RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Targeted Next-Generation Sequencing in Rare Diseases

Targeted next-generation sequencing (NGS) in rare disease focuses on genetic analysis of specific regions in genome that are linked to a rare disease. In addition to library preparation, sequencing, and data analysis, targeted NGS includes an additional step of target enrichment of selected genes and regions. It allows for more sensitive and profound sequencing, as it is a fast and cost-effective approach with less data burden and is therefore often a method of choice for identifying rare variants in known genes, especially in diagnostics of rare diseases. Several in silico tools address the pathogenicity predictions of rare variants of unknown significance (VUS) and can therefore facilitate clinical interpretation.

GoFCards: an integrated database and analytic platform for gain of function variants in humans

Gain-of-function (GOF) variants, which introduce new or amplify protein functions, are essential for understanding disease mechanisms. Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database limitations, underscoring the difficulty in accessing critical genetic information. To address this challenge, we manually reviewed the literature, pinpointing 3089 single-nucleotide variants and 72 insertions and deletions in 579 genes associated with 1299 diseases from 2069 studies, and integrated these with the 3.5 million predicted GOF variants. Our approach is complemented by a proprietary scoring system that prioritizes GOF variants on the basis of the evidence supporting their GOF effects and provides predictive scores for variants that lack existing documentation. We then developed a database named GoFCards for general geneticists and clinicians to easily obtain GOF variants in humans (http://www.genemed.tech/gofcards). This database also contains data from >150 sources and offers comprehensive variant-level and gene-level annotations, with the aim of providing users with convenient access to detailed and relevant genetic information. Furthermore, GoFCards empowers users with limited bioinformatic skills to analyze and annotate genetic data, and prioritize GOF variants. GoFCards offers an efficient platform for interpreting GOF variants and thereby advancing genetic research.

A de novo ARIH2 gene mutation was detected in a patient with autism spectrum disorders and intellectual disability

E3 ubiquitin protein ligase encoded by ARIH2 gene catalyses the ubiquitination of target proteins and plays a crucial role in posttranslational modifications across various cellular processes. As prior documented, mutations in genes involved in the ubiquitination process are often associated with autism spectrum disorder (ASD) and/or intellectual disability (ID). In the current study, a de novo heterozygous mutation was identified in the splicing intronic region adjacent to the last exon of the ARIH2 gene using whole exome sequencing (WES). We hypothesize that this mutation, found in an ASD/ID patient, disrupts the protein Ariadne domain which is involved in the autoinhibition of ARIH2 enzyme. Predictive analyses elucidated the implications of the novel mutation in the splicing process and confirmed its autosomal dominant inheritance model. Nevertheless, we cannot exclude the possibility that other genetic factors, undetectable by WES, such as mutations in non-coding regions and polygenic risk in inter-allelic complementation, may contribute to the patient's phenotype. This work aims to suggest potential relationship between the detected mutation in ARIH2 gene and both ASD and ID, even though functional studies combined with new sequencing approaches will be necessary to validate this hypothesis.

Three candidate SNPs show associations with thyroid-stimulating hormone in euthyroid subjects: Tehran thyroid study

Objectives

Previous studies have shown interindividual variation in free thyroxine (FT4) serum levels and thyroid stimulating hormone (TSH) in healthy persons. Genetic factors mainly determine this variation, and genome-wide association studies have increased the number of thyroid function-associated variants. The present study investigates the association of candidate variants with FT4 and TSH in a euthyroid Iranian population.

Method

A total of 2931 unrelated euthyroid subjects (FT4 10.29-21.88 pmol/L; TSH 0.32-10 mIU/L, thyroid peroxidase antibody TPOAb < 33 IU/mL in men and < 35 IU/mL in women), with available genotypes were chosen from the Tehran Thyroid Study (TTS), to examine the impact of selected SNPs on thyroid hormone under the additive genetic model. In order to evaluate regional associations with FT4 and TSH levels, a haplotype analysis was done.

Results

We identified a strong association between the rs4338740-C allele and TSH in the adjusted model (β = -0.095, P-value = 0.0004). Also, findings indicated that rs4954192 ACMSD and rs4445669 CADM1 correlated with normal TSH levels (P-value = 0.011, P-value = 0.014, respectively). Haplotype analysis revealed that two haplotypes were significantly associated with TSH levels in euthyroid individuals. The ACGA and AC haplotypes on chromosomes 8 and 14 were significantly correlated with normal TSH levels, respectively (P-value = 0.014, P-value = 0.016).

Conclusions

This is the first genetic association study with TSH and FT4 reference values in an Iranian population. Our findings indicate that a few gene variants associated with the reference values of TSH in other populations are also associated with the reference values of TSH in Iranians.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01383-2.

MC1R and age heteroclassification of face phenotypes in the Rio Grande do Sul population

BACKGROUND

Forensic DNA phenotyping (FDP) consists of the use of methodologies for predicting externally visible characteristics (EVCs) from the genetic material of biological samples found in crime scenes and has proven to be a promising tool in aiding human identification in police activities. Currently, methods based on multiplex assays and statistical models of prediction of EVCs related to hair, skin, and iris pigmentation using panels of SNP and INDEL biomarkers have already been developed and validated by the forensic scientific community. As well as traces of pigmentation, an individual's perceived age (PA) can also be considered an EVC and its estimation in unknown individuals can be useful for the progress of investigations. Liu and colleagues (2016) were pioneers in evidencing that, in addition to lifestyle and environmental factors, the presence of SNP and INDEL variants in the MC1R gene - which encodes a transmembrane receptor responsible for regulating melanin production - seems to contribute to an individual's PA. The group highlighted the association between these MC1R gene polymorphisms and the PA in the European population, where carriers of risk haplotypes appeared to be up to 2 years older in comparison to their chronological age (CA).

PURPOSE

Understanding that genotype-phenotype relationships cannot be extrapolated between different population groups, this study aimed to test this hypothesis and verify the applicability of this variant panel in the Rio Grande do Sul admixed population.

METHODS

Based on genomic data from a sample of 261 volunteers representative of gaucho population and using a multiple linear regression (MLR) model, our group was able to verify a significant association among nine intronic variants in loci adjacent to MC1R (e.g., AFG3L1P, TUBB3, FANCA) and facial age appearance, whose PA was defined after age heteroclassification of standard frontal face images through 11 assessors.

RESULTS

Different from that observed in European populations, our results show that the presence of effect alleles (R) of the selected variants in our sample influenced both younger and older face phenotypes. The influence of each variant on PA is expressed as β values.

CONCLUSIONS

There are important molecular mechanisms behind the effects of MC1R locus on PA, and the genomic background of each population seems to be crucial to determine this influence.

PTCH1 Gene Variants, mRNA Expression, and Bioinformatics Insights in Mexican Cutaneous Squamous Cell Carcinoma Patients

BACKGROUND

Skin cancer is one of the most frequent types of cancer, and cutaneous squamous cell carcinoma (cSCC) constitutes 20% of non-melanoma skin cancer (NMSC) cases. PTCH1, a tumor suppressor gene involved in the Sonic hedgehog signaling pathway, plays a crucial role in neoplastic processes.

METHODS

An analytical cross-sectional study, encompassing 211 cSCC patients and 290 individuals in a control group (CG), was performed. A subgroup of samples was considered for the relative expression analysis, and the results were obtained using quantitative real-time PCR (qPCR) with TaqMan® probes. The functional, splicing, and disease-causing effects of the proposed variants were explored via bioinformatics.

RESULTS

cSCC was predominant in men, especially in sun-exposed areas such as the head and neck. No statistically significant differences were found regarding the rs357564, rs2236405, rs2297086, and rs41313327 variants of PTCH1, or in the risk of cSCC, nor in the mRNA expression between the cSCC group and CG. A functional effect of rs357564 and a disease-causing relation to rs41313327 was identified.

CONCLUSION

The proposed variants were not associated with cSCC risk in this Mexican population, but we recognize the need for analyzing larger population groups to elucidate the disease-causing role of rare variants.

Detecting and understanding meaningful cancerous mutations based on computational models of mRNA splicing

Cancer research has long relied on non-silent mutations. Yet, it has become overwhelmingly clear that silent mutations can affect gene expression and cancer cell fitness. One fundamental mechanism that apparently silent mutations can severely disrupt is alternative splicing. Here we introduce Oncosplice, a tool that scores mutations based on models of proteomes generated using aberrant splicing predictions. Oncosplice leverages a highly accurate neural network that predicts splice sites within arbitrary mRNA sequences, a greedy transcript constructor that considers alternate arrangements of splicing blueprints, and an algorithm that grades the functional divergence between proteins based on evolutionary conservation. By applying this tool to 12M somatic mutations we identify 8K deleterious variants that are significantly depleted within the healthy population; we demonstrate the tool's ability to identify clinically validated pathogenic variants with a positive predictive value of 94%; we show strong enrichment of predicted deleterious mutations across pan-cancer drivers. We also achieve improved patient survival estimation using a proposed set of novel cancer-involved genes. Ultimately, this pipeline enables accelerated insight-gathering of sequence-specific consequences for a class of understudied mutations and provides an efficient way of filtering through massive variant datasets - functionalities with immediate experimental and clinical applications.

PTCH1 gene variants rs357564, rs2236405, rs2297086 and rs41313327, mRNA and tissue expression in basal cell carcinoma patients from Western Mexico

BACKGROUND

Basal cell carcinoma (BCC) represents about 80% of all cases of skin cancer. The PTCH1 is a transmembrane protein of the Sonic Hedgehog signaling pathway that regulates cell proliferation. Genetic variants in PTCH1 gene have been previously described in association with BCC development. In addition, PTCH1 mRNA and protein expression analysis are also significant to understand its role in skin cancer physiopathology.

METHODS

An analytical cross-sectional study was performed, and a total of 250 BCC patients and 290 subjects from the control group (CG) were included, all born in western Mexico. The genotypes and relative expression of the mRNA were determined by TaqMan® assay. The protein expression was investigated in 70 BCC paraffin-embedded samples with PTCH1 antibodies. Semi-quantitative analysis was performed to determine the expression level in the immunostained cells.

RESULTS

We did not find evidence of an association between PTCH1 rs357564, rs2297086, rs2236405, and rs41313327 genetic variants and susceptibility to BCC. Likewise, no statistically significant differences were found in the comparison of the mRNA level expression between BCC and CG (p > 0.05). The PTCH1 protein showed a low expression in 6 of the analyzed samples and moderate expression in 1 sample. No association was found between genetic variants, protein expression, and demographic-clinical characteristics (p > 0.05).

CONCLUSION

The studied PTCH1 variants may not be associated with BCC development in the Western Mexico population. The PTCH1 mRNA levels were lower in patients with BCC compared to the control group, but its protein was underexpressed in the tissue samples.

Prioritizing de novo potential non-canonical splicing variants in neurodevelopmental disorders

BACKGROUND

Genomic variants outside of the canonical splicing site (±2) may generate abnormal mRNA splicing, which are defined as non-canonical splicing variants (NCSVs). However, the clinical interpretation of NCSVs in neurodevelopmental disorders (NDDs) is largely unknown.

METHODS

We investigated the contribution of NCSVs to NDDs from 345,787 de novo variants (DNVs) in 47,574 patients with NDDs. We performed functional enrichment and protein-protein interaction analysis to assess the association between genes carrying prioritised NCSVs and NDDs. Minigene was used to validate the impact of NCSVs on mRNA splicing.

FINDINGS

We observed significantly more NCSVs (p = 0.02, odds ratio [OR] = 2.05) among patients with NDD than in controls. Both canonical splicing variants (CSVs) and NCSVs contributed to an equal proportion of patients with NDD (0.76% vs. 0.82%). The candidate genes carrying NCSVs were associated with glutamatergic synapse and chromatin remodelling. Minigene successfully validated 59 of 79 (74.68%) NCSVs that led to abnormal splicing in 40 candidate genes, and 9 of the genes (ARID1B, KAT6B, TCF4, SMARCA2, SHANK3, PDHA1, WDR45, SCN2A, SYNGAP1) harboured recurrent NCSVs with the same variant present in more than two unrelated patients with NDD. Moreover, 36 of 59 (61.02%) NCSVs are novel clinically relevant variants, including 34 unreported and 2 clinically conflicting interpretations or of uncertain significance NCSVs in the ClinVar database.

INTERPRETATION

This study highlights the common pathology and clinical importance of NCSVs in unsolved patients with NDD.

FUNDING

The present study was funded by grants from the National Natural Science Foundation of China, China Postdoctoral Science Foundation, the Hunan Youth Science and Technology Innovation Talent Project, the Provincial Natural Science Foundation of Hunan, The Scientific Research Program of FuRong laboratory, and the Natural Science Project of the University of Anhui Province.

Role of SIRT1 Gene Polymorphisms and Serum Levels in Patients with Multiple Sclerosis

AIM

The purpose of this work was to investigate the prevalence of SIRT1 rs3818292, rs3758391, and rs7895833 single nucleotide polymorphisms and SIRT1 serum levels associated with multiple sclerosis (MS) in the Lithuanian population.

METHODS

A total of 250 MS patients and 250 healthy controls were included in the study. Genotyping was performed using the RT-PCR method. Statistical analysis was performed using "IBM SPSS version 29.0". The serum SIRT1 level was determined by the ELISA method.

RESULTS

We found that rs3818292 was associated with increased odds of developing MS under the dominant (p = 0.007) and allelic genetic (p = 0.004) models. rs3758391 was associated with increased odds of developing under the co-dominant (p < 0.001), overdominant (p < 0.001), dominant (p < 0.001), and allelic (p = 0.002) genetic models. rs7895833 was associated with increased odds of developing MS under co-dominant (p < 0.001), overdominant (p < 0.001), dominant (p < 0.001), and allelic (p < 0.001) genetic models. Additional sex-differentiated analysis within females revealed that the rs3758391 was associated with an increased odds ratio for the occurrence of MS among the co-dominant (p = 0.006), dominant (p = 0.002), and allelic (p = 0.001). rs7895833 was associated with an increased odds ratio for the development of MS under the co-dominant (p < 0.001), overdominant (p < 0.001), dominant (p < 0.001), and allelic (p < 0.001) genetic models. Age-differentiated analysis showed that rs3758391 was associated with an increased odds ratio for the development of MS in younger patients under the codominant (p = 0.002), overdominant (p = 0.003), and dominant (p = 0.004) genetic models. rs7895833 was associated with an increased odds ratio for the occurrence of MS under the overdominant genetic model (p = 0.013). In elderly patients, rs3818292 was associated with an increased odds ratio for the occurrence of MS under the dominant (p = 0.008) and allelic (p = 0.009) genetic models. rs7895833 was associated with an increased odds ratio for the occurrence of MS under the codominant (p = 0.011 and p = 0.012), dominant (p = 0.001), and allelic (p < 0.001) genetic models. We also found that serum SIRT1 levels were statistically significantly different between MS patients and control group subjects (p < 0.001). In addition, comparison of SIRT1 levels between study groups and genotypes showed that rs3818292 AA (p = 0.001), rs3758391 CT (p < 0.001), and rs7895833 AA (p = 0.002) and AG (p = 0.004) had higher SIRT1 levels in the control group than in the MS group. All results were provided after strict Bonferroni correction.

CONCLUSIONS

Genetic variations in SIRT1 rs3818292, rs3758391, and rs7895833 are associated with multiple sclerosis, with possible differences in gender and age, as well as lower serum SIRT1 levels.

Intronic position +9 and -9 are potentially splicing sites boundary from intronic variants analysis of whole exome sequencing data

Whole exome sequencing (WES) can also detect some intronic variants, which may affect splicing and gene expression, but how to use these intronic variants, and the characteristics about them has not been reported. This study aims to reveal the characteristics of intronic variant in WES data, to further improve the clinical diagnostic value of WES. A total of 269 WES data was analyzed, 688,778 raw variants were called, among these 367,469 intronic variants were in intronic regions flanking exons which was upstream/downstream region of the exon (default is 200 bps). Contrary to expectation, the number of intronic variants with quality control (QC) passed was the lowest at the +2 and -2 positions but not at the +1 and -1 positions. The plausible explanation was that the former had the worst effect on trans-splicing, whereas the latter did not completely abolish splicing. And surprisingly, the number of intronic variants that passed QC was the highest at the +9 and -9 positions, indicating a potential splicing site boundary. The proportion of variants which could not pass QC filtering (false variants) in the intronic regions flanking exons generally accord with "S"-shaped curve. At +5 and -5 positions, the number of variants predicted damaging by software was most. This was also the position at which many pathogenic variants had been reported in recent years. Our study revealed the characteristics of intronic variant in WES data for the first time, we found the +9 and -9 positions might be a potentially splicing sites boundary and +5 and -5 positions were potentially important sites affecting splicing or gene expression, the +2 and -2 positions seem more important splicing site than +1 and -1 positions, and we found variants in intronic regions flanking exons over ± 50 bps may be unreliable. This result can help researchers find more useful variants and demonstrate that WES data is valuable for intronic variants analysis.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.

The Roles of MTRR and MTHFR Gene Polymorphisms in Colorectal Cancer Survival

Background: Paradoxically epidemiological data illustrate a negative relationship between dietary folate intake and colorectal cancer (CRC) risk. The occurrence and progression of CRC may be influenced by variants in some key enzyme coding genes in the folate metabolic pathway. We investigated the correlation between genetic variants in methionine synthase reductase (MTRR) and methylenetetrahydrofolate reductase (MTHFR) and CRC survival. Methods: This study used data collected from the Newfoundland Familial Colorectal Cancer Study. A total of 532 patients diagnosed with CRC for the first time from 1999 to 2003 were enrolled, and their mortality were tracked until April 2010. DNA samples were genotyped by Illumina’s integrated quantum 1 million chip. Cox models were established to assess 33 tag single-nucleotide polymorphisms in MTRR and MTHFR in relation to overall survival (OS), disease-free survival (DFS) and CRC-specific survival. Results: The MTRR and MTHFR genes were associated with DFS and CRC-specific survival in CRC patients at the gene level. After multiple comparison adjustment, MTRR rs1801394 A (vs. G) allele was associated with increased DFS (p = 0.024), while MTHRT rs3737966 (G vs. A), rs4846049 (T vs. G), rs1476413 (A vs. G), rs1801131 (C vs. A), rs12121543 (A vs. C), rs1801133 (C vs. T), rs4846052 (T vs. C), rs2066471 (A vs. G) and rs7533315 (T vs. C) were related to worse CRC-specific survival. Additionally, significant interactions were seen among pre-diagnostic alcohol consumption with MTRR rs1801394, rs3776467, rs326124, rs162040, and rs3776455, with superior OS associated with those protective variant alleles limited to patients with alcohol consumption under the median. The MTHFR rs3737966 (G vs. A) allele seemed to be detrimental to CRC survival only among subjects with fruit intake below the median. Conclusions: Polymorphic variants in MTRR and MTHFR genes that code for key enzymes for folate metabolism may be associated with survival in patients with CRC. The gene-CRC outcome association seems modulated by alcohol drinking and fruit intake.

Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing

It is estimated that 10%-30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, systematic diagnosis or prediction of splicing variants is yet to be established, especially for the near-exon intronic splice region. The major challenge lies in the redundant and ill-defined branch sites and other splicing motifs therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants that overlapped with branch sites and collected 5,884 variants across the 5' splice region. We found that strong splice sites and exonic features preserve splicing from intronic sequence variation. Whereas the splice-altering mechanism of the 3' intronic variants is complex, that of the 5' is mainly splice-site destruction. Statistical learning combined with these molecular features allows precise prediction of altered splicing from an intronic variant. This statistical model provides the identity and ranking of biological features that determine splicing, which serves as transferable knowledge and out-performs the benchmarking predictive tool. Moreover, we demonstrated that intronic splicing variants may associate with disease risks in the human population. Our study elucidates the mechanism of splicing response of intronic variants, which classify disease-associated splicing variants for the promise of precision medicine.

PEMT variants are associated with nonsyndromic cleft lip with or without cleft palate in Chile

Aim: To assess the association between PEMT variants and nonsyndromic cleft lip with or without cleft palate in Chile and the effects of these variants on global DNA methylation. Subjects & methods: The authors obtained genotypes for nine variants from 247 cases and 453 controls for genotype-phenotype associations. The effect of significant polymorphisms on global DNA methylation (percentage of long interspersed element-1 methylation) was evaluated in a subsample of 95 controls. Results: After multiple comparison corrections, variants rs7649 and rs4646409 were associated with nonsyndromic cleft lip with or without cleft palate. Carriers of risk alleles presented lower DNA methylation levels than noncarriers. Conclusion: According to functional analysis for risk variants from previous reports, the authors infer that a decrease of methyl group availability is occurring in affected subjects.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework

A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.

Testing for Dihydropyrimidine Dehydrogenase Deficiency to Individualize 5-Fluorouracil Therapy

Severe adverse events (toxicity) related to the use of the commonly used chemotherapeutic drug 5-fluorouracil (5-FU) affect one in three patients and are the primary reason cited for premature discontinuation of therapy. Deficiency of the 5-FU catabolic enzyme dihydropyrimidine dehydrogenase (DPD, encoded by DPYD) has been recognized for the past 3 decades as a pharmacogenetic syndrome associated with high risk of 5-FU toxicity. An appreciable fraction of patients with DPD deficiency that receive 5-FU-based chemotherapy die as a result of toxicity. In this manuscript, we review recent progress in identifying actionable markers of DPD deficiency and the current status of integrating those markers into the clinical decision-making process. The limitations of currently available tests, as well as the regulatory status of pre-therapeutic DPYD testing, are also discussed.

Quantitative prediction of variant effects on alternative splicing in MAPT using endogenous pre-messenger RNA structure probing

Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the B^act complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.

Clotting factor genes are associated with preeclampsia in high-altitude pregnant women in the Peruvian Andes

Preeclampsia is a multi-organ complication of pregnancy characterized by sudden hypertension and proteinuria that is among the leading causes of preterm delivery and maternal morbidity and mortality worldwide. The heterogeneity of preeclampsia poses a challenge for understanding its etiology and molecular basis. Intriguingly, risk for the condition increases in high-altitude regions such as the Peruvian Andes. To investigate the genetic basis of preeclampsia in a population living at high altitude, we characterized genome-wide variation in a cohort of preeclamptic and healthy Andean families (n = 883) from Puno, Peru, a city located above 3,800 meters of altitude. Our study collected genomic DNA and medical records from case-control trios and duos in local hospital settings. We generated genotype data for 439,314 SNPs, determined global ancestry patterns, and mapped associations between genetic variants and preeclampsia phenotypes. A transmission disequilibrium test (TDT) revealed variants near genes of biological importance for placental and blood vessel function. The top candidate region was found on chromosome 13 of the fetal genome and contains clotting factor genes PROZ, F7, and F10. These findings provide supporting evidence that common genetic variants within coagulation genes play an important role in preeclampsia. A selection scan revealed a potential adaptive signal around the ADAM12 locus on chromosome 10, implicated in pregnancy disorders. Our discovery of an association in a functional pathway relevant to pregnancy physiology in an understudied population of Native American origin demonstrates the increased power of family-based study design and underscores the importance of conducting genetic research in diverse populations.

SIRT1: Genetic Variants and Serum Levels in Age-Related Macular Degeneration

Background: The aim of this paper was to determine the frequency of SIRT1 rs3818292, rs3758391, rs7895833 single nucleotide polymorphism genotypes and SIRT1 serum levels associated with age-related macular degeneration (AMD) in the Lithuanian population. Methods: Genotyping of SIRT1 rs3818292, rs3758391 and rs7895833 was performed using RT-PCR. SIRT1 serum level was determined using the ELISA method. Results: We found that rs3818292 and rs7895833 were associated with an increased risk of developing exudative AMD. Additional sex-differentiated analysis revealed only rs7895833 was associated with an increased risk of developing exudative AMD in women after strict Bonferroni correction. The analysis also revealed that individuals carrying rs3818292, rs3758391 and rs7895833 haplotype G-T-G are associated with increased odds of exudative AMD. Still, the rare haplotypes were associated with the decreased odds of exudative AMD. After performing an analysis of serum SIRT1 levels and SIRT1 genetic variant, we found that carriers of the SIRT1 rs3818292 minor allele G had higher serum SIRT1 levels than the AA genotype. In addition, individuals carrying at least one SIRT1 rs3758391 T allele also had elevated serum SIRT1 levels compared with individuals with the wild-type CC genotype. Conclusions: Our study showed that the SIRT1 polymorphisms rs3818292 and rs7895833 and rs3818292-rs3758391-rs7895833 haplotype G-T-G could be associated with the development of exudative AMD. Also, two SNPs (rs3818292 and rs3758391) are associated with elevated SIRT1 levels.

Vitamin B12 Transport Genes and Nonsyndromic Cleft Lip With or Without Cleft Palate in Chile

The aims of this study were to assess the association between polymorphisms within genes involved in vitamin B12 transport and nonsyndromic cleft lip with or without cleft palate (NSCL/P) and global DNA methylation in Chile. From 247 cases and 453 controls, we obtained variant genotypes for CBLIF, CUBN, AMN, ABCC1, CD320, and TCN2 from a single nucleotide polymorphisms array. Global DNA methylation in 95 controls was obtained through LINE-1 methylation. After multiple comparison corrections, only rs780807 in CUBN remains associated with NSCL/P at dominant model (OR 0.564, p-value = 0.0006, q-value = 0.0450). Carriers of protective allele showed lower levels of DNA methylation than non-carriers (p = 0.0259). Further studies are necessary in order to explain relations with the phenotype and DNA methylation due to the absence of functional evidence for rs780807 in CUBN.

Analysis of the APOB Gene and Apolipoprotein B Serum Levels in a Mexican Population with Acute Coronary Syndrome: Association with the Single Nucleotide Variants rs1469513, rs673548, rs676210, and rs1042034

Apolipoprotein B (APOB) is associated with the development of atherosclerosis and consequently in the acute coronary syndrome (ACS) physiopathology. Single number variants (SNVs) in apolipoprotein B gene (APOB) influence over the susceptibility for this syndrome. The aim of this study was to determine the impact of the rs1469513, rs673548, rs676210, and rs1042034 SNVs and serum levels of APOB in the risk of ACS in a population from western Mexico. We included 300 patients in the group of cases (ACSG) and 300 individuals in the control group (CG). APOB levels were evaluated by immunonephelometry, and SNVs were genotyped with TaqMan probes. We found significant allelic and genotypic differences between groups for rs673548 and rs676210 (OR = 1.33, p=0.030, OR = 2.69, p < 0.001) and rs1042034 (OR = 0.50, p=0.037) SNVs. We found a risk haplotype TAGT (OR: 2.14, IC 1.50–3.04, p < 0.001). Our findings support a significant risk association between rs673548 and rs676210 variants for ACS; meanwhile, rs1042034 could be considered protective factor in a western Mexican population. Also, in this population, haplotype TAGT may confer 2.14 times a higher risk. APOB serum levels were compared by genotype variants in both groups without any significant statistical difference.

A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed.

Identification and management of GCK-MODY complicating pregnancy in Chinese patients with gestational diabetes

Precise differentiation of glucokinase (GCK) monogenic diabetes from gestational diabetes mellitus (GDM) is critical for accurate management of the pregnancy outcome. We screened GCK-MODY complicating pregnancies in Chinese GDM patients, explored the pathogenesis of novel GCK mutations, and evaluated the patients' pregnancy outcome and management. The GCK gene from 411 GDM patients was screened with PCR-direct sequencing and multiplex ligation-dependent probe amplification (MLPA) and 15 GCK mutations were identified. We also retrospectively analyzed a total of 65 pregnancies from 21 GCK-MODY families, wherein 41 were from 15 maternal families and 24 were from six paternal families. Bioinformatic analysis and biochemical functional study were conducted to identify novel GCK mutations. In total, we identified 21 GCK mutations: 15 from the 411 GDM patients and six from 24 fathers. Of th Asp78Asn (GAC → AAC), Met87Arg (ATG → AGG), Leu451Val (CTT → GTT), Leu451Pro (CTG → CCG) and 1019 + 20G > A e mutations, five, i.e., were novel and deleterious, with markedly decreased enzyme activity and thermal stability. The unaffected offspring of GCK mutation-affected mothers were heavier than affected offspring (p < 0.001). Of 21 insulin-treated affected mothers, 10 had maternal hypoglycemia (47.6%) and seven had perinatal complications (33.3%), and the affected offspring of the insulin-treated affected mothers had significantly lower birth weights than that of the 20 diet-control affected mothers (p = 0.031). In this study, the prevalence of GCK-MODY complicating pregnancy in Chinese GDM patients was 3.6% (15/411). The defective GCK may contribute to the hyperglycemia in GCK-MODY. Insulin therapy is not beneficial for GCK-MODY complicating pregnancy and therefore should not be recommended.

Whole-Exome Sequencing Identifies a Novel Variant (c.1538T > C) of TNNI3K in Arrhythmogenic Right Ventricular Cardiomyopathy

Backgrounds

Arrhythmic right ventricular cardiomyopathy (ARVC) is a cardiomyopathy with a genetic predisposition that can lead to a sudden cardiac death and heart failure. According to the 2010 Task Force Criteria, genetic diagnosis is one of the most important methods, but, so far, only a few genes related to ARVC have been identified.

Methods

In this study, the pathogenic gene of a patient with ARVC was examined using whole-exome sequencing. The plasmids of TNNI3K were constructed, and the effects of the TNNI3K variant was investigated by a real-time polymerase chain reaction (PCR) and western blot.

Results

A novel variant (c.1538T > C) of TNNI3K was identified, with phenotypes of dominant right ventricular (RV) disease preliminarily fulfilling the diagnosis of ARVC. A comprehensive assessment revealed that the variant was pathogenic. We found that this variant would lead to a decrease in the level of TNNI3K mRNA and protein, as well as a decrease in the expression of the RYR2 gene, which further proves that TNNI3K plays an important role in cardiomyopathy and expands the spectrum of the TNNI3K variants.

Conclusion

In this study, we reported a TNNI3K variant in ARVC for the first time, and the results not only contribute to the diagnosis of ARVC, but also provide a reference for genetic counseling and promote the understanding of the genetic mechanism of cardiomyopathy.

Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology

During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.

Association of CLOCK gene variants with obesity and adiposity-related anthropometric, metabolic, and behavioral parameters

The CLOCK gene is a core component of the circadian clock and regulates various aspects of metabolism. Therefore, any variation that affects the function/expression of the CLOCK gene may contribute to the manifestation of metabolic disorders such as obesity. This study investigated whether the CLOCK variants rs4864548 and rs6843722 are associated with obesity and related traits in Pakistanis. A total of 306 overweight/obese cases and 306 age- and gender-matched control subjects were recruited (males 336 and females 276, age range 12–63 years). Anthropometric and metabolic parameters were taken by standard procedures and biochemical analyses, respectively. Behavior-related information was collected with a questionnaire. The genotypes of the variants were determined by allelic discrimination Taqman assays. Both variants were found to have a significant association with overweight/obesity according to the over-dominant model. The rs4864548 and rs6843722 were observed to escalate the risk of overweight/obesity by 1.611 ( p = 0.004) and 1.657 ( p = 0.002) times, respectively. These variants were also seen to be significantly associated with various other adiposity-related anthropometric parameters ( p < 0.05). However, no association of both variants with metabolic and behavioral parameters was observed ( p > 0.05). Thus, these variants may contribute to increasing the risk of overweight/obesity and related anthropometric traits in Pakistanis.

Compound heterozygous c.598_612del and c.1746-20C > G CAPN3 genotype cause autosomal recessive limb-girdle muscular dystrophy-1: a case report

BACKGROUND

Autosomal recessive limb-girdle muscular dystrophy-1 (LGMDR1), also known as calpainopathy, is a genetically heterogeneous disorder characterised by progression of muscle weakness. Homozygous or compound heterozygous variants in the CAPN3 gene are known genetic causes of this condition. The aim of this study was to confirm the molecular consequences of the CAPN3 variant NG_008660.1(NM_000070.3):c.1746-20C > G of an individual with suspected LGMDR1 by extensive complementary DNA (cDNA) analysis.

CASE PRESENTATION

In the present study, we report on a male with proximal muscular weakness in his lower limbs. Compound heterozygous NM_000070.3:c.598_612del and NG_008660.1(NM_000070.3):c.1746-20C > G genotype was detected on the CAPN3 gene by targeted next-generation sequencing (NGS). To confirm the pathogenicity of the variant c.1746-20C > G, we conducted genetic analysis based on Sanger sequencing of the proband's cDNA sample. The results revealed that this splicing variant disrupts the original 3' splice site on intron 13, thus leading to the skipping of the DNA fragment involving exon 14 and possibly exon 15. However, the lack of exon 15 in the CAPN3 isoforms present in a blood sample was explained by cell-specific alternative splicing rather than an aberrant splicing mechanism. In silico the c.1746-20C > G splicing variant consequently resulted in frameshift and formation of a premature termination codon (NP_000061.1:p.(Glu582Aspfs*62)).

CONCLUSIONS

Based on the results of our study and the literature we reviewed, both c.598_612del and c.1746-20C > G variants are pathogenic and together cause LGMDR1. Therefore, extensive mRNA and/or cDNA analysis of splicing variants is critical to understand the pathogenesis of the disease.

Clinical Relevance of Novel Polymorphisms in the Dihydropyrimidine Dehydrogenase (DPYD) Gene in Patients with Severe Fluoropyrimidine Toxicity: A Spanish Case-Control Study

Among cancer patients treated with fluoropyrimidines, 10-40% develop severe toxicity. Polymorphism of the dihydropyrimidine dehydrogenase (DPYD) gene may reduce DPD function, the main enzyme responsible for the metabolism of fluoropyrimidines. This leads to drug accumulation and to an increased risk of toxicity. Routine genotyping of this gene, which usually includes DPYD *HapB3, *2A, *13 and c.2846A > T (D949V) variants, helps predict approximately 20-30% of toxicity cases. For DPD intermediate (IM) or poor (PM) metabolizers, a dose adjustment or drug switch is warranted to avoid toxicity, respectively. Societies such as the Spanish Society of Pharmacogenetics and Pharmacogenomics (SEFF), the Dutch Pharmacogenetics Working Group (DPWG) or the Clinical Pharmacogenetics Implementation Consortium (CPIC) and regulatory agencies (e.g., the Spanish Medicines Agency, AEMPS) already recommend DPYD routine genotyping. However, the predictive capacity of genotyping is currently still limited. This can be explained by the presence of unknown polymorphisms affecting the function of the enzyme. In this case-control work, 11 cases of severe fluoropyrimidine toxicity in patients who did not carry any of the four variants mentioned above were matched with 22 controls, who did not develop toxicity and did not carry any variant. The DPYD exome was sequenced (Sanger) in search of potentially pathogenic mutations. DPYD rs367619008 (c.187 A > G, p.Lys63Glu), rs200643089 (c.2324 T > G, p.Leu775Trp) and rs76387818 (c.1084G > A, p.Val362Ile) increased the percentage of explained toxicities to 38-48%. Moreover, there was an intronic variant considered potentially pathogenic: rs944174134 (c.322-63G > A). Further studies are needed to confirm its clinical relevance. The remaining variants were considered non-pathogenic.

Case report of atypical Leigh syndrome in an adolescent male with novel biallelic variants in NDUFAF5 and review of the natural history of NDUFAF5-related disorders

NDUFAF5 encodes a Complex I assembly factor which is critical to the modification of a core subunit, NDUFS7, in early Complex I factor assembly. Mutations in NDUFAF5 have been previously shown to cause Complex I deficiency leading to mitochondrial respiratory chain impairment. More than 15 individuals affected by variants in NDUFAF5 have been described; however, there is phenotypic heterogeneity within this cohort. Some individuals display features of classical Leigh syndrome with early onset neurodegeneration whereas others live into early adulthood with progressive neurological deficits. Here, we present a clinical report of a 17-year-old African American individual with compound heterozygous mutations in NDUFAF5. The individual presented with childhood onset bilateral optic atrophy and developed progressive neuromuscular decline with relatively preserved cognition over time.

Integrative assessment of CIP2A overexpression and mutational effects in human malignancies identifies possible deleterious variants

KIAA1524 is the gene encoding the human cancerous inhibitor of PP2A (CIP2A) protein which is regarded as a novel target for cancer therapy. It is overexpressed in 65%-90% of tissues in almost all studied human cancers. CIP2A expression correlates with cancer progression, disease aggressivity in lung cancer besides poor survival and resistance to chemotherapy in breast cancer. Herein, a pan-cancer analysis of public gene expression datasets was conducted showing significant upregulation of CIP2A in cancerous and metastatic tissues. CIP2A overexpression also correlated with poor survival of cancer patients. To determine the non-coding variants associated with CIP2A overexpression, 5'UTR and 3'UTR variants were annotated and scored using RegulomeDB and Enformer deep learning model. The 5'UTR variants rs1239349555, rs1576326380, and rs1231839144 were predicted to be potential regulators of CIP2A overexpression scoring best on RegulomeDB annotations with a high "2a" rank of supporting experimental data. These variants also scored the highest on Enformer predictions. Analysis of the 3'UTR variants of CIP2A predicted rs56255137 and rs58758610 to alter binding sites of hsa-miR-500a-5 and (hsa-miR-3671, hsa-miR-5692a) respectively. Both variants were also found in linkage disequilibrium with rs11709183 and rs147863209 respectively at r² ≥ 0.8. The aforementioned variants were found to be eQTL hits significantly associated with CIP2A overexpression. Further, analysis of rs11709183 and rs147863209 revealed a high "2b" rank on RegulomeDB annotations indicating a probable effect on DNAse transcription factors binding. The MuTarget analysis indicated that somatic mutations in TP53 are significantly associated with upregulated CIP2A in human cancers. Analysis of missense SNPs on CIP2A solved structure predicted seven deleterious effects. Four of these variants were also predicted as structurally and functionally destabilizing to CIP2A including; rs375108755, rs147942716, rs368722879, and rs367941403. Variant rs1193091427 was predicted as a potential intronic splicing mutation that might be responsible for the novel CIP2A variant (NOCIVA) in multiple myeloma. Finally, Enrichment of the Wnt/β-catenin pathway within the CIP2A regulatory gene network suggested potential of therapeutic combinations between FTY720 with Wnt/β-catenin, Plk1 and/or HDAC inhibitors to downregulate CIP2A which has been shown to be essential for the survival of different cancer cell lines.

IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling

Prediction of the effect of a single-nucleotide variant (SNV) in an intronic region on aberrant pre-mRNA splicing is challenging except for an SNV affecting the canonical GU/AG splice sites (ss). To predict pathogenicity of SNVs at intronic positions −50 (Int-50) to −3 (Int-3) close to the 3’ ss, we developed light gradient boosting machine (LightGBM)-based IntSplice2 models using pathogenic SNVs in the human gene mutation database (HGMD) and ClinVar and common SNVs in dbSNP with 0.01 ≤ minor allelic frequency (MAF) < 0.50. The LightGBM models were generated using features representing splicing cis-elements. The average recall/sensitivity and specificity of IntSplice2 by fivefold cross-validation (CV) of the training dataset were 0.764 and 0.884, respectively. The recall/sensitivity of IntSplice2 was lower than the average recall/sensitivity of 0.800 of IntSplice that we previously made with support vector machine (SVM) modeling for the same intronic positions. In contrast, the specificity of IntSplice2 was higher than the average specificity of 0.849 of IntSplice. For benchmarking (BM) of IntSplice2 with IntSplice, we made a test dataset that was not used to train IntSplice. After excluding the test dataset from the training dataset, we generated IntSplice2-BM and compared it with IntSplice using the test dataset. IntSplice2-BM was superior to IntSplice in all of the seven statistical measures of accuracy, precision, recall/sensitivity, specificity, F1 score, negative predictive value (NPV), and matthews correlation coefficient (MCC). We made the IntSplice2 web service at https://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice2.

Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity

The determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals.

Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study

BACKGROUND

To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups.

METHODS

Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina's Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping.

RESULTS

Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects.

CONCLUSIONS

This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS.

Next-Generation Sequencing of CYP2C19 in Stent Thrombosis: Implications for Clopidogrel Pharmacogenomics

PURPOSE

Describe CYP2C19 sequencing results in the largest series of clopidogrel-treated cases with stent thrombosis (ST), the closest clinical phenotype to clopidogrel resistance. Evaluate the impact of CYP2C19 genetic variation detected by next-generation sequencing (NGS) with comprehensive annotation and functional studies.

METHODS

Seventy ST cases on clopidogrel identified from the PLATO trial (n = 58) and Mayo Clinic biorepository (n = 12) were matched 1:1 with controls for age, race, sex, diabetes mellitus, presentation, and stent type. NGS was performed to cover the entire CYP2C19 gene. Assessment of exonic variants involved measuring in vitro protein expression levels. Intronic variants were evaluated for potential splicing motif variations.

RESULTS

Poor metabolizers (n = 4) and rare CYP2C19*8, CYP2C19*15, and CYP2C19*11 alleles were identified only in ST cases. CYP2C19*17 heterozygote carriers were observed more frequently in cases (n = 29) than controls (n = 18). Functional studies of CYP2C19 exonic variants (n = 11) revealed 3 cases and only 1 control carrying a deleterious variant as determined by in vitro protein expression studies. Greater intronic variation unique to ST cases (n = 169) compared with controls (n = 84) was observed with predictions revealing 13 allele candidates that may lead to a potential disruption of splicing and a loss-of-function effect of CYP2C19 in ST cases.

CONCLUSION

NGS detected CYP2C19 poor metabolizers and paradoxically greater number of so-called rapid metabolizers in ST cases. Rare deleterious exonic variation occurs in 4%, and potentially disruptive intronic alleles occur in 16% of ST cases. Additional studies are required to evaluate the role of these variants in platelet aggregation and clopidogrel metabolism.

Parathyroid Hormone Gene and Genes Involved in the Maintenance of Vitamin D Levels Association with Mandibular Retrognathism

In this study we evaluated whether single nucleotide polymorphisms (SNPs) in the genes encoding PTH, VDR, CYP24A1, and CYP27B1 were associated with mandibular retrognathism (MR). Samples from biologically-unrelated Brazilian patients receiving orthodontic treatment were included in this study. Pre-orthodontic lateral cephalograms were used to determine the phenotype. Patients with a retrognathic mandible were selected as cases and those with an orthognathic mandible were selected as controls. Genomic DNA was used for genotyping analysis of SNPs in PTH (rs694, rs6256, and rs307247), VDR (rs7975232), CYP24A1 (rs464653), and CYP27B1 (rs927650). Chi-squared or Fisher’s tests were used to compare genotype and allele distribution among groups. Haplotype analysis was performed for the SNPs in PTH. The established alpha was p < 0.05. Multifactor dimensionality reduction (MDR) was used to identify SNP–SNP interactions. A total of 48 (22 males and 26 females) MR and 43 (17 males and 26 females) controls were included. The linear mandibular and the angular measurements were statistically different between MR and controls (p < 0.05). In the genotype and allele distribution analysis, the SNPs rs694, rs307247, and rs464653 were associated with MR (p < 0.05). MDR analyses predicted the best interaction model for MR was rs694–rs927650, followed by rs307247–rs464653–rs927650. Some haplotypes in the PTH gene presented statistical significance. Our results suggest that SNPs in PTH, VDR, CYP24A1, and CYP27B1 genes are associated with the presence of mandibular retrognathism.

Disparities in COVID-19 severities and casualties across ethnic groups around the globe and patterns of ACE2 and PIR variants

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) mediated Coronavirus disease-19 (COVID-19) has affected millions of individuals around all corners of the globe. Symptoms and severities of infection with this highly contagious virus vary among individuals and there is disparity in the number of COVID-19-related casualties across different ethnic groups. The primary receptor for SARS-CoV-2 entry into the host cells is angiotensin-converting enzyme 2 (ACE2). Certain variants of ACE2 are known to be associated with COVID-19 comorbidities such as hypertension, cardiovascular complications, diabetes, chronic lung disease, etc. In this study, we looked into the geographic distribution of disease-associated variants of ACE2 as well as closely located PIR gene to explore any possible correlation with the disparities in COVID-19 severities and casualties across ethnic groups. Frequencies of the ACE2 variants associated with COVID-19 comorbidities are higher in the European and the admixed American populations. These variants are also present with stronger pairwise linkage disequilibrium (LD) in the European and the admixed American populations. On the other hand, the variants with protective role are more prevalent in the East and the South Asian populations. Strong pairwise LD exists among the activity modifying (modifier) variants of the PIR and ACE2 genes only in the European super-population. Absence of these PIR variants in the South Asian population may contribute to the overall lower COVID-19 case fatality rates (CFR) despite the dense population in this region.

Genetic analysis of 39 erythrocytosis and hereditary hemochromatosis-associated genes in the Slovenian family with idiopathic erythrocytosis

BACKGROUND

Erythrocytosis is a condition with an excessive number of erythrocytes, accompanied by an elevated haemoglobin and/or haematocrit value. Congenital erythrocytosis has a diverse genetic background with several genes involved in erythropoiesis. In clinical practice, nine genes are usually examined, but in approximately 70% of patients, no causative mutation can be identified. In this study, we screened 39 genes, aiming to identify potential disease-driving variants in the family with erythrocytosis of unknown cause.

PATIENTS AND METHODS

Two affected family members with elevated haemoglobin and/or haematocrit and negative for acquired causes and one healthy relative from the same family were selected for molecular-genetic analysis of 24 erythrocytosis and 15 hereditary haemochromatosis-associated genes with targeted NGS. The identified variants were further analysed for pathogenicity using various bioinformatic tools and review of the literature.

RESULTS

Of the 12 identified variants, two heterozygous variants, the missense variant c.471G>C (NM_022051.2) (p.(Gln157His)) in the EGLN1 gene and the intron variant c.2572-13A>G (NM_004972.3) in the JAK2 gene, were classified as low-frequency variants in European population. None of the two variants were present in a healthy family member. Variant c.2572-13A>G has potential impact on splicing by one prediction tool.

CONCLUSION

For the first time, we included 39 genes in the erythrocytosis clinical panel and identified two potential disease-driving variants in the Slovene family studied. Based on the reported functional in vitro studies combined with our bioinformatics analysis, we suggest further functional analysis of variant in the JAK2 gene and evaluation of a cumulative effect of both variants.

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual’s sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer.

Genetic variants in S-adenosyl-methionine synthesis pathway and nonsyndromic cleft lip with or without cleft palate in Chile

BACKGROUND

The S-adenosyl-methionine (SAM) availability is crucial for DNA methylation, an epigenetic mechanism involved in nonsyndromic cleft lip with or without cleft palate (NSCL/P) expression. The aim of this study was to assess the association between single-nucleotide polymorphisms (SNPs) of genes involved in SAM synthesis and NSCL/P in a Chilean population.

METHODS

In 234 cases and 309 controls, 18 SNPs in AHCY, MTR, MTRR, and MAT2A were genotyped, and the association between them and the phenotype was evaluated based on additive (allele), dominant, recessive and haplotype models, by odds ratio (OR) computing.

RESULTS

Three deep intronic SNPs of MTR showed a protective effect on NSCL/P expression: rs10925239 (OR 0.68; p = 0.0032; q = 0.0192), rs10925254 (OR 0.66; p = 0.0018; q = 0.0162), and rs3768142 (OR 0.66; p = 0.0015; q = 0.0162). Annotations in expression database demonstrate that the protective allele of the three SNPs is associated with a reduction of MTR expression summed to the prediction by bioinformatic tools of its potentiality to modify splicing sites.

CONCLUSIONS

The protective effect against NSCL/P of these intronic MTR SNPs seems to be related to a decrease in MTR enzyme expression, modulating the SAM availability for proper substrate methylation. However, functional analyses are necessary to confirm our findings.

IMPACT

SAM synthesis pathway genetic variants are factors associated to NSCL/P. This article adds new evidence for folate related genes in NSCL/P in Chile. Its impact is to contribute with potential new markers for genetic counseling.

Unique roles of rare variants in the genetics of complex diseases in humans

Genome-wide association studies have identified >10,000 genetic variants associated with various phenotypes and diseases. Although the majority are common variants, rare variants with >0.1% of minor allele frequency have been investigated by imputation and using disease-specific custom SNP arrays. Rare variants sequencing analysis mainly revealed have played unique roles in the genetics of complex diseases in humans due to their distinctive features, in contrast to common variants. Unique roles are hypothesis-free evidence for gene causality, a precise target of functional analysis for understanding disease mechanisms, a new favorable target for drug development, and a genetic marker with high disease risk for personalized medicine. As whole-genome sequencing continues to identify more rare variants, the roles associated with rare variants will also increase. However, a better estimation of the functional impact of rare variants across whole genome is needed to enhance their contribution to improvements in human health.

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.