Clinical application of targeted long read sequencing in prenatal beta-thalassemia testing and genetic counseling.

BACKGROUND: Beta thalassemia, related to HBB mutation and associated with elevated hemoglobin A2 (HbA2), is an important genetic hemoglobinopathy with high incidences of disease and carrier rates in Singapore. Carrier screening is essential to facilitate prenatal counseling and testing. However, when individuals with elevated HbA2 do not have an identifiable HBB disease-associated variant, there is ambiguity on risk to their offspring. METHODS: We describe a case report of a proband with elevated HbA2, no identifiable HBB disease-associated variant, whose partner was a beta thalassemia carrier. Through clinical HBB gene sequencing, multiplex ligation-dependent probe amplification (MLPA) analysis, as well as targeted Nanopore long read sequencing of selected genes, we performed a complete analysis of HBB including the promoter region, 5'UTR and coding gene sequence, as well as evaluation for potential modifier variants and other rare structural variants. RESULTS: This process identified that the proband was heterozygous for KLF1:c.544T>C (p.Phe182Leu), a potential functional polymorphism previously known to be associated with benign elevated HbA2 levels. The presence of disease variants in the HBB locus was excluded. CONCLUSION: This finding provided clarity and enabled family planning for the proband and her family.

Robust preimplantation genetic testing of the common F8 Inv22 pathogenic variant of severe hemophilia A using a highly polymorphic multi-marker panel encompassing the paracentric inversion.

BACKGROUND: Hemophilia A (HEMA) is an X-linked bleeding disorder caused by reduced/absent coagulation factor VIII expression, as a result of pathogenic variants in the F8 gene. Preimplantation prevention of HEMA should ideally include direct pathogenic F8 variant detection, complemented by linkage analysis of flanking markers to identify the high-risk F8 allele. Linkage analysis is particularly indispensable when the pathogenic variant cannot be detected directly or identified. This study evaluated the suitability of a panel of F8 intragenic and extragenic short tandem repeat markers for standalone linkage-based preimplantation genetic testing for monogenic disorder (PGT-M) of the Inv22 pathogenic variant, an almost 600 kb paracentric inversion responsible for almost half of all severe HEMA globally, for which direct detection is challenging. METHODS: Thirteen markers spanning 1 Mb and encompassing both F8 and the Inv22 inversion interval were genotyped in 153 unrelated females of Viet Kinh ethnicity. RESULTS: All individuals were heterozygous for ≥ 1 marker, ~ 90% were heterozygous for ≥ 1 of the five F8 intragenic markers, and almost 98% were heterozygous for ≥ 1 upstream (telomeric) and ≥ 1 downstream (centromeric) markers. A prospective PGT-M couple at risk of transmitting F8 Inv22 were fully informative at four marker loci (2 intra-inversion, 1 centromeric, 1 telomeric) and partially informative at another five (2 intra-inversion, 3 centromeric), allowing robust phasing of low- and high-risk haplotypes. In vitro fertilization produced three embryos, all of which clearly inherited the low-risk maternal allele, enabling reliable unaffected diagnoses. A single embryo transfer produced a clinical pregnancy, which was confirmed as unaffected by amniocentesis and long-range PCR, and a healthy baby girl was delivered at term. CONCLUSION: Robust and reliable PGT-M of HEMA, including the common F8 Inv22 pathogenic variant, can be achieved with sufficient informative intragenic and flanking markers.

Triplet-primed PCR and Melting Curve Analysis for Rapid Molecular Screening of Spinocerebellar Ataxia Types 1, 2, and 3.

There are more than 40 types of spinocerebellar ataxia (SCA), most of which are caused by abnormal expansion of short tandem repeats at various gene loci. These phenotypically similar disorders require molecular testing at multiple loci by fluorescent PCR and capillary electrophoresis to identify the causative repeat expansion. We describe a simple strategy to screen for the more common SCA1, SCA2, and SCA3 by rapidly detecting the abnormal CAG repeat expansion at the ATXN1, ATXN2, and ATXN3 loci using melting curve analysis of triplet-primed PCR products. Each of the three separate assays employs a plasmid DNA carrying a known repeat size to generate a threshold melt peak temperature, which effectively distinguishes expansion-positive samples from those without a repeat expansion. Samples that are screened positive based on their melt peak profiles are subjected to capillary electrophoresis for repeat sizing and genotype confirmation. These screening assays are robust and provide accurate detection of the repeat expansion while eliminating the need for fluorescent PCR and capillary electrophoresis for every sample.

Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.

Third-Generation Single-Molecule Sequencing for Preimplantation Genetic Testing of Aneuploidy and Segmental Imbalances.

BACKGROUND: Current strategies for preimplantation genetic testing for aneuploidy or structural rearrangements (PGT-A/SR) rely mainly on next-generation sequencing (NGS) and microarray platforms, which are robust but require expensive instrumentation. We explored the suitability of third-generation single-molecule sequencing as a PGT-A/SR screening platform for both aneuploidy and segmental imbalance. METHODS: Single-cell and multicell replicates from aneuploid or segmentally unbalanced cell lines (n = 208) were SurePlex-amplified, randomized, and subjected to (a) Nanopore-based single-molecule sequencing (Oxford Nanopore Technologies) and (b) NGS using a leading commercial PGT-A solution (Illumina VeriSeq PGS). Archival SurePlex-amplified trophectoderm biopsy samples (n = 96) previously analyzed using the commercial kit were blinded and reanalyzed using Nanopore. RESULTS: Nanopore-based PGT-A identified the specific aberration in 95.45% (84/88) and 97.78% (88/90) of single-/multicells with an aneuploidy or segmental imbalance (10-30.5 Mb), respectively. Comparison against the commercial kit's results revealed concordances of 98.86% (87/88) and 98.89% (89/90) for the aneuploid and segmentally unbalanced (10-30.5 Mb aberration) samples, respectively. Detection sensitivity for smaller segmental imbalances (5-5.8 Mb aberration, n = 30) decreased markedly on both platforms. Nanopore-based PGT-A reanalysis of trophectoderm biopsy samples was 97.92% (94/96) concordant with the commercial kit results. CONCLUSION: Up to 24 SurePlex-amplified single-cell, multicell, or trophectoderm samples could be sequenced in a single MinION flow-cell for subsequent preimplantation genetic testing for aneuploidy or structural rearrangements (PGT-A/SR) analysis, with results obtainable in ≤3 days and at per-sample costs that are competitive with commercial offerings. Nanopore's third-generation single-molecule sequencing represents a viable alternative to current commercial NGS-based PGT-A solutions for aneuploidy and segmental imbalance (≥10 Mb) screening of single-/multicell or trophectoderm biopsy samples.

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

BACKGROUND: The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease. METHODS: This study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature. RESULTS: Through robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10-16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed ( https://xistance.shinyapps.io/prs-ra/ ) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs. CONCLUSIONS: These findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

REViewer: haplotype-resolved visualization of read alignments in and around tandem repeats.

BACKGROUND: Expansions of short tandem repeats are the cause of many neurogenetic disorders including familial amyotrophic lateral sclerosis, Huntington disease, and many others. Multiple methods have been recently developed that can identify repeat expansions in whole genome or exome sequencing data. Despite the widely recognized need for visual assessment of variant calls in clinical settings, current computational tools lack the ability to produce such visualizations for repeat expansions. Expanded repeats are difficult to visualize because they correspond to large insertions relative to the reference genome and involve many misaligning and ambiguously aligning reads. RESULTS: We implemented REViewer, a computational method for visualization of sequencing data in genomic regions containing long repeat expansions and FlipBook, a companion image viewer designed for manual curation of large collections of REViewer images. To generate a read pileup, REViewer reconstructs local haplotype sequences and distributes reads to these haplotypes in a way that is most consistent with the fragment lengths and evenness of read coverage. To create appropriate training materials for onboarding new users, we performed a concordance study involving 12 scientists involved in short tandem repeat research. We used the results of this study to create a user guide that describes the basic principles of using REViewer as well as a guide to the typical features of read pileups that correspond to low confidence repeat genotype calls. Additionally, we demonstrated that REViewer can be used to annotate clinically relevant repeat interruptions by comparing visual assessment results of 44 FMR1 repeat alleles with the results of triplet repeat primed PCR. For 38 of these alleles, the results of visual assessment were consistent with triplet repeat primed PCR. CONCLUSIONS: Read pileup plots generated by REViewer offer an intuitive way to visualize sequencing data in regions containing long repeat expansions. Laboratories can use REViewer and FlipBook to assess the quality of repeat genotype calls as well as to visually detect interruptions or other imperfections in the repeat sequence and the surrounding flanking regions. REViewer and FlipBook are available under open-source licenses at https://github.com/illumina/REViewer and https://github.com/broadinstitute/flipbook respectively.

Triplet-Primed PCR Assays for Accurate Screening of FMR1 CGG Repeat Expansion and Genotype Verification.

Fragile X syndrome and other fragile X-associated disorders are caused by the full-mutation (>200 copies) and premutation (55 to 200 copies) expansion, respectively, of the CGG short tandem repeat in the fragile X messenger ribonucleoprotein 1 (FMR1) gene. Clinical diagnostic laboratories use Southern blot analysis and polymerase chain reaction (PCR)-based tests to detect and/or size the FMR1 CGG repeats. The development of sensitive and high-throughput triplet-primed PCR (TP-PCR) assays has diminished the need to subject all samples to Southern blot analysis, which is both labor- and time-intensive. In this article, we describe two direct TP-PCR (dTP-PCR) assays for the detection of FMR1 CGG repeat expansions. We outline a protocol that is based on melting curve analysis of dTP-PCR amplicons for a rapid and cost-effective first-tier screening and identification of individuals with premutation and full-mutation expansions. We also describe a protocol that employs capillary electrophoresis to resolve the dTP-PCR amplicon fragments and to estimate the repeat sizes of normal (5 to 44 copies), intermediate (45 to 54 copies), and premutation alleles, as well as to detect full mutations and determine the structure of the FMR1 alleles. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Direct triplet-primed PCR master mix preparation and amplification of the FMR1 CGG repeat locus for melting curve analysis Basic Protocol 2: Melting curve analysis of direct triplet-primed PCR amplicons on the Rotor-Gene Q MD × 5plex high-resolution melt platform Alternate Protocol: Melting curve analysis of direct triplet-primed PCR amplicons on the LightCycler 480 system Basic Protocol 3: Generation of direct triplet-primed PCR melting curve analysis profiles Basic Protocol 4: Direct triplet-primed PCR master mix preparation and amplification of the FMR1 CGG repeat locus for capillary electrophoresis Basic Protocol 5: Generation of control FMR1 plasmids for direct triplet-primed PCR melting curve analysis Basic Protocol 6: Sanger sequencing assay to verify FMR1 CGG repeat size and structure of plasmid DNA controls.

Single-Tube Screen for Rapid Detection of Repeat Expansions in Seven Common Spinocerebellar Ataxias.

BACKGROUND: The autosomal dominantly inherited and genetically heterogeneous spinocerebellar ataxias (SCAs) exhibit highly similar clinical presentations. Many are caused by repeat expansions, of which at least 8 involve CAG repeats. Repeat expansion detection is the only method to confirm disease status in symptomatic individuals. We present a novel strategy to simultaneously screen for the presence of CAG repeat expansion in the genes responsible for SCAs 1, 2, 3, 6, 7, 12, and dentatorubral-pallidoluysian atrophy using a simplified single-tube assay. METHODS: The method employs differentially labeled locus-specific primers and a common triplet-primed primer. Amplified products from each locus are distinguished by a combination of the product size and the fluorophore tag. The upper size limit of the normal allele range was used as the cutoff for distinguishing normal from potentially affected samples, with repeat expansion detected by presence of electrophoretic peaks extending beyond the cutoff. RESULTS: Blinded evaluation of the assay on 60 genotype-known DNA samples correctly detected repeat expansion in the expected SCA repeat locus for all 31 DNA samples. CONCLUSIONS: In principle, this strategy can be applied to the simultaneous screening of any group of disease genes sharing the same repetitive units and/or their reverse complement.

Machine learning using genetic and clinical data identifies a signature that robustly predicts methotrexate response in rheumatoid arthritis.

OBJECTIVE: To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. METHODS: MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. RESULTS: The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855-0.916; sensitivity: 0.715-0.892; and specificity: 0.733-0.862) and the unseen test set (area under the curve: 0.751-0.826; sensitivity: 0.581-0.839; and specificity: 0.641-0.923). CONCLUSION: Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.

High-Throughput Methylation-Specific Triplet-Primed PCR and Melting Curve Analysis for Selective and Reliable Identification of Actionable FMR1 Genotypes.

Methylated FMR1 full-mutation expansions cause fragile X syndrome. FMR1 premutation carriers are susceptible to other late-onset conditions, and women with premutation are at risk of transmitting a fully expanded FMR1 allele to offspring. Identification of individuals with actionable FMR1 genotypes (full-mutation males and females, and premutation females at risk for primary ovarian insufficiency and/or having fragile X-affected offspring) can enable timely access to intervention services and genetic counseling. This study presents a rapid, first-tier test based on melting curve analysis of methylation-specific triplet-primed PCR amplicons (msTP-PCR MCA) for concurrent detection of FMR1 CGG-repeat expansions and their methylation status. The msTP-PCR MCA assay was optimized on 20 fragile X reference samples, and its performance was evaluated on 111 peripheral blood-derived DNA samples from patients who have undergone prior molecular testing with PCR and/or Southern blot analysis. The msTP-PCR MCA assay detected all samples with a methylated FMR1 CGG-repeat expansion, and had sensitivity, specificity, positive predictive value, and negative predictive values of 100%, 92.06%, 91.1%, and 100%, respectively. The msTP-PCR MCA assay identified premutation/full-mutation mosaicism down to 1%, detected skewed inactivation in females with FMR1 expansions, and enabled selective identification of all individuals with an actionable FMR1 genotype. The msTP-PCR MCA assay may aid in fragile X screening of at-risk populations and newborns and voluntary carrier screening of women of reproductive age.

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

BACKGROUND: Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients. METHODS: Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set. FINDINGS: Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients. INTERPRETATION: Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways. FUNDING: Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.

Clinicopathological-Associated Regulatory Network of Deregulated circRNAs in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide. Here, we present a novel strategy to identify key circRNA signatures of clinically relevant co-expressed circRNA-mRNA networks in pertinent cancer-pathways that modulate prognosis of HCC patients, by integrating clinic-pathological features, circRNA and mRNA expression profiles. Through further integration with miRNA expression profiles, clinically relevant competing-endogenous-RNA (ceRNA) networks of circRNA-miRNA-mRNAs were constructed. At least five clinically relevant nodal-circRNAs, co-expressed with numerous genes, were identified from the circRNA-mRNA networks. These nodal circRNAs upregulated proliferation (except circRaly) and transformation in cells. The most upregulated nodal-circRNA, circGPC3, associated with higher-grade tumors and co-expressed with 33 genes, competes with 11 mRNAs for two shared miRNAs. circGPC3 was experimentally demonstrated to upregulate cell-cycle and migration/invasion in both transformed and non-transformed liver cell-lines. circGPC3 was further shown to act as a sponge of miR-378a-3p to regulate APSM (Abnormal spindle-like microcephaly associated) expression and modulate cell transformation. This study identifies 5 key nodal master circRNAs in a clinically relevant circRNA-centric network that are significantly associated with poorer prognosis of HCC patients and promotes tumorigenesis in cell-lines. The identification and characterization of these key circRNAs in clinically relevant circRNA-mRNA and ceRNA networks may facilitate the design of novel strategies targeting these important regulators for better HCC prognosis.

Simultaneous Screening of the FRAXA and FRAXE Loci for Rapid Detection of FMR1 CGG and/or AFF2 CCG Repeat Expansions by Triplet-Primed PCR.

Moderate to hyper-expansion of trinucleotide repeats at the FRAXA and FRAXE fragile sites, with or without concurrent hypermethylation, has been associated with intellectual disability and other conditions. Unlike molecular diagnosis of FMR1 CGG repeat expansions in FRAXA, current detection of AFF2 CCG repeat expansions in FRAXE relies on low-throughput and otherwise inefficient techniques combining Southern blot analysis and PCR. A novel triplet-primed PCR assay was developed for simultaneous screening for trinucleotide repeat expansions at the FRAXA and FRAXE fragile sites, and was validated using archived clinical samples of known FMR1 and AFF2 genotypes. Population samples and FRAXE-affected samples were sequenced for the evaluation of variations in the AFF2 CCG repeat structure. The duplex assay accurately identified expansions at the FMR1 and AFF2 trinucleotide repeat loci. On Sanger sequencing of the AFF2 CCG repeat, the single-nucleotide polymorphism variant rs868914124(C) that effectively adds two CCG repeats at the 5'-end, was enriched in the Malay population and with short repeats (<11 CCGs), and was present in all six expanded AFF2 alleles of this study. All expanded AFF2 alleles contained multiple non-CCG interruptions toward the 5'-end of the repeat. A sensitive, robust, and rapid assay has been developed for the simultaneous detection of expansion mutations at the FMR1 and AFF2 trinucleotide repeat loci, simplifying screening for FRAXA- and FRAXE-associated disorders.

Highly deregulated lncRNA LOC is associated with overall worse prognosis in Hepatocellular Carcinoma patients.

Although numerous long non-coding RNAs (lncRNAs) were reported to be deregulated in Hepatocellular Carcinoma (HCC), experimentally characterized, and/or associated with patient's clinical characteristics, there is, thus far, minimal concerted research strategy to identify deregulated lncRNAs that modulate prognosis of HCC patients. Here, we present a novel strategy where we identify lncRNAs, which are not only de-regulated in HCC patients, but are also associated with pertinent clinical characteristics, potentially contributing to the prognosis of HCC patients. LOC101926913 (LOC) was further characterized because it is the most highly differentially expressed amongst those that are associated with the most number of clinical features (tumor-stage, vascular and tumor invasion and poorer overall survival). Experimental gain- and loss-of-function manipulation of LOC in liver cell-lines highlight LOC as a potential onco-lncRNA promoting cell proliferation, anchorage independent growth and invasion. LOC expression in cells up-regulated genes involved in GTPase-activities and downregulated genes associated with cellular detoxification, oxygen- and drug-transport. Hence, LOC may represent a novel therapeutic target, modulating prognosis of HCC patients through up-regulating GTPase-activities and down-regulating detoxification, oxygen- and drug-transport. This strategy may thus be useful for the identification of clinically relevant lncRNAs as potential biomarkers/targets that modulate prognosis in other cancers as well.

Robust Performance of Potentially Functional SNPs in Machine Learning Models for the Prediction of Atorvastatin-Induced Myalgia.

Statins can cause muscle symptoms resulting in poor adherence to therapy and increased cardiovascular risk. We hypothesize that combinations of potentially functional SNPs (pfSNPs), rather than individual SNPs, better predict myalgia in patients on atorvastatin. This study assesses the value of potentially functional single nucleotide polymorphisms (pfSNPs) and employs six machine learning algorithms to identify the combination of SNPs that best predict myalgia. Methods: Whole genome sequencing of 183 Chinese, Malay and Indian patients from Singapore was conducted to identify genetic variants associated with atorvastatin induced myalgia. To adjust for confounding factors, demographic and clinical characteristics were also examined for their association with myalgia. The top factor, sex, was then used as a covariate in the whole genome association analyses. Variants that were highly associated with myalgia from this and previous studies were extracted, assessed for potential functionality (pfSNPs) and incorporated into six machine learning models. Predictive performance of a combination of different models and inputs were compared using the average cross validation area under ROC curve (AUC). The minimum combination of SNPs to achieve maximum sensitivity and specificity as determined by AUC, that predict atorvastatin-induced myalgia in most, if not all the six machine learning models was determined. Results: Through whole genome association analyses using sex as a covariate, a larger proportion of pfSNPs compared to non-pf SNPs were found to be highly associated with myalgia. Although none of the individual SNPs achieved genome wide significance in univariate analyses, machine learning models identified a combination of 15 SNPs that predict myalgia with good predictive performance (AUC >0.9). SNPs within genes identified in this study significantly outperformed SNPs within genes previously reported to be associated with myalgia. pfSNPs were found to be more robust in predicting myalgia, outperforming non-pf SNPs in the majority of machine learning models tested. Conclusion: Combinations of pfSNPs that were consistently identified by different machine learning models to have high predictive performance have good potential to be clinically useful for predicting atorvastatin-induced myalgia once validated against an independent cohort of patients.

Rapid Molecular Screen of Spinocerebellar Ataxia Types 1, 2, and 3 by Triplet-Primed PCR and Melting Curve Analysis.

The autosomal dominantly inherited spinocerebellar ataxias (SCAs) can be caused by dynamic mutations of short tandem repeats within various genes. Because of the significant clinical overlap among the various SCA types, molecular screening of multiple genetic loci by fluorescent PCR and capillary electrophoresis is necessary to identify the causative repeat expansion. We describe a simple, rapid, and inexpensive strategy to screen for CAG repeat expansion mutations at the ATXN1, ATXN2, and ATXN3 loci using melting curve analysis of triplet-primed PCR products. Plasmid DNAs of known repeat sizes were used to generate threshold melt peak temperatures, which rapidly and effectively distinguish samples carrying an expanded allele from those carrying nonexpanded alleles. Melting curve analysis-positive samples were confirmed by capillary electrophoresis sizing of the triplet-primed PCR products. All three assays achieved 100% sensitivity, with 95% CIs of 67.86% to 100% (SCA1), 74.65% to 100% (SCA2), and 91.58% to 100% (SCA3). The SCA1 assay also achieved 100% specificity (95% CI, 97.52%-100%), whereas the SCA2 and SCA3 assays achieved specificity of 99.46% (95% CI, 96.56%-99.97%) and 99.32% (95% CI, 95.70%-99.96%), respectively. These screening assays provide robust and highly accurate detection of expanded alleles and are amenable to large-scale screening while minimizing the need for capillary electrophoresis sizing for every sample.

FMR1 gene CGG repeat distribution among the three individual cohorts with intellectual disability, autism, and primary ovarian insufficiency from Tamil Nadu, Southern India.

Fragile X syndrome is the most common genetic cause of intellectual disability (ID) and is also well known to have a role in primary ovarian insufficiency (POI) and fragile X-associated tremor ataxia syndrome (FXTAS) that expresses across generations. The objective was to compare the CGG repeat variants in FMR1 gene among three correlating cohorts of ID, autism and idiopathic POI. Thirty-six patients with ID, 12 with autism spectrum disorder (ASD) and 13 females with idiopathic POI were screened for FMR1 CGG repeat size by fluorescent methylation-specific PCR and GeneScan analysis, irrespective of Hagerman checklist clinical scores. Among 29 males and seven females, 11 FMR1 allelic variants ranging from 21 to >200 CGG repeats were observed. Three (CF2-3, 39-5, 44-2) out of 29 males had full mutation alleles accounting for a 10.34% incidence of FXS among idiopathic ID males. One of them was a mosaic for CGG repeats with both premutation and full mutation alleles. The frequency of fragile X syndrome is high among patients with idiopathic ID; they also had a high score for the clinical check list. A cascade testing that begins with checklist evaluation prior to DNA analysis will be cost-effective for establishing early diagnosis in South India. With the huge disease burden, there is a need for the establishment of more molecular diagnostics and self-help groups for fragile X syndrome.

Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients.

Long non-coding RNAs (lncRNAs) are often aberrantly expressed in Hepatocellular Carcinoma (HCC). We hypothesize that lncRNAs modulate HCC prognoses through differential deregulation of key lncRNAs affecting important gene network in key cancer pathways associated with pertinent clinical phenotype. Here, we present a novel approach integrating lncRNA-mRNA expression profiles with clinical characteristics to identify lncRNA signatures in clinically-relevant co-expression lncRNA-mRNA networks residing in pertinent cancer pathways. Notably one network, associated with poorer prognosis, comprises five up-regulated lncRNAs significantly correlated (|Pearson Correlation Coefficient|≥ 0.9) with 91 up-regulated genes in the cell-cycle and Rho-GTPase pathways. All 5 lncRNAs and 85/91 (93.4%) of the correlated genes were significantly associated with higher tumor-grade while 3/5 lncRNAs were also associated with no tumor capsule. Interestingly, 2/5 lncRNAs that are correlated with numerous genes in this oncogenic network were experimentally shown to up-regulate genes involved in cell-cycle and transcriptional regulation. Another network comprising 4 down-regulated lncRNAs and 8 down-regulated metallothionein-family genes are significantly associated with tumor invasion. The identification of these key lncRNAs signatures that deregulate important network of genes in key cancer pathways associated with pertinent clinical phenotype may facilitate the design of novel therapeutic strategies targeting these 'master' regulators for better patient outcome.