INTRODUCTION A major challenge in genomics is discerning which bases among billions alter organismal phenotypes and affect health and disease risk. Evidence of past selective pressure on a base, whether highly conserved or fast evolving, is a marker of functional importance. Bases that are unchanged in all mammals may shape phenotypes that are essential for organismal health. Bases that are evolving quickly in some species, or changed only in species that share an adaptive trait, may shape phenotypes that support survival in specific niches. Identifying bases associated with exceptional capacity for cellular recovery, such as in species that hibernate, could inform therapeutic discovery. RATIONALE The power and resolution of evolutionary analyses scale with the number and diversity of species compared. By analyzing genomes for hundreds of placental mammals, we can detect which individual bases in the genome are exceptionally conserved (constrained) and likely to be functionally important in both coding and noncoding regions. By including species that represent all orders of placental mammals and aligning genomes using a method that does not require designating humans as the reference species, we explore unusual traits in other species. RESULTS Zoonomia’s mammalian comparative genomics resources are the most comprehensive and statistically well-powered produced to date, with a protein-coding alignment of 427 mammals and a whole-genome alignment of 240 placental mammals representing all orders. We estimate that at least 10.7% of the human genome is evolutionarily conserved relative to neutrally evolving repeats and identify about 101 million significantly constrained single bases (false discovery rate < 0.05). We cataloged 4552 ultraconserved elements at least 20 bases long that are identical in more than 98% of the 240 placental mammals. Many constrained bases have no known function, illustrating the potential for discovery using evolutionary measures. Eighty percent are outside protein-coding exons, and half have no functional annotations in the Encyclopedia of DNA Elements (ENCODE) resource. Constrained bases tend to vary less within human populations, which is consistent with purifying selection. Species threatened with extinction have few substitutions at constrained sites, possibly because severely deleterious alleles have been purged from their small populations. By pairing Zoonomia’s genomic resources with phenotype annotations, we find genomic elements associated with phenotypes that differ between species, including olfaction, hibernation, brain size, and vocal learning. We associate genomic traits, such as the number of olfactory receptor genes, with physical phenotypes, such as the number of olfactory turbinals. By comparing hibernators and nonhibernators, we implicate genes involved in mitochondrial disorders, protection against heat stress, and longevity in this physiologically intriguing phenotype. Using a machine learning–based approach that predicts tissue-specific cis - regulatory activity in hundreds of species using data from just a few, we associate changes in noncoding sequence with traits for which humans are exceptional: brain size and vocal learning. CONCLUSION Large-scale comparative genomics opens new opportunities to explore how genomes evolved as mammals adapted to a wide range of ecological niches and to discover what is shared across species and what is distinctively human. High-quality data for consistently defined phenotypes are necessary to realize this potential. Through partnerships with researchers in other fields, comparative genomics can address questions in human health and basic biology while guiding efforts to protect the biodiversity that is essential to these discoveries. Comparing genomes from 240 species to explore the evolution of placental mammals. Our new phylogeny (black lines) has alternating gray and white shading, which distinguishes mammalian orders (labeled around the perimeter). Rings around the phylogeny annotate species phenotypes. Seven species with diverse traits are illustrated, with black lines marking their branch in the phylogeny. Sequence conservation across species is described at the top left. IMAGE CREDIT: K. MORRILL
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This content will become publicly available on April 28, 2024
Leveraging base-pair mammalian constraint to understand genetic variation and human disease
INTRODUCTION Thousands of genetic variants have been associated with human diseases and traits through genome-wide association studies (GWASs). Translating these discoveries into improved therapeutics requires discerning which variants among hundreds of candidates are causally related to disease risk. To date, only a handful of causal variants have been confirmed. Here, we leverage 100 million years of mammalian evolution to address this major challenge. RATIONALE We compared genomes from hundreds of mammals and identified bases with unusually few variants (evolutionarily constrained). Constraint is a measure of functional importance that is agnostic to cell type or developmental stage. It can be applied to investigate any heritable disease or trait and is complementary to resources using cell type– and time point–specific functional assays like Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTEx). RESULTS Using constraint calculated across placental mammals, 3.3% of bases in the human genome are significantly constrained, including 57.6% of coding bases. Most constrained bases (80.7%) are noncoding. Common variants (allele frequency ≥ 5%) and low-frequency variants (0.5% ≤ allele frequency < 5%) are depleted for constrained bases (1.85 versus 3.26% expected by chance, P < 2.2 × 10 −308 ). Pathogenic ClinVar variants are more constrained than benign variants ( P < 2.2 × 10 −16 ). The most constrained common variants are more enriched for disease single-nucleotide polymorphism (SNP)–heritability in 63 independent GWASs. The enrichment of SNP-heritability in constrained regions is greater (7.8-fold) than previously reported in mammals and is even higher in primates (11.1-fold). It exceeds the enrichment of SNP-heritability in nonsynonymous coding variants (7.2-fold) and fine-mapped expression quantitative trait loci (eQTL)–SNPs (4.8-fold). The enrichment peaks near constrained bases, with a log-linear decrease of SNP-heritability enrichment as a function of the distance to a constrained base. Zoonomia constraint scores improve functionally informed fine-mapping. Variants at sites constrained in mammals and primates have greater posterior inclusion probabilities and higher per-SNP contributions. In addition, using both constraint and functional annotations improves polygenic risk score accuracy across a range of traits. Finally, incorporating constraint information into the analysis of noncoding somatic variants in medulloblastomas identifies new candidate driver genes. CONCLUSION Genome-wide measures of evolutionary constraint can help discern which variants are functionally important. This information may accelerate the translation of genomic discoveries into the biological, clinical, and therapeutic knowledge that is required to understand and treat human disease. Using evolutionary constraint in genomic studies of human diseases. ( A ) Constraint was calculated across 240 mammal species, including 43 primates (teal line). ( B ) Pathogenic ClinVar variants ( N = 73,885) are more constrained across mammals than benign variants ( N = 231,642; P < 2.2 × 10 −16 ). ( C ) More-constrained bases are more enriched for trait-associated variants (63 GWASs). ( D ) Enrichment of heritability is higher in constrained regions than in functional annotations (left), even in a joint model with 106 annotations (right). ( E ) Fine-mapping (PolyFun) using a model that includes constraint scores identifies an experimentally validated association at rs1421085. Error bars represent 95% confidence intervals. BMI, body mass index; LF, low frequency; PIP, posterior inclusion probability.
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- Award ID(s):
- 2046550
- NSF-PAR ID:
- 10423946
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Science
- Volume:
- 380
- Issue:
- 6643
- ISSN:
- 0036-8075
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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