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Key Points The copy number variation (CNV) map of the human genome documents the extent and characteristics of CNV among healthy populations. Depending on the level of stringency of the map, 4.8–9.7% of the human genome contributes to CNVs. CNVs are distributed unevenly in the genome; the pericentromeric and subtelomeric regions of chromosomes show a particularly high rate of variation. Various gene groups are affected differently by copy number variants. Genes that are associated with disease are the least affected by copy number variants, whereas paralogous genes have the most copy number variants. More than 100 genes can be completely removed from the genome without producing apparent phenotypic consequences. The CNV map will aid the interpretation of copy number variants of medical importance. Copy number variation (CNV) accounts for much of the variability across genomes and can influence phenotypes. In this Analysis, the authors construct a CNV map using high-quality data from published studies to provide more detailed insights into CNV, which will be useful for both clinical and research applications in the future. A major contribution to the genome variability among individuals comes from deletions and duplications — collectively termed copy number variations (CNVs) — which alter the diploid status of DNA. These alterations may have no phenotypic effect, account for adaptive traits or can underlie disease. We have compiled published high-quality data on healthy individuals of various ethnicities to construct an updated CNV map of the human genome. Depending on the level of stringency of the map, we estimated that 4.8–9.5% of the genome contributes to CNV and found approximately 100 genes that can be completely deleted without producing apparent phenotypic consequences. This map will aid the interpretation of new CNV findings for both clinical and research applications.

Long-read sequencing (LRS) technologies have enhanced the ability to resolve complex genomic architecture and determine the ‘phase’ relationships of genetic variants over long distances. Although genome-wide association studies (GWAS) identify individual variants associated with complex traits, they do not typically account for whether multiple associated signals at a locus may act in cis or trans , or whether they reflect allelic heterogeneity. As a result, effects that arise specifically from phase relationships may remain hidden in analyses using short-read and microarray data. While the advent of LRS has enabled accurate measurement of phase in population cohorts, statistical methods that leverage phase in genetic association analysis remain underdeveloped. Here, we introduce the Regression on Phase (RoP) method, which directly models cis and trans phase effects between variants under a regression framework. In simulations, RoP outperforms genotype interaction tests that detect phase effects indirectly, and distinguishes in- cis from in- trans phase effects. We implemented RoP at two cystic fibrosis (CF) modifier loci discovered by GWAS. At the chromosome 7q35 trypsinogen locus, RoP confirmed that two variants contributed independently (allelic heterogeneity). At the SLC6A14 locus on chromosome X, phase analysis uncovered a coordinated regulatory mechanism in which a promoter variant modulates lung phenotypes in individuals with CF when acting in cis with a lung-specific enhancer (E2765449/enhD). This coordinated regulation was confirmed in functional studies. These findings highlight the potential of leveraging phase information from LRS in genetic association studies. Analyzing phase effects with RoP can provide deeper insights into the complex genetic architectures underlying disease phenotypes, ultimately guiding more informed functional investigations and potentially revealing new therapeutic targets.

High-altitude hypoxia (reduced inspired oxygen tension due to decreased barometric pressure) exerts severe physiological stress on the human body. Two high-altitude regions where humans have lived for millennia are the Andean Altiplano and the Tibetan Plateau. Populations living in these regions exhibit unique circulatory, respiratory, and hematological adaptations to life at high altitude. Although these responses have been well characterized physiologically, their underlying genetic basis remains unknown. We performed a genome scan to identify genes showing evidence of adaptation to hypoxia. We looked across each chromosome to identify genomic regions with previously unknown function with respect to altitude phenotypes. In addition, groups of genes functioning in oxygen metabolism and sensing were examined to test the hypothesis that particular pathways have been involved in genetic adaptation to altitude. Applying four population genetic statistics commonly used for detecting signatures of natural selection, we identified selection-nominated candidate genes and gene regions in these two populations (Andeans and Tibetans) separately. The Tibetan and Andean patterns of genetic adaptation are largely distinct from one another, with both populations showing evidence of positive natural selection in different genes or gene regions. Interestingly, one gene previously known to be important in cellular oxygen sensing, EGLN1 (also known as PHD2), shows evidence of positive selection in both Tibetans and Andeans. However, the pattern of variation for this gene differs between the two populations. Our results indicate that several key HIF-regulatory and targeted genes are responsible for adaptation to high altitude in Andeans and Tibetans, and several different chromosomal regions are implicated in the putative response to selection. These data suggest a genetic role in high-altitude adaption and provide a basis for future genotype/phenotype association studies necessary to confirm the role of selection-nominated candidate genes and gene regions in adaptation to altitude.

Although the full aetiology of autism spectrum disorder (ASD) is unknown, familial and twin studies demonstrate high heritability of 60–90%, indicating a predominant role of genetics in the development of the disorder. The genetic architecture of ASD consists of a complex array of rare and common variants of all classes of genetic variation usually acting additively to augment individual risk. The relative contribution of heredity in ASD persists despite selective pressures against the classic autistic phenotype; a phenomenon thought to be explained, in part, by the incidence of spontaneous (or de novo) mutations. Notably, environmental exposures attributed as salient risk factors for ASD may play a causal role in the emergence of deleterious de novo variations, with several ASD-associated agents having significant mutagenic potential. To explore this hypothesis, this review article assesses published epidemiological data with evidence derived from assays of mutagenicity, both in vivo and in vitro, to determine the likely role such agents may play in augmenting the genetic liability in ASD. Broadly, these exposures were observed to elicit genomic alterations through one or a combination of: (1) direct interaction with genetic material; (2) impaired DNA repair; or (3) oxidative DNA damage. However, the direct contribution of these factors to the ASD phenotype cannot be determined without further analysis. The development of comprehensive prospective birth cohorts in combination with genome sequencing is essential to forming a causal, mechanistic account of de novo mutations in ASD that links exposure, genotypic alterations, and phenotypic consequences.

Genetic testing of patient constitutional DNA (i.e., their genome) is increasingly performed in medical practice. Sequencing an entire human genome (about 3.2 billion nucleotides) is now possible to complete in days to weeks, and at a similar cost to some advanced imaging tests or to a brief admission to hospital. Genome sequencing is being integrated into health care systems internationally, most notably in the UK. Starting in 2021, genome sequencing is being performed as a clinical genetic test in Ontario, Canada. Genome sequencing is broader in scope than other commonly used genetic tests, and data can be analyzed in both hypothesis-driven and hypothesis-generating ways. For these reasons, genome sequencing will likely eventually supplant exome sequencing, large next-generation sequencing gene panel tests and chromosomal microarray analysis. Genome sequencing is a consideration for children and adults with suspected genetic disorders for whom a targeted genetic testing approach is unlikely to succeed or has already failed.

Glioblastoma (GBM) is a cancer comprised of morphologically, genetically, and phenotypically diverse cells. However, an understanding of the functional significance of intratumoral heterogeneity is lacking. We devised a method to isolate and functionally profile tumorigenic clones from patient glioblastoma samples. Individual clones demonstrated unique proliferation and differentiation abilities. Importantly, naüïve patient tumors included clones that were temozolomide resistant, indicating that resistance to conventional GBM therapy can preexist in untreated tumors at a clonal level. Further, candidate therapies for resistant clones were detected with clone-specific drug screening. Genomic analyses revealed genes and pathways that associate with specific functional behavior of single clones. Our results suggest that functional clonal profiling used to identify tumorigenic and drug-resistant tumor clones will lead to the discovery of new GBM clone-specific treatment strategies. Significance Glioblastoma is an incurable brain tumor. It is characterized by intratumoral phenotypic and genetic heterogeneity, but the functional significance of this heterogeneity is unclear. We devised an integrated functional and genomic strategy to obtain single cell-derived tumor clones directly from patient tumors to identify mechanisms of aggressive clone behavior and drug resistance. Genomic analysis of single clones identified genes associated with clonal phenotypes. We predict that integration of functional and genomic analysis at a clonal level will be essential for understanding evolution and therapeutic resistance of human cancer, and will lead to the discovery of novel driver mechanisms and clone-specific cancer treatment.

Purpose For neurodevelopmental disorders (NDDs), etiological evaluation can be a diagnostic odyssey involving numerous genetic tests, underscoring the need to develop a streamlined algorithm maximizing molecular diagnostic yield for this clinical indication. Our objective was to compare the yield of exome sequencing (ES) with that of chromosomal microarray (CMA), the current first-tier test for NDDs. Methods We performed a PubMed scoping review and meta-analysis investigating the diagnostic yield of ES for NDDs as the basis of a consensus development conference. We defined NDD as global developmental delay, intellectual disability, and/or autism spectrum disorder. The consensus development conference included input from genetics professionals, pediatric neurologists, and developmental behavioral pediatricians. Results After applying strict inclusion/exclusion criteria, we identified 30 articles with data on molecular diagnostic yield in individuals with isolated NDD, or NDD plus associated conditions (such as Rett-like features). Yield of ES was 36% overall, 31% for isolated NDD, and 53% for the NDD plus associated conditions. ES yield for NDDs is markedly greater than previous studies of CMA (15–20%). Conclusion Our review demonstrates that ES consistently outperforms CMA for evaluation of unexplained NDDs. We propose a diagnostic algorithm placing ES at the beginning of the evaluation of unexplained NDDs.

Autism spectrum disorder (ASD) is often grouped with other brain-related phenotypes into a broader category of neurodevelopmental disorders (NDDs). In clinical practice, providers need to decide which genes to test in individuals with ASD phenotypes, which requires an understanding of the level of evidence for individual NDD genes that supports an association with ASD. Consensus is currently lacking about which NDD genes have sufficient evidence to support a relationship to ASD. Estimates of the number of genes relevant to ASD differ greatly among research groups and clinical sequencing panels, varying from a few to several hundred. This Roadmap discusses important considerations necessary to provide an evidence-based framework for the curation of NDD genes based on the level of information supporting a clinically relevant relationship between a given gene and ASD.A curated list of genes that are relevant to autism spectrum disorder (ASD) would greatly benefit clinical genetic testing. This Roadmap discusses the need for an evidence-based framework for gene curation that is based on the level of information supporting a clinically relevant relationship between a given gene and ASD.

Structural genetic changes, especially copy number variants (CNVs), represent a major source of genetic variation contributing to human disease. Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease, but to date little is known about the role of CNVs in the etiology of TOF. Using high-resolution genome-wide microarrays and stringent calling methods, we investigated rare CNVs in a prospectively recruited cohort of 433 unrelated adults with TOF and/or pulmonary atresia at a single centre. We excluded those with recognized syndromes, including 22q11.2 deletion syndrome. We identified candidate genes for TOF based on converging evidence between rare CNVs that overlapped the same gene in unrelated individuals and from pathway analyses comparing rare CNVs in TOF cases to those in epidemiologic controls. Even after excluding the 53 (10.7%) subjects with 22q11.2 deletions, we found that adults with TOF had a greater burden of large rare genic CNVs compared to controls (8.82% vs. 4.33%, p = 0.0117). Six loci showed evidence for recurrence in TOF or related congenital heart disease, including typical 1q21.1 duplications in four (1.18%) of 340 Caucasian probands. The rare CNVs implicated novel candidate genes of interest for TOF, including PLXNA2, a gene involved in semaphorin signaling. Independent pathway analyses highlighted developmental processes as potential contributors to the pathogenesis of TOF. These results indicate that individually rare CNVs are collectively significant contributors to the genetic burden of TOF. Further, the data provide new evidence for dosage sensitive genes in PLXNA2-semaphorin signaling and related developmental processes in human cardiovascular development, consistent with previous animal models.

Most eukaryotic messenger RNAs (mRNAs) are spliced to remove introns. Splicing generates uninterrupted open reading frames that can be translated into proteins. Splicing is often highly regulated, generating alternative spliced forms that code for variant proteins in different tissues. RNA-binding proteins that bind specific sequences in the mRNA regulate splicing. Xiong et al. develop a computational model that predicts splicing regulation for any mRNA sequence (see the Perspective by Guigó and Valcárcel). They use this to analyze more than half a million mRNA splicing sequence variants in the human genome. They are able to identify thousands of known disease-causing mutations, as well as many new disease candidates, including 17 new autism-linked genes. Science , this issue 10.1126/science.1254806 ; see also p. 124 A model predicts how thousands of disease-linked nucleotide variants affect messenger RNA splicing. [Also see Perspective by Guigó and Valcárcel ] To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of more than 650,000 intronic and exonic variants revealed widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations that are more than 30 nucleotides from any splice site alter splicing nine times as often as common variants, and missense exonic disease mutations that have the least impact on protein function are five times as likely as others to alter splicing. We detected tens of thousands of disease-causing mutations, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole-genome sequencing of individuals with autism revealed misspliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine.