Systems and methods for genomic annotation and distributed variant interpretation

Abstract

A computer-based genomic annotation system, including a database configured to store genomic data, non-transitory memory configured to store instructions, and at least one processor coupled with the memory, the processor configured to implement the instructions in order to implement an annotation pipeline and at least one module filtering or analysis of the genomic data.

1 .- 93 . (canceled) 
     
     
         94 . A computer-based method for predicting a risk of an individual developing a disease, the method comprising the steps of:
 obtaining genetic variant data describing a plurality of genetic variants in a genome of the individual, the genome comprising a plurality of genes;   determining a percent functionality for each gene based on the genetic variant data;   generating a weighted genetic network comprising the plurality of genes of the genome having weighted connections there between;   obtaining a global centrality score for each of the plurality of genes in the weighted genetic network;   generating a weighted genetic disease network comprising a plurality of genes having weighted connections therebetween;   assigning a high importance score in the weighted genetic disease network for at least one gene that is associated with the disease;   obtaining a disease-specific centrality score for each of the plurality of genes in the weighted genetic disease network;   for each of the plurality of genes, determining, a difference between the global centrality score for the gene for each of the respective genes and the disease-specific centrality score for the gene for each of the respective genes and multiplying the difference by the percent functionality for the gene for each of the respective genes to produce a product for each gene;   generating a disease score for the individual based on a sum of the products of the plurality of genes; and   generating a risk of developing a disease for the individual based at least in part on the disease score.   
     
     
         95 . The method of  claim 94 , wherein obtaining a global centrality score for each of the plurality of genes in the weighted genetic network further comprises using a pagerank algorithm, a heat diffusion algorithm, or a degree centrality calculation to obtain a global centrality score for each of the plurality of genes in the weighted genetic network. 
     
     
         96 . The method of  claim 94 , wherein obtaining a disease-specific centrality score for each of the plurality of genes in the weighted genetic disease network comprises using a pagerank algorithm, a heat diffusion algorithm, or a degree centrality calculation to obtain a disease specific centrality score for each of the plurality of genes in the weighted genetic disease network. 
     
     
         97 . The method of  claim 94 , wherein determining a percent functionality for each gene based on the genetic variant data comprises:
 annotating the genetic variant data comprising determining at least one gene of the genome with which each variant is associated, and   determining a weighted score for each gene of the genome indicating a combined impact of the genetic variants on the gene, wherein the weighted score for each gene is used to determine a percent functionality for each gene.   
     
     
         98 . The method of  claim 94 , wherein generating a weighted genetic network comprises determining an importance of each gene within the network based on at least one of a number of connections each gene makes with other genes in the network and an importance of each of the other genes in the network. 
     
     
         99 . The method of  claim 94 , wherein obtaining genetic variant data describing a plurality of genetic variants in a genome of the individual comprises generating at least one gene-based annotation. 
     
     
         100 . The method of  claim 99 , wherein the gene-based annotations comprise at least one of the following levels of annotation information: genomic elements, prediction of impact information, linking element information, and prior knowledge. 
     
     
         101 . The method of  claim 100 , wherein the genomic elements comprise at least one of known genes, protein domains, transcription factor binding sites, conserved elements, miRNA, binding sites, splice sites, splicing enhancers, splicing silencers, common SNPs, UTR regulatory motifs, post translational modification sites, and custom elements. 
     
     
         102 . The method of  claim 100 , wherein the prediction of impact information comprises at least one of coding impact, nonsynonymous impact prediction, protein domain impact prediction, motif based impact scores, nucleotide conservation, targetScan, splicing changes, binding energy, and codon abundance. 
     
     
         103 . The method of  claim 100 , wherein the linking elements information comprises at least one of phase information, molecular information, biological information, protein-protein interactions, co-expression, and genomic context. 
     
     
         104 . The method of  claim 100 , wherein the prior knowledge comprises at least one of phenotype associations, biological processes, molecular function, drug metabolism, GWAS catalog, allele frequency, eQTL frequency, and text mining information.