Addition of multiple rare SNPs to known common variants improves the association between disease and gene in the Genetic Analysis Workshop 17 data

Towards efficiency in rare disease research: what is distinctive and important?

Characterized by their low prevalence, rare diseases are often chronically debilitating or life threatening. Despite their low prevalence, the aggregate number of individuals suffering from a rare disease is estimated to be nearly 400 million worldwide. Over the past decades, efforts from researchers, clinicians, and pharmaceutical industries have been focused on both the diagnosis and therapy of rare diseases. However, because of the lack of data and medical records for individual rare diseases and the high cost of orphan drug development, only limited progress has been achieved. In recent years, the rapid development of next-generation sequencing (NGS)-based technologies, as well as the popularity of precision medicine has facilitated a better understanding of rare diseases and their molecular etiology. As a result, molecular subclassification can be identified within each disease more clearly, significantly improving diagnostic accuracy. However, providing appropriate care for patients with rare diseases is still an enormous challenge. In this review, we provide a brief introduction to the challenges of rare disease research and make suggestions on where and how our efforts should be focused.

Single-nucleotide polymorphisms in the vascular endothelial growth factor pathway and outcomes of patients treated with first-line cytotoxic chemotherapy combined with bevacizumab for advanced colorectal cancer

OBJECTIVE

The aim of this study was to evaluate the association between the efficacy of first-line cytotoxic chemotherapy plus bevacizumab and single-nucleotide polymorphisms (SNPs) of angiogenic genes in patients with advanced colorectal cancer (CRC).

METHODS

DNA was extracted from blood samples of 125 patients, and 12 SNPs were evaluated for association with the objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).

RESULTS

The vascular endothelial growth factor A (VEGFA) rs833061 T/T was associated with superior ORR compared to its alternative genotypes (75.9 vs. 50.8%; p = 0.008), and the interleukin 8 rs4073 A/A genotype tended to be associated with poor ORR (45.0 vs. 66.0%; p = 0.067). The median PFS and OS were superior in patients with the fms-related tyrosine kinase 1 (FLT1) rs9513070 A/A genotype (8.7 vs. 6.6 months; p = 0.001 and 26.4 vs. 16.1 months; p = 0.038, respectively). The kinase insert domain receptor rs1531289 G/G genotype tended to be associated with improved PFS (8.0 vs. 7.1 months; p = 0.069). In haplotype analysis, the FLT1 rs9513070/rs9554320/rs9582036 GCA haplotype was associated with inferior PFS and OS (p = 0.004 and p = 0.041, respectively).

CONCLUSION

The VEGFA rs833061 SNP is associated with the ORR, and the FLT1 rs9513070 SNP and FLT1 GCA haplotypes are associated with PFS and OS in advanced CRC patients treated with cytotoxic chemotherapy plus bevacizumab.

Detecting rare variant associations: methods for testing haplotypes and multiallelic genotypes

We summarize the work done by the contributors to Group 13 at Genetic Analysis Workshop 17 (GAW17) and provide a synthesis of their data analyses. The Group 13 contributors used a variety of approaches to test associations of both rare variants and common single-nucleotide polymorphisms (SNPs) with the GAW17 simulated traits, implementing analytic methods that incorporate multiallelic genotypes and haplotypes. In addition to using a wide variety of statistical methods and approaches, the contributors exhibited a remarkable amount of flexibility and creativity in coding the variants and their genes and in evaluating their proposed approaches and methods. We describe and contrast their methods along three dimensions: (1) selection and coding of genetic entities for analysis, (2) method of analysis, and (3) evaluation of the results. The contributors consistently presented a strong rationale for using multiallelic analytic approaches. They indicated that power was likely to be increased by capturing the signals of multiple markers within genetic entities defined by sliding windows, haplotypes, genes, functional pathways, and the entire set of SNPs and rare variants taken in aggregate. Despite this variability, the methods were fairly consistent in their ability to identify two associated genes for each simulated trait. The first gene was selected for the largest number of causal alleles and the second for a high-frequency causal SNP. The presumed model of inheritance and choice of genetic entities are likely to have a strong effect on the outcomes of the analyses.