Interpretation Of Genetic Variants Research Articles

Context-adjusted proportion of singletons (CAPS): a novel metric for assessing negative selection in the human genome.

Interpretation of genetic variants remains challenging, partly due to the lack of well-established ways of determining the potential pathogenicity of genetic variation, especially for understudied classes of variants. Addressing this, population genetics methods offer a practical solution by evaluating variant effects through human population distributions. Negative selection influences the ratio of singleton variants and can serve as a proxy for deleteriousness, as exemplified by the Mutability-Adjusted Proportion of Singletons (MAPS) metric. However, MAPS is sensitive to the calibration of the singletons-by-mutability linear model, which results in biased estimates for certain variant classes. Building up on the methodology used in MAPS, we introduce the Context-Adjusted Proportion of Singletons (CAPS) metric for assessing negative selection in the human genome. CAPS produces corrected estimates with more accurate confidence intervals by eliminating the mutability layer in the model. Retaining the advantageous features of MAPS, CAPS emerges as a robust and reliable tool. We believe that CAPS has the potential to enhance the identification of new disease-variant associations in clinical and research settings, offering improved accuracy in assessing negative selection for diverse SNV classes.

Enhancing SNV identification in whole-genome sequencing data through the incorporation of known genetic variants into the minimap2 index

MotivationAlignment of reads to a reference genome sequence is one of the key steps in the analysis of human whole-genome sequencing data obtained through Next-generation sequencing (NGS) technologies. The quality of the subsequent steps of the analysis, such as the results of clinical interpretation of genetic variants or the results of a genome-wide association study, depends on the correct identification of the position of the read as a result of its alignment. The amount of human NGS whole-genome sequencing data is constantly growing. There are a number of human genome sequencing projects worldwide that have resulted in the creation of large-scale databases of genetic variants of sequenced human genomes. Such information about known genetic variants can be used to improve the quality of alignment at the read alignment stage when analysing sequencing data obtained for a new individual, for example, by creating a genomic graph. While existing methods for aligning reads to a linear reference genome have high alignment speed, methods for aligning reads to a genomic graph have greater accuracy in variable regions of the genome. The development of a read alignment method that takes into account known genetic variants in the linear reference sequence index allows combining the advantages of both sets of methods.ResultsIn this paper, we present the minimap2_index_modifier tool, which enables the construction of a modified index of a reference genome using known single nucleotide variants and insertions/deletions (indels) specific to a given human population. The use of the modified minimap2 index improves variant calling quality without modifying the bioinformatics pipeline and without significant additional computational overhead. Using the PrecisionFDA Truth Challenge V2 benchmark data (for HG002 short-read data aligned to the GRCh38 linear reference (GCA_000001405.15) with parameters k = 27 and w = 14) it was demonstrated that the number of false negative genetic variants decreased by more than 9500, and the number of false positives decreased by more than 7000 when modifying the index with genetic variants from the Human Pangenome Reference Consortium.

SwissGenVar: A Platform for Clinical-Grade Interpretation of Genetic Variants to Foster Personalized Healthcare in Switzerland

Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows. This includes extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUSs) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of the standards used to describe genetic variants and the associated phenotypes when searching for relevant information to support clinical decision making. To address this, all five Swiss academic institutions for Medical Genetics joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its aim is to provide a protected environment for expert evidence sharing about individual variants to harmonize and upscale their significance interpretation at the clinical grade according to international standards. To corroborate the clinical assessment, the variant-related data will be combined with consented high-quality clinical information. Broader visibility will be achieved by interfacing with international databases, thus supporting global initiatives in personalized healthcare.

From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics

Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.

The detection of a strong episignature for Chung–Jansen syndrome, partially overlapping with Börjeson–Forssman–Lehmann and White–Kernohan syndromes

Chung-Jansen syndrome is a neurodevelopmental disorder characterized by intellectual disability, behavioral problems, obesity and dysmorphic features. It is caused by pathogenic variants in the PHIP gene that encodes for the Pleckstrin homology domain-interacting protein, which is part of an epigenetic modifier protein complex. Therefore, we hypothesized that PHIP haploinsufficiency may impact genome-wide DNA methylation (DNAm). We assessed the DNAm profiles of affected individuals with pathogenic and likely pathogenic PHIP variants with Infinium Methylation EPIC arrays and report a specific and sensitive DNAm episignature biomarker for Chung–Jansen syndrome. In addition, we observed similarities between the methylation profile of Chung–Jansen syndrome and that of functionally related and clinically partially overlapping genetic disorders, White–Kernohan syndrome (caused by variants in DDB1 gene) and Börjeson–Forssman–Lehmann syndrome (caused by variants in PHF6 gene). Based on these observations we also proceeded to develop a common episignature biomarker for these disorders. These newly defined episignatures can be used as part of a multiclass episignature classifier for screening of affected individuals with rare disorders and interpretation of genetic variants of unknown clinical significance, and provide further insights into the common molecular pathophysiology of the clinically-related Chung–Jansen, Börjeson–Forssman–Lehmann and White–Kernohan syndromes.

Predicting functional UTR variants by integrating region-specific features.

The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.

Clinical Interpretation of Genetic Variants in the Evaluation and Management of Thoracic Aortic Aneurysm and Dissection

BackgroundWe aimed to elucidate clinical implications of genetic variant interpretation in assessing disease severity and progression in thoracic aortic aneurysm and dissection (TAAD) patients. MethodsConsecutive TAAD patients with aortic root and/or ascending aortic aneurysms seen between 2011 and 2020 were included. Serial echocardiography, family history of TAAD, and management information were retrospectively collected and analyzed. Patients were classified into gene-positive (Gen-P), variants of uncertain significance, and gene-negative (Gen-N) groups. ResultsA total of 407 patients were included: mean age 53.7 ± 15.4 years, 64.4% men, and 38% with reported family history of TAAD. Thirty-seven (9.1%) were Gen-P; 147 (36.1%) had a variant of uncertain significance. The maximal aneurysm diameter was 4.78 mm larger in Gen-P than the other groups (P < .001). In 162 unoperated TAAD patients with serial echocardiographic measurements, aneurysms enlarged at a significantly higher rate in the Gen-P (1.36 mm/year, 95% CI: 0.77-1.95) than variants of uncertain significance and Gen-N groups (0.83 mm/year vs 0.89 mm/year, respectively; P < .001). Aneurysms were 20% more likely to require surgical intervention for every millimeter increase in diameter. When considered on an individual basis, the highest growth rates were found in the variants of uncertain significance group. ConclusionsWhile aneurysms linked to variants of uncertain significance demonstrate average growth rates comparable to those in Gen-N, close follow-up and genetic counseling in the variants of uncertain significance group are recommended for assessment of pathogenicity on a case-by-case basis. Early familial gene testing in TAAD is important to develop individualized preventive and therapeutic criteria.

Abstract 3536: Paracell: A high throughput, deep learning-based pipeline for single-cell phenotypic profiling

Abstract Variants of uncertain significance constitute the vast majority of missense variants, hampering the utility of genetic testing, despite its remarkable impacts on clinical diagnosis and decision-making. Although the sequencing and interpretation of genetic variants have been drastically improved in recent years, only about 2% of novel missense variants are currently clinically actionable. Therefore, a robust and scalable pipeline is desperately needed to provide functional evidence critical for variant interpretation. In this work, we demonstrate Paracell, a deep learning-based phenotypic profiling pipeline that leverages subcellular segmentation, high-dimensional single-cell phenotyping, and machine learning to classify the functional impact of variants. Paracell captures heterogeneity in cellular phenotypes and protein colocalization between variants and their signaling partners, as well as subcellular markers, and enables analysis at a single-cell resolution. Using a classification model trained on single-cell features extracted by Paracell, we were able to accurately classify cells from loss-of-function (LoF) variants based on their impact. After aggregating cellular profiles on a variant level, our pipeline was able to distinguish LoF variants from functional ones with high sensitivity and specificity. Our work demonstrates that Paracell is a robust and scalable method that can sensitively detect differences in single-cell phenotypic profiles. The systematic application of this pipeline will provide valuable functional evidence for variant interpretation, enhancing their clinical utility and accelerating personalized cancer care. Citation Format: David L. Nguyen, Jesse T. Chao. Paracell: A high throughput, deep learning-based pipeline for single-cell phenotypic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3536.

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BackgroundThe Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.ResultsPerformance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.ConclusionsResults show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Pycofitness-Evaluating the fitness landscape of RNA and protein sequences.

The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue. We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability. https://github.com/KIT-MBS/pycofitness.

KCNQ1 p.D446E Variant as a Risk Allele for Arrhythmogenic Phenotypes: Electrophysiological Characterization Reveals a Complex Phenotype Affecting the Slow Delayed Rectifier Potassium Current (IKs) Voltage Dependence by Causing a Hyperpolarizing Shift and a Lack of Response to Protein Kinase A Activation.

Genetic testing is crucial in inherited arrhythmogenic channelopathies; however, the clinical interpretation of genetic variants remains challenging. Incomplete penetrance, oligogenic, polygenic or multifactorial forms of channelopathies further complicate variant interpretation. We identified the KCNQ1/p.D446E variant in 2/63 patients with long QT syndrome, 30-fold more frequent than in public databases. We thus characterized the biophysical phenotypes of wildtype and mutant IKs co-expressing these alleles with the β-subunit minK in HEK293 cells. KCNQ1 p.446E homozygosity significantly shifted IKs voltage dependence to hyperpolarizing potentials in basal conditions (gain of function) but failed to shift voltage dependence to hyperpolarizing potentials (loss of function) in the presence of 8Br-cAMP, a protein kinase A activator. Basal IKs activation kinetics did not differ among genotypes, but in response to 8Br-cAMP, IKs 446 E/E (homozygous) activation kinetics were slower at the most positive potentials. Protein modeling predicted a slower transition of the 446E Kv7.1 tetrameric channel to the stabilized open state. In conclusion, biophysical and modelling evidence shows that the KCNQ1 p.D446E variant has complex functional consequences including both gain and loss of function, suggesting a contribution to the pathogenesis of arrhythmogenic phenotypes as a functional risk allele.

Gene editing and cardiac disease modelling for the interpretation of genetic variants of uncertain significance in congenital heart disease

BackgroundGenomic sequencing in congenital heart disease (CHD) patients often discovers novel genetic variants, which are classified as variants of uncertain significance (VUS). Functional analysis of each VUS is required in specialised laboratories, to determine whether the VUS is disease causative or not, leading to lengthy diagnostic delays. We investigated stem cell cardiac disease modelling and transcriptomics for the purpose of genetic variant classification using a GATA4 (p.Arg283Cys) VUS in a patient with CHD.MethodsWe performed high efficiency CRISPR gene editing with homology directed repair in induced pluripotent stem cells (iPSCs), followed by rapid clonal selection with amplicon sequencing. Genetic variant and healthy matched control cells were compared using cardiomyocyte disease modelling and transcriptomics.ResultsGenetic variant and healthy cardiomyocytes similarly expressed Troponin T (cTNNT), and GATA4. Transcriptomics analysis of cardiomyocyte differentiation identified changes consistent with the patient’s clinical human phenotype ontology terms. Further, transcriptomics revealed changes in calcium signalling, and cardiomyocyte adrenergic signalling in the variant cells. Functional testing demonstrated, altered action potentials in GATA4 genetic variant cardiomyocytes were consistent with patient cardiac abnormalities.ConclusionsThis work provides in vivo functional studies supportive of a damaging effect on the gene or gene product. Furthermore, we demonstrate the utility of iPSCs, CRISPR gene editing and cardiac disease modelling for genetic variant interpretation. The method can readily be applied to other genetic variants in GATA4 or other genes in cardiac disease, providing a centralised assessment pathway for patient genetic variant interpretation.

A taxonomic schema of potential pitfalls in clinical variant analysis based on real-world evidence.

The classification and interpretation of genetic variants associated with genetic diseases have been shown to vary between clinical genetic laboratories. This can lead to errors introduced in the interpretation and public presentation of genetic findings in the literature and available databases. This qualitative study utilizes real-world evidence to introduce a taxonomic schema of potential pitfalls associated with public and commercial resources commonly used for sequence variant analysis. Databases, articles and other resources continue to evolve over time. A modified and expanded version of Reason's Model of Human Error with respect to variant analysis is proposed and discussed. This study complements professional standards and published recommendations of interpretive considerations associated with variant analysis and expands the scope of professional competency.

Clinical relevance of genetic variants in the von Willebrand factor according to in-silico methods.

Clinical interpretation of genetic variants in the context of the patient's phenotype is a time-consuming and costly process. In-silico analysis using in-silico prediction tools, and molecular modeling have been developed to predict the influence of genetic variants on the quality and/or quantity of the resulting translated protein, and in this way, to alert clinicians of disease likelihood in the absence of previous evidence. Our objectives were to evaluate the success rate of the in-silico analysis in predicting the disease-causing variants as pathogenic and the single-nucleotide variants as neutral, and to establish the reliability of in-silico analysis for determining pathogenicity or neutrality of von Willebrand factor gene-associated genetic variants. Using in-silico analysis, we studied pathogenicity in 31 disease-causing variants, and neutrality in 61 single-nucleotide variants from patients previously diagnosed as type 2 von Willebrand disease. Disease-causing variants and non-synonymous single-nucleotide variants were explored by in-silico tools that analyze the amino acidic sequence. Intronic and synonymous single-nucleotide variants were analyzed by in-silico methods that evaluate the nucleotidic sequence. We found a consistent agreement between predictions achieved by in-silico prediction tools and molecular modeling, both for defining the pathogenicity of disease-causing variants and the neutrality of single-nucleotide variants. Based on our results, the in-silico analysis would help to define the pathogenicity or neutrality in novel genetic variants observed in patients with clinical and laboratory phenotypes suggestive of von Willebrand disease.

How human genetic context can inform pathogenicity classification: FGFR1 variation in idiopathic hypogonadotropic hypogonadism.

Precision medicine requires precise genetic variant interpretation, yet many disease-associated genes have unresolved variants of unknown significance (VUS). We analyzed variants in a well-studied gene, FGFR1, a common cause of Idiopathic Hypogonadotropic Hypogonadism (IHH) and examined whether regional genetic enrichment of missense variants could improve variant classification. FGFR1 rare sequence variants (RSVs) were examined in a large cohort to (i) define regional genetic enrichment, (ii) determine pathogenicity based on the American College of Medical Genetics/Association for Molecular Pathology (ACMG/AMP) variant classification framework, and (iii) characterize the phenotype of FGFR1 variant carriers by variant classification. A total of 143 FGFR1 RSVs were identified in 175 IHH probands (n = 95 missense, n = 48 protein-truncating variants). FGFR1 missense RSVs showed regional enrichment across biologically well-defined domains: D1, D2, D3, and TK domains and linker regions (D2-D3, TM-TK). Using these defined regions of enrichment to augment the ACMG/AMP classification reclassifies 37% (20/54) of FGFR1 missense VUS as pathogenic or likely pathogenic (PLP). Non-proband carriers of FGFR1 missense VUS variants that were reclassified as PLP were more likely to express IHH or IHH-associated phenotypes [anosmia or delayed puberty] than non-proband carriers of FGFR1 missense variants that remained as VUS (76.9% vs 34.7%, p = 0.035). Using the largest cohort of FGFR1 variant carriers, we show that integration of regional genetic enrichment as moderate evidence for pathogenicity improves the classification of VUS and that reclassified variants correlated with phenotypic expressivity. The addition of regional genetic enrichment to the ACMG/AMP guidelines may improve clinical variant interpretation.

The challenges in the interpretation of genetic variants detected by genomics techniques in patients with congenital anomalies.

Despite the efforts that have been made to standardize the interpretation of variants, in some cases, their pathogenicity remains vague and confusing, and sometimes their interpretation does not help clinicians to establish clinical correlation using genetic test results. This study aims to shed more lights on these challenging variants. In a clinical setting, the variants found from 81 array CGH and 79 whole exome sequencing (WES) in patients with congenital anomalies were interpreted based on American College of Medical Genetics and Genomics guidelines. In this study, the interpretation of the disease-causing variants and the variants with uncertain clinical significance detected by WES was far more challenging than the variants detected by array CGH. The presence of unreported clinical symptoms, incomplete penetrance, variable expressivity, parents' reluctance to analyze segregation in the family, and the limitations of prenatal tests, were among the challenging factors in the interpretation of variants in this study. A careful study of the pedigree and disease mode of inheritance, as well as a careful clinical examination of the carrier parents in diseases with autosomal dominant inheritance, are among the primary strategies for determining the clinical significance of the variants. Continued efforts to mitigate these challenges are needed to improve the interpretation of variants.

Landscape of germline BRCA1/BRCA2 variants in breast and ovarian cancer in Peru.

There is an increasing amount of data from Latin America on the characterization of BRCA variants; however, there is limited information from Peru. We conducted a retrospective study to describe germline pathogenic/likely pathogenic(P/LP) variants and variants of uncertain/unknown significance (VUS) in the BRCA1 and BRCA2 genes in Peru, in patients with breast and ovarian cancer, candidates for treatment with poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors. The patients were evaluated during the period 2019-2021. Genomic DNA was isolated from peripheral blood samples and targeted sequencing was performed using the Ampliseq BRCA panel. Genetic variant interpretation was carried out in accordance with the recommendations of the American College of Medical Genetics and ClinVar. During this period, 525 patients (143 with breast cancer and 382 with ovarian cancer) were studied. We found that 14.7% (21/143) of breast cancer patients and 20.7% (79/382) of ovarian cancer patients were carriers of P/LP variants in BRCA1/2. The most frequent pathogenic variants detected in BRCA1 were c.2105dupT (BIC: 2224insT, n=12, 18.75%), c.68_69delAG (BIC: 185delAG, n=6, 9.38%), c.140G>T and c.815_824dupAGCCATGTGG (n=5, 7.81%), while in BRCA2 were c.8023A>G (n=6, 16.67%), c.6024dupG (BIC: 6252insG, n=4, 11.11%), and c.9235delG (BIC: 9463delG, n=3, 8.33%). Regarding VUS, we found that 6.99% (10/143) of breast cancer patients and 7.33% (28/382) of ovarian cancer patients were carriers of a VUS in BRCA1/2. For BRCA1, the most frequent VUS was c.93C>G (n=2), and for BRCA2, c.5465A>T (n=4), c.3101T>C (n=3), c.205C>A and c.437T>C (n=2). We found a frequency of 14.7% germline mutations in breast cancer patients and 20.7% in ovarian cancer patients. The most recurrent mutations were BRCA1 c.2105dupT and BRCA2 c.8023A>G. We found that BRCA2 c.8023A>G, c.6024dupG, and c.9235delG were not previously reported in Peruvian patients. BRCA1 c.2344dupA is a novel mutation that has not been previously reported in any database. The frequency of VUS in our cohort was 7.2%.

Muscle magnetic resonance imaging involvement patterns in nemaline myopathies.

Characterise the diagnostic and prognostic value of muscle MRI patterns as biomarkers in a genetically heterogeneous nemaline myopathy (NM) patient cohort. Modified Mercuri scoring of lower limb MRI in genetically characterised NM patients referred to the highly specialised service for congenital myopathies at Great Ormond Street Hospital. Findings were compared to clinical data and MRI patterns derived from collated published data. Twenty-seven patients with MRI were identified (8 NEB-NM, 13 ACTA1-NM, 6 TPM3-NM). NEB-NM demonstrated sparing of the thigh. ACTA1-NM demonstrated diffuse thigh involvement, notable in the vasti, sartorius and biceps-femoris, with relative adductor and gracilis sparing. TPM3-NM demonstrated diffuse thigh involvement notable in biceps-femoris and adductor magnus with relative rectus femoris, adductor longus and gracilis sparing. In the lower leg, the soleus and tibialis anterior are notably involved in all three genotypes. NEB-NM and ACTA1-NM demonstrated relative gastrocnemii and tibialis posterior sparing, while TPM3-NM showed significantly more tibialis posterior involvement (P =< 0.05). Comparison of involvement patterns with literature datasets highlighted preferential adductor and gracilis sparing in our ACTA1-NM cohort, consistent tibialis posterior involvement in our TPM3-NM cohort and a distinct MRI pattern from those derived from other NM genotypes and congenital myopathies. Greater tibialis anterior involvement correlated with foot drop (P = 0.02). Greater tibialis anterior and extensor hallucis longus involvement correlated with worse mobility (P =< 0.04). This is the widest NM MRI data set described to date; we describe distinct muscle involvement patterns for NEB-NM, ACTA1-NM and TPM3-NM which may have utility as diagnostic and prognostic biomarkers and aid in genetic variant interpretation.

Identifying Protein Quantitative Trait Loci in the Cerebrospinal Fluid Proteome

AbstractBackgroundGenome‐wide association studies (GWAS) on dementia have successfully identified variants conferring risk to disease. Still, how such variants impact exact biological mechanisms underlying disease remains largely unclear. Protein quantitative trait loci (pQTLs) measured in cerebrospinal fluid (CSF) may provide insight into these neurobiological mechanisms, since CSF protein levels can reflect ongoing processes in the brain and have been related to genetic variants. We used a CSF pQTL approach in individuals along the Alzheimer’s disease (AD) spectrum to reveal intermediate molecular pathways by which genetic variance influences neurobiological processes. Improving the functional interpretation of genetic variants in a matrix relevant to neurological disease helps deciphering biological mechanisms underlying neurological traits.MethodWe selected 243 subjects (106 controls/67 mild cognitive impairment/70 AD‐dementia, age 66.8±8.1 years, 53.9%female) from the EMIF‐AD MBD study, who had genetic data and CSF data (n = 1,351 proteins, tandem mass tag (TMT) mass spectrometry) available. Association signals between genetic variants and CSF proteins were tested using linear regression, adjusted for principal components (PC1‐3), age and sex using the genome‐wide significance threshold (P&lt;5.0e−8). CSF pQTLs were identified as novel if the association has not been previously reported in Yang et al., (2021), Sasayama et al., (2017) or Kauwe et al., (2014).ResultsTwenty‐four out of 1,351 CSF proteins (1.78%) showed genome‐wide significant associations with in total 147 SNPs (Table 1; Figure 1). We identified 25 CSF pQTLs, representing three cis‐acting (IGH, LTF and ADAMTS8) and 22 trans‐acting pQTLs. Results included mostly novel CSF pQTL associations, exception for the cis association of LTF with LTF protein levels. Two novel cis‐acting pQTLs were previously reported in a study on human lymphocytes (IGH on IGHV2‐70D) (Theusch et al., 2020) and prefrontal cortex brain data (ADAMTS8 on ADAMTS8) (Ng et al., 2017).ConclusionsThese results contribute to the functional interpretation of genetic variance on neurobiological processes, by pinpointing inflammation and tissue remodeling as potential contributing mechanisms in AD.

Perturbations in fatty acid metabolism and collagen production infer pathogenicity of a novel MBTPS2 variant in Osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is a heritable and chronically debilitating skeletal dysplasia. Patients with OI typically present with reduced bone mass, tendency for recurrent fractures, short stature and bowing deformities of the long bones. Mutations causative of OI have been identified in over 20 genes involved in collagen folding, posttranslational modification and processing, and in bone mineralization and osteoblast development. In 2016, we described the first X-linked recessive form of OI caused by MBTPS2 missense variants in patients with moderate to severe phenotypes. MBTPS2 encodes site-2 protease, a Golgi transmembrane protein that activates membrane-tethered transcription factors. These transcription factors regulate genes involved in lipid metabolism, bone and cartilage development, and ER stress response. The interpretation of genetic variants in MBTPS2 is complicated by the gene's pleiotropic properties; MBTPS2 variants can also cause the dermatological conditions Ichthyosis Follicularis, Atrichia and Photophobia (IFAP), Keratosis Follicularis Spinulosa Decalvans (KFSD) and Olmsted syndrome (OS) without skeletal abnormalities typical of OI. Using control and patient-derived fibroblasts, we previously identified gene expression signatures that distinguish MBTPS2-OI from MBTPS2-IFAP/KFSD and observed stronger suppression of genes involved in fatty acid metabolism in MBTPS2-OI than in MBTPS2-IFAP/KFSD; this was coupled with alterations in the relative abundance of fatty acids in MBTPS2-OI. Furthermore, we observed a reduction in collagen deposition in the extracellular matrix by MBTPS2-OI fibroblasts. Here, we extrapolate our observations in the molecular signature unique to MBTPS2-OI to infer the pathogenicity of a novel MBTPS2 c.516A>C (p.Glu172Asp) variant of unknown significance in a male proband. The pregnancy was terminated at gestational week 21 after ultrasound scans showed bowing of femurs and tibiae and shortening of long bones particularly of the lower extremity; these were further confirmed by autopsy. By performing transcriptional analyses, gas chromatography-tandem mass spectrometry-based quantification of fatty acids and immunocytochemistry on fibroblasts derived from the umbilical cord of the proband, we observed perturbations in fatty acid metabolism and collagen production similar to what we previously described in MBTPS2-OI. These findings support pathogenicity of the MBTPS2 variant p.Glu172Asp as OI-causative and highlights the value of extrapolating molecular signatures identified in multiomics studies to characterize novel genetic variants.