Toward DNA Sequencing as a Primary Newborn Screen for Treatable Disorders not Amenable to Current Screening

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We propose to evaluate whole exome sequencing (WES) and whole genome sequencing (WGS) as an approach for population screening of early-onset treatable recessively inherited conditions. Our analysis will be based on prior and additional work on the large, ancestrally diverse cohort of children constituting all 1,334 cases of inborn errors of metabolism (IEM) diagnosed by tandem mass spectrometry screening in California over an 8.5 year period. Our prior analysis found that WES lacked adequate sensitivity and specificity to replace current newborn screening by tandem mass spectrometry (MS/MS). A screening-optimized DNA variant interpretation pipeline identified two known or likely pathogenic variants in most, but not all, affected cases. The pipeline was 88% sensitive and 98.4% specific, numbers too low to replace MS/MS. We found that 1/3 of known or likely pathogenic variants were novel, of which roughly 2/3 were missense. Adequate sensitivity and specificity therefore require accurate annotation of missense variants. DNA variant annotation has focused on individual variants and is most applicable for dominant disorders for which pathogenicity is determined by a single variant. For recessive diseases, expression is determined by two variants on different chromosomes. We propose a new framework for disease prediction in recessive conditions, in which the bi-allelic variants, or diplotype, is used to assess pathogenicity. We will improve disease detection for autosomal recessive diseases through screening utilizing DNA sequencing by 1) improving interpretation of bi-allelic missense variants by using variant co-evolution, conservation, amino acid proximity and other features to derive a risk score, 2) considering both variants in recessive pathogenicity prediction, and 3) using the California newborn screening data set as a training set with a small number of positives for each disorder and a large number of controls (those cases positive for a different disease) to develop a machine learning algorithm to predict likelihood of disease. To address sensitivity, for 103 exome negative IEM cases from our data set we will fully interrogate the exome data, both for known IEM genes and also the entire exome to identify novel genes. Finally, for those remaining unsolved, we will perform WGS to identify additional causal variants. In so doing, we will also compare WES and WGS in terms of overall sensitivity and specificity for screening. We anticipate that the proposed study will be a significant advance in assessing DNA sequencing as a newborn screening tool for those early-onset treatable diseases for which there is currently no screening test, leading to decreased death and disability.