Biology subjects

Hou, K.

Publications and source records attributed to Hou, K..

26 records · Page 2Linked to original sources

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↗

bioRxiv2021

Powerful eQTL mapping through low coverage RNA sequencing

Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-Seq limits sample size, sequencing depth, and therefore, discovery power. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-Seq of whole blood tissue across 1490 individuals at low-coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-Seq study of 570 individuals at high-coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-Seq data to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power. Our study suggests that lowering coverage while increasing the number of individuals is an effective approach to increase discovery power in RNA-Seq studies.

genomics↗

bioRxiv2021

Analysis of rhizosphere bacterial diversity of Angelica dahurica var. formosana based on different experimental sites and varieties (strains)

Only with good producing areas and good germplasm can good medicinal materials be produced. In this study, in order to explore the effects of soil environment and germplasm on yield and quality of Angelica dahurica var. formosana from microorganism level, Illumina MiSeq was used to study the rhizosphere bacterial diversity of A. dahurica var. formosana based on different origin and varieties (strains). The results showed that the bacterial community of A. dahurica var. formosana was stable and conserved to a certain extent, and the bacteria of Proteobacteria and Firmicutes might play an important role in improving the yield and quality of A. dahurica var. formosana. Soil variables were the key factors affecting the rhizosphere bacterial community composition of A. dahurica var. formosana. These results are of great significance for further understanding the growth promotion mechanism of beneficial rhizosphere bacteria, reducing fertilizer application, expanding planting area, improving soil environment, and improving yield and quality. ImportanceRhizosphere microorganisms play an important role in plant growth and accumulation of secondary metabolites. In this study, it was found for the first time that the rhizosphere bacterial community of A. dahurica var. formosana was relatively stable and conserved which could adapt to different environmental changes and resist the interference of external factors to a certain extent, providing a theoretical basis for distant planting and promotion of planting. The bacterial taxa of Proteobacteria and Firmicutes might play an important role in improving the yield and quality of A. dahurica var. formosana. This provided a theoretical basis for the exploitation and utilization of rhizosphere microbial resources of A. dahurica var. formosana to artificially control the soil environment and provided a new idea for increasing yield and quality of A. dahurica var. formosana.

microbiology↗

bioRxiv2021

Inhibition of amyloid formation of the Nucleoprotein of SARS-CoV-2

The SARS-CoV-2 Nucleoprotein (NCAP) functions in RNA packaging during viral replication and assembly. Computational analysis of its amino acid sequence reveals a central low-complexity domain (LCD) having sequence features akin to LCDs in other proteins known to function in liquid-liquid phase separation. Here we show that in the presence of viral RNA, NCAP, and also its LCD segment alone, form amyloid-like fibrils when undergoing liquid-liquid phase separation. Within the LCD we identified three 6-residue segments that drive amyloid fibril formation. We determined atomic structures for fibrils formed by each of the three identified segments. These structures informed our design of peptide inhibitors of NCAP fibril formation and liquid-liquid phase separation, suggesting a therapeutic route for Covid-19. One Sentence SummaryAtomic structures of amyloid-driving peptide segments from SARS-CoV-2 Nucleoprotein inform the development of Covid-19 therapeutics.

biochemistry↗

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

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

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↗

bioRxiv2019

Scalable multi-component linear mixed models with application to SNP heritability estimation

Variance components analysis has emerged as a powerful tool in complex trait genetics, with applications ranging from heritability estimation to association mapping. While the application of these methods to large-scale genetic datasets can potentially reveal important insights into genetic architecture, existing methods for fitting variance components do not scale well to these datasets. Here, we present a new algorithm for variance components analysis that is accurate and highly efficient, capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating variation in a trait explained by genotyped SNPs (SNP heritability) as well in partitioning heritability across population and functional genomic annotations. Analyzing 22 diverse traits with genotypes from 300, 000 individuals across about 8 million common and low frequency SNPs (minor allele frequency > 0.1%), we observe that the allelic effect size increases with decreasing MAF (minor allele frequency) and LD (linkage disequilibrium) across the analyzed traits consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders.

bioinformatics↗