To test how this bioinformatic pipeline performs, we also mapped male and female reads against autosomal transcripts, and categorized variants in the same manner as for the neo-sex linked genes.
In the second, we categorize variants selectively into two subsets: one in which variants are overrepresented in cases, and the other in which variants are overrepresented in controls.
We generated a filter to categorize variants into three groups: likely pathogenic, benign and unknown significance (see Fig. 1).
In humans, several large scale projects have been carried out to categorize variants of the human genome, such as the 1000 genomes project (4).
This resulted in categorizing variants into five classes: pathogenic, likely pathogenic, unknown significance (VUS), likely benign, and benign (Table 1; Richards et al. 2015).
RegulomeDB categorizes variants into a seven-level functional score based on a synthesis of regulatory data derived from ENCODE and other sources.
Here, we review and assess the limitations of computational techniques for categorizing variants according to functional classes, prioritizing variants for experimental follow-up and generating hypotheses about the possible molecular mechanisms to inform downstream experiments.
RUNES categorized each variant according to ACMG recommendations for reporting sequence variation [ 18– 21] and with an allele frequency (MAF) derived from CPGM's Variant Warehouse database of approximately 90 million variants and 3,900 individuals [ 6, 11, 12].
RUNES categorizes each variant according to American College of Medical Genetics and Genomics recommendations for reporting sequence variation [ 7, 8] as well as an allele frequency derived from the Children's Mercy Hospital Center for Pediatric Genomic Medicine Variant Warehouse database [ 23].
Biopsy specimens categorized as the variant form were from patients who were younger and had greater phosphorus and parathyroid hormone levels.
