Biology subjects

Biesecker, L. G.

Publications and source records attributed to Biesecker, L. G..

9 recordsLinked to original sources

bioRxiv2025

Specification of frequency criteria for secondary findings genes to improve variant classification concordance

PurposeThe return of secondary findings (SF) is well-established in clinical testing and is increasingly employed in clinical research testing. Variant classification can be challenging due to the lack of monogenic disease entity (MDE, defined as a gene-phenotype pair) specific variant classification recommendations. A key criterion for variant classification is disease allele frequency thresholds (DAFTs), which are not established for all MDEs recommended for the return of SF. MethodsWe calculated DAFTs for SF MDEs considering prevalence, gene contribution, and penetrance. ACMG criterion BS1 was set at the calculated DAFT value, with BA1 set at ten times the calculated DAFT. For genes associated with multiple SF MDEs without clear genotype-phenotype correlation, DAFT values were combined. GnomAD Grpmax filtering allele frequencies (FAF) for pathogenic/likely pathogenic classified variants were compared to calculated thresholds. ResultsWe determined BS1 and BA1 values for 58 SF MDEs (47 genes). No pathogenic/likely pathogenic variant Grpmax FAF was greater than the relevant MDE-specific BA1. ConclusionSetting BA1 and BS1 thresholds should improve variant classification consistency and reduce misclassifications. For SF MDEs without current ClinGen Variant Curation Expert Panel (VCEP) specifications, these frequency specifications can be used until full criteria are available.

genetics↗

bioRxiv2025

Genetics of growth rate in induced pluripotent stem cells

Human induced pluripotent stem cells (iPSCs) have transformed biomedical research by enabling the generation of diverse cell types from accessible somatic tissues. However, certain fundamental biological properties, such as the genetic and epigenetic determinants of iPSC proliferation, remain poorly characterized. We measured the growth of iPSC lines derived from 602 unique donors using high-throughput time-lapse imaging, quantified proliferation through a growth Area-Under-the-Curve (gAUC) phenotype, and correlated gAUC with the gene expression and genotype of the cell lines. We identified 3,091 genes associated with gAUC, many of which are well established regulators of cell proliferation. We also found that rare deleterious variants in WDR54 were associated with reduced iPSC growth and that WDR54 was differentially expressed with respect to gAUC. Although no common variants showed a genome-wide association with gAUC, iPSC lines from monozygotic twins were highly correlated, and common genetic variation explained approximately 71-75% of the variance in iPSC growth rates. These results indicate a complex genetic architecture of iPSC growth rates, where rare, large-effect variants in important growth regulators, including WDR54, are layered onto a highly polygenic background. These findings have important implications for the design of pooled iPSC-based studies and disease models, which may be confounded by intrinsic growth differences.

developmental biology↗

bioRxiv2025

Direct RNA nanopore sequencing reveals rapid RNA modification changes following glucose stimulation of human pancreatic beta-cell lines

RNA modifications are critical regulators of gene expression and cellular processes; however, the epitranscriptome is less well studied than the epigenome. Here, we studied transcriptome-wide changes in RNA modifications and expression levels in two human pancreatic beta-cell lines, EndoC-BH1 and EndoC-BH3, after one hour of glucose stimulation. Using direct RNA nanopore sequencing (dRNA-seq), we measured N6-methyladenosine (m6A), 5-methylcytosine (m5C), inosine, and pseudouridine concurrently across the transcriptome. We developed a differential RNA modification method and identified 1,697 differentially modified sites (DMSs) across all modifications. These DMSs were largely independent of changes in gene expression levels and enriched in transcripts for type 2 diabetes (T2D) genes. Our study demonstrates how dRNA-seq can be used to detect and quantify RNA modification changes in response to cellular stimuli at the single-nucleotide level and provides new insights into RNA-mediated mechanisms that may contribute to normal beta-cell response and potential dysfunction in T2D.

genomics↗

bioRxiv2024

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria

PurposeWe previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches have since been released, and these methods must be independently calibrated for clinical application. MethodUsing our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by three new tools (AlphaMissense, ESM1b, VARITY) and calibrated scores meeting each evidence strength. Results All three tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, though sometimes for few variants. Compared to previously recommended tools, these yielded at best only modest improvements in the tradeoffs of evidence strength and false positive predictions. ConclusionAt calibrated thresholds, three new computational predictors provided evidence for variant pathogenicity at similar strength to the four previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

bioinformatics↗

bioRxiv2022

Updated Variant Curation Expert Panel Criteria and Pathogenicity Classifications for 251 Variants for RYR1-related Malignant Hyperthermia Susceptibility

The ClinGen malignant hyperthermia susceptibility (MHS) variant curation expert panel specified the ACMG/AMP criteria for RYR1-related MHS and a pilot analysis of 84 variants was published. We have now classified an additional 251 variants for RYR1-related MHS according to current ClinGen standards and updated the criteria where necessary. Criterion PS4 was modified such that individuals with multiple RYR1 variants classified as pathogenic (P), likely pathogenic (LP) or variant of uncertain significance (VUS) were not considered as providing evidence for pathogenicity. Critera PS1 and PM5 were revised to consider LP variants at the same amino acid residue as providing evidence for pathogenicity at reduced strength. Finally, PM1 was revised such that if PS1 or PM5 are used PM1, if applicable, should be downgraded to supporting. Of the 251 RYR1 variants, 42 were classified as P/LP, 16 as B/LB, and 193 as VUS. The primary driver of 176 VUS classifications was insufficient evidence supporting pathogenicity, rather than evidence against pathogenicity. Functional data supporting PS3/BS3 was identified for only 13 variants. Based on the posterior probabilities of pathogenicity and variant frequencies in gnomAD, we estimated the prevalence of individuals with RYR1-related MHS pathogenic variants to be between 1/300 and 1/1,075, considerably higher than current estimates. We have updated ACMG/AMP criteria for RYR1-related MHS and classified 251 variants. We suggest that prioritization of functional studies is needed to resolve the large number of VUS classifications and allow for appropriate risk assessment. RYR1-related MHS pathogenic variants are likely to be more common than currently appreciated.

genetics↗

bioRxiv2022

Evidence-based calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for clinical use of PP3/BP4 criteria

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as Supporting level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (Supporting, Moderate, Strong, Very Strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tools scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved Supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying Moderate and several reached Strong evidence levels. One tool reached Very Strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

bioinformatics↗

bioRxiv2021

Evaluating the impact of in silico predictors on clinical variant classification

BackgroundIn silico evidence is important to consider when interpreting genetic variants. According to the ACMG/AMP, in silico evidence is applied at the supporting strength level using the PP3 and BP4 criteria, for pathogenic and benign evidence, respectively. While PP3 has been determined to be one of the most commonly applied criteria, less is known about the effect of these two criteria on variant classification outcomes. MethodsIn this study, a total of 727 missense variants curated by Clinical Genome Resource (ClinGen) Variant Curation Expert Panels (VCEPs) were analyzed to determine how often PP3 and BP4 were applied and how often they influenced final variant classifications. The current categorical system of variant classification was compared with a point-based system being developed by the ClinGen Sequence Variant Interpretation Working Group. In addition, the performance of four in silico tools (REVEL, VEST, FATHMM, and MPC) was assessed by using a gold set of 237 variants (classified as benign or pathogenic independent of PP3 or BP4) to calculate pathogenicity likelihood ratios. ResultsCollectively, the PP3 and BP4 criteria were applied by ClinGen VCEPs to 55% of missense variants in this data set. Removing in silico criteria from variants where they were originally applied caused variants to change classification from pathogenic to likely pathogenic (14%), likely pathogenic to variant of uncertain significance (VUS) (24%), or likely benign to VUS (64%). The proportion of downgrades with the categorical classification system was similar to that of the point-based system, though the latter resolved borderline classifications. REVEL and VEST performed at a level consistent with moderate strength towards either benign or pathogenic evidence, while FATHMM performed at the supporting level. ConclusionsOverall, this study demonstrates that in silico criteria PP3 and BP4 are commonly applied in variant classification and often affect the final classification. Our results suggest that when sufficient thresholds for in silico predictors are established, PP3 and BP4 may be appropriate to use at a moderate strength. However, further calibration with larger datasets is needed to optimize the performance of current in silico tools given the impact they have on clinical variant classification.

genomics↗

bioRxiv2020

Variant Curation Expert Panel Recommendations for RYR1 Pathogenicity Assertions in Malignant Hyperthermia Susceptibility

PurposePrevention of malignant hyperthermia (MH) requires an understanding of RYR1 variant pathogenicity to assess the risk of exposure to triggering agents. Personalized medicine, especially secondary findings and eventually genomic screening, will contribute toward this goal. MethodsWe specified ACMG/AMP criteria for variant interpretation for RYR1 and MH. Proposed rules were piloted on 84 variants. We applied quantitative evidence calibration for several criteria using likelihood ratios based on the Bayesian framework. ResultsSeven ACMG/AMP criteria were adopted without changes, ten were adopted with RYR1-specific modifications, and nine were dropped. The in silico (PP3 and BP4) and hot spot criteria (PM1) were evaluated quantitatively. REVEL gave an OR of 23:1 for PP3 and 16:1 for BP4 using trichotomized cut-offs of >0.85 (pathogenic) and <0.5 (benign). The PM1 hotspot criterion had an OR of 24:1. PP3 and PM1 were implemented at moderate strength. Applying the revised ACMG criteria to 44 recognized MH variants, 30 were assessed as pathogenic, 12 as likely pathogenic, and two as VUS. ConclusionCuration of these variants will facilitate interpretation of RYR1/MH genomic testing results, which is especially important for secondary findings analyses. Our approach to quantitatively calibrating criteria are generalizable to other variant curation expert panels.

genetics↗

bioRxiv2019

Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework

BackgroundThe American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria (PS3/BS3) for functional assays that specified a \"strong\" level of evidence. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes is a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation.\n\nMethodsThe Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development.\n\nResultsThe SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are: 1. Define the disease mechanism; 2. Evaluate applicability of general classes of assays used in the field; 3. Evaluate validity of specific instances of assays; 4. Apply evidence to individual variant interpretation. We found that a minimum of eleven total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis.\n\nConclusionsThe recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

genetics↗