Evidence map›Paper›PMID 40027236›Full record

ArticleGenetics in medicine open2025

CFTR haplotype phasing using long-read genome sequencing from ultralow input DNA.

Neeru Gandotra, Antariksh Tyagi, Irina Tikhonova, Caroline Storer, Curt Scharfe

Abstract read
In one paragraph

Article in Genetics in medicine open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Neeru GandotraDepartment of Genetics, Yale School of Medicine, New Haven, CT.
Antariksh TyagiDepartment of Genetics, Yale School of Medicine, New Haven, CT.
Irina TikhonovaDepartment of Genetics, Yale School of Medicine, New Haven, CT.
Caroline StorerPacBio, Menlo Park, CA.
Curt ScharfeDepartment of Genetics, Yale School of Medicine, New Haven, CT.

Funding

Multiplex gene sequencing and metabolomics analysis from newborn dried blood spots to improve screening and diagnosis of metabolic disorders.R01HD102537 · NICHD · YALE UNIVERSITY · PI SCHARFE, CURT · 2020 to 2024
$3.0M
NICHD NIH HHS R01 HD102537
6 · The paper itself

Abstract

Purpose: Newborn screening identifies rare diseases that result from the recessive inheritance of pathogenic variants in both copies of a gene. Long-read genome sequencing (LRS) is used for identifying and phasing genomic variants, but further efforts are needed to develop LRS for applications using low-yield DNA samples. Methods: In this study, genomic DNA with high molecular weight was obtained from 2 cystic fibrosis patients, comprising a whole-blood sample (CF1) and a newborn dried blood spot sample (CF2). Library preparation and genome sequencing (30-fold coverage) were performed using 20 ng of DNA input on both the PacBio Revio system and the Illumina NovaSeq short-read sequencer. Single-nucleotide variants, small indels, and structural variants were identified for each data set. Results: Our results indicated that the genotype concordance between long- and short-read genome sequencing data was higher for single-nucleotide variants than for small indels. Both technologies accurately identified known pathogenic variants in the Conclusion: Haplotype phasing of rare pathogenic variants from minimal DNA input is achieved through LRS. This approach has the potential to eliminate the need for parental testing, thereby shortening the time to diagnosis in genetic disease screening.

Indexed as

Cystic fibrosisDried blood spotsHaplotype phasingLong-read sequencingNewborn screening

Identifiers

PMID40027236
PMCPMC11869909

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.