The statistical analysis of the large quantity of data generated from sequencing approaches is a challenge. Even by only sequencing the exomes of individuals, a large quantity of data and sequence information is generated which requires a significant amount of data analysis. Challenges associated with the analysis of this data include changes in programs used to align and assemble sequence reads. Various sequencing technologies also have different error rates and generate various read-lengths which can pose challenges in comparing results from different sequencing platforms.
False positive and false negative findings are associated with genomic resequencing approaches and are critical issues. A few strategies have been developed to improve the quality of exome data such as:
* Comparing the genetic variants identified between sequencing and array-based genotyping
* Comparing the coding SNPs to a whole genome sequenced individual with the disorder
* Comparing the coding SNPs with Sanger sequencing of HapMap individuals
Rare recessive disorders may not have single nucleotide polymorphisms (SNPs) in public databases such as dbSNP. More common recessive phenotypes would be more likely to have disease-causing variants reported in dbSNP. For example, the most common cystic fibrosis variant has an allele frequency of about 3% in most populations. Screening out such variants might erroneously exclude such genes from consideration. Genes for recessive disorders are usually easier to identify than dominant disorders because the genes are less likely to have more than one rare nonsynonymous variant. The system that screens common genetic variants relies on dbSNP which may not have accurate information about the variation of alleles. Using lists of common variation from a study exome or genome-wide sequenced individual would be more reliable. A challenge in this approach is that as the number of exomes sequenced increases, dbSNP will also increase in the number of uncommon variants. It will be necessary to develop thresholds to define the common variants that are unlikely to be associated with a disease phenotype.
Genetic heterogeneity and population ethnicity are also major limitations as they may increase the number of false positive and false negative findings which will make the identification of candidate genes more difficult. Of course, it is possible to reduce the stringency of the thresholds in the presence of heterogeneity and ethnicity, however this will reduce the power to detect variants as well. Using a genotype-first approach to identify candidate genes might also offer a solution to overcome these limitations.
Unlike common variant analysis, the analysis of rare variants in whole-exome sequencing studies evaluates variant sets rather than single variants. Functional annotations predict the effect or function of rare variants and help prioritize rare functional variants. Incorporating these annotations can effectively boost the power of genetic association of rare variants analysis of whole genome sequencing studies. Some methods and tools have been developed to perform functionally-informed rare variant association analysis by incorporating functional annotations to empower analysis in whole exome sequencing studies.