LMO1 polymorphisms and the risk of neuroblastoma: Assessment of meta-analysis of case-control studies.

Neuroblastoma (NB), a neuroendocrine tumour, is one of the most prevalent cancers in children. The link between LMO1 polymorphisms and NB has been investigated by several groups, rendering inconclusive results. Here, with this comprehensive systematic review and up-to-date meta-analysis, we aim to distinctively elucidate the possible correlation between LMO1 polymorphisms and NB susceptibility. Eligible studies were systematically researched and identified using PubMed, Web of Science and Scopus databases up to 10 February 2019. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the strength of the associations. Our findings revealed that rs110419 and rs2168101 polymorphisms were significantly associated with a decreased risk of NB in all genetic models. In addition, the rs4758051 variant appeared protective against NB in homozygous, dominant and allele genetic models, whereas the rs10840002 variant markedly decreased the risk of NB in the allele model. In contrast, the rs204938 polymorphism showed a positive association with NB susceptibility in allele genetic models. In summary, our meta-analysis is the first to provide clear evidence of an association between specific polymorphisms of LMO1 and susceptibility to NB. Of note, additional larger well-designed studies would be helpful to further evaluate and confirm this association.

A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays.

BACKGROUND: Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development. RESULTS: We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events. Based on a set of scores, an exon score and an array score, a decision regarding alternative splicing can be made. The model enables the ability to distinguish a differential expressed gene from a differential spliced exon. The proposed model was applied to three case studies concerning both exon and HTA arrays. CONCLUSION: The REIDS model provides a work flow for the identification of alternative splicing events relying on the established linear mixed model. The model can be applied to different types of arrays.

Targeted analysis of whole genome sequence data to diagnose genetic cardiomyopathy.

BACKGROUND: Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of >50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated $1000 genome, it is expected that genetic testing will shift toward comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. METHODS AND RESULTS: Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused on 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1 to 14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and segregation analysis, where available. Three of 3 previously identified primary mutations were detected by this analysis. In 6 subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and had additional pathological correlation to provide evidence for causality. For 2 subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. CONCLUSIONS: These pilot data demonstrate that ≈30 to 40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.

Variants associated with type 2 diabetes identified by the transethnic meta-analysis study: assessment in American Indians and evidence for a new signal in LPP.

AIM/HYPOTHESIS: A recent genome-wide trans-ancestry meta-analysis identified seven new loci associated with type 2 diabetes. We assessed the replication of the seven lead single nucleotide polymorphisms (SNPs) and evaluated these loci for additional signals in American Indians. METHODS: Seven SNPs were genotyped in 7,710 individuals from a longitudinally studied American Indian population, and associations with type 2 diabetes, BMI and related phenotypes were assessed. Previous genome-wide association study (GWAS) data from these individuals were used to screen for additional type 2 diabetes signals at these loci. A variant independent of the trans-ancestry meta-analysis was identified within LPP, and its replication was assessed in an additional 3,106 urban American Indians. RESULTS: SNP rs6813195 near to TMEM154 was nominally associated with type 2 diabetes (p = 0.01, OR 1.12 [95% CI 1.03, 1.22]) and adiposity: the type 2 diabetes risk allele was associated with a lower percentage body fat (ß = -1.451%, p = 4.8 × 10(-4)). Another SNP, rs3130501 near to POU5F1-TCF19, was associated with BMI (ß = -0.012, p = 0.004), type 2 diabetes adjusted for BMI (p = 0.02, OR 1.11 [95% CI 1.02, 1.22]), 2 h glucose concentrations (ß = 0.080 mmol/l, p = 0.02) and insulin resistance estimated by homeostatic model (ß = 0.039, p = 0.009). The independent variant identified at the LPP locus in our American Indian GWAS for type 2 diabetes was replicated in the additional samples (all American Indian meta-analysis, p = 8.9 × 10(-6), OR 1.29 [95% CI 1.15, 1.45]). CONCLUSIONS/INTERPRETATION: For two of the seven newly identified variants, there was nominal evidence for association with type 2 diabetes and related traits in American Indians. Identification of an independent variant at the LPP locus suggests the existence of more than one type 2 diabetes signal at this locus.