Multiplexed functional genomic assays to decipher the noncoding genome

GWAS-Informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density

Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblasts (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from stratified LD-score regression involving 98 cell types grouped into 15 tissues. 23 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed unexpectedly that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues. Extending our CRISPRi screening approach to these tissues could play a key role in fully elucidating the etiology of BMD.

Pharmacogenetic analysis of structural variation in the 1000 genomes project using whole genome sequences

While significant strides have been made in understanding pharmacogenetics (PGx) and gene-drug interactions, there remains limited characterization of population-level PGx variation. This study aims to comprehensively profile global star alleles (haplotype patterns) and phenotype frequencies in 58 pharmacogenes associated with drug absorption, distribution, metabolism, and excretion. PyPGx, a star-allele calling tool, was employed to identify star alleles within high-coverage whole genome sequencing (WGS) data from the 1000 Genomes Project (N = 2504; 26 global populations). This process involved detecting structural variants (SVs), such as gene deletions, duplications, hybrids, as well as single nucleotide variants and insertion-deletion variants. The majority of our PyPGx calls for star alleles and phenotype frequencies aligned with the Pharmacogenomics Knowledge Base, although notable population-specific frequencies differed at least twofold. Validation efforts confirmed known SVs while uncovering several novel SVs currently undefined as star alleles. Additionally, we identified 210 small nucleotide variants associated with severe functional consequences that are not defined as star alleles. The study serves as a valuable resource, providing updated population-level star allele and phenotype frequencies while incorporating SVs. It also highlights the burgeoning potential of cost-effective WGS for PGx genotyping, offering invaluable insights to improve tailored drug therapies across diverse populations.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Functional neurogenomics in autism spectrum disorders: A decade of progress

Advances in autism spectrum disorder (ASD) genetics have identified many genetic causes, reflecting remarkable progress while at the same time identifying challenges such as heterogeneity and pleiotropy, which complicate attempts to connect genetic risk to mechanisms. High-throughput functional genomic approaches have yielded progress by defining a molecular pathology in the brain of individuals with ASD and in identifying convergent biological pathways through which risk genes are predicted to act. These studies indicate that ASD genetic risk converges in early brain development, primarily during the period of cortical neurogenesis. Over development, genetic perturbations in turn lead to broad neuronal signaling dysregulation, most prominent in glutamatergic cortical-cortical projecting neurons and somatostatin positive interneurons, which is accompanied by glial dyshomeostasis throughout the cerebral cortex. Connecting these developmental perturbations to disrupted neuronal and glial function in the postnatal brain is an important direction in current research. Coupling functional genomic approaches with advances in induced pluripotent stem cell-derived neural organoid development provides a promising avenue for connecting brain pathology to developmental mechanisms.

gRNA-SeqRET: a universal tool for targeted and genome-scale gRNA design and sequence extraction for prokaryotes and eukaryotes

High-throughput genetic screening is frequently employed to rapidly associate gene with phenotype and establish sequence-function relationships. With the advent of CRISPR technology, and the ability to functionally interrogate previously genetically recalcitrant organisms, non-model organisms can be investigated using pooled guide RNA (gRNA) libraries and sequencing-based assays to quantitatively assess fitness of every targeted locus in parallel. To aid the construction of pooled gRNA assemblies, we have developed an in silico design workflow for gRNA selection using the gRNA Sequence Region Extraction Tool (gRNA-SeqRET). Built upon the previously developed CCTop, gRNA-SeqRET enables automated, scalable design of gRNA libraries that target user-specified regions or whole genomes of any prokaryote or eukaryote. Additionally, gRNA-SeqRET automates the bulk extraction of any regions of sequence relative to genes or other features, aiding in the design of homology arms for insertion or deletion constructs. We also assess in silico the application of a designed gRNA library to other closely related genomes and demonstrate that for very closely related organisms Average Nucleotide Identity (ANI) > 95% a large fraction of the library may be of relevance. The gRNA-SeqRET web application pipeline can be accessed at https://grna.jgi.doe.gov. The source code is comprised of freely available software tools and customized Python scripts, and is available at https://bitbucket.org/berkeleylab/grnadesigner/src/master/ under a modified BSD open-source license (https://bitbucket.org/berkeleylab/grnadesigner).

Characterization of Gene Regulatory Elements in Human Fetal Cortical Development: Enhancing Our Understanding of Neurodevelopmental Disorders and Evolution

The neocortex is the region that most distinguishes human brain from other mammals and primates [Annu Rev Genet. 2021 Nov;55(1):555-81]. Studying the development of human cortex is important in understanding the evolutionary changes occurring in humans relative to other primates, as well as in elucidating mechanisms underlying neurodevelopmental disorders. Cortical development is a highly regulated process, spatially and temporally coordinated by expression of essential transcriptional factors in response to signaling pathways [Neuron. 2019 Sep;103(6):980-1004]. Enhancers are the most well-understood cis-acting, non-protein-coding regulatory elements that regulate gene expression [Nat Rev Genet. 2014 Apr;15(4):272-86]. Importantly, given the conservation of both DNA sequence and molecular function of the majority of proteins across mammals [Genome Res. 2003 Dec;13(12):2507-18], enhancers [Science. 2015 Mar;347(6226):1155-9], which are far more divergent at the sequence level, likely account for the phenotypes that distinguish the human brain by changing the regulation of gene expression. In this review, we will revisit the conceptual framework of gene regulation during human brain development, as well as the evolution of technologies to study transcriptional regulation, with recent advances in genome biology that open a window allowing us to systematically characterize cis-regulatory elements in developing human brain [Hum Mol Genet. 2022 Oct;31(R1):R84-96]. We provide an update on work to characterize the suite of all enhancers in the developing human brain and the implications for understanding neuropsychiatric disorders. Finally, we discuss emerging therapeutic ideas that utilize our emerging knowledge of enhancer function.