Background Countries with culturally accepted consanguinity provide a unique resource for the study of rare recessively inherited genetic diseases. Although hereditary hearing loss (HHL) is not uncommon, it is genetically heterogeneous, with over 85 genes causally implicated in non-syndromic hearing loss (NSHL). This heterogeneity makes many gene-specific types of NSHL exceedingly rare. We sought to define the spectrum of autosomal recessive HHL in Iran by investigating both common and rarely diagnosed deafness-causing genes. Design Using a custom targeted genomic enrichment (TGE) panel we simultaneously interrogating all known genetic causes of NSHL in a cohort of 302 GJB2-negative Iranian families. Results We established a genetic diagnosis for 67% of probands and their families, with over half of all diagnoses attributable to variants in five genes: SLC26A4, MYO15A, MYO7A, CDH23, and PCDH15. As a reflection of the power of consanguinity mapping, 26 genes were identified as causative for NSHL in the Iranian population for the first time. In total, 179 deafness-causing variants were identified in 40 genes in 201 probands, including 110 novel single nucleotide or small insertion-deletion variants and 3 novel copy number variations. Several variants represent founder mutations. Conclusion This study attests to the power of TGE and massively parallel sequencing (TGE+MPS) as a diagnostic tool for the evaluation of hearing loss in Iran, and expands on our understanding of the genetics of HHL in this country. Families negative for variants in the genes represented on this panel represent an excellent cohort for novel gene discovery.

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Transcriptome-wide association studies (TWAS) can prioritize trait-associated genes by finding correlations between a trait and the genetically regulated component of gene expression. A basic ingredient of a TWAS is a regression model, typically trained in an external reference data set, used to impute the genetically-regulated expression. We devised a model that improves the accuracy of the imputation by using, as predictors, not the genotypes directly but rather the sequence composition of the proximal gene regulatory region, expressed as its profile of affinities for a set of position weight matrices. When trained on 48 tissues from GTEx, the regression model showed improved performance compared with models regressing expression directly on the genotype. We imputed the expression levels in genotyped individuals from the ADNI data set, and used the imputed expression to perform a TWAS. We also developed a method to perform the TWAS based on summary statistics from genome-wide association studies, and applied it to 11 complex traits from the UK Biobank. The greater accuracy in the prediction of gene expression allowed us to report hundreds of new gene-phenotype association candidates.