Best practices for Genomic analysis
Nowadays, rare genetic variants begin to be discovered more and more often. And still no clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are available. Without accurate standards an acceleration of false-positive reports of causality is at a high probability, therefore obstructing the translation of genomic research findings into clinical diagnostics setting and hinder biological understanding of disease. So what are the best practices for genomic analysis?
In the paper Guidelines for investigating causality of sequence variants in human disease D. G. MacArthur et al discuss the primary challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality and introduce guidelines for summarizing in variant pathogenicity and highlight several areas that require further resource development.
For us the most interesting part of the paper is the emphasis on the value of sharing sequence and phenotype data from clinical and research samples to the fullest possible extent. D. G. MacArthur team recognises that many investigators and research funders look at data sharing as a moral and professional imperative, nevertheless, sharing of sequence data among testing laboratories has often been blocked, so that many potentially pathogenic mutations and associated phenotypes are only known to individual laboratories.
In this paper, they urge, whenever possible, that investigators assess the results of genetic, informatic and functional analyses within a quantitative statistical framework, such as determining the probability of the observed distribution of genetic variants in cases and controls under the null hypothesis, and the a priori power to detect variants of a specified frequency and effect size. The specificity of experimental or informatic results provided in support of implication should also be assessed whenever possible by asking how often a similar result would be obtained by chance among a set of random variants or genes. However, we all agree that investigators should definitely take advantage of the steadily increasing availability of genome-scale sequencing and functional data, and help to build these resources by contributing their findings to public databases.
If you would like to read the guidelines D. G. MacArthur et al propose at the Guidelines for investigating causality of sequence variants in human disease follow this link and enjoy the paper.