Ancestry-specific performance of variant effect predictors in clinical variant classification

We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches has since been released, and these methods must be independently calibrated for clinical application. Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by 3 new tools (AlphaMissense, ESM1b, and VARITY) and calibrated scores meeting each evidence strength. All 3 tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, although sometimes for few variants. Compared with previously recommended tools, these yielded at best only modest improvements in the trade-offs between evidence strength and false-positive predictions. At calibrated thresholds, 3 new computational predictors provided evidence for variant pathogenicity at similar strength to the 4 previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

BackgroundSevere equine asthma is a chronic inflammatory disease of the lung in horses similar to low-Th2 late-onset asthma in humans. This study aimed to determine the utility of RNA-Seq to call gene sequence variants, and to identify sequence variants of potential relevance to the pathogenesis of asthma.MethodsRNA-Seq data were generated from endobronchial biopsies collected from six asthmatic and seven non-asthmatic horses before and after challenge (26 samples total). Sequences were aligned to the equine genome with Spliced Transcripts Alignment to Reference software. Read preparation for sequence variant calling was performed with Picard tools and Genome Analysis Toolkit (GATK). Sequence variants were called and filtered using GATK and Ensembl Variant Effect Predictor (VEP) tools, and two RNA-Seq predicted sequence variants were investigated with both PCR and Sanger sequencing. Supplementary analysis of novel sequence variant selection with VEP was based on a score of <0.01 predicted with Sorting Intolerant from Tolerant software, missense nature, location within the protein coding sequence and presence in all asthmatic individuals. For select variants, effect on protein function was assessed with Polymorphism Phenotyping 2 and screening for non-acceptable polymorphism 2 software. Sequences were aligned and 3D protein structures predicted with Geneious software. Difference in allele frequency between the groups was assessed using a Pearson’s Chi-squared test with Yates’ continuity correction, and difference in genotype frequency was calculated using the Fisher’s exact test for count data.ResultsRNA-Seq variant calling and filtering correctly identified substitution variants in PACRG and RTTN. Sanger sequencing confirmed that the PACRG substitution was appropriately identified in all 26 samples while the RTTN substitution was identified correctly in 24 of 26 samples. These variants of uncertain significance had substitutions that were predicted to result in loss of function and to be non-neutral. Amino acid substitutions projected no change of hydrophobicity and isoelectric point in PACRG, and a change in both for RTTN. For PACRG, no difference in allele frequency between the two groups was detected but a higher proportion of asthmatic horses had the altered RTTN allele compared to non-asthmatic animals.DiscussionRNA-Seq was sensitive and specific for calling gene sequence variants in this disease model. Even moderate coverage (<10–20 counts per million) yielded correct identification in 92% of samples, suggesting RNA-Seq may be suitable to detect sequence variants in low coverage samples. The impact of amino acid alterations in PACRG and RTTN proteins, and possible association of the sequence variants with asthma, is of uncertain significance, but their role in ciliary function may be of future interest.

Background. Severe equine asthma is a chronic inflammatory disease of the lung in horses similar to low-Th2 late-onset asthma in humans. This study aimed to determine the utility of RNA-Seq to call gene sequence variants, and to identify sequence variants or potential relevance to the pathogenesis of asthma. Methods. RNA-Seq data were generated from endobronchial biopsies collected from 6 asthmatic and 7 non-asthmatic horses before and after challenge (26 samples total). Sequences were aligned to the equine genome with Spliced Transcripts Alignment to Reference software. Read preparation for sequence variant calling was performed with Picard tools and Genome Analysis Toolkit (GATK). Sequence variants were called and filtered using GATK and Ensembl Variant Effect Predictor (VEP) tools, and two RNA-Seq predicted sequence variants were investigated with both PCR and Sanger sequencing. Supplementary analysis of novel sequence variant selection with VEP was based on a score of &lt;0.01 predicted with Sorting Intolerant From Tolerant (SIFT) software, missense nature, location within the protein coding sequence and presence in all asthmatic individuals. For select variants, effect on protein function was assessed with Polymorphism Phenotyping (PolyPhen) 2 and Screening for Non-Acceptable Polymorphism (SNAP) 2 software. Sequences were aligned and 3D protein structures predicted with Geneious software. Difference in allele frequency between the groups was assessed using a Pearson's Chi-squared test with Yates' continuity correction, and difference in genotype frequency was calculated using the Fisher's exact test for count data. Results. RNA-Seq variant calling and filtering correctly identified substitution variants in PACRG and RTTN. Sanger sequencing confirmed that the PACRG substitution was appropriately identified in all 26 samples while the RTTN substitution was identified correctly in 24 of 26 samples. These variants of uncertain significance had substitutions that were predicted to result in loss of function and to be non-neutral. Amino acid substitutions projected no change of hydrophobicity and isoelectric point in PACRG, and a change in both for RTTN. For PACRG, no difference in allele frequency between the two groups was detected but a higher proportion of asthmatic horses had the altered RTTN allele compared to non-asthmatic animals. Discussion. RNA-Seq was sensitive and specific for calling gene sequence variants in this disease model. Even moderate coverage (&lt;10-20 cpm) yielded correct identification in 92% of samples, suggesting RNA-Seq may be suitable to detect sequence variants in low coverage samples. The impact of amino acid alterations in PACRG and RTTN proteins, and possible association of the sequence variants with asthma, is of uncertain significance, but their role in ciliary function may be of future interest.

The need to interpret thepathogenicity of novel missense variants of unknown significance identified in the homeodomain of X-chromosome aristaless-related homeobox (ARX) gene prompted us to assess the utility of conservation and constraint across these domains in multiple genes compared to conventional in vitro functional analysis. Pathogenic missense variants clustered in the homeodomain of ARX contribute to intellectual disability (ID) and epilepsy, with and without brain malformation in affected males. Here we report novel c.1112G>A, p.Arg371Gln and c.1150C>T, p.Arg384Cys variants in male patients with ID and severe seizures. The third case of a male patient with a c.1109C>T, p.Ala370Val variant is perhaps the first example of ID and autism spectrum disorder (ASD), without seizures or brain malformation. We compiled data sets of pathogenic variants from ClinVar and presumed benign variation from gnomAD and demonstrated that the high levels of sequence conservation and constraint of benign variation within the homeodomain impacts upon the ability of publicly available in silico prediction tools to accurately discern likely benign from likely pathogenic variants in these data sets. Despite this, considering the inheritance patterns of the genes and disease variants with the conservation and constraint of disease variants affecting the homeodomain in conjunction with current clinical assessments may assist in predicting the pathogenicity of missense variants, particularly for genes with autosomal recessive and X-linked patterns of disease inheritance, such as ARX. In vitro functional analysis demonstrates that the transcriptional activity of all three variants wasdiminished compared to ARX-Wt. We review the associated phenotypes of the published cases of patients with ARX homeodomain variants and propose expansion of the ARX-related phenotype to include severe ID and ASD without brain malformations or seizures. We propose that the use of the constraint and conservation data in conjunction with consideration of the patient phenotype and inheritance pattern may negate the need for the experimental functional validation currently required to achieve a diagnosis.

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

AIMSTo determine the frequency of the FAS-ligand gene (FASLG) variant associated with feline autoimmune lymphoproliferative syndrome (FALPS) and the proportion of carriers of the variant in three British shorthair (BSH) breeding catteries in New Zealand.METHODSBuccal swabs were collected from all cats in two BSH breeding catteries from the South Island and one from the North Island of New Zealand. DNA was extracted and was tested for the presence of the FASLG variant using PCR. Cats with the FASLG variant were identified and the frequency of the FASLG variant allele calculated. Pedigree analysis was performed and inbreeding coefficients were calculated for cats with the FASLG variant.RESULTSOf 32 BSH cats successfully tested for the presence of the FASLG variant, one kitten (3%) was homozygous (FALPS-affected), and seven (22%) cats were heterozygous (carriers) for the FASLG variant allele, and 24 (75%) cats were homozygous for the wild type allele. The overall frequency of the FASLG variant allele in these 32 cats was 0.14. Cats carrying the FASLG variant were from all three breeding catteries sampled, including two catteries that had not previously reported cases of FALPS. Pedigree analysis revealed common ancestry of FALPS-affected and carrier cats within six generations, as well as frequent inbreeding, with inbreeding coefficients >0.12 for five cats with the FASLG variant.CONCLUSIONS AND CLINICAL RELEVANCEThere was a high frequency of the FASLG variant allele (0.14) in this small sample of BSH cats, with 22% of healthy cats identified as carriers of the FASLG variant. For an inherited disease, lethal at a young age, in a small population in which inbreeding is common, these results are significant. To prevent future cases of disease and stop further spread of the FASLG variant allele within the BSH population in New Zealand, it is recommended that all BSH and BSH-cross cats be tested for the presence of the FASLG variant before mating. Cats identified as carriers of the variant allele should be desexed and not used for breeding. Results support the need for further investigations of the true frequency of the FASLG variant allele and occurrence of FALPS in the wider population of BSH cats in New Zealand.

The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study revealed poorer antidepressant treatment response among black compared with white participants. This racial disparity persisted even after socioeconomic and baseline clinical factors were taken into account. Some studies have suggested genetic contributions to this disparity, but none have attempted to disentangle race and genetic ancestry. Here we used genome-wide single-nucleotide polymorphism (SNP) data to examine independent contributions of race and genetic ancestry to citalopram response. Secondary data analyses included 1877 STAR*D participants who completed an average of 10 weeks of citalopram treatment and provided DNA samples. Participants reported their race as White (n=1464), black (n=299) or other/mixed (n=114). Genetic ancestry was estimated by multidimensional scaling (MDS) analyses of about 500 000 SNPs. Ancestry proportions were estimated by STRUCTURE. Structural equation modeling was used to examine the direct and indirect effects of observed and latent predictors of response, defined as change in the Quick Inventory of Depressive Symptomatology (QIDS) score from baseline to exit. Socioeconomic and baseline clinical factors, race, and anxiety significantly predicted response, as previously reported. However, direct effects of race disappeared in all models that included genetic ancestry. Genetic African ancestry predicted lower treatment response in all models. Although socioeconomic and baseline clinical factors drive racial differences in antidepressant response, genetic ancestry, rather than self-reported race, explains a significant fraction of the residual differences. Larger samples would be needed to identify the specific genetic mechanisms that may be involved, but these findings underscore the importance of including more African-American patients in drug trials.

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

Variant interpretation remains one of the most significant challenges in clinical genetics. Variants of uncertain significance (VUS) undermine precision medicine implementation because they have an unknown relationship to disease and cannot be used for clinical decision-making. While evidence from multiplexed assays of variant effect (MAVEs) and other functional assays can help classify variants, major barriers prevent routine use in clinical variant classification, including fragmentation across multiple repositories, insufficient data standards, and the need to calibrate assays clinically. Here we address these challenges by presenting a new interface for the MaveDB database called MaveMD (MAVEs for MeDicine) that integrates with external resources such as ClinVar and the ClinGen Allele Registry, displays clinical evidence calibrations, provides intuitive visualizations, and exports structured evidence compatible with ACMG/AMP variant classification guidelines. MaveMD implements automatic mapping of MaveDB datasets to the human reference genome, dramatically simplifying the clinical translation of new MAVE data. We also defined a new metadata model after curating 438,318 variant effect measurements from 74 MAVE datasets spanning 32 disease-associated genes, and created an interface aimed at enabling effective clinical decision-making. Thus, MaveMD makes MAVE data accessible and easily usable for variant classification, and will scale seamlessly with future data generation efforts to empower the use of MAVE evidence in clinical practice.

No evidence of the APC D1822V missense variant's pathogenicity in Tunisian patients with sporadic colorectal cancer

Parkinson's disease (PD) is a genetically complex neurodegenerative disorder. Up to 15% of cases are considered monogenic. However, research on monogenic PD has largely focused on populations of European ancestry, leaving gaps in our understanding of genetic variability in other populations. This study addresses this gap by analysing the allele frequencies of pathogenic and likely pathogenic variants in known monogenic PD genes across eight global populations, using data from the gnomAD database. We compiled a list of 27 genes associated with Mendelian PD from the Online Mendelian Inheritance in Man (OMIM) database, and identified pathogenic and likely pathogenic variants using ClinVar. We then performed pairwise comparisons of allele frequencies across populations included in the gnomAD database. Variants with significant frequency differences were further assessed using in silico pathogenicity predictions. We identified 81 variants across 17 genes with statistically significant allele frequency differences between at least two populations. Variants in GBA1 were the most prevalent among monogenic PD-related genes, followed by PLA2G6, ATP13A2, VPS13C, and PRKN. GBA1 exhibited the greatest variability in allele frequencies, particularly the NM_000157.4:c.1226A>G (p.Asn409Ser) variant. Additionally, we observed significant population-specific differences in PD-related variants, such as the NM_032409.3:c.1040T>C (p.Leu347Pro) variant in PINK1, which was most prevalent in East Asian populations. Our findings reveal substantial population-specific differences in the allele frequencies of pathogenic variants linked to monogenic PD, emphasising the need for broader genetic studies beyond European populations. These insights have important implications for PD research, genetic screening, and understanding the pathogenesis of PD in diverse populations.

The analysis of ancient DNA (aDNA) can inspire both the public and the scientific community. Knowing about ancient human genomes and comparing them with those of modern humans can give us a new perspective on evolution and the migration of humans over time. aDNA is DNA isolated from ancient specimens. It can also be loosely described as any DNA recovered from biological samples that have not been preserved specifically for later DNA analysis. Examples include DNA recovered from archaeological and historical skeletal material, mummified tissues, archival collections of non-frozen medical specimens, preserved plant remains, ice and permafrost cores, Holocene plankton in marine and lake sediments, and so on. Due to considerable anthropological, archaeological, and public interest, human remains receive ample attention from the DNA community. Genetic genealogy is the use of DNA testing in combination with traditional genealogical methods to infer relationships between individuals and to find ancestors. Genetic genealogy involves the use of genealogical DNA testing to determine the level and type of genetic relationship between individuals. DNA markers such as autosomal single nucleotide polymorphisms (SNPs), Y SNPs, and mitochondrial DNA (mtDNA) SNPs are used. By analyzing the sequence of mtDNA and the Y-chromosome, the path of human migration throughout history and the common ancestor of humans can be identified. mtDNA analysis is a field of research in genetics and molecular archaeology that is efficient in less than ideal conditions, such as with biologically degraded materials. The mtDNA molecule not only has a high copy number, but it can also be extracted from very decayed biological specimens. Its D-loop region is polymorphic, consisting of two hypervariable regions (HVI and HVII) with a large variety in different human populations. The analysis of such mtDNA regions using ancient excavated human bones will determine the genetic composition of human mtDNA known as haplogroups and can be used to identify ancient ethnic groups, trace descendants of ancestors, and follow man’s migration trails.

This study investigates the distribution and allele frequencies of the ABO and Rh blood systems in the Al-Jabal Al-Akhdar region of Libya. A total of 3,363 individuals from ten towns in the region were included. ABO blood types and Rh factor were recorded from families, and ABO genotypes were inferred by pedigree analysis. Chi-square goodness-of-fit tests were used to compare the proportions of observed ABO phenotypes and Rh antigens across towns. Overall, blood type A had the highest frequency at 33.37%, followed by O at 29.63%, B at 27.92%, and AB at 9.09%. Regarding Rh status, 88.91% of individuals were Rh-positive, and 11.09% were Rh-negative. Allele frequencies for the ABO system in the overall population were estimated as p = 0.280, q = 0.242, and r = 0.478. The allele frequencies of the Rh system were p = 0.766 and q = 0.234. Significant differences across towns were observed for blood types A, AB, and O (p-value = 0.025, 0.000, and 0.001, respectively). However, no significant differences were found for blood types B, Rh-positive, and Rh-negative (p-value = 0.301, 0.990, and 0.093, respectively) across towns. These findings provide useful data for population genetics and transfusion medicine in the region. Further work is needed to examine genetic diversity and population structure in other blood group systems.

The deleterious genetic variants contributing to certain diseases may differ in terms of number and allele frequency from population to population depending on their evolutionary background. Here, we prioritize the deleterious variants from Pakistani population in manually curated gene list already reported to be associated with common, Mendelian, and congenital cardiovascular diseases (CVDs) using the genome/exome sequencing data of Pakistani individuals publically available in 1000 Genomes Project (PJL), and Exome Aggregation Consortium (ExAC) South Asia. By applying a set of tools such as Combined Annotation Dependent Depletion (CADD), ANNOVAR, and Variant Effect Predictor (VEP), we highlighted 561 potentially detrimental variants from PJL data, and 7374 variants from ExAC South Asian data. Likewise, filtration from ClinVar for CVDs revealed 03 pathogenic and 02 likely pathogenic variants from PJL and 112 pathogenic and 42 likely pathogenic variants from ExAC South Asians. The comparison of derived allele frequencies (DAF) revealed many of these prioritized variants having two fold and higher DAF in Pakistani individuals than in other populations. The highest number of deleterious variants contributing to common CVDs in descending order includes hypertension, atherosclerosis, heart failure, aneurysm, and coronary heart disease, and for Mendelian and congenital CVDs cardiomyopathies, cardiac arrhythmias, and atrioventricular septal defects.

Characterization of Germline Variants in Multiple Myeloma