Biology subjects

Burch, K. S.

Publications and source records attributed to Burch, K. S..

12 recordsLinked to original sources

bioRxiv2026

Electrical Readout Strategies of GFET Biosensors for Real-World Requirements

Graphene Field-Effect Transistors (GFETs) are increasingly employed as biochemical sensors due to their exceptional electronic properties, surface sensitivity, and potential for miniaturization. A critical challenge in deploying GFETs is determining the optimal electrical readout strategy. GFETs are typically operated with either of two modalities: one measuring current in real time (amperometric) and the other monitoring the change in voltage for charge neutrality (potential potentiometric). Here, we undertake a systematic study of the two modalities to determine their relative advantages/disadvantages towards guiding the future use of GFETs in sensing. We focus on viral proteins in wastewater, given the matrixs complexity and the growing interest in the field of wastewater surveillance. Our results show that transconductance offers far superior limits of detection (LOD) but suffers from limited reproducibility, a narrower dynamic range, and is ineffective for some viral proteins. In comparison, we find that Dirac point tracking offers higher reproducibility and superior robustness, but at a higher LOD. Interestingly, both techniques exhibit similar sensitivity, highlighting the importance of the aptamers employed. Systematic experiments also help explain differences in dynamic range and limited functionality in detecting some proteins, resulting from hidden electrophoresis, and shifting the high transconductance point away from the active region. Thus, our findings provide crucial considerations for designing and operating resilient graphene biosensors suitable for real-time pathogen monitoring in environmental scenarios.

biophysics↗

bioRxiv2023

Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.

bioinformatics↗

bioRxiv2021

Rapid, multianalyte detection of opioid metabolites in wastewater

By monitoring opioid metabolites, wastewater-based epidemiology (WBE) could be an excellent tool for real-time information on consumption of illicit drugs. A key limitation of WBE is the reliance on costly laboratory-based techniques that require substantial infrastructure and trained personnel, resulting in long turnaround times. Here, we present an aptamer-based graphene field effect transistor (AptG-FET) platform for simultaneous detection of three different opioid metabolites. This platform provides a reliable, rapid, and inexpensive method for quantitative analysis of opioid metabolites in wastewater (WW). The platform delivers a limit of detection (LOD) 2-3 orders of magnitude lower than previous reports, but in line with the concentrations range (pg/ml to ng/ml) of these opioid metabolites present in real samples. To enable multianalyte detection we developed a facile, reproducible, and high yield fabrication process producing twenty G-FETs with integrated side gate platinum (Pt) electrodes on a single chip. Our devices achieved the simultaneous and selective multianalyte detection of three different metabolites: Noroxycodone (NX), 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), and Norfentanyl (NF) in wastewater.

biophysics↗

bioRxiv2021

Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes

Recent works have shown that SNP-heritability--which is dominated by low-effect common variants--may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene (genelevel heritability). We partition gene-level heritability across minor allele frequency (MAF) classes to find genes whose gene-level heritability is explained exclusively by "low-frequency/rare" variants (0.5% [≤] MAF < 1%). Applying our method to ~17K protein-coding genes and 25 quantitative traits in the UK Biobank (N=290K), we find that, on average across traits, ~2.5% of nonzero-heritability genes have a rare-variant component, and only ~0.8% (370 gene-trait pairs) have heritability exclusively from rare variants. Of these 370 gene-trait pairs, 37% were not detected by existing gene-level association testing methods, likely because existing methods combine signal from all variants in a region irrespective of MAF class. Many of the additional genes we identify are implicated in phenotypically related Mendelian disorders or congenital developmental disorders, providing further evidence of their trait-relevance. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. We conclude that the proportion of gene-level heritability attributable to low-frequency/rare variation can yield novel insights into complex-trait genetic architecture.

genetics↗

bioRxiv2020

Large uncertainty in individual PRS estimation impacts PRS-based risk stratification

Large-scale genome-wide association studies have enabled polygenic risk scores (PRS), which estimate the genetic value of an individual for a given trait. Since PRS accuracy is typically assessed using cohort-level metrics (e.g., R2), uncertainty in PRS estimates at individual level remains underexplored. Here we show that Bayesian PRS methods can estimate the variance of an individuals PRS and can yield well-calibrated credible intervals for the genetic value of a single individual. For real traits in the UK Biobank (N=291,273 unrelated "white British") we observe large variance in individual PRS estimates which impacts interpretation of PRS-based stratification; for example, averaging across 13 traits, only 0.8% (s.d. 1.6%) of individuals with PRS point estimates in the top decile have their entire 95% credible intervals fully contained in the top decile. We provide an analytical estimator for individual PRS variance--a function of SNP-heritability, number of causal SNPs, and sample size--and observe high concordance with individual variances estimated via posterior sampling. Finally as an example of the utility of individual PRS uncertainties, we explore a probabilistic approach to PRS-based stratification that estimates the probability of an individuals genetic value to be above a prespecified threshold. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.

genomics↗

bioRxiv2020

Quantifying the contribution of dominance effects to complex trait variation in biobank-scale data

The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of Biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method that can partition the variation in complex traits into variance that can be attributed to additive (additive heritability) and dominance (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency, MAF > 1%) and 50 quantitative traits measured in 291, 273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no evidence for dominance heritability ([Formula] accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.

genetics↗

bioRxiv2020

Detection of a multi-disease biomarker in Salivawith Graphene Field Effect Transistors

Human carbonic anhydrase 1 (CA1) has been suggested as a biomarker for identification of several diseases including cancers, pancreatitis, diabetes, and Sjogrens syndrome. However, the lack of a rapid, cheap, accurate, and easy-to-use quantification technique has prevented widespread utilization of CA1 for practical clinical applications. To this end, we present a label-free electronic biosensor for detection of CA1 utilizing highly sensitive graphene field effect transistors (G-FETs) as a transducer and specific RNA aptamers as a probe. The binding of CA1 with aptamers resulted in a positive shift in Dirac voltage VD of the G-FETs, the magnitude of which depended on target concentration. These aptameric G-FET biosensors showed the binding affinity (KD) of [~] 2.3 ng/ml (70 pM), which is four orders lower than that reported using a gel shift assay. This lower value of KD enabled us to achieve a detection range (10 pg/ml - 100 ng/ml) which is well in line with the clinically relevant range. These highly sensitive devices allowed us to further prove their clinical relevance by successfully detecting the presence of CA1 in human saliva samples. Utilization of this label-free biosensor could facilitate the early stage identification of various diseases associated with changes in concentration of CAs.

biophysics↗

bioRxiv2020

A scalable method for estimating the regional polygenicity of complex traits

A key question in human genetics is understanding the proportion of SNPs modulating a particular phenotype or the proportion of susceptibility SNPs for a disease, termed polygenicity. Previous studies have observed that complex traits tend to be highly polygenic, opposing the previous belief that only a handful of SNPs contribute to a trait. Beyond these genome-wide estimates, the distribution of polygenicity across genomic regions as well as the genomic factors that affect regional polygenicity remain poorly understood. A reason for this gap is that methods for estimating polygenicity utilize SNP effect sizes from GWAS. However, estimating regional polygenicity from GWAS effect sizes involves untangling the correlation between SNPs due to LD, leading to intractable computations for even a small number of SNPs. In this work, we propose a scalable method, BEAVR, to estimate the regional polygenicity of a trait given marginal effect sizes from GWAS and LD information. We implement a Gibbs sampler to estimate the posterior distribution of the regional polygenicity and derive a fast, algorithmic update to circumvent the computational bottlenecks associated with LD. The runtime of our algorithm is [O](MK) for M SNPs and K susceptibility SNPs, where the number of susceptibility SNPs is typically K << M. By modeling the full LD structure, we show that BEAVR provides unbiased estimates of polygenicity compared to previous methods that only partially model LD. Finally, we show how estimates of regional polygenicity for BMI, eczema, and high cholesterol provide insight into the regional genetic architecture of each trait.

genetics↗

bioRxiv2019

Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

genomics↗

bioRxiv2019

Rapid, selective and single cell electrical detection of antibiotic resistant bacteria

The rapid increase in antibiotic resistant pathogenic bacteria has become a global threat, which besides the development of new drugs, requires rapid, cheap, scalable, and accurate diagnostics. Label free biosensors relying on electrochemical, mechanical, and mass based detection of whole bacterial cells have attempted to meet these requirements. However, the trade-off between selectivity and sensitivity of such sensors remains a key challenge. In particular, point-of-care diagnostics that are able to reduce and/or prevent unneeded antibiotic prescriptions require highly specific probes with sensitive and accurate transducers that can be miniaturized and multiplexed, and that are easy to operate and cheap. Towards achieving this goal, we present a number of advances in the use of graphene field effect transistors (G-FET) including the first use of peptide probes to electrically detect antibiotic resistant bacteria in a highly specific manner. In addition, we dramatically reduce the needed concentration for detection by employing dielectrophoresis for the first time in a G-FET, allowing us to monitor changes in the Dirac point due to individual bacterial cells. Specifically, we realized rapid binding of bacterial cells to a G-FET by electrical field guiding to the device to realize an overall 3 order of magnitude decrease in cell-concentration enabling a single-cell detection limit, and 9-fold reduction in needed time to 5 minutes. Utilizing our new biosensor and procedures, we demonstrate the first selective, electrical detection of the pathogenic bacterial species Staphylococcus aureus and antibiotic resistant Acinetobacter baumannii on a single platform.

microbiology↗

bioRxiv2019

Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture

The proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-heritability reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.

genomics↗

bioRxiv2018

Integrative analysis of Dupuytren’s disease identifies novel risk locus and reveals a shared genetic etiology with BMI

Dupuytrens disease is the common inherited tissue-specific fibrotic disorder. Its characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand, with an onset typically in the sixth decade of life. Although genome-wide association studies (GWAS) have identified 24 genomic regions associated with Dupuytrens risk, the biological mechanisms driving signal at these regions remain elusive. We identify potential biological mechanisms for Dupuytrens disease by integrating the most recent, largest GWAS (ncases = 3,871, ncontrols = 4,686) with eQTLs (47 tissue panels from five consortia, total n = 3,975) to perform a transcriptome-wide association study (TWAS). We identify 43 tissue-specific gene associations with Dupuytrens risk, one of which resides at least 0.5 Mb away from the 24 risk regions previously identified. We also estimate the genome-wide genetic correlation between Dupuytrens disease and 45 complex traits and find significant genetic correlations between Dupuytrens disease and body mass index ([Formula], P = 1.6 x 10-6), type II diabetes ([Formula], P = 1.7 x 10-4), triglycerides ([Formula], P = 3.5 x 10-4), and high-density lipoprotein ([Formula], P = 4.1 x 10-4), which suggests a shared genetic etiology. We further refine the genome-wide genetic correlation signal to identify 8 regions significantly negatively correlated with BMI and 3 regions significantly correlated (1 positively and 2 negatively correlated) with HDL; none of these regions contained the novel gene association identified by TWAS. Our results are consistent with previous epidemiological findings which show that lower BMI increases risk for Dupuytrens disease. These 12 novel risk regions provide new insight into the biological mechanisms of Dupuytrens disease and serve as a starting point for functional validation.

genetics↗