It has proven challenging to simultaneously achieve sufficient breadth to detect CNVs and depth to detect SNVs on samples of limited input amount. The objective of this study was to validate a new methodology for detection of SNVs and CNVs in a single assay. We used a massively multiplex PCR/NGS approach combining an SNV panel covering 585 point mutation hotspots in breast cancer (Cosmic) and a CNV panel targeting 28,000 SNPs designed to detect copy number at chromosomes 1, 2, 13, 18, 21, and X, and focal regions 4p16, 5p15, 7q11, 15q, 17p, 22q11, and 22q13. We applied these panels to breast cancer cell lines and fresh frozen (FF) breast tumor samples; the presence of CNVs in circulating cell-free tumor DNA (ctDNA) in the plasma of breast cancer patients was also investigated. The CNV assay methodology was validated using genomic DNA isolated from 96 human samples with known karyotype; sensitivity to single region deletions or duplications was 100% (71/71) and specificity was 100% for normal regions in the same samples. Single-molecule sensitivity for the detection of CNVs was established by analyzing isolated single cells. Performance of the mutation assay was demonstrated with the analysis of 5 matched tumor and normal cell lines, with 24 out of 27 SNVs known to be present in these cell lines detected. The 3 undetected SNVs were determined to be a result of assay design failure. Also, multiple somatic CNVs (median: 13) were detected in all 5 tumor cell lines. Analysis of the normal cell lines found no cancer related SNVs or CNVs. In 32 FF tumor samples, 78.1% (25/32) had SNVs detected; of samples with SNVs, 88% (22/25) had SNVs in TP53 or PIK3CA. Of the same 32 FF breast tumor samples, 96.9% (31/32) showed full or partial CNVs in at least 1 and up to 15 regions; of the 31 samples with detected CNVs, 93.5% had a CNV of either 1q or 17p, two of the three most prevalent breast cancer CNVs (the 16q region was not represented in this panel). Overall, a combination of SNV and CNV testing allowed identification of genetic changes in 100% of the breast tumor samples, a significant improvement in diagnostic yield than using SNV detection alone. Of the 12 breast cancer patients with matched tumor tissue and plasma samples, 83.3% (10/12) had CNVs detected in tissue. The CNVs present in each primary tumor sample were identified in corresponding plasma ctDNA samples (1 stage IIa, 7 stage IIb, and 2 stage III). The ctDNA fractions in these samples ranged from 0.58 to 4.33%; detection required as few as 86 heterozygous SNPs per CNV. Analysis of ctDNA for cancer-associated mutations may allow earlier, safer and more accurate profiling and monitoring of breast cancer. Thus, this targeted PCR approach offers the promise of an assay able to detect both cancer-associated SNVs and CNVs in the same sample with good sensitivity and specificity, and improved detection rates compared to assays that only detect SNVs. Citation Format: Robert J Pelham, Bernhard G Zimmermann, Eser Kirkizlar, Ryan K Swenerton, Bin Hoang, Onur Sakarya, Joshua E Babiarz, Nicholas Wayham, Tudor Constantin, Styrmir Sigurjonsson, Matthew Rabinowitz, Matthew Hill. Detection of single nucleotide variations and copy number variations in breast cancer tissue and ctDNA samples using single-nucleotide polymorphism-targeted massively multiplexed PCR [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-02-03.

DNA copy number variants (CNVs) that alter the copy number of a particular DNA segment in the genome play an important role in human phenotypic variability and disease susceptibility. A number of CNVs overlapping with genes have been shown to confer risk to a variety of human diseases thus highlighting the relevance of addressing the variability of CNVs at a higher resolution. So far, it has not been possible to deterministically infer the allelic composition of different haplotypes present within the CNV regions. We have developed a novel computational method, called PiCNV, which enables to resolve the haplotype sequence composition within CNV regions in nuclear families based on SNP genotyping microarray data. The algorithm allows to i) phase normal and CNV-carrying haplotypes in the copy number variable regions, ii) resolve the allelic copies of rearranged DNA sequence within the haplotypes and iii) infer the heritability of identified haplotypes in trios or larger nuclear families. To our knowledge this is the first program available that can deterministically phase null, mono-, di-, tri- and tetraploid genotypes in CNV loci. We applied our method to study the composition and inheritance of haplotypes in CNV regions of 30 HapMap Yoruban trios and 34 Estonian families. For 93.6% of the CNV loci, PiCNV enabled to unambiguously phase normal and CNV-carrying haplotypes and follow their transmission in the corresponding families. Furthermore, allelic composition analysis identified the co-occurrence of alternative allelic copies within 66.7% of haplotypes carrying copy number gains. We also observed less frequent transmission of CNV-carrying haplotypes from parents to children compared to normal haplotypes and identified an emergence of several de novo deletions and duplications in the offspring.

Introduction Despite many advances, the treatment for most patients with advanced solid tumors in the abdomen continues to be a clinical challenge. Copy number variants (CNVs) are an important part of the genomic landscape of these patients. Next-generation sequencing (NGS) tests used in the clinical setting are predominantly multi-gene targeted panels (FM, Guardant, NeoGenomics). Although this is the current standard, there is still doubt about the reliability and accuracy of such panels (PMID: 28472276), and panel based CNV-calling in clinical labs use stringent thresholds that may filter out many CNVs. The purpose of our study was to evaluate the clinical utility of a 125-gene targeted sequencing panel in detecting actionable CNVs in a cohort of colorectal cancer patients, with the eventual goal of identifying appropriate genome-guided therapy options. Materials and Methods We performed (i) targeted (350X) NGS based on the IDT xGen 125-gene Pan-Cancer panel on tumor and matched normal blood samples of 54 patients as well as (ii) very low-coverage (LC-WGS, 0.1X) whole genome sequencing on tumor and matched blood samples for the same set of patients. Bioinformatics methods included adapter trimming with bbduk, alignment with bwa-mem, quality control with FASTQC and Qualimap. CNVkit, ichorNCA, and Control-FREEC were used to call CNVs. For CNVkit and ichorNCA, the normal samples were combined to create a ‘reference normal'. Multiple window sizes were utilized to call CNVs at different scales. Results We compared the results of CNV calls from CNVkit, ichorCNA and Control-FREEC from targeted and LC-WGS data. This analysis indicates that targeted panels are at least currently not capable of reliably identifying the copy number landscape of clinical colorectal samples when matched normal samples are utilized. While within the targeted region we identified a median of five clinically actionable CNVs per sample for both targeted and whole genome sequencing (CNV amplification threshold of 5 copies), discordance of called CNVs on average was 56 percent. Numerical copy number calls tend to be higher for the targeted panel (mean: 10) vs LC-WGS (mean: 6) Beyond the targeted regions, LC-WGS detected a median of one additional clinical actionable CNV in genes such as PD-L1, JAK2, LRP1B, VEGFA, BCL2, and CCND3 which are not part of the targeted gene panel. Conclusions Our analysis suggests that CNV calling from targeted panels can be improved upon by incorporating very low-coverage whole genome sequencing. Citation Format: Anu Amallraja, Janina Fuß, Casey B. Williams, Michael Forster, Sebastian Hinz, Tobias Meißner, Alexander Hendricks. Very low coverage whole genome sequencing improves clinically relevant copy number variation calling compared to targeted sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3617.

BackgroundThe prevalence of Non-alcoholic fatty liver disease (NAFLD) is increasing and emerging as a global health burden. In addition to environmental factors, numerous studies have shown that genetic factors play an important role in the development of NAFLD. Copy number variation (CNV) as a genetic variation plays an important role in the evaluation of disease susceptibility and genetic differences. The aim of the present study was to assess the contribution of CNV to the evaluation of NAFLD in a Chinese population.MethodsGenome-wide analysis of CNV was performed using high-density comparative genomic hybridisation microarrays (ACGH). To validate the CNV regions, TaqMan real-time quantitative PCR (qPCR) was utilized.ResultsA total of 441 CNVs were identified, including 381 autosomal CNVs and 60 sex chromosome CNVs. By merging overlapping CNVs, a genomic CNV map of NAFLD patients was constructed. A total of 338 autosomal CNVRs were identified, including 275 CNVRs with consistent trends (197 losses and 78 gains) and 63 CNVRs with inconsistent trends. The length of the 338 CNVRs ranged from 5.7 kb to 2.23 Mb, with an average size of 117.44 kb. These CNVRs spanned 39.70 Mb of the genome and accounted for ~ 1.32% of the genome sequence. Through Gene Ontology and genetic pathway analysis, we found evidence that CNVs involving nine genes may be associated with the pathogenesis of NAFLD progression. One of the genes (NLRP4 gene) was selected and verified by quantitative PCR (qPCR) method with large sample size. We found the copy number deletion of NLRP4 was related to the risk of NAFLD.ConclusionsThis study indicate the copy number variation is associated with NAFLD. The copy number deletion of NLRP4 was related to the risk of NAFLD. These results could prove valuable for predicting patients at risk of developing NAFLD.

Parkinson's disease is the second most common neurodegenerative disorder and affects people from all ethnic backgrounds, yet little is known about the genetics of Parkinson's disease in non-European populations. In addition, the overall identification of copy number variants at a genome-wide level has been understudied in Parkinson's patients. The objective of this study was to understand the genome-wide burden of copy number variants in Latinos and its association with Parkinson's disease. We used genome-wide genotyping data from 747 Parkinson's disease patients and 632 controls from the Latin American Research Consortium on the Genetics of Parkinson's disease. Genome-wide copy number burden analysis showed that patients were significantly enriched for copy number variants overlapping known Parkinson's disease genes compared with controls (odds ratio, 3.97; 95%CI, 1.69-10.5; P = 0.018). PRKN showed the strongest copy number burden, with 20 copy number variant carriers. These patients presented an earlier age of disease onset compared with patients with other copy number variants (median age at onset, 31 vs 57 years, respectively; P = 7.46 × 10-7 ). We found that although overall genome-wide copy number variant burden was not significantly different, Parkinson's disease patients were significantly enriched with copy number variants affecting known Parkinson's disease genes. We also identified that of 250 patients with early-onset disease, 5.6% carried a copy number variant on PRKN in our cohort. Our study is the first to analyze genome-wide copy number variant association in Latino Parkinson's disease patients and provides insights about this complex disease in this understudied population. © 2020 International Parkinson and Movement Disorder Society.

Extensive Copy-Number Variation of the Human Olfactory Receptor Gene Family

Background and ObjectivesThe spinocerebellar ataxias (SCAs) are a genetically heterogeneous group of neurodegenerative disorders generally caused by single nucleotide variants (SNVs) or indels in coding regions or by repeat expansions in coding and noncoding regions of SCA genes. Copy number variants (CNVs) have now also been reported for 3 genes—ITPR1, FGF14, and SPTBN2—but not all SCA genes have been screened for CNVs as the underlying cause of the disease in patients. In this study, we aim to assess the prevalence of CNVs encompassing 36 known SCA genes.MethodsA cohort of patients with cerebellar ataxia who were referred to the University Medical Center Groningen for SCA genetic diagnostics was selected for this study. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using the Infinium Global Screening Array. Following data processing, genotyping data were uploaded into NxClinical software to perform CNV analysis per patient and to visualize identified CNVs in 36 genes with allocated SCA symbols. The clinical relevance of detected CNVs was determined using evidence from studies based on PubMed literature searches for similar CNVs and phenotypic features.ResultsOf the 338 patients with cerebellar ataxia, we identified putative clinically relevant CNV deletions in 3 patients: an identical deletion encompassing ITPR1 in 2 patients, who turned out to be related, and a deletion involving PPP2R2B in another patient. Although the CNV deletion in ITPR1 was clearly the underlying cause of SCA15 in the 2 related patients, the clinical significance of the deletion in PPP2R2B remained unknown.DiscussionWe showed that CNVs detectable with the limited resolution of SNP array are a very rare cause of SCA. Nevertheless, we suggest adding CNV analysis alongside SNV analysis to SCA gene diagnostics using next-generation sequencing approaches, at least for ITPR1, to improve the genetic diagnostics for patients.

Global patterns of copy number variations (CNVs) in chromosomes are required to understand the dynamics of genome organization and complexity. For this study, analysis was performed using the Affymetrix Genome-Wide Human SNP Array 6.0 chip and CytoScan High-Density arrays. We identified a total of 44 109 CNVs from 1715 genomes with a mean of 25 CNVs in an individual, which established the first drafts of population-specific CNV maps providing a rationale for prioritizing chromosomal regions. About 19 905 ancient CNVs were identified across all chromosomes and populations at varying frequencies. CNV count, and sometimes CNV size, contributed to the bulk CNV size of the chromosome. Population specific lengthening and shortening of chromosomal length was observed. Sex bias for CNV presence was largely dependent on ethnicity. Lower CNV inheritance rate was observed for India, compared to YRI and CEU. A total of 33 candidate CNV hotspots from 5382 copy number (CN) variable region (CNVR) clusters were identified. Population specific CNV distribution patterns in p and q arms disturbed the assumption that CNV counts in the p arm are less common compared to long arms, and the CNV occurrence and distribution in chromosomes is length independent. This study unraveled the force of independent evolutionary dynamics on genome organization and complexity across chromosomes and populations.

BackgroundCopy number variation is an important class of genomic variation that has been reported in 75% of the human genome. However, it is underreported in African populations. Copy number variants (CNVs) could have important impacts on disease susceptibility and environmental adaptation. To describe CNVs and their possible impacts in Africans, we sequenced genomes of 232 individuals from three major African ethno-linguistic groups: (1) Niger Congo A from Guinea and Côte d’Ivoire, (2) Niger Congo B from Uganda and the Democratic Republic of Congo and (3) Nilo-Saharans from Uganda. We used GenomeSTRiP and cn.MOPS to identify copy number variant regions (CNVRs).ResultsWe detected 7608 CNVRs, of which 2172 were only deletions, 2384 were only insertions and 3052 had both. We detected 224 previously un-described CNVRs. The majority of novel CNVRs were present at low frequency and were not shared between populations. We tested for evidence of selection associated with CNVs and also for population structure. Signatures of selection identified previously, using SNPs from the same populations, were overrepresented in CNVRs. When CNVs were tagged with SNP haplotypes to identify SNPs that could predict the presence of CNVs, we identified haplotypes tagging 3096 CNVRs, 372 CNVRs had SNPs with evidence of selection (iHS > 3) and 222 CNVRs had both. This was more than expected (p < 0.0001) and included loci where CNVs have previously been associated with HIV, Rhesus D and preeclampsia. When integrated with 1000 Genomes CNV data, we replicated their observation of population stratification by continent but no clustering by populations within Africa, despite inclusion of Nilo-Saharans and Niger-Congo populations within our dataset.ConclusionsNovel CNVRs in the current study increase representation of African diversity in the database of genomic variants. Over-representation of CNVRs in SNP signatures of selection and an excess of SNPs that both tag CNVs and are subject to selection show that CNVs may be the actual targets of selection at some loci. However, unlike SNPs, CNVs alone do not resolve African ethno-linguistic groups. Tag haplotypes for CNVs identified may be useful in predicting African CNVs in future studies where only SNP data is available.