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1. Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens

   https://doi.org/10.1126/science.adh7699  

   Morris, John A.; Caragine, Christina; Daniloski, Zharko; Domingo, Júlia; Barry, Timothy; Lu, Lu; Davis, Kyrie; Ziosi, Marcello; Glinos, Dafni A.; Hao, Stephanie; et al (May 2023, Science)

   INTRODUCTION Genome-wide association studies (GWASs) have identified thousands of human genetic variants associated with diverse diseases and traits, and most of these variants map to noncoding loci with unknown target genes and function. Current approaches to understand which GWAS loci harbor causal variants and to map these noncoding regulators to target genes suffer from low throughput. With newer multiancestry GWASs from individuals of diverse ancestries, there is a pressing and growing need to scale experimental assays to connect GWAS variants with molecular mechanisms. Here, we combined biobank-scale GWASs, massively parallel CRISPR screens, and single-cell sequencing to discover target genes of noncoding variants for blood trait loci with systematic targeting and inhibition of noncoding GWAS loci with single-cell sequencing (STING-seq). RATIONALE Blood traits are highly polygenic, and GWASs have identified thousands of noncoding loci that map to candidate cis -regulatory elements (CREs). By combining CRE-silencing CRISPR perturbations and single-cell readouts, we targeted hundreds of GWAS loci in a single assay, revealing target genes in cis and in trans . For select CREs that regulate target genes, we performed direct variant insertion. Although silencing the CRE can identify the target gene, direct variant insertion can identify magnitude and direction of effect on gene expression for the GWAS variant. In select cases in which the target gene was a transcription factor or microRNA, we also investigated the gene-regulatory networks altered upon CRE perturbation and how these networks differ across blood cell types. RESULTS We inhibited candidate CREs from fine-mapped blood trait GWAS variants (from ~750,000 individual of diverse ancestries) in human erythroid progenitors. In total, we targeted 543 variants (254 loci) mapping to candidate CREs, generating multimodal single-cell data including transcriptome, direct CRISPR gRNA capture, and cell surface proteins. We identified target genes in cis (within 500 kb) for 134 CREs. In most cases, we found that the target gene was the closest gene and that specific enhancer-associated biochemical hallmarks (H3K27ac and accessible chromatin) are essential for CRE function. Using multiple perturbations at the same locus, we were able to distinguished between causal variants from noncausal variants in linkage disequilibrium. For a subset of validated CREs, we also inserted specific GWAS variants using base-editing STING-seq (beeSTING-seq) and quantified the effect size and direction of GWAS variants on gene expression. Given our transcriptome-wide data, we examined dosage effects in cis and trans in cases in which the cis target is a transcription factor or microRNA. We found that trans target genes are also enriched for GWAS loci, and identified gene clusters within trans gene networks with distinct biological functions and expression patterns in primary human blood cells. CONCLUSION In this work, we investigated noncoding GWAS variants at scale, identifying target genes in single cells. These methods can help to address the variant-to-function challenges that are a barrier for translation of GWAS findings (e.g., drug targets for diseases with a genetic basis) and greatly expand our ability to understand mechanisms underlying GWAS loci. Identifying causal variants and their target genes with STING-seq. Uncovering causal variants and their target genes or function are a major challenge for GWASs. STING-seq combines perturbation of noncoding loci with multimodal single-cell sequencing to profile hundreds of GWAS loci in parallel. This approach can identify target genes in cis and trans , measure dosage effects, and decipher gene-regulatory networks. 

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2. Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

   https://doi.org/10.7554/eLife.73983  

   Nguyen Ba, Alex N; Lawrence, Katherine R; Rego-Costa, Artur; Gopalakrishnan, Shreyas; Temko, Daniel; Michor, Franziska; Desai, Michael M (February 2022, eLife)

   Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits. 

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3. MARS: leveraging allelic heterogeneity to increase power of association testing

   https://doi.org/10.1186/s13059-021-02353-8  

   Hormozdiari, Farhad; Jung, Junghyun; Eskin, Eleazar; J. Joo, Jong Wha (December 2021, Genome Biology)

   null (Ed.)

   Abstract In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error. 

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4. A comparative approach for selecting orthologous candidate genes underlying signal in genome-wide association studies across multiple species

   https://doi.org/10.1101/2023.10.05.561051  

   Whitt, L; Mahood, E H; Ziegler, G; Luebbert, C; Gillman, J D; Norton, G J; Price, A H; Salt, DE; Dilkes, B; Baxter, I (October 2023, bioRxiv)

   Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, genomic regions can contain hundreds to thousands of genes, and progressing from genomic regions to candidate genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, a mere 5% of loci are associated with a known ionomic gene - indicating that many causal genes are still unknown. To select candidates for the remaining 95% of loci, we developed a method to identify conserved genes underlying GWAS loci in multiple species. For 19 ionomic traits, we identified 14,336 candidates across Arabidopsis, soybean, rice, maize, and sorghum. We calculated the likelihood of candidates with random permutations of the data and determined that most of the top 10% of candidates were orthologous genes linked to GWAS loci across all five species. The candidate list also includes orthologous genes with previously established ionomic functions in Arabidopsis and rice. Our methods highlight the conserved nature of ionomic genetic regulators and enable the identification of previously unknown ionomic genes. 

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5. Statistical Methods in Genome-Wide Association Studies

   https://doi.org/10.1146/annurev-biodatasci-030320-041026  

   Sun, Ning; Zhao, Hongyu (July 2020, Annual Review of Biomedical Data Science)

   null (Ed.)

   Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges. 

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