Microarray-based genome investigation: molecular karyotyping or segmental aneuploidy profiling?

From genes to therapy: A comprehensive exploration of congenital heart disease through the lens of genetics and emerging technologies

Congenital heart disease (CHD) affects approximately 1 % of live births worldwide, making it the most common congenital anomaly in newborns. Recent advancements in genetics and genomics have significantly deepened our understanding of the genetics of CHDs. While the majority of CHD etiology remains unclear, evidence consistently indicates that genetics play a significant role in its development. CHD etiology holds promise for enhancing diagnosis and developing novel therapies to improve patient outcomes. In this review, we explore the contributions of both monogenic and polygenic factors of CHDs and highlight the transformative impact of emerging technologies on these fields. We also summarized the state-of-the-art techniques, including targeted next-generation sequencing (NGS), whole genome and whole exome sequencing (WGS, WES), single-cell RNA sequencing (scRNA-seq), human induced pluripotent stem cells (hiPSCs) and others, that have revolutionized our understanding of cardiovascular disease genetics both from diagnosis perspective and from disease mechanism perspective in children and young adults. These molecular diagnostic techniques have identified new genes and chromosomal regions involved in syndromic and non-syndromic CHD, enabling a more defined explanation of the underlying pathogenetic mechanisms. As our knowledge and technologies continue to evolve, they promise to enhance clinical outcomes and reduce the CHD burden worldwide.

A three-step workflow procedure for the interpretation of array-based comparative genome hybridization results in patients with idiopathic mental retardation and congenital anomalies

One of the aims of clinical genetics is to identify gene mutations or genomic rearrangements that may underlie complex presentations of phenotypic features, such as multiple congenital malformations and mental retardation. During the decade after publication of the first article on array-based comparative genome hybridization, this technique has supplemented karyotyping as the prime genome-wide screening method in patients with idiopathic multiple congenital malformations and mental retardation. The use of this novel, discovery-based, approach has dramatically increased the detection rate of genomic imbalances. Array-based comparative genome hybridization detects copy number changes in the genome of patients and healthy subjects, some of which may represent phenotypically neutral copy number variations. This prompts the need for properly distinguishing between those copy number changes that may contribute to the clinical phenotype amid a pool of neutral copy number variations. We briefly review the characteristics of copy number changes in relation to their clinical relevance. Second, we discuss several published workflow schemes to identify copy number changes putatively contributing to the phenotype, and third, we propose a three-step procedure aiming to rapidly evaluate copy number changes on a case-by-case basis as to their potential contribution to the phenotype of patients with idiopathic multiple congenital malformations and mental retardation. This workflow is gene-centered and should aid in identification of disease-related candidate genes and in estimating the recurrence risk for the disorder in the family.

Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies

Chromosomal microarray (CMA) is increasingly utilized for genetic testing of individuals with unexplained developmental delay/intellectual disability (DD/ID), autism spectrum disorders (ASD), or multiple congenital anomalies (MCA). Performing CMA and G-banded karyotyping on every patient substantially increases the total cost of genetic testing. The International Standard Cytogenomic Array (ISCA) Consortium held two international workshops and conducted a literature review of 33 studies, including 21,698 patients tested by CMA. We provide an evidence-based summary of clinical cytogenetic testing comparing CMA to G-banded karyotyping with respect to technical advantages and limitations, diagnostic yield for various types of chromosomal aberrations, and issues that affect test interpretation. CMA offers a much higher diagnostic yield (15%-20%) for genetic testing of individuals with unexplained DD/ID, ASD, or MCA than a G-banded karyotype ( approximately 3%, excluding Down syndrome and other recognizable chromosomal syndromes), primarily because of its higher sensitivity for submicroscopic deletions and duplications. Truly balanced rearrangements and low-level mosaicism are generally not detectable by arrays, but these are relatively infrequent causes of abnormal phenotypes in this population (<1%). Available evidence strongly supports the use of CMA in place of G-banded karyotyping as the first-tier cytogenetic diagnostic test for patients with DD/ID, ASD, or MCA. G-banded karyotype analysis should be reserved for patients with obvious chromosomal syndromes (e.g., Down syndrome), a family history of chromosomal rearrangement, or a history of multiple miscarriages.

A large-scale survey of genetic copy number variations among Han Chinese residing in Taiwan

BACKGROUND

Copy number variations (CNVs) have recently been recognized as important structural variations in the human genome. CNVs can affect gene expression and thus may contribute to phenotypic differences. The copy number inferring tool (CNIT) is an effective hidden Markov model-based algorithm for estimating allele-specific copy number and predicting chromosomal alterations from single nucleotide polymorphism microarrays. The CNIT algorithm, which was constructed using data from 270 HapMap multi-ethnic individuals, was applied to identify CNVs from 300 unrelated Han Chinese individuals in Taiwan.

RESULTS

Using stringent selection criteria, 230 regions with variable copy numbers were identified in the Han Chinese population; 133 (57.83%) had been reported previously, 64 displayed greater than 1% CNV allele frequency. The average size of the CNV regions was 322 kb (ranging from 1.48 kb to 5.68 Mb) and covered a total of 2.47% of the human genome. A total of 196 of the CNV regions were simple deletions and 27 were simple amplifications. There were 449 genes and 5 microRNAs within these CNV regions; some of these genes are known to be associated with diseases.

CONCLUSION

The identified CNVs are characteristic of the Han Chinese population and should be considered when genetic studies are conducted. The CNV distribution in the human genome is still poorly characterized, and there is much diversity among different ethnic populations.

Genome-wide copy number analysis using copy number inferring tool (CNIT) and DNA pooling

Copy number variation (CNV) has become an important genomic structure element in the human population, and some CNVs are related to specific traits and diseases. Moreover, analysis of human genomes has been potentiated by the use of high-resolution microarrays that assess single nucleotide polymorphisms (SNPs). Although many programs have been designed to analyze data from Affymetrix SNP microarrays, they all have high false-positive rates (FPRs) in copy number (CN) analyses. Copy number analysis tool (CNAT) 4.0 is a recently developed program that offers improved CN estimation, but small amplifications and deletions are lost when using the smoothing procedure. Here, we propose a copy number inferring tool (CNIT) algorithm for the 100K SNP microarray to investigate CNVs at 29.6-kb resolution. CNIT estimated SNP allelic and total CN with reliable P values based on intensity data. In addition, the hidden Markov model (HMM) method was applied to predict regions having altered CN by considering contiguous SNPs. Based on a CN analysis of 23 unrelated Taiwanese and 30 HapMap Centre d'Etude du Polymorphisme Humain (CEPH) trios, CNIT showed higher accuracy and power than other programs. The FPRs and false-negative rates (FNRs) of CNIT were 0.1% and 0.16%, respectively. CNIT also showed better sensitivity for detecting small amplifications and deletions. Furthermore, DNA pooling of 10 and 30 normal unrelated individuals were applied to the 100K SNP microarray, respectively, and 12 common CN-variable regions were identified, suggesting that DNA pooling can be applied to discover common CNVs.

QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data

Array-based technologies have been used to detect chromosomal copy number changes (aneuploidies) in the human genome. Recent studies identified numerous copy number variants (CNV) and some are common polymorphisms that may contribute to disease susceptibility. We developed, and experimentally validated, a novel computational framework (QuantiSNP) for detecting regions of copy number variation from BeadArray SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM). Objective Bayes measures are used to set certain hyperparameters in the priors using a novel re-sampling framework to calibrate the model to a fixed Type I (false positive) error rate. Other parameters are set via maximum marginal likelihood to prior training data of known structure. QuantiSNP provides probabilistic quantification of state classifications and significantly improves the accuracy of segmental aneuploidy identification and mapping, relative to existing analytical tools (Beadstudio, Illumina), as demonstrated by validation of breakpoint boundaries. QuantiSNP identified both novel and validated CNVs. QuantiSNP was developed using BeadArray SNP data but it can be adapted to other platforms and we believe that the OB-HMM framework has widespread applicability in genomic research. In conclusion, QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy detection with application to clinical genetics, cancer and disease association studies.

Molecular karyotyping of patients with MCA/MR: the blurred boundary between normal and pathogenic variation

Molecular karyotyping has revealed that microdeletions/duplications in the human genome are a major cause of multiple congenital anomalies associated with mental retardation (MCA/MR). The identification of a de novo chromosomal imbalance in a patient with MCA/MR is usually considered causal for the phenotype while a chromosomal imbalance inherited from a phenotypically normal parent is considered as a benign variation and not related to the disorder. Around 40% of imbalances in patients with MCA/MR in this series is inherited from a healthy parent and the majority of these appear to be (extremely) rare variants. As some of these contain known disease-causing genes and have also been found to be de novo in MCA/MR patients, this challenges the general view that such familial variants are innocent and of no major phenotypic consequence. Rather, we argue, that human genomes can be tolerant of genomic copy number variations depending on the genetic and environmental background and that different mechanisms play a role in determining whether these chromosomal imbalances manifest themselves.