Dataset Information

Unraveling haplotype errors in the DFNA33 locus.

ABSTRACT: Genetic heterogeneity makes it difficult to identify the causal genes for hearing loss. Studies from previous decades have mapped numerous genetic loci, providing critical supporting evidence for gene discovery studies. Despite widespread sequencing accessibility, many historically mapped loci remain without a causal gene. The DFNA33 locus was mapped in 2009 and coincidentally contains ATP11A, a gene recently associated with autosomal dominant hearing loss and auditory neuropathy type 2. In a rare opportunity, we genome-sequenced a member of the original family to determine whether the DFNA33 locus may also be assigned to ATP11A. We identified a deep intronic variant in ATP11A that showed evidence of functionally normal splicing. Furthermore, we re-assessed haplotypes from the originally published DFNA33 family and identified two double recombination events and one triple recombination event in the pedigree, a highly unlikely occurrence, especially at this scale. This brief research report also serves as a call to the community to revisit families who have previously been involved in gene mapping studies, provide closure, and resolve these historical loci.

SUBMITTER: Vona B

PROVIDER: S-EPMC10475583 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Unraveling haplotype errors in the DFNA33 locus.

Vona Barbara B Regele Sabrina S Rad Aboulfazl A Strenzke Nicola N Pater Justin A JA Neumann Katrin K Sturm Marc M Haack Tobias B TB Am Zehnhoff-Dinnesen Antoinette G AG

Frontiers in genetics 20230821

Genetic heterogeneity makes it difficult to identify the causal genes for hearing loss. Studies from previous decades have mapped numerous genetic loci, providing critical supporting evidence for gene discovery studies. Despite widespread sequencing accessibility, many historically mapped loci remain without a causal gene. The DFNA33 locus was mapped in 2009 and coincidentally contains <i>ATP11A</i>, a gene recently associated with autosomal dominant hearing loss and auditory neuropathy type 2. ...[more]

PMID: 37671045

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Project description:In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.

Dataset Information

Unraveling haplotype errors in the DFNA33 locus.

Publications

Unraveling haplotype errors in the DFNA33 locus.

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