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Wagenknecht, J.

Publications and source records attributed to Wagenknecht, J..

2 recordsLinked to original sources

Defining Pseudo-Haplotype Analysis Reveals Multi-Gene Genetic Pattern Across BAF Chromatin Remodeling Complexes

BRG1-associated factor (BAF) is a crucial chromatin remodeling complex. Variants in genes encoding BAF complex components cause human diseases, including cancers and developmental disorders. However, the genetic diversity and variant co-occurrence patterns within BAF genes remain incompletely understood. It is feasible, though largely untested, that rare patterns of common variations could alter function similarly to rare deleterious variants. Further, there is no modern census of how often individual people simultaneously carry multiple rare and common variations, nor means for genomics practitioners to assess their combined effects. Approaches are needed to characterize complete sequences from individual samples. In this study, we introduce a pseudo-haplotype analysis (PHA) framework, combining multiple protein-coding sequence variants, observed concurrently within individual samples, into discrete BAF patterns. In this cohort, 78.44% of pseudo-haplotype (PH) copies carry at least one BAF coding variation. Among these, 56.18% contain at least two distinct variants, and 32.39% contain three or more, indicating a substantial burden of multi-variant configurations across individuals. Notably, 25.30% of unique PHs are observed only once, highlighting a considerable proportion of people who are affected by rare or private combinations of genetic variations. We identify multiple significant (FDR < 0.05) co-occurrence combinations across global populations. These findings underscore the importance of considering population-specific genetic structures, and complete individual variant configurations when investigating disease associations and genetic mechanisms. Our approach provides a generalizable framework for characterizing multi-variant architectures within chromatin remodeling genes at a population scale, with potential applications in elucidating disease etiology and advancing precision medicine.

bioinformatics↗

Defining the molecular tolerance-to-damage landscape of SMARCA4 helicase genetic alterations

Evaluating the impact of genomic variation is essential for identifying underlying mechanistic causes of human diseases. The spectrum of neurodevelopmental disorders is driven by diverse genetic alterations with genes like SMARCA4 being prototypical examples. There have been significant hurdles to implementing the protein-specific and mechanism-informed variation effect predictors that are anticipated to have the highest yield of mechanistic information. Yet, there is a pressing need, for example, within SMARCA4 where 98% of the 2780 reported variants lack a disposition and remain of uncertain significance (VUS). Further, the field has yet to identify each variants specific molecular mechanism, which will inform targeted therapeutic development strategies. In this study we developed a mechanistic structure-informed helicase-specific variant effect predictor by leveraging diverse information with state-specific calculations. Our approach has 100% recall of pathogenic variants while classifying 87.23% of VUS into damaging (55.74%, n=262) versus tolerated effects (31.49%, n=148), including those with conflicting interpretations. This analysis reveals significant enrichment of integrated functional metrics, such as conservation and solvent exposure, that parallel allele frequences in health populations, and emphasizes the robustness of the method. Thus, we have demonstrated a novel approach for the development of mechanism-informed protein-specific interpretation of human genetic information.

genetics↗