Quality control and analytic best practices for testing genetic models of sex differences in large populations.

Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.

Population immunity predicts evolutionary trajectories of SARS-CoV-2.

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.

The deep population history of northern East Asia from the Late Pleistocene to the Holocene.

Northern East Asia was inhabited by modern humans as early as 40 thousand years ago (ka), as demonstrated by the Tianyuan individual. Using genome-wide data obtained from 25 individuals dated to 33.6-3.4 ka from the Amur region, we show that Tianyuan-related ancestry was widespread in northern East Asia before the Last Glacial Maximum (LGM). At the close of the LGM stadial, the earliest northern East Asian appeared in the Amur region, and this population is basal to ancient northern East Asians. Human populations in the Amur region have maintained genetic continuity from 14 ka, and these early inhabitants represent the closest East Asian source known for Ancient Paleo-Siberians. We also observed that EDAR V370A was likely to have been elevated to high frequency after the LGM, suggesting the possible timing for its selection. This study provides a deep look into the population dynamics of northern East Asia.

The genomic history of the Middle East.

The Middle East region is important to understand human evolution and migrations but is underrepresented in genomic studies. Here, we generated 137 high-coverage physically phased genome sequences from eight Middle Eastern populations using linked-read sequencing. We found no genetic traces of early expansions out-of-Africa in present-day populations but found Arabians have elevated Basal Eurasian ancestry that dilutes their Neanderthal ancestry. Population sizes within the region started diverging 15-20 kya, when Levantines expanded while Arabians maintained smaller populations that derived ancestry from local hunter-gatherers. Arabians suffered a population bottleneck around the aridification of Arabia 6 kya, while Levantines had a distinct bottleneck overlapping the 4.2 kya aridification event. We found an association between movement and admixture of populations in the region and the spread of Semitic languages. Finally, we identify variants that show evidence of selection, including polygenic selection. Our results provide detailed insights into the genomic and selective histories of the Middle East.

A natural single-guide RNA repurposes Cas9 to autoregulate CRISPR-Cas expression.

CRISPR-Cas systems provide prokaryotes with acquired immunity against viruses and plasmids, but how these systems are regulated to prevent autoimmunity is poorly understood. Here, we show that in the S. pyogenes CRISPR-Cas system, a long-form transactivating CRISPR RNA (tracr-L) folds into a natural single guide that directs Cas9 to transcriptionally repress its own promoter (Pcas). Further, we demonstrate that Pcas serves as a critical regulatory node. De-repression causes a dramatic 3,000-fold increase in immunization rates against viruses; however, heightened immunity comes at the cost of increased autoimmune toxicity. Using bioinformatic analyses, we provide evidence that tracrRNA-mediated autoregulation is widespread in type II-A CRISPR-Cas systems. Collectively, we unveil a new paradigm for the intrinsic regulation of CRISPR-Cas systems by natural single guides, which may facilitate the frequent horizontal transfer of these systems into new hosts that have not yet evolved their own regulatory strategies.

Identifying and Interpreting Apparent Neanderthal Ancestry in African Individuals.

Admixture has played a prominent role in shaping patterns of human genomic variation, including gene flow with now-extinct hominins like Neanderthals and Denisovans. Here, we describe a novel probabilistic method called IBDmix to identify introgressed hominin sequences, which, unlike existing approaches, does not use a modern reference population. We applied IBDmix to 2,504 individuals from geographically diverse populations to identify and analyze Neanderthal sequences segregating in modern humans. Strikingly, we find that African individuals carry a stronger signal of Neanderthal ancestry than previously thought. We show that this can be explained by genuine Neanderthal ancestry due to migrations back to Africa, predominately from ancestral Europeans, and gene flow into Neanderthals from an early dispersing group of humans out of Africa. Our results refine our understanding of Neanderthal ancestry in African and non-African populations and demonstrate that remnants of Neanderthal genomes survive in every modern human population studied to date.

An Organoid Biobank of Neuroendocrine Neoplasms Enables Genotype-Phenotype Mapping.

Gastroenteropancreatic (GEP) neuroendocrine neoplasm (NEN) that consists of neuroendocrine tumor and neuroendocrine carcinoma (NEC) is a lethal but under-investigated disease owing to its rarity. To fill the scarcity of clinically relevant models of GEP-NEN, we here established 25 lines of NEN organoids and performed their comprehensive molecular characterization. GEP-NEN organoids recapitulated pathohistological and functional phenotypes of the original tumors. Whole-genome sequencing revealed frequent genetic alterations in TP53 and RB1 in GEP-NECs, and characteristic chromosome-wide loss of heterozygosity in GEP-NENs. Transcriptome analysis identified molecular subtypes that are distinguished by the expression of distinct transcription factors. GEP-NEN organoids gained independence from the stem cell niche irrespective of genetic mutations. Compound knockout of TP53 and RB1, together with overexpression of key transcription factors, conferred on the normal colonic epithelium phenotypes that are compatible with GEP-NEN biology. Altogether, our study not only provides genetic understanding of GEP-NEN, but also connects its genetics and biological phenotypes.

Global Genetic Networks and the Genotype-to-Phenotype Relationship.

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Megabase Length Hypermutation Accompanies Human Structural Variation at 17p11.2.

DNA rearrangements resulting in human genome structural variants (SVs) are caused by diverse mutational mechanisms. We used long- and short-read sequencing technologies to investigate end products of de novo chromosome 17p11.2 rearrangements and query the molecular mechanisms underlying both recurrent and non-recurrent events. Evidence for an increased rate of clustered single-nucleotide variant (SNV) mutation in cis with non-recurrent rearrangements was found. Indel and SNV formation are associated with both copy-number gains and losses of 17p11.2, occur up to ∼1 Mb away from the breakpoint junctions, and favor C > G transversion substitutions; results suggest that single-stranded DNA is formed during the genesis of the SV and provide compelling support for a microhomology-mediated break-induced replication (MMBIR) mechanism for SV formation. Our data show an additional mutational burden of MMBIR consisting of hypermutation confined to the locus and manifesting as SNVs and indels predominantly within genes.

Identifying Epistasis in Cancer Genomes: A Delicate Affair.

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, "mutually exclusive." Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters. APARENT's predictions are highly accurate when tasked with inferring APA in synthetic and human 3'UTRs. Visualizing features learned across all network layers reveals that APARENT recognizes sequence motifs known to recruit APA regulators, discovers previously unknown sequence determinants of 3' end processing, and integrates these features into a comprehensive, interpretable, cis-regulatory code. We apply APARENT to forward engineer functional polyadenylation signals with precisely defined cleavage position and isoform usage and validate predictions experimentally. Finally, we use APARENT to quantify the impact of genetic variants on APA. Our approach detects pathogenic variants in a wide range of disease contexts, expanding our understanding of the genetic origins of disease.

A Reverse Ecology Approach Based on a Biological Definition of Microbial Populations.

Delineating ecologically meaningful populations among microbes is important for identifying their roles in environmental and host-associated microbiomes. Here, we introduce a metric of recent gene flow, which when applied to co-existing microbes, identifies congruent genetic and ecological units separated by strong gene flow discontinuities from their next of kin. We then develop a pipeline to identify genome regions within these units that show differential adaptation and allow mapping of populations onto environmental variables or host associations. Using this reverse ecology approach, we show that the human commensal bacterium Ruminococcus gnavus breaks up into sharply delineated populations that show different associations with health and disease. Defining populations by recent gene flow in this way will facilitate the analysis of bacterial and archaeal genomes using ecological and evolutionary theory developed for plants and animals, thus allowing for testing unifying principles across all biology.

Human Disease Variation in the Light of Population Genomics.

Identifying the causes of similarities and differences in genetic disease prevalence among humans is central to understanding disease etiology. While present-day humans are not strongly differentiated, vast amounts of genomic data now make it possible to study subtle patterns of genetic variation. This allows us to trace our genomic history thousands of years into the past and its implications for the distribution of disease-associated variants today. Genomic analyses have shown that demographic processes shaped the distribution and frequency of disease-associated variants over time. Furthermore, local adaptation to new environmental conditions-including pathogens-has generated strong patterns of differentiation at particular loci. Researchers are also beginning to uncover the genetic architecture of complex diseases, affected by many variants of small effect. The field of population genomics thus holds great potential for providing further insights into the evolution of human disease.

Optimal Decoding of Cellular Identities in a Genetic Network.

In developing organisms, spatially prescribed cell identities are thought to be determined by the expression levels of multiple genes. Quantitative tests of this idea, however, require a theoretical framework capable of exposing the rules and precision of cell specification over developmental time. We use the gap gene network in the early fly embryo as an example to show how expression levels of the four gap genes can be jointly decoded into an optimal specification of position with 1% accuracy. The decoder correctly predicts, with no free parameters, the dynamics of pair-rule expression patterns at different developmental time points and in various mutant backgrounds. Precise cellular identities are thus available at the earliest stages of development, contrasting the prevailing view of positional information being slowly refined across successive layers of the patterning network. Our results suggest that developmental enhancers closely approximate a mathematically optimal decoding strategy.

Physical and Functional Compartmentalization of Archaeal Chromosomes.

The three-dimensional organization of chromosomes can have a profound impact on their replication and expression. The chromosomes of higher eukaryotes possess discrete compartments that are characterized by differing transcriptional activities. Contrastingly, most bacterial chromosomes have simpler organization with local domains, the boundaries of which are influenced by gene expression. Numerous studies have revealed that the higher-order architectures of bacterial and eukaryotic chromosomes are dependent on the actions of structural maintenance of chromosomes (SMC) superfamily protein complexes, in particular, the near-universal condensin complex. Intriguingly, however, many archaea, including members of the genus Sulfolobus do not encode canonical condensin. We describe chromosome conformation capture experiments on Sulfolobus species. These reveal the presence of distinct domains along Sulfolobus chromosomes that undergo discrete and specific higher-order interactions, thus defining two compartment types. We observe causal linkages between compartment identity, gene expression, and binding of a hitherto uncharacterized SMC superfamily protein that we term "coalescin."

Dosage Compensation of the X Chromosome: A Complex Epigenetic Assignment Involving Chromatin Regulators and Long Noncoding RNAs.

X chromosome regulation represents a prime example of an epigenetic phenomenon where coordinated regulation of a whole chromosome is required. In flies, this is achieved by transcriptional upregulation of X chromosomal genes in males to equalize the gene dosage differences in females. Chromatin-bound proteins and long noncoding RNAs (lncRNAs) constituting a ribonucleoprotein complex known as the male-specific lethal (MSL) complex or the dosage compensation complex mediate this process. MSL complex members decorate the male X chromosome, and their absence leads to male lethality. The male X chromosome is also enriched with histone H4 lysine 16 acetylation (H4K16ac), indicating that the chromatin compaction status of the X chromosome also plays an important role in transcriptional activation. How the X chromosome is specifically targeted and how dosage compensation is mechanistically achieved are central questions for the field. Here, we review recent advances, which reveal a complex interplay among lncRNAs, the chromatin landscape, transcription, and chromosome conformation that fine-tune X chromosome gene expression.

Nuclear Genomic Instability and Aging.

The nuclear genome decays as organisms age. Numerous studies demonstrate that the burden of several classes of DNA lesions is greater in older mammals than in young mammals. More challenging is proving this is a cause rather than a consequence of aging. The DNA damage theory of aging, which argues that genomic instability plays a causal role in aging, has recently gained momentum. Support for this theory stems partly from progeroid syndromes in which inherited defects in DNA repair increase the burden of DNA damage leading to accelerated aging of one or more organs. Additionally, growing evidence shows that DNA damage accrual triggers cellular senescence and metabolic changes that promote a decline in tissue function and increased susceptibility to age-related diseases. Here, we examine multiple lines of evidence correlating nuclear DNA damage with aging. We then consider how, mechanistically, nuclear genotoxic stress could promote aging. We conclude that the evidence, in toto, supports a role for DNA damage as a nidus of aging.

Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model.

The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent "omnigenic" or "core genes" model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.

The Biology of CRISPR-Cas: Backward and Forward.

In bacteria and archaea, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system against phages and other foreign genetic elements. Here, we review the biology of the diverse CRISPR-Cas systems and the major progress achieved in recent years in understanding the underlying mechanisms of the three stages of CRISPR-Cas immunity: adaptation, crRNA biogenesis, and interference. The ecology and regulation of CRISPR-Cas in the context of phage infection, the roles of these systems beyond immunity, and the open questions that propel the field forward are also discussed.

Statistical Detection of Relatives Typed with Disjoint Forensic and Biomedical Loci.

In familial searching in forensic genetics, a query DNA profile is tested against a database to determine whether it represents a relative of a database entrant. We examine the potential for using linkage disequilibrium to identify pairs of profiles as belonging to relatives when the query and database rely on nonoverlapping genetic markers. Considering data on individuals genotyped with both microsatellites used in forensic applications and genome-wide SNPs, we find that ∼30%-32% of parent-offspring pairs and ∼35%-36% of sib pairs can be identified from the SNPs of one member of the pair and the microsatellites of the other. The method suggests the possibility of performing familial searches of microsatellite databases using query SNP profiles, or vice versa. It also reveals that privacy concerns arising from computations across multiple databases that share no genetic markers in common entail risks, not only for database entrants, but for their close relatives as well.