Genetic characterization of Spinocerebellar ataxia 1 in a South Indian cohort

Identification of Genetic Risk Factors of Severe COVID-19 Using Extensive Phenotypic Data: A Proof-of-Concept Study in a Cohort of Russian Patients

The COVID-19 pandemic has drawn the attention of many researchers to the interaction between pathogen and host genomes. Over the last two years, numerous studies have been conducted to identify the genetic risk factors that predict COVID-19 severity and outcome. However, such an analysis might be complicated in cohorts of limited size and/or in case of limited breadth of genome coverage. In this work, we tried to circumvent these challenges by searching for candidate genes and genetic variants associated with a variety of quantitative and binary traits in a cohort of 840 COVID-19 patients from Russia. While we found no gene- or pathway-level associations with the disease severity and outcome, we discovered eleven independent candidate loci associated with quantitative traits in COVID-19 patients. Out of these, the most significant associations correspond to rs1651553 in MYH14p = 1.4 × 10^-7), rs11243705 in SETX (p = 8.2 × 10^-6), and rs16885 in ATXN1 (p = 1.3 × 10^-5). One of the identified variants, rs33985936 in SCN11A, was successfully replicated in an independent study, and three of the variants were found to be associated with blood-related quantitative traits according to the UK Biobank data (rs33985936 in SCN11A, rs16885 in ATXN1, and rs4747194 in CDH23). Moreover, we show that a risk score based on these variants can predict the severity and outcome of hospitalization in our cohort of patients. Given these findings, we believe that our work may serve as proof-of-concept study demonstrating the utility of quantitative traits and extensive phenotyping for identification of genetic risk factors of severe COVID-19.

Genetics of Ataxias in Indian Population: A Collative Insight from a Common Genetic Screening Tool

Cerebellar ataxias (CAs) represent a group of autosomal dominant and recessive neurodegenerative disorders affecting cerebellum with or without spinal cord. Overall, CAs have preponderance for tandem nucleotide repeat expansions as an etiological factor (10 TREs explain nearly 30-40% of ataxia cohort globally). The experience of 10 years of common genetic ataxia subtypes for ≈5600 patients' referrals (Pan-India) received at a single center is shared herein. Frequencies (in %, n) of SCA types and FRDA in the sample cohort are observed as follows: SCA12 (8.6%, 490); SCA2 (8.5%, 482); SCA1 (4.8%, 272); SCA3 (2%, 113); SCA7 (0.5%, 28); SCA6 (0.1%, 05); SCA17 (0.1%, 05), and FRDA (2.2%, 127). A significant amount of variability in TRE lengths at each locus is observed, we noted presence of biallelic expansion, co-occurrence of SCA-subtypes, and the presence of premutable normal alleles. The frequency of mutated GAA-FRDA allele in healthy controls is 1/158 (0.63%), thus an expected FRDA prevalence of 1:100 000 persons. The data of this study are relevant not only for clinical decision making but also for guidance in direction of genetic investigations, transancestral comparison of genotypes, and lastly provide insight for policy decision for the consideration of SCAs under rare disease category.

Macular Morpho-Functional and Visual Pathways Functional Assessment in Patients with Spinocerebellar Type 1 Ataxia with or without Neurological Signs

Spinocerebellar ataxia type 1 (SCA-ATXN1) is an autosomal dominant, neurodegenerative disease, caused by CAG repeat expansion in the ataxin-1 gene (ATXN1). In isolated reports of patients with neurological signs [symptomatic patients (SP)], macular abnormalities have been described. However, no reports exist about macular anomalies in SCA1 subjects carrying the ATXN1 mutation without neurological signs [not symptomatic carriers (NSC)]. Therefore, the main aim of our work was to evaluate whether the macular functional and morphological abnormalities could be detectable in SP, genetically confirmed and with neurological signs, as well as in SCA-ATXN1-NSC, harboring pathogenic CAG expansion in ATXN1. In addition, we investigated whether the macular involvement could be associated or not to an impairment of RGCs and of their fibers and of the neural conduction along the visual pathways. Herein, nine SCA-ATXN1 subjects (6 SP and 3 NSC) underwent the following examinations: visual acuity and chromatic test assessments, fundus oculi (FO) examination, macular and peripapillary retinal nerve fiber layer thickness (RNFL-T) analysis by Spectral domain-Optical Coherence Tomography (Sd-OCT) acquisition, multifocal electroretinogram (mfERG), pattern reversal electroretinogram (PERG) and visual evoked potentials (VEP) recordings. In four eyes of two SP, visual acuity reduction and chromatic abnormalities were observed; in three of them FO changes associated with macular thinning and outer retinal defects were also detected. In three NSC eyes, slight FO abnormalities were associated with qualitative macular morphological changes. By contrast, abnormal mfERG responses (exclusively from foveal and parafoveal areas) were detected in all SP and NSC (18 eyes). No abnormalities of PERG values, RNFL-T, and VEP responses were found, but in one SP, presenting abnormal papillo-macular bundle neural conduction. Results from our SCA-ATXN1 cohort suggest that a macular dysfunction, detectable by mfERG recordings, may occur in the overt disorder, and unexpectedly in the stage of the disease in which there is still an absence of neurological signs. In NSC, an exclusive dysfunction of preganglionic macular elements can be observed, and this is associated with both normal RGCs function and neural conduction along the visual pathways.

Molecular spectrum, family screening and genetic counselling of Spinocerebellar Ataxia (SCA) cases in an Indian scenario

Spinocerebellar Ataxia (SCA) is a heterogeneous adult-onset disorder with an autosomal dominant inheritance pattern mainly caused by triplet repeat expansions. Clinical diagnosis of SCA is based on phenotypic features followed by confirmation through molecular diagnosis. To identify status of repeat range in Indian SCA cases and provide extended family screening, we enrolled 70 clinical SCA suspects. For molecular diagnosis, multiplex PCR (M-PCR) was used for common Indian SCA subtypes 1, 2, 3, 6, 7, 10, 12 and 17. TP-PCR was further used in SCA2, 7 and 10 to identify larger expansions. Eighteen out of 70 SCA suspects (25%) were found to be positive for various SCA subtypes- (5 SCA1 (28%), 6 SAC2 (34%), 2 SCA3 (12%), 3 SCA7 (16%) and one each for SCA6 (1%) and SCA17 (1%) subtypes). Genetic counselling and extended family screening were offered to all positive cases and yielded additional nine cases. We have established M-PCR and TP-PCR to detect the CAG repeat expansion in SCA suspects. This method can confirm SCA subtypes in a reliable, rapid and cost-effective way. Genetic characterization of SCA-related genes has great clinical relevance, as it could provide additional information and guidance to clinicians and family members regarding prognosis.

Systems genetics analysis of pharmacogenomics variation during antidepressant treatment

Selective serotonin reuptake inhibitors (SSRIs) are the most widely used antidepressants, but the efficacy of the treatment varies significantly among individuals. It is believed that complex genetic mechanisms play a part in this variation. We have used a network based approach to unravel the involved genetic components. Moreover, we investigated the potential difference in the genetic interaction networks underlying SSRI treatment response over time. We found four hub genes (ASCC3, PPARGC1B, SCHIP1 and TMTC2) with different connectivity in the initial SSRI treatment period (baseline to week 4) compared with the subsequent period (4-8 weeks after initiation), suggesting that different genetic networks are important at different times during SSRI treatment. The strongest interactions in the initial SSRI treatment period involved genes encoding transcriptional factors, and in the subsequent period genes involved in calcium homeostasis. In conclusion, we suggest a difference in genetic interaction networks between initial and subsequent SSRI response.