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Abstract Hi-C characterizes three-dimensional chromatin organization, facilitates haplotype phasing, and enables genome-assembly scaffolding, but encounters difficulties across complex regions. By coupling chromosome conformation capture (3C) with PacBio HiFilong-read sequencing, here we develop a method (CiFi) that enables analysis of genomic interactions across repetitive regions. Starting with as little as 60,000 cells (sub-microgram DNA), the method produces multi-kilobasepair HiFi reads that contain multiple interacting, concatenated segments (~350 bp to 2 kbp). This multiplicity and increase in segment length versus standard short-read-based Hi-C improves read-mapping efficiency and coverage in repetitive regions and enhances haplotype phasing. CiFi pairwise interactions are largely concordant with Hi-C from a human lymphoblastoid cell line, with gains in assigning topologically associating domains across centromeres, segmental duplications, and human disease-associated genomic hotspots. As CiFi requires less input versus established methods, we apply the approach to characterize single small insects: assaying chromatin interactions across the genome from anAnopheles coluzziimosquito and producing a chromosome-scale scaffolded assembly from aCeratitis capitataMediterranean fruit fly. Together, CiFi enables assessment of chromosome-scale interactions of previously recalcitrant low-complexity loci, low-input samples, and small organisms. 

Synopsis The trajectory of evolution is impacted by molecular constraints and biases that are difficult to validate experimentally. Repeated evolution of similar traits across the Tree of Life serves as a natural experiment to discern common factors that drive the evolution of these traits. The architecture of genomes in one-dimensional, two-dimensional, and three-dimensional space is emerging as a potential factor that may predict repeated phenotypic evolution. For example, chromatin packaging and the 3D organization of the genome within the nucleus can impose evolutionary constraints by predisposing genomic regions for particular types of mutations, while the evolution of genome sequence can also drive reorganization of chromatin. With the explosion of new library preparation and sequencing technologies that are accessible for non-model species, we envision a great opportunity to understand how genome architecture across phylogenetically disparate species may impact repeated phenotypic evolution. We provide examples of the known and potential avenues of phenotypic convergence at each level of genome architecture and how integration of these data can provide unique insights into the constraints, trajectory, and predictability of evolution. 

Abstract Structural variants (SVs)—including duplications, deletions, and inversions of DNA—can have significant genomic and functional impacts but are technically difficult to identify and assay compared with single‐nucleotide variants. With the aid of new genomic technologies, it has become clear that SVs account for significant differences across and within species. This phenomenon is particularly well‐documented for humans and other primates due to the wealth of sequence data available. In great apes, SVs affect a larger number of nucleotides than single‐nucleotide variants, with many identified SVs exhibiting population and species specificity. In this review, we highlight the importance of SVs in human evolution by (1) how they have shaped great ape genomes resulting in sensitized regions associated with traits and diseases, (2) their impact on gene functions and regulation, which subsequently has played a role in natural selection, and (3) the role of gene duplications in human brain evolution. We further discuss how to incorporate SVs in research, including the strengths and limitations of various genomic approaches. Finally, we propose future considerations in integrating existing data and biospecimens with the ever‐expanding SV compendium propelled by biotechnology advancements. 

High-resolution profiling of dynamic gene regulatory changes during brain development remains challenging. In this issue of Neuron, Jia, Hou, Cui, Luo, and Duan et al. present SCOPE-C, a low-input method to simultaneously capture open chromatin and dynamic cis-regulatory contacts, revealing cell- type-specific and human-biased regulatory hubs during corticogenesis. 

Human-specific segmental duplications (HSDs) contain millions of base pairs of sequence unique to the human genome, including genes that shape neurodevelopment. Despite their young age (<6 million years), HSD genes exhibit widespread regulatory divergence, with paralog-specific expression patterns documented across a variety of tissues and cell types. Using long-read expression and epigenomic data, we show that human-specific paralogs tend to have lower activity than the shared, ancestral ones. To systematically characterize the cis-regulatory elements (CREs) within HSDs and understand patterns of regulatory change in recently evolved gene families, we conducted a massively parallel reporter assay of 7,160 human duplicated and chimpanzee orthologous sequences in lymphoblastoid (GM12878) and neuroblastoma (SH-SY5Y) cell lines. A large proportion (14–24%) of sequences exhibited differential activity relative to the chimpanzee ortholog (or between human paralogs), mostly with small fold-differences. Combining measured activity levels across all assayed sequences, predicted differences in cis-regulatory activity correlated with mRNA levels in SH-SY5Y. Differentially active CREs were validated for CHRFAM7A, HYDIN2, and SRGAP2C that may contribute to paralog-specific expression patterns and thereby to human-specific traits. While we find some changes in CRE activity shared between duplicate paralogs likely driving regulatory divergence in gene expression, consideration of non-shared adjacent sequences to duplications suggests a larger role for altered genome positional effects. In all, this work suggests that functional divergence of duplicated CREs contributes moderately to regulatory divergence of HSD genes and uncovers enhancers that are candidate drivers of human-specific regulatory patterns.