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Mutation detection
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Platform selection
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Different sequencing platforms have variable error rates
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Increased sequencing coverage for platforms with high error rates
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Sequencing target selection
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Exome sequencing may miss regulatory variants that are disease causal
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Use whole genome sequencing when budget is not a concern, or when diseases other than well-studied classical Mendelian diseases are encountered
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Variant generation
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Genotype calling algorithms differ from each other and have specific limitations
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Use multiple alignment and variant calling algorithms and look for concordant calls. Use local assembly to improve indel calls
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Variant annotation
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Multiple gene models and multiple function prediction algorithms are available
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Perform comprehensive set of annotations and make informed decisions; use probabilistic model for ranking genes/variants
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Variant validation
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Predicted disease causal mutations may be false positives
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Secondary validation by Sanger sequencing or capture-based sequencing on specific genes/regions
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Type of mutations
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Coding and splice variants
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Many prediction algorithms are available
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Evaluate all prediction algorithms under different settings. Develop consensus approaches for combining evidence from multiple algorithms
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Untranslated region, synonymous and non-coding variants
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Little information on known causal variants in databases such as HGMD
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Improved bioinformatics predictions using multiple sources of information (ENCODE data, multispecies conservation, RNA structure, and so on)
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Specific application areas
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Somatic mutations in cancer
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Tissues selected for sequencing may not harbor large fractions of cells with causal mutations due to heterogeneity; variant calling is complicated by stromal contamination; current databases on allele frequencies do not apply to somatic mutations; current function prediction algorithms focus on loss-of-function mutations
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Sample several tissue locations for sequencing; utilize algorithms specifically designed for tumor with consideration for heterogeneity; use somatic mutation databases such as COSMIC; develop function prediction algorithms specifically for gain-of-function mutations in cancer-related genes/pathways
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Non-invasive fetal sequencing
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Variants from fetal and maternal genomes need to be teased apart; severe consequences when variants are incorrectly detected and predicted to be highly pathogenic
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Much increased sequence depth and more sophisticated statistical approaches that best leverage prior information for inferring fetal alleles; far more stringent criteria to predict pathogenic variants
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Inheritance pattern
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Inherited from affected parents
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Rare/private mutations may be neutral
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Evaluate extended pedigrees and 'clans' to assess the potential role of private variants
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De novo mutations from unaffected parents
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Every individual is expected to carry three de novo mutations, including about one amino acid altering mutation per newborn
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Detailed functional analysis of the impacted genes
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Biological validation
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Known disease causal genes
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Difficult to conclude causality when a mutation is found in a well-known disease causal gene
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Examine public databases such as locus-specific databases
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Previously characterized genes not known to cause the disease of interest
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Relate known molecular function to phenotype of interest
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Evaluate loss of function by biochemical assays where available
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Genes without known function
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Difficult to design functional follow-up assays
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Evaluate gene expression data. Use model organisms to recapitulate the phenotype of interest
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Statistical validation
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Rare diseases
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Limited power to declare association
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Sequence candidate genes in unrelated patients to identify additional causal variants
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Idiopathic diseases
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Lack of additional unrelated patients
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Comprehensive functional follow-up of the biospecimens from patients to prove causality
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Mendelian diseases or traits
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Finding rare, unrelated individuals with same phenotype and same mutation to help prove causality
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Networking of science through online databases can help find similarly affected people with same phenotype and mutation
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Type of phenotypes
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Mendelian forms of complex diseases or traits
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Several major-effect mutations may work together to cause disease
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Statistical models of combined effects (additive and epistatic) of multiple variants within each individual
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Complex diseases or traits
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Many variants may contribute to disease risk, each with small effect sizes
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Refrain from making predictions unless prior evidence suggested that such predictive models are of practical utility (for example, receiver operating characteristic >0.8)
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