Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

A Large-Scale Candidate-Gene Association Mapping for Drought Tolerance and Agronomic Traits in Sugarcane

Dissection of the genetic loci controlling drought tolerance traits with a complex genetic inheritance is important for drought-tolerant sugarcane improvement. In this study, we conducted a large-scale candidate gene association study of 649 candidate genes in a sugarcane diversity panel to identify genetic variants underlying agronomic traits and drought tolerance indices evaluated in plant cane and ratoon cane under water-stressed (WS) and non-stressed (NS) environments. We identified 197 significant marker-trait associations (MTAs) in 141 candidate genes associated with 18 evaluated traits with the Bonferroni correction threshold (α = 0.05). Out of the total, 95 MTAs in 78 candidate genes and 62 MTAs in 58 candidate genes were detected under NS and WS conditions, respectively. Most MTAs were found only in specific water regimes and crop seasons. These MTAs explained 7.93-30.52% of phenotypic variation. Association mapping results revealed that 34, 59, and 104 MTAs involved physiological and molecular adaptation, phytohormone metabolism, and drought-inducible genes. They identified 19 pleiotropic genes associated with more than one trait and many genes related to drought tolerance indices. The genetic and genomic resources identified in this study will enable the combining of yield-related traits and sugar-related traits with agronomic value to optimize the yield of sugarcane cultivars grown under drought-stressed and non-stressed environments.

Toward Identification of Functional Sequences and Variants in Noncoding DNA

Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power.

BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data

We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at [Formula: see text] of the associated loci.

Reassessment of candidate gene studies for idiopathic restless legs syndrome in a large genome-wide association study dataset of European ancestry

Study Objectives

Several candidate gene studies have been published for idiopathic restless legs syndrome (RLS) in populations of European ancestry, but the reported associations have not been confirmed in independent samples. Our aim was to reassess these findings in a large case–control dataset in order to evaluate their validity.

Methods

We screened PubMed for RLS candidate gene studies. We used the genome-wide association study (GWAS) dataset of the International EU-RLS-GENE Consortium as our replication sample, which provided genome-wide single-variant association data based on at most 17 220 individuals of European ancestry. We performed additional gene-based tests using the software MAGMA and assessed the power of our study using the genpwr R package.

Results

We identified 14 studies conducted in European samples which assessed 45 variants in 27 genes of which 5 variants had been reported as significantly associated. None of these individual variants were replicated in our GWAS-based reassessment (nominal p > 0.05) and gene-based tests for the respective five genes ADH1B, GABRR3, HMOX1, MAOA, and VDR, were also nonsignificant (nominal p > 0.05). Our replication dataset was well powered to detect the reported effects, even when adjusting for effect size overestimation due to winner’s curse. Power estimates were close to 100% for all variants.

Conclusion

In summary, none of the significant single-variant associations from candidate gene studies were confirmed in our GWAS dataset. Therefore, these associations were likely false positive. Our observations emphasize the need for large sample sizes and stringent significance thresholds in future association studies for RLS.