Biology subjects

Krueger, R. F.

Publications and source records attributed to Krueger, R. F..

3 recordsLinked to original sources

bioRxiv2025

A benchmark of the somatic mutation landscape using single-cell and single-molecule whole-genome sequencing

Accurate detection of somatic mutations in noncancerous cells is critical for studying somatic mosaicism, a process implicated in aging and multiple chronic diseases. However, single-cell and single-molecule DNA sequencing platforms differ in their error profiles, coverage biases, and sensitivity to specific mutation types, complicating cross-platform comparisons. Here, we present in vitro and in silico benchmarks to quantify true-positive and false-positive rates in single-cell whole-genome sequencing using Single-Cell Multiple Displacement Amplification, and in single-molecule sequencing using Nanorate Sequencing (NS) and whole-genome NS (WGNS). Using standard cell lines, we show that all three methods detect single-nucleotide variants (sSNVs) and small insertions and deletions (sINDELs) with high accuracy, but differ in genomic coverage and susceptibility to artifacts. Method-specific biases influence mutational signatures and hotspot detection. Applying results of the benchmark to IMR-90 fibroblasts, we estimate higher in vitro mutation rates using NS than expected from in vivo data, consistent with potential replication stress and culture-associated DNA damage. Overall, our study highlights the substantial impact of sequencing platform-specific biases on somatic mutation detection and interpretation, and lays the foundation for standardized, cross-platform-comparable analyses of somatic mosaicism in normal human tissues.

genomics↗

bioRxiv2025

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

Polygenic indexes (PGIs) -- DNA-based predictors of individual phenotypes -- have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGIs predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repositorys scope, and introduce novel resources that reduce confounding bias and improve interpretability.

genomics↗

bioRxiv2019

Educational attainment, body mass index, and smoking initiation: Using genetic path analysis to control for pleiotropy in a Mendelian randomization study

BackgroundWhen a randomized experimental study is not possible, Mendelian randomization studies use genetic variants or polygenic scores as instrumental variables to control for gene-environment correlation while estimating the association between an exposure and outcome. Polygenic scores have become increasingly potent predictors of their respective phenotypes, satisfying the relevance criteria of an instrumental variable. Evidence for pleiotropy, however, casts doubt on whether the exclusion criteria of an instrumental variable is likely to hold for polygenic scores of complex phenotypes, and a number of methods have been developed to adjust for pleiotropy in Mendelian randomization studies.\n\nMethodUsing multiple polygenic scores and path analysis we implement an extension of genetic instrumental variable regression, genetic path analysis, and use it to test whether educational attainment is associated with two health-related outcomes in adulthood, body mass index and smoking initiation, while estimating and controlling for both gene-environment correlations and pleiotropy.\n\nResultsGenetic path analysis provides compelling evidence for a complex set of gene-environment transactions that undergird the relations between educational attainment and health-related outcomes in adulthood. Importantly, results are consistent with education having a protective effect on body mass index and smoking initiation, even after controlling for gene-environment correlations and pleiotropy.\n\nConclusionsThe proposed method is capable of addressing the exclusion criteria for a sound instrumental variable and, consequently, has the potential to help advance Mendelian randomization studies of complex phenotypes.

genetics↗