An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome

Evaluating the efficacy of a long-read sequencing-based approach in the clinical diagnosis of neonatal congenital adrenocortical hyperplasia

Congenital adrenal hyperplasia (CAH) is a group of autosomal recessive disorders predominantly characterized by impaired corticosteroid synthesis. Clinical phenotypes include hypoadrenocorticism, electrolyte disturbances, abnormal gonadal development, and short stature, of which severe hyponadrenocorticism and salt wasting can be life-threatening. Genetic analysis can help in the clinical diagnosis of CAH. However, the 21-OHD-causing gene CYP21A2 is arranged in tandem with the highly homologous CYP21A1P pseudogene, making it difficult to determine the exact genotypes using the traditional method of multiplex ligation-dependent probe amplification (MLPA) plus Sanger sequencing or next-generation sequencing (NGS). We applied a long-read sequencing-based approach termed comprehensive analysis of CAH (CACAH) to 48 newborns with CAH that were diagnosed by clinical features and the traditional MLPA plus Sanger sequencing method for retrospective analysis, to evaluate its efficacy in the clinical diagnosis of neonatal CAH. Compared with the MLPA plus Sanger sequencing method, CACAH showed 100 % consistency in detecting SNV/indel variants located in exons and exon-intron boundary regions of CAH-related genes. It can directly determine the cis-trans relationship without the need to analyze parental genotypes, which reduces the time to diagnosis. Moreover, CACAH was able to distinguish different CYP21A1P/CYP21A2 and TNXA/TNXB chimeras, and detect additional variants (CYP21A2 variants c.-121C > T, c.*13G > A, c.*52C > T, c.*440C > T, c.*443 T > C, and TNXB variants c.12463 + 2 T > C, c.12204 + 5G > A). We also identified the TNXB variant c.11435_11524 + 30del alone instead of as a part of the TNXA/TNXB-CH-1 chimera in two newborns, which might be introduced by gene conversion. All of these characteristics enabled clinicians to better explain the phenotype of subjects and manage them more effectively. CACAH has a great advantage over the traditional MLPA and Sanger sequencing methods, showing substantial potential in the genetic diagnosis and screening of neonatal CAH.

VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

Somatic ARAF mutations in pediatric Langerhans cell histiocytosis: clinicopathologic, genetic and functional profiling

ARAF mutations have been identified in a limited subset of patients with Langerhans cell histiocytosis (LCH), a rare disorder characterized by abnormal proliferation of Langerhans cells. LCH is primarily instigated by mutations in the mitogen-activated protein kinase (MAPK) signaling pathway, with BRAFV600E and MAP2K1 mutations constituting most cases. ARAF mutations in LCH highlight the heterogeneity of the disease and provide insights into its underlying molecular mechanisms. However, the occurrence of ARAF-positive LCH cases is extremely rare, with only two reported globally. Although they may be linked to a more aggressive form of LCH and a more severe clinical progression, the clinical significance and functional consequences of these mutations remain uncertain. We performed next-generation sequencing (NGS) to explore driver mutations in 148 pediatric LCH patients and recognized a series of mutations, including an identical novel somatic ARAF mutation, c.1046_1051delAGGCTT (p.Q349_F351delinsL), in four pediatric LCH patients. It was considered an ARAF hotspot mutation. All reported ARAF-positive patients worldwide exhibited characteristic pathological features of LCH, albeit with involvement across multiple systems. In vitro functional studies showed that this mutation could trigger the MAPKinase pathway and phosphorylate its downstream effectors MEK1/2 and ERK1/2 (relatively weaker than BRAFV600E). Over-activation of mutant A-Raf kinase could be inhibited by the BRAF inhibitor vemurafenib. LCH is uncommon, and ARAF mutation is even rarer. In our study, we have identified a novel hotspot somatic ARAF mutation, which has been verified through functional analysis to be an activating mutation. LCH patients with ARAF mutation typically have an unfavorable prognosis due to limited treatment experiences, although they do not exhibit a high relapse rate. To aid in the development of personalized treatment approaches and prognostic markers for LCH patients, it is recommended to conduct typical pathological and immunohistochemical examinations, as well as genetic tests utilizing a targeted gene panel or whole exome sequencing (WES), for LCH diagnosis, thereby promoting the use of inhibitor treatment strategies.

Genetic control of N-glycosylation of human blood plasma proteins

Glycosylation is an important protein modification, which influences the physical and chemical properties as well as biological function of these proteins. Large-scale population studies have shown that the levels of various plasma protein N-glycans are associated with many multifactorial human diseases. Observed associations between protein glycosylation levels and human diseases have led to the conclusion that N-glycans can be considered a potential source of biomarkers and therapeutic targets. Although biochemical pathways of glycosylation are well studied, the understanding of the mechanisms underlying general and tissue-specific regulation of these biochemical reactions in vivo is limited. This complicates both the interpretation of the observed associations between protein glycosylation levels and human diseases, and the development of glycan-based biomarkers and therapeutics. By the beginning of the 2010s, high-throughput methods of N-glycome profiling had become available, allowing research into the genetic control of N-glycosylation using quantitative genetics methods, including genome-wide association studies (GWAS). Application of these methods has made it possible to find previously unknown regulators of N-glycosylation and expanded the understanding of the role of N-glycans in the control of multifactorial diseases and human complex traits. The present review considers the current knowledge of the genetic control of variability in the levels of N-glycosylation of plasma proteins in human populations. It briefly describes the most popular physical-chemical methods of N-glycome profiling and the databases that contain genes involved in the biosynthesis of N-glycans. It also reviews the results of studies of environmental and genetic factors contributing to the variability of N-glycans as well as the mapping results of the genomic loci of N-glycans by GWAS. The results of functional in vitro and in silico studies are described. The review summarizes the current progress in human glycogenomics and suggests possible directions for further research.

Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels

BACKGROUND

The use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied.

METHODS

We created a dataset of 3964 small (< 100 bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1-5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2021, PROVEAN, SIFT-indel, VEST-indel and VVP.

RESULTS

Our dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n = 809), ClinVar (n = 2882) and, DDD (n = 273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81-0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools' training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64-0.87. Several of the tools performed similarly however, VEST-indel had the highest AUCs of 0.93 (full dataset) and 0.87 (DDD subset).

CONCLUSIONS

Algorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools.

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions

MOTIVATION

Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the non-coding regions, are underdeveloped.

RESULTS

By leveraging labeled nc-sindels identified by cis-expression quantitative trait loci analyses across 44 tissues in Genotype-Tissue Expression (GTEx), and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), which is a supervised computational framework for predicting non-coding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the 'Whole Blood' tissue in GTEx and test the model using 15 immune cell types from an independent study named Database of Immune Cell Expression. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lichen-lab/TIVAN-indel.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deletions in GSN gene associated with growth traits of four Chinese cattle breeds

The aim of this study was to assess the potential of 21 bp mutation in the second intron of the GSN gene as a molecular marker-assisted by exploring the effect of 21 bp mutation on growth traits in four beef cattle breeds. Gelsolin (GSN), a member of the superfamily of gel proteins, is involved in the regulation of a variety of cellular activities in the organism and plays an important function in cell motility, apoptosis, signal transduction and inflammatory responses. Gelatin can not only negatively regulate the pro-apoptotic effect of P53 protein, but also promote apoptosis by blocking the interaction between actin and deoxyribonuclease, so, the GSN gene was selected as a candidate gene in this study. In this study, a 21 bp mutation on the second intron to the GSN gene was verified in 573 individuals of Yunling (YL, n = 220), Jiaxian (JX, n = 140), Xianan (XN, n = 114) and Qinchuan (QC, n = 97) cattle breeds using Once PCR and agarose gel electrophoresis. The association analysis of polymorphisms in the GSN gene with growth traits in four breeds was revealed: in YL cattle, the heart girth and forehead size of heterozygous ID genotype were significantly higher than II genotype (P < 0.05). In JX cattle, the withers height, body length and heart girth of II and ID genotype were significantly highest than DD genotype (P < 0.01); the height at hip cross and height at sacrum of II genotype was significantly highest than DD genotype (P < 0.01), but ID genotype was significantly higher than DD genotype. In XN cattle, the abdominal girth and circumference of the cannon bone of II genotype were significantly higher than ID genotype (P < 0.05). In QC cattle, the hucklebone width of ID genotype was significantly the highest than II genotype (P < 0.01). The results suggest that GSN may be an important candidate gene and that a 21 bp mutation on the second intron to the GSN gene can be used for molecular marker-assisted selection of four beef cattle breeds.

Screening of the TMEM151A Gene in Patients With Paroxysmal Kinesigenic Dyskinesia and Other Movement Disorders

Background

Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder characterized by recurrent involuntary movements usually triggered by sudden movements. Mutations in the TMEM151A gene were found to be the causative factor of PKD in recent studies. It has also been revealed that loss-of-function is the mechanism by which TMEM151A mutations cause PKD.

Methods

To investigate the genetic basis of PKD and broaden the clinical spectrum of the TMEM151A mutations, we recruited 181 patients of Chinese origin with movement disorders (MDs), including 39 PRRT2-negative PKD, 3 paroxysmal exercise-induced dyskinesia (PED), 2 paroxysmal non-kinesigenic dyskinesia (PNKD), 127 isolated dystonia, 8 choreas, and 2 myoclonus-dystonia syndromes. Whole-exome sequencing was applied to identify their possible disease-causing mutations. Then, Sanger sequencing was performed for validation and co-segregation analysis. Genetic analysis was also performed on additional family members of patients with TMEM151A mutations. Clinical manifestations of all PKD cases with mutations in TMEM151A reported, so far, were reviewed.

Results

Two novel variants of the TMEM151A gene (NM_153266.4, NP_694998.1), c.627_643dup (p.A215Gfs^*53) and c.627delG (p.L210Wfs^*52), were identified in 2 patients with PKD by whole-exome sequencing and further Sanger sequencing. Both variants were inherited by the patients from their respective mothers. No mutation of the TMEM151A gene was found in the other type of movement disorders. In reviewing the clinical presentation of TMEM151A-related PKD, no statistically significant difference in the age of onset, family history, duration of attacks, laterality, and phenotype was found between genders. More male patients received treatment and had a good response. A higher proportion of female patients did not receive any treatment, possibly because they had a milder condition of the disease.

Conclusions

This study further validated the role of TMEM151A in PKD. Future studies on protein function will be needed to ascertain the pathogenesis of TMEM151A in PKD.

Novel Loss-of-Function Variants in CHD2 Cause Childhood-Onset Epileptic Encephalopathy in Chinese Patients

Developmental and epileptic encephalopathy-94 (DEE94) is a severe form of epilepsy characterized by a broad spectrum of neurodevelopmental disorders. It is caused by pathogenic CHD2 variants. While only a few pathogenic CHD2 variants have been reported with detailed clinical phenotypes, most of which lack molecular analysis. In this study, next-generation sequencing (NGS) was performed to identify likely pathogenic CHD2 variants in patients with epilepsy. Three likely pathogenic variants were finally identified in different patients. The seizure onset ages were from two years to six years. Patients 1 and 2 had developmental delays before epilepsy, while patient 3 had intellectual regression after the first seizure onset. The observed seizures were myoclonic, febrile, and generalized tonic-clonic, which had been controlled by different combinations of antiepileptic drugs. Two de novo (c.1809_1809+1delGGinsTT, p.? and c.3455+2_3455+3insTG, p.?) and one maternal (c.3783G>A, p.W1261*) variant were identified, which were all predicted to be pathogenic/likely pathogenic. Molecular analysis was performed in patient 1, and we detected aberrantly spliced products, proving the pathogenicity of this CHD2 variant. New cases with novel variants, along with a detailed clinical and molecular analysis, are important for a better understanding of CHD2-related epileptic encephalopathy.

Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic

Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.

Genetic variations of bovine PCOS-related DENND1A gene identified in GWAS significantly affect female reproductive traits

Genome-wide association studies (GWAS) have identified DENND1A as a potential candidate gene linked to the fertility-related phenotypes in dairy cows. However, to date, no studies have examined the association of the DENND1A insertion/deletions (indels) to bovine fertility on a large scale. Herein, two indel sites, including P4-del-26-bp and P8-ins-15-bp were identified in 1064 Holstein cows. The values of the minor allelic frequency (MAF) ranged between 0.471 (deletion) and 0.230 (deletion), respectively, and combined four different haplotypes by analyzing the haplotype combination. It is noteworthy that P4-del-26-bp is associated with the ovarian width (P = 0.0004) and corpus luteum diameter (P = 0.004). Meanwhile, P8-ins-15-bp was found to have a significant association with the ovarian width (P = 0.020), ovarian weight (P = 0.004), the number of mature follicles (P = 0.020), and diameter of the mature follicles (P = 0.016). Furthermore, the combinatorial analysis showed that the two indel combined-genotypes were significantly related to several reproductive traits (ovarian width, ovarian weight, etc.). Collectively, our findings indicated that these two novel indels and their combinations are correlated with the reproductive traits, and hence, they can serve in the marker-assisted selection (MAS) in cattle breeding. Nevertheless, further functional experiments are needed for understanding the mechanisms of these indels in cattle reproduction in a better way.

Prediction of driver variants in the cancer genome via machine learning methodologies

Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research.

SVLR: Genome Structural Variant Detection Using Long-Read Sequencing Data

Genome structural variants (SVs) have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long-read sequencing technologies arise to make it possible to find SVs of as long as 10,000 nucleotides. Thus, long read-based SV detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect SVs recently. In this article, we present a new method, called SVLR, to detect SVs based on long-read sequencing data. Comparing with existing methods, SVLR can detect three new kinds of SVs: block replacements, block interchanges, and translocations. Although these new SVs are structurally more complicated, SVLR achieves accuracies that are comparable with those of the classic SVs. Moreover, for the classic SVs that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up to 38% without harming the precisions (i.e., >78%). We also point out three directions to further improve SV detection in the future. Source codes: https://github.com/GWYSDU/SVLR.

Transcript analysis for variant classification resolution in a child with primary ciliary dyskinesia

Transcriptional analysis can be utilized to reconcile variants of uncertain significance, particularly those predicted to impact splicing. Laboratory analysis of the predicted mRNA transcript may allow inference of the in vivo impact of the variant and aid prediction of its clinical significance. We present a patient with classical features of primary ciliary dyskinesia (PCD) who was identified to have compound heterozygous variants in the DNAH11 gene (c.10691 + 2T > C, c.13523_13543dup21) via trio whole-exome sequencing in 2013. These variants were originally classified as Mutation and Likely Mutation. However, these variants were downgraded to variants of uncertain significance (VUSs) during reanalysis in 2016 because of uncertainty that they caused a loss of function of the gene. c.10691 + 2T > C is predicted to abrogate the canonical splice site and lead to the skipping of exon 65, but the adjoining of exon 64 and exon 66 in the DNAH11 transcript preserves the reading frame of the resultant protein. c.13523_13543dup21 is located in the last exon of the DNAH11 coding sequence, upstream of the canonical stop codon, which suggests a reduced likelihood to trigger nonsense-mediated decay (NMD). Transcriptional analysis was performed to characterize the impact of the variants, resulting in reclassification of c.10691 + 2T > C to Likely Pathogenic by providing evidence that it results in a deleterious effect and subsequent downstream reclassification of c.13523_13543dup21 to Likely Pathogenic as well. Our case illustrates the potential impact of transcriptional analysis on variant resolution, supporting its usage on variants that exert an unpredictable effect on splicing.

Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

Comparative assessments of indel annotations in healthy and cancer genomes with next-generation sequencing data

BACKGROUND

Insertion and deletion (indel) is one of the major variation types in human genomes. Accurate annotation of indels is of paramount importance in genetic variation analysis and investigation of their roles in human diseases. Previous studies revealed a high number of false positives from existing indel calling methods, which limits downstream analyses of the effects of indels on both healthy and disease genomes. In this study, we evaluated seven commonly used general indel calling programs for germline indels and four somatic indel calling programs through comparative analysis to investigate their common features and differences and to explore ways to improve indel annotation accuracy.

METHODS

In our comparative analysis, we adopted a more stringent evaluation approach by considering both the indel positions and the indel types (insertion or deletion sequences) between the samples and the reference set. In addition, we applied an efficient way to use a benchmark for improved performance comparisons for the general indel calling programs RESULTS: We found that germline indels in healthy genomes derived by combining several indel calling tools could help remove a large number of false positive indels from individual programs without compromising the number of true positives. The performance comparisons of somatic indel calling programs are more complicated due to the lack of a reliable and comprehensive benchmark. Nevertheless our results revealed large variations among the programs and among cancer types.

CONCLUSIONS

While more accurate indel calling programs are needed, we found that the performance for germline indel annotations can be improved by combining the results from several programs. In addition, well-designed benchmarks for both germline and somatic indels are key in program development and evaluations.

Detecting novel Indel variants within the GHR gene and their associations with growth traits in Luxi Blackhead sheep

The growth hormone is important in the regulation of metabolism and energy homeostasis and acts through a growth hormone receptor (GHR). In this work, genetic variations within the ovine GHR gene were identified and tested for associations with body morphometric traits in Chinese Luxi Blackhead (LXBH) sheep. Novel deletion loci in the LXBH GHR gene included P2-del-23 bp and P8-del-23 bp indel variants. The polymorphic information content (PIC) was 0.329 in P2-del-23 bp and 0.257 in P8-del-23 bp. Moreover, both indel polymorphisms were not at Hardy-Weinberg equilibrium (p < 0.05) in the LXBH population. Statistical analyses revealed that the P2-del-23 bp and P8-del-23 bp indels were significantly associated (p < 0.05) with several growth traits in rams and ewes, including body weight, body height, chest depth, chest width, chest circumference, cannon circumference, paunch girth and hip width. Among the tested sheep, the body traits of those with genotype DD were superior to those with II and ID genotypes, suggesting that the 'D' allele was responsible for the positive effects on growth traits. Thus, these results indicate that the P2-del-23 bp and P8-del-23 bp indel sites and the DD genotype can be useful in marker-assisted selection in sheep.

Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions

Chronic musculoskeletal pain affects all aspects of human life. However, mechanisms of its genetic control remain poorly understood. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. Here, we apply principal component analysis to reduce phenotype heterogeneity of chronic musculoskeletal pain at four locations: the back, neck/shoulder, hip, and knee. Using matrices of genetic covariances, we constructed four genetically independent phenotypes (GIPs) with the leading GIP (GIP1) explaining 78.4% of the genetic variance of the analyzed conditions, and GIP2–4 explain progressively less. We identified and replicated five GIP1-associated loci and one GIP2-associated locus and prioritized the most likely causal genes. For GIP1, we showed enrichment with multiple nervous system-related terms and genetic correlations with anthropometric, sociodemographic, psychiatric/personality traits and osteoarthritis. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and reflecting pain perception and processing.

Yakov Tsepilov, Maxim Freidin et al. find that chronic musculoskeletal pain conditions at four distinct anatomical sites share a common genetic background. The authors constructed genetically independent phenotypes (GIP) from principal components analysis of the different pain phenotypes and used the GIPs to perform genome-wide association studies to identify underlying genetic factors.

Lamotrigine induced Brugada-pattern in a patient with genetic epilepsy associated with a novel variant in SCN9A

BACKGROUND

A 30-year-old man presented with intellectual disability associated with epilepsy. The epilepsy was initially treated with sodium valproate and since he was 28 years-old with lamotrigine. With the addition of lamotrigine, a pattern of Brugada syndrome appeared on the electrocardiogram. The family history was positive for epilepsy from the motheŕs side, who had never been treated with lamotrigine.

OBJECTIVE

Determine the genetic cause of the intellectual disability, epilepsy and Brugada syndrome of the patient and try to establish a possible correlation between the genetic background and the Brugada syndrome pattern under lamotrigine treatment.

METHODS

A standard karyotype, array comparative genomic hybridization and two different NGS panels have done to the index case to identify the genetic causes of the intellectual disability, epilepsy and Brugada syndrome pattern.

RESULTS

Genetic analyses in the family identified a de novo duplication of 1.3 Mb in 8p21.3 as well as two novel heterozygous rare variants in SCN9A and AKAP9 genes, both inherited from the mother.

CONCLUSION

We hypothesize that in this family the SCN9A variant was responsible for the epileptic syndrome. In addition, given that SCN9A is lightly expressed in the heart tissue, we postulate that this SCN9A variant, alone or in combination with AKAP9 variant, might be responsible for the Brugada pattern when challenged by lamotrigine.

Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours

For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.

Defining the genetic control of human blood plasma N-glycome using genome-wide association study

Glycosylation is a common post-translational modification of proteins. Glycosylation is associated with a number of human diseases. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people measured by Ultra Performance Liquid Chromatography (UPLC) technology. Starting with the 36 original traits measured by UPLC, we computed an additional 77 derived traits leading to a total of 113 glycan traits. We studied associations between these traits and genetic polymorphisms located on human autosomes. We discovered and replicated 12 loci. This allowed us to demonstrate an overlap in genetic control between total plasma protein and IgG glycosylation. The majority of revealed loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3 and MGAT5) and a known regulator of plasma protein fucosylation (HNF1A). However, we also found loci that could possibly reflect other more complex aspects of glycosylation process. Functional genomic annotation suggested the role of several genes including DERL3, CHCHD10, TMEM121, IGH and IKZF1. The hypotheses we generated may serve as a starting point for further functional studies in this research area.

The molecular landscape of glioma in patients with Neurofibromatosis 1

Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the prevalent tumors. Gliomagenesis in NF1 results in a heterogeneous spectrum of low- to high-grade neoplasms occurring during the entire lifespan of patients. The pattern of genetic and epigenetic alterations of glioma that develops in NF1 patients and the similarities with sporadic glioma remain unknown. Here, we present the molecular landscape of low- and high-grade gliomas in patients affected by NF1 (NF1-glioma). We found that the predisposing germline mutation of the NF1 gene was frequently converted to homozygosity and the somatic mutational load of NF1-glioma was influenced by age and grade. High-grade tumors harbored genetic alterations of TP53 and CDKN2A, frequent mutations of ATRX associated with Alternative Lengthening of Telomere, and were enriched in genetic alterations of transcription/chromatin regulation and PI3 kinase pathways. Low-grade tumors exhibited fewer mutations that were over-represented in genes of the MAP kinase pathway. Approximately 50% of low-grade NF1-gliomas displayed an immune signature, T lymphocyte infiltrates, and increased neo-antigen load. DNA methylation assigned NF1-glioma to LGm6, a poorly defined Isocitrate Dehydrogenase 1 wild-type subgroup enriched with ATRX mutations. Thus, the profiling of NF1-glioma defined a distinct landscape that recapitulates a subset of sporadic tumors.

Principles and methods of in-silico prioritization of non-coding regulatory variants

Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.