Exome Sequencing: Genome Sequencing Focusing on Exonic Regions

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

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Lessons learnt from multifaceted diagnostic approaches to the first 150 families in Victoria’s Undiagnosed Diseases Program

Journal of Medical Genetics ◽

10.1136/jmedgenet-2021-107902 ◽

2021 ◽

pp. jmedgenet-2021-107902

Author(s):

Thomas Cloney ◽

Lyndon Gallacher ◽

Lynn S Pais ◽

Natalie B Tan ◽

Alison Yeung ◽

...

Keyword(s):

High Resolution ◽

Exome Sequencing ◽

Genome Sequencing ◽

Rare Diseases ◽

Strategic Decision ◽

Clinical Genetics ◽

Rna Seq ◽

Lessons Learnt ◽

Family Based ◽

Undiagnosed Diseases

BackgroundClinical exome sequencing typically achieves diagnostic yields of 30%–57.5% in individuals with monogenic rare diseases. Undiagnosed diseases programmes implement strategies to improve diagnostic outcomes for these individuals.AimWe share the lessons learnt from the first 3 years of the Undiagnosed Diseases Program-Victoria, an Australian programme embedded within a clinical genetics service in the state of Victoria with a focus on paediatric rare diseases.MethodsWe enrolled families who remained without a diagnosis after clinical genomic (panel, exome or genome) sequencing between 2016 and 2018. We used family-based exome sequencing (family ES), family-based genome sequencing (family GS), RNA sequencing (RNA-seq) and high-resolution chromosomal microarray (CMA) with research-based analysis.ResultsIn 150 families, we achieved a diagnosis or strong candidate in 64 (42.7%) (37 in known genes with a consistent phenotype, 3 in known genes with a novel phenotype and 24 in novel disease genes). Fifty-four diagnoses or strong candidates were made by family ES, six by family GS with RNA-seq, two by high-resolution CMA and two by data reanalysis.ConclusionWe share our lessons learnt from the programme. Flexible implementation of multiple strategies allowed for scalability and response to the availability of new technologies. Broad implementation of family ES with research-based analysis showed promising yields post a negative clinical singleton ES. RNA-seq offered multiple benefits in family ES-negative populations. International data sharing strategies were critical in facilitating collaborations to establish novel disease–gene associations. Finally, the integrated approach of a multiskilled, multidisciplinary team was fundamental to having diverse perspectives and strategic decision-making.

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