title: Incorporating Non-Coding Annotations into Rare Variant Analysis.
abstract: The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches.
  
  
  
  We conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with 'nonsynonymous' and 'loss of function' consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa.
  
  
  
  Incorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease.
authors.affiliation: MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom.
authors.forename: Tom G
authors.initials: TG
authors.lastname: Richardson
authors.affiliation: Intelligent Systems Laboratory, University of Bristol, Bristol, United Kingdom.
authors.forename: Colin
authors.initials: C
authors.lastname: Campbell
authors.affiliation: MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Timpson
authors.affiliation: MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom.
authors.forename: Tom R
authors.initials: TR
authors.lastname: Gaunt
mesh_terms: D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods; D005787:Gene Frequency; D030342:Genetic Diseases, Inborn / Q000175:diagnosis / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D014644:Genetic Variation; D015894:Genome, Human / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D006113:United Kingdom