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Background: Sandhoff disease (SD) is an autosomal recessive lysosomal storage disorder, resulting in accumulation of GM2 ganglioside, particular in neuronal cells. The disorder is caused by deficiency of β-hexosaminidase B (HEX-B), due to pathogenic variant of human HEXB gene., Method: This study describes clinical features, biochemical, and genetic defects among Thai patients with infantile SD during 2008-2019., Results: Five unrelated Thai patients presenting with developmental regression, axial hypotonia, seizures, exaggerated startle response to noise, and macular cherry red spot were confirmed to have infantile SD based on deficient HEX enzyme activities and biallelic variants of the HEXB gene. In addition, an uncommon presenting feature, cardiac defect, was observed in one patient. All the patients died in their early childhood. Plasma total HEX and HEX-B activities were severely deficient. Sequencing analysis of HEXB gene identified two variants including c.1652G>A (p.Cys551Tyr) and a novel variant of c.761T>C (p.Leu254Ser), in 90 and 10% of the mutant alleles found, respectively. The results from in silico analysis using multiple bioinformatics tools were in agreement that the p.Cys551Tyr and the p.Leu254Ser are likely pathogenic variants. Molecular modelling suggested that the Cys551Tyr disrupt disulfide bond, leading to protein destabilization while the Leu254Ser resulted in change of secondary structure from helix to coil and disturbing conformation of the active site of the enzyme. Genome-wide SNP array analysis showed no significant relatedness between the five affected individuals. These two variants were not present in control individuals. The prevalence of infantile SD in Thai population is estimated 1 in 1,458,521 and carrier frequency at 1 in 604., Conclusion: The study suggests that SD likely represents the most common subtype of rare infantile GM2 gangliosidosis identified among Thai patients. We firstly described a potential common variant in HEXB in Thai patients with infantile onset SD. The data can aid a rapid molecular confirmation of infantile SD starting with the hotspot variant and the use of expanded carrier testing.

We investigate the possible replication of "known" associated single-nucleotide polymorphisms (SNPs) with blood pressure and expression phenotypes. Previous studies have provided a list of 95 SNPs thought to be associated with blood pressure phenotypes, of which 44 were present in the Genetic Analysis Workshop 19 (GAW19) family-imputed genome-wide association studies (GWAS) data and 4 in the GAW19 unrelateds sequence data. Using only the real (not simulated) GAW19 data, we show through the use of statistical tests that account for family relatedness, using FaST-LMM (Factored Spectrally Transformed Linear Mixed Model), that none of our candidate SNPs yields a significant p value. Furthermore, a study of epistasis, aiming to detect statistical interactions between loci with respect to their association with transcription levels has provided a list of 30 associated interacting SNP pairs, of which 13 are present in the GAW19 family GWAS and expression data. We show for this set of results, using the program GEMMA (genome-wide efficient mixed-model analysis) to account for family relatedness, that there is evidence of replication within the real GAW19 data. Two individual SNP pairs reach significance, and the set of remaining results give a combined p value of 0.017 that at least 1 of these remaining SNP pairs interacts to influence an expression phenotype.

In the last few years, a bewildering variety of methods/software packages that use linear mixed models to account for sample relatedness on the basis of genome-wide genomic information have been proposed. We compared these approaches as implemented in the programs EMMAX, FaST-LMM, Gemma, and GenABEL (FASTA/GRAMMAR-Gamma) on the Genetic Analysis Workshop 18 data. All methods performed quite similarly and were successful in reducing the genomic control inflation factor to reasonable levels, particularly when the mean values of the observations were used, although more variation was observed when data from each time point were used individually. From a practical point of view, we conclude that it makes little difference to the results which method/software package is used, and the user can make the choice of package on the basis of personal taste or computational speed/convenience.