Copy number variation analysis using next-generation sequencing identifies the CFHR3/CFHR1 deletion in atypical hemolytic uremic syndrome: a case report

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. Combined, we provide a database of genes impacted by build choice, and recommend that transcriptomics-guided analyses and diagnoses are cross-referenced with these data for robustness.

Disease profiles in the Indigenous Australian population are suggestive of a common complement control haplotype

Aboriginal and Torres Strait Islander People (respectfully referred to as Indigenous Australians herein) are disparately burdened by many infectious and chronic diseases relative to Australians with European genetic ancestry. Some of these diseases are described in other populations to be influenced by the inherited profile of complement genes. These include complement factor B, H, I and complement factor H-related (CFHR) genes that can contribute to a polygenic complotype. Here the focus is on the combined deletion of CFHR1 and 3 to form a common haplotype (CFHR3-1Δ). The prevalence of CFHR3-1Δ is high in people with Nigerian and African American genetic ancestry and correlates to a higher frequency and severity of systemic lupus erythematosus (SLE) but a lower prevalence of age-related macular degeneration (AMD) and IgA-nephropathy (IgAN). This pattern of disease is similarly observed among Indigenous Australian communities. Additionally, the CFHR3-1Δ complotype is also associated with increased susceptibility to infection with pathogens, such as Neisseria meningitidis and Streptococcus pyogenes, which also have high incidences in Indigenous Australian communities. The prevalence of these diseases, while likely influenced by social, political, environmental and biological factors, including variants in other components of the complement system, may also be suggestive of the CFHR3-1Δ haplotype in Indigenous Australians. These data highlight a need to define the Indigenous Australian complotypes, which may lead to the discovery of new risk factors for common diseases and progress towards precision medicines for treating complement-associated diseases in Indigenous and non-Indigenous populations. Herein, the disease profiles suggestive of a common complement CFHR3-1Δ control haplotype are examined.