Estimating linkage disequilibrium and selection from allele frequency trajectories

An integrative approach to identifying NPC1 as a susceptibility gene for gestational diabetes mellitus

OBJECTIVE

The objective of this study was to identify a novel gene and its potential mechanisms associated with susceptibility to gestational diabetes mellitus (GDM) through an integrative approach.

METHODS

We analyzed data from genome-wide association studies (GWAS) of GDM in the FinnGen R11 dataset (16,802 GDM cases and 237,816 controls) and Genotype Tissue Expression v8 expression quantitative trait locus data. We used summary-data-based Mendelian randomization to determine associations between transcript levels and phenotypes, transcriptome-wide association studies to provide insights into gene-trait associations, multi-marker analysis of genomic annotation to perform gene-based analysis, genome-wide complex trait analysis-multivariate set-based association test-combo to determine gene prioritization, and polygenic priority scores to prioritize the causal genes to screen candidate genes. Subsequent Mendelian randomization analysis was performed to infer causality between the candidate genes and GDM and phenome-wide association study (PheWAS) analysis was used to explore the associations between selected genes and other characteristics. Furthermore, to gain a deeper understanding of the functional implications of these susceptibility genes, GeneMANIA analysis was used to determine the fundamental biological functions of the therapeutic targets and protein-protein interaction network analysis was performed to identify intracellular protein interactions.

RESULTS

We identified two novel susceptibility genes associated with GDM: NPC1 and KIAA1191. Magnetic resonance imaging revealed a strong correlation between NPC1 expression levels and a lower incidence of GDM (odds ratio: 0.922, 95% confidence interval: 0.866-0.981, p = 0.011). PheWAS at the gene level indicated that NPC1 was not associated with any other trait. The biological significance of this gene was evidenced by its strong association with sterol metabolism.

CONCLUSION

Our study identified NPC1 as a novel gene whose predicted expression level is linked to a reduced risk of GDM, providing new insights into the genetic framework of this disease.

Causal association between varicose veins and atrial fibrillation: A 2-sample bidirectional Mendelian randomization study

This study selected genome-wide association study data from the FinnGen database and utilized a bidirectional 2-sample Mendelian randomization (MR) method to explore the causal association between varicose veins (VV) and atrial fibrillation (AF). Inverse variance weighted (IVW) was used as the primary analytical method to assess the causal relationship between VV and AF, supplemented by Weighted median, MR-Egger and Simple 1mode. Cochran's Q test, MR-Egger regression intercept and Mendelian randomization pleiotropy residual sum and outlier were used as sensitivity analyses to detect heterogeneity and multilevel pleiotropy. Additionally, reverse MR was conducted to analyze the causal association between AF and VV. The IVW method indicated a positive causal relationship between VV and AF (odds ratio = 1.1571, 95% confidence interval = 1.0810-1.2384, P = 2.59 × 10-5). Reverse MR analysis shows no potential reverse causal relationships. The results showed a significant causal effect of VV on AF, suggesting that VV may increase the risk of developing AF. It also elaborates on the common risk factors and pathophysiological conditions between VV and AF.

Parallel HIV-1 fitness landscapes shape viral dynamics in humans and macaques that develop broadly neutralizing antibodies

Human immunodeficiency virus (HIV)-1 exhibits remarkable genetic diversity. For this reason, an effective HIV-1 vaccine must elicit antibodies that can neutralize many variants of the virus. While broadly neutralizing antibodies (bnAbs) have been isolated from HIV-1 infected individuals, a general understanding of the virus-antibody coevolutionary processes that lead to their development remains incomplete. We performed a quantitative study of HIV-1 evolution in two individuals who developed bnAbs. We observed strong selection early in infection for mutations affecting HIV-1 envelope glycosylation and escape from autologous strain-specific antibodies, followed by weaker selection for bnAb resistance later in infection. To confirm our findings, we analyzed data from rhesus macaques infected with viruses derived from the same two individuals. We inferred remarkably similar fitness effects of HIV-1 mutations in humans and macaques. Moreover, we observed a striking pattern of rapid HIV-1 evolution, consistent in both humans and macaques, that precedes the development of bnAbs. Our work highlights strong parallels between infection in rhesus macaques and humans, and it reveals a quantitative evolutionary signature of bnAb development.

Efficient epistasis inference via higher-order covariance matrix factorization

Epistasis can profoundly influence evolutionary dynamics. Temporal genetic data, consisting of sequences sampled repeatedly from a population over time, provides a unique resource to understand how epistasis shapes evolution. However, detecting epistatic interactions from sequence data is technically challenging. Existing methods for identifying epistasis are computationally demanding, limiting their applicability to real-world data. Here, we present a novel computational method for inferring epistasis that significantly reduces computational costs without sacrificing accuracy. We validated our approach in simulations and applied it to study HIV-1 evolution over multiple years in a data set of 16 individuals. There we observed a strong excess of negative epistatic interactions between beneficial mutations, especially mutations involved in immune escape. Our method is general and could be used to characterize epistasis in other large data sets.

Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data

In evolving populations where the rate of beneficial mutations is large, subpopulations of individuals with competing beneficial mutations can be maintained over long times. Evolution with this kind of clonal structure is commonly observed in a wide range of microbial and viral populations. However, it can be difficult to completely resolve clonal dynamics in data. This is due to limited read lengths in high-throughput sequencing methods, which are often insufficient to directly measure linkage disequilibrium or determine clonal structure. Here, we develop a method to infer clonal structure using correlated allele frequency changes in time-series sequence data. Simulations show that our method recovers true, underlying clonal structures when they are known and accurately estimate linkage disequilibrium. This information can then be combined with other inference methods to improve estimates of the fitness effects of individual mutations. Applications to data suggest novel clonal structures in an E. coli long-term evolution experiment, and yield improved predictions of the effects of mutations on bacterial fitness and antibiotic resistance. Moreover, our method is computationally efficient, requiring orders of magnitude less run time for large data sets than existing methods. Overall, our method provides a powerful tool to infer clonal structures from data sets where only allele frequencies are available, which can also improve downstream analyses.

Bézier interpolation improves the inference of dynamical models from data

Many dynamical systems, from quantum many-body systems to evolving populations to financial markets, are described by stochastic processes. Parameters characterizing such processes can often be inferred using information integrated over stochastic paths. However, estimating time-integrated quantities from real data with limited time resolution is challenging. Here, we propose a framework for accurately estimating time-integrated quantities using Bézier interpolation. We applied our approach to two dynamical inference problems: Determining fitness parameters for evolving populations and inferring forces driving Ornstein-Uhlenbeck processes. We found that Bézier interpolation reduces the estimation bias for both dynamical inference problems. This improvement was especially noticeable for data sets with limited time resolution. Our method could be broadly applied to improve accuracy for other dynamical inference problems using finitely sampled data.