Biomedical subjects

Jeremiah M Scharf

Publications and source records attributed to Jeremiah M Scharf.

4 recordsLinked to original sources

PubMed2026

Persistent tic disorders are associated with 17q12 duplications.

Tourette Syndrome (TS) and Persistent Tic Disorder (PTD) are childhood-onset neuropsychiatric conditions with high heritability. Due to current sample size limitations, identifying TS/PTD risk genes has been challenging. This study addressed this issue by conducting a meta-analysis of microarray copy number variant (CNV) studies from three TS/PTD genomics consortia, supplemented with new data from 3291 cases. This approach more than doubled the sample size of previous TS/PTD CNV studies, with CNV calls generated from 5725 TS/PTD cases and 10,982 matched controls. The results confirmed that TS/PTD cases 1) have a higher burden of ultra-rare deletions overlapping loss-of-function intolerant genes (OR = 1.68, P = 9.3×10-5) and 2) are more likely to carry established neurodevelopmental CNVs (OR = 1.42, P = 3.9×10-2) compared to controls. Additionally, a novel, genome-wide significant CNV locus for TS/PTD was discovered, involving duplications at 17q12 (hg19 chr17:34.8 - 36.2 Mb). This locus is associated with a known duplication syndrome associated with variable neuropsychiatric traits, but has not been previously linked to tic disorders. Eight cases and one control carried the canonical ~1.4 Mb duplication at chr17:34.8-36.2 Mb, while one additional case had a smaller 110 kb duplication within this known CNV that included only one gene, ACACA (acetyl-CoA carboxylase, OR = 26.7, P = 5.69×10-7). Overall, this study provides further evidence that rare, genic CNVs play a substantial role in the genetic architecture of TS/PTD and identifies a new genome-wide significant association with this neurodevelopmental disorder.

Journal Article↗

PubMed2026

MarkerMatch: a proximity-based probe-matching algorithm for joint analysis of copy-number variants from different genotyping arrays.

MOTIVATION: Copy-number variants (CNVs) are a form of genetic structural variation with increasing importance in complex human disorders. Both DNA sequencing and microarray data can be used to detect CNVs, which can be used in genetic association tests. Unlike genotypes, CNV detection in microarrays requires the use of observed intensity signals at each probe, which limits the imputability for analyses that span multiple array types. Thus far, a consensus set of probes (those present on all arrays) has been used to circumvent the problem of differing array-specific sensitivities. This has led to excessive reduction in overall sensitivity since arrays can have an undesirably low probe overlap. To overcome this limitation, we developed MarkerMatch, a proximity-based algorithm that matches probes across different genotyping microarrays to maximize the number of probes considered in the CNV calling algorithm, thereby increasing the resolution and sensitivity while preserving precision. RESULTS: By analyzing CNV calls from 4906 individuals genotyped across three different arrays, we show that the MarkerMatch approach improves sensitivity by increasing the density of probes available for CNV calling while maintaining precision or improving it relative to the current practice (e.g. use of consensus probes only). We further demonstrate that MarkerMatch matches the CNV detection from current practice in terms of F1 score and PPV for larger CNVs. We also optimize MarkerMatch parameters, DMAX and Method, and find an optimal DMAX setting at 10 kb, with no clear optimal candidate based on Method, indicating that parameters for this metric should be determined on a use case basis. AVAILABILITY: The R package for MarkerMatch is available at: https://github.com/FranjoIM/MarkerMatch. The code used for analysis and implementation is available at: https://doi.org/10.5281/zenodo.18460979. The live notebook is available at https://fivankovic.notion.site/2026-markermatch.

DNA Copy Number Variations↗

PubMed2025

Distinct patterns of de novo coding variants contribute to Tourette Syndrome etiology.

Tourette syndrome (TS) is a highly heritable childhood-onset neuropsychiatric disorder characterized by persistent motor and vocal tics. While both common and rare variants contribute to TS susceptibility, the role of rare de novo mutations (DNMs) remains incompletely characterized. Here, we report findings from the largest TS whole-exome sequencing study to date, analyzing 1,466 TS trios alongside 6,714 autism spectrum disorder (ASD) trios and 5,880 unaffected sibling controls from the Simons Simplex Collection (SSC) and SPARK cohorts. Leveraging a trio-based design across these cohorts enabled calibrated assessment of DNM burden while controlling for background mutation rates. We observed a significant exome-wide enrichment of protein-truncating DNMs in TS probands, particularly within genes intolerant to loss-of-function variation (pLI ≥ 0.9), with little contribution from damaging missense variants. Notably, TS probands did not exhibit enrichment in previously implicated ASD or developmental delay (DD) genes, but elsewhere in the genome, suggesting a distinct rare variant architecture. Using a Bayesian statistical framework that integrates both de novo and rare inherited coding variants, we identified three candidate TS risk genes with FDR ≤ 0.05: PPP5C , EXOC1 , and GXYLT1 . Literature shows that they have prior links to neurodevelopmental and psychiatric disorders. These findings reveal a rare variant burden in TS that is genetically distinguishable from ASD, underscore the importance of loss-of-function mutations in TS risk, and nominate novel candidate genes for future functional investigation.

Journal Article↗

PubMed2025

MarkerMatch: A Proximity-Based Probe-Matching Algorithm for Joint Analysis of Copy-Number Variants from Different Genotyping Arrays.

MOTIVATION: Copy-number variants (CNVs) are a form of genetic structural variation with increasing importance in complex human disorders. Both DNA sequencing and microarray data can be used to call CNVs, which can be used in association tests, such as association between CNV number and disease status. Unlike genotypes, CNV detection in microarrays requires the use of observed intensity signals at each probe, which limits the imputability for analyses that span multiple array types. Thus far, a consensus set of probes (the intersection encompassing the probes that occur in common on all arrays) has been used to circumvent the problem of differing array-specific sensitivities. This has, however, led to excessive reduction in overall sensitivity of CNV calls as arrays can have an undesirably low overlap of probe sets. To overcome this limitation, we developed MarkerMatch, a proximity-based algorithm that matches probes across different genotyping microarrays to maximize the number of probes considered in the CNV calling algorithm, thereby increasing the resolution and sensitivity while preserving precision. RESULTS: By analyzing CNV calls from 4,906 individuals genotyped across three different arrays (Global Screening Array, Omni2.5 array, and Omni Express Exome array), we show that the MarkerMatch approach improves sensitivity by increasing the density of probes available for CNV calling while maintaining precision or improving it relative to the current practice (e.g., use of consensus probes only). We further demonstrate that MarkerMatch exceeds the output from current practice in terms of F1 score, Fowlkes-Mallows index, and Jaccard index. We also optimize MarkerMatch parameters, D MAX and Method, and find an optimal D MAX setting at 10kb, with no clear optimal candidate based on Method, indicating that parameters for this metric should be determined on a use case basis.

Journal Article↗