Diagnostic Gene Panel Testing in (Non)-Syndromic Patients with Cleft Lip, Alveolus and/or Palate in the Netherlands

Objectives: Clefts of the lip, alveolus and/or palate (CLA/P) are the most common craniofacial congenital malformations in humans. These oral clefts can be divided into non-syndromic (isolated) and syndromic forms. Many cleft-related syndromes are clinically variable and genetically heterogeneous, making it challenging to distinguish syndromic from non-syndromic cases. Recognition of syndromic/genetic causes is important for personalized tailored care, identification of (unrecognized) comorbidities, and accurate genetic counseling. Therefore, next generation sequencing (NGS)-based targeted gene panel testing is increasingly implemented in diagnostics of CLA/P patients. In this retrospective study, we assess the yield of NGS gene panel testing in a cohort of CLA/P cases.Methods: Whole exome sequencing (WES) followed by variant detection and interpretation in an a priori selected set of genes associated with CLA/P phenotypes was performed in 212 unrelated CLA/P patients after genetic counseling between 2015 and 2020. Medical records including family history and results of additional genetic tests were evaluated.Results: In 24 CLA/P cases (11.3%), a pathogenic genetic variant was identified. Twenty out of these 24 had a genetic syndrome requiring specific monitoring and follow-up. Six of these 24 cases (25%) were presumed to be isolated CLA/P cases prior to testing, corresponding to 2.8% of the total cohort. In eight CLA/P cases (3.8%) without a diagnosis after NGS-based gene panel testing, a molecular diagnosis was established by additional genetic analyses (e.g., SNP array, single gene testing, trio WES).Conclusion: This study illustrates NGS-based gene panel testing is a powerful diagnostic tool in the diagnostic workup of CLA/P patients. Also, in apparently isolated cases and non-familial cases, a genetic diagnosis can be identified. Early diagnosis facilitates personalized care for patients and accurate genetic counseling of their families.</p

52 Genetic Loci Influencing Myocardial Mass.

BACKGROUND: Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death. OBJECTIVES: This meta-analysis sought to gain insights into the genetic determinants of myocardial mass. METHODS: We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment. RESULTS: We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo. CONCLUSIONS: Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets

OccuPeak: ChIP-Seq Peak Calling Based on Internal Background Modelling

<div><p>ChIP-seq has become a major tool for the genome-wide identification of transcription factor binding or histone modification sites. Most peak-calling algorithms require input control datasets to model the occurrence of background reads to account for local sequencing and GC bias. However, the GC-content of reads in Input-seq datasets deviates significantly from that in ChIP-seq datasets. Moreover, we observed that a commonly used peak calling program performed equally well when the use of a simulated uniform background set was compared to an Input-seq dataset. This contradicts the assumption that input control datasets are necessary to fatefully reflect the background read distribution. Because the GC-content of the abundant single reads in ChIP-seq datasets is similar to those of randomly sampled regions we designed a peak-calling algorithm with a background model based on overlapping single reads. The application, OccuPeak, uses the abundant low frequency tags present in each ChIP-seq dataset to model the background, thereby avoiding the need for additional datasets. Analysis of the performance of OccuPeak showed robust model parameters. Its measure of peak significance, the excess ratio, is only dependent on the tag density of a peak and the global noise levels. Compared to the commonly used peak-calling applications MACS and CisGenome, OccuPeak had the highest sensitivity in an enhancer identification benchmark test, and performed similar in an overlap tests of transcription factor occupation with DNase I hypersensitive sites and H3K27ac sites. Moreover, peaks called by OccuPeak were significantly enriched with cardiac disease-associated SNPs. OccuPeak runs as a standalone application and does not require extensive tweaking of parameters, making its use straightforward and user friendly. Availability: <a href="http://occupeak.hfrc.nl" target="_blank">http://occupeak.hfrc.nl</a></p></div

Consistency of different peak-calling methods.

<p>OccuPeak, MACS and CisGenome were used to call peaks for each of the two replicate p300 ChIP-seq experiments generated by the ENCODE consortium (GSE29184). <b>A</b>. Peaks are considered common (green) if they were identified in both replicates and singleton if they were only found in the current replicate (yellow and blue), as depicted in the UCSC genome browser example (<b>B</b>).</p

Biological Validation: overlap with cardiac enhancers.

<p>OccuPeak, MACS and CisGenome were used to call peaks from the TBX3 and the two replicate p300 ChIP-seq datasets. Peaks were then sorted on peak significance and overlap with cardiac enhancers was determined. For visualization, the number of most significant peaks was incremented in steps of 1000 peaks. A set of 102 validated cardiac enhancers was used to assess the sensitivity of the peak-calling method and the biological relevance of the called peaks. The number of enhancers identified using the default threshold of each peak calling method is plotted in the bar graphs.</p

Biological Validation: overlap with cardiac H3K27ac sites.

<p>OccuPeak, MACS and CisGenome were used to call peaks from the TBX3 and the two replicate p300 ChIP-seq datasets. Peaks were then sorted on peak significance and overlap with cardiac enhancers was determined. For visualization, the number of most significant peaks was incremented in steps of 1000 peaks. Overlap of peaks with H3K27ac sites was assessed as measure for active enhancers. In the p300(2) dataset the performance of OccuPeak was significantly better when only uniquely mappable tags were considered. The results of the statistical comparison at the maximum common number of peaks (vertical dotted line) is given as a string in which ' = ' indicates that the overlap is not significantly different between the methods and '>' that the overlap differs significantly at p<0.0001 or less (O = OccuPeak, all reads; OU = OccuPeak, uniquely mappable reads; M = MACS; C = Cisgenome).</p

Correlation between Input-seq datasets depends on repeated sequences.

<p><b>A</b>. UCSC genome browser snapshot showing tag counts (log scale) in 1 KB bins of two replicate Input-seq datasets. High tag counts are related to annotated genomic repeats. <b>B</b>. Correlation between tag counts in two replicate Input-seq datasets for bins without or with genomic repeats (yellow area: bins with tag counts between 1 and 8, blue: between 1 and 20, red: between 1 and infinity). Bins without any tags were excluded from the analysis because they might be the result of unmappable regions. <b>C</b>. The small overlap (green) between peaks called in ChIP-seq datasets (yellow) and an Input-seq dataset (blue) is significantly reduced when only uniquely mappable (um) reads are considered in peak calling. This is effect is independent of the number of called peaks.</p

Association with cardiac GWAS SNPs.

<p>Peaks called by OccuPeak for TBX3 and both p300 replicate datasets were converted from the mouse genome (mm9) to the human genome (hg18) to enable overlap analysis with known human SNPs. A two-sample Z-test was performed to test whether called peaks overlap more frequently with SNPs associated with cardiac function than with control SNPs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099844#pone.0099844-Sherry1" target="_blank">[40]</a>. Control SNPs were randomly selected from a population of SNPs not significantly associated with any GWAS signal and located within 1 Mb of known UCSC genes. SNPs associated with TBX3 peaks are listed, including their phenotype. Further details and references are in the Results section of the main text.</p

Visualization of overlap analysis.

<p>Visual inspection with the UCSC genome browser can show where and why certain enhancers are missed by a particular peak-calling method. <b>A</b>. Relatively small local increases in input control tag density can result in a locally decreased sensitivity of the method. An enhancer on the Foxl1 locus is missed by MACS when heart Input-seq data is used as input control, but detected when a simulated uniform dataset is used as control instead. <b>B</b>. Similarly, an enhancer located on the Tbx20 locus is missed by MACS when an input control is used on the p300(2) data. When applying the same input control on the more abundant TBX3 data, the enhancer is marked by all methods. Abbreviations: um  =  dataset in which only unique tags are mapped; sim-control  =  dataset where simulated uniform data is used as input control for peak-calling.</p