PubMed Health⌕ Search

PubMed · 15173476

Population-based newborn screening for genetic disorders when multiple mutation DNA testing is incorporated: a cystic fibrosis newborn screening model demonstrating increased sensitivity but more carrier detections.

Abstract

OBJECTIVES: Newborn screening for cystic fibrosis (CF) provides a model to investigate the implications of applying multiple-mutation DNA testing in screening for any disorder in a pediatric population-based setting, where detection of affected infants is desired and identification of unaffected carriers is not. Widely applied 2-tiered CF newborn screening strategies first test for elevated immunoreactive trypsinogen (IRT) with subsequent analysis for a single CFTR mutation (DeltaF508), systematically missing CF-affected infants with any of the >1000 less common or population-specific mutations. Comparison of CF newborn screening algorithms that incorporate single- and multiple-mutation testing may offer insights into strategies that maximize the public health value of screening for CF and other genetic disorders. The objective of this study was to evaluate technical feasibility and practical implications of 2-tiered CF newborn screening that uses testing for multiple mutations (multiple-CFTR-mutation testing). METHODS: We implemented statewide CF newborn screening using a 2-tiered algorithm: all specimens were assayed for IRT; those with elevated IRT then had multiple-CFTR-mutation testing. Infants who screened positive by detection of 1 or 2 mutations or extremely elevated IRT (>99.8%; failsafe protocol) were then referred for definitive diagnosis by sweat testing. We compared the number of sweat-test referrals using single- with multiple-CFTR-mutation testing. Initial physician assessments and diagnostic outcomes of these screened-positive infants and any affected infants missed by the screen were analyzed. We evaluated compliance with our screening and follow-up protocols. All Massachusetts delivery units, the Newborn Screening Program, pediatric health care providers who evaluate and refer screened-positive infants, and the 5 Massachusetts CF Centers and their affiliated genetic services participated. A 4-year cohort of 323 506 infants who were born in Massachusetts between February 1, 1999, and February 1, 2003, and screened for CF at approximately 2 days of age was studied. RESULTS: A total of 110 of 112 CF-affected infants screened (negative predictive value: 99.99%) were detected with IRT/multiple-CFTR-mutation screening; 2 false-negative screens did not show elevated IRT. A total of 107 (97%) of the 110 had 1 or 2 mutations detected by the multiple- CFTR-mutation screen, and 3 had positive screens on the basis of the failsafe protocol. In contrast, had we used single-mutation testing, only 96 (87%) of the 110 would have had 1 or 2 mutations detectable by single-mutation screen, 8 would have had positive screens on the basis of the failsafe protocol, and an additional 6 infants would have had false-negative screens. Among 110 CF-affected screened-positive infants, a likely "genetic diagnosis" was made by the multiple-CFTR-mutation screen in 82 (75%) versus 55 (50%) with DeltaF508 alone. Increased sensitivity from multiple-CFTR-mutation testing yielded 274 (26%) more referrals for sweat testing and carrier identifications than testing with DeltaF508 alone. CONCLUSIONS: Use of multiple-CFTR-mutation testing improved sensitivity and postscreening prediction of CF at the cost of increased referrals and carrier identification.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anne Marie Comeau, Richard B Parad, Henry L Dorkin, Mark Dovey, Robert Gerstle, Kenan Haver, Allen Lapey, Brian P O'Sullivan, David A Waltz, Robert G Zwerdling, Roger B Eaton. 2004. Population-based newborn screening for genetic disorders when multiple mutation DNA testing is incorporated: a cystic fibrosis newborn screening model demonstrating increased sensitivity but more carrier detections.. https://doi.org/10.1542/peds.113.6.1573

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗