Protein-altering variants at copy number-variable regions influence diverse human phenotypes.

Copy number variants (CNVs) are among the largest genetic variants, yet CNVs have not been effectively ascertained in most genetic association studies. Here we ascertained protein-altering CNVs from UK Biobank whole-exome sequencing data (n = 468,570) using haplotype-informed methods capable of detecting subexonic CNVs and variation within segmental duplications. Incorporating CNVs into analyses of rare variants predicted to cause gene loss of function (LOF) identified 100 associations of predicted LOF variants with 41 quantitative traits. A low-frequency partial deletion of RGL3 exon 6 conferred one of the strongest protective effects of gene LOF on hypertension risk (odds ratio = 0.86 (0.82-0.90)). Protein-coding variation in rapidly evolving gene families within segmental duplications-previously invisible to most analysis methods-generated some of the human genome's largest contributions to variation in type 2 diabetes risk, chronotype and blood cell traits. These results illustrate the potential for new genetic insights from genomic variation that has escaped large-scale analysis to date.

Repeat polymorphisms underlie top genetic risk loci for glaucoma and colorectal cancer.

Many regions in the human genome vary in length among individuals due to variable numbers of tandem repeats (VNTRs). To assess the phenotypic impact of VNTRs genome-wide, we applied a statistical imputation approach to estimate the lengths of 9,561 autosomal VNTR loci in 418,136 unrelated UK Biobank participants and 838 GTEx participants. Association and statistical fine-mapping analyses identified 58 VNTRs that appeared to influence a complex trait in UK Biobank, 18 of which also appeared to modulate expression or splicing of a nearby gene. Non-coding VNTRs at TMCO1 and EIF3H appeared to generate the largest known contributions of common human genetic variation to risk of glaucoma and colorectal cancer, respectively. Each of these two VNTRs associated with a >2-fold range of risk across individuals. These results reveal a substantial and previously unappreciated role of non-coding VNTRs in human health and gene regulation.

Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions.

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)-present in some but not all cells-remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e-4), with recurrent somatic deletions of exons 1-5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5' deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk.

Hidden protein-altering variants influence diverse human phenotypes.

Structural variants (SVs) comprise the largest genetic variants, altering from 50 base pairs to megabases of DNA. However, SVs have not been effectively ascertained in most genetic association studies, leaving a key gap in our understanding of human complex trait genetics. We ascertained protein-altering SVs from UK Biobank whole-exome sequencing data (n=468,570) using haplotype-informed methods capable of detecting sub-exonic SVs and variation within segmental duplications. Incorporating SVs into analyses of rare variants predicted to cause gene loss-of-function (pLoF) identified 100 associations of pLoF variants with 41 quantitative traits. A low-frequency partial deletion of RGL3 exon 6 appeared to confer one of the strongest protective effects of gene LoF on hypertension risk (OR = 0.86 [0.82-0.90]). Protein-coding variation in rapidly-evolving gene families within segmental duplications-previously invisible to most analysis methods-appeared to generate some of the human genome's largest contributions to variation in type 2 diabetes risk, chronotype, and blood cell traits. These results illustrate the potential for new genetic insights from genomic variation that has escaped large-scale analysis to date.

Influences of rare copy-number variation on human complex traits.

The human genome contains hundreds of thousands of regions harboring copy-number variants (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs have been ascertainable from SNP-array data generated by large biobanks. We developed a computational approach leveraging haplotype sharing in biobank cohorts to more sensitively detect CNVs. Applied to UK Biobank, this approach accounted for approximately half of all rare gene inactivation events produced by genomic structural variation. This CNV call set enabled a detailed analysis of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (p < 5 × 10-8) likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling insights into dosage sensitivity of these genes and uncovering several gene-trait relationships. These results demonstrate the ability of haplotype-informed analysis to provide insights into the genetic basis of human complex traits.

Developmental dynamics of RNA translation in the human brain.

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity and cognitive function. Although several studies have profiled transcription in the developing human brain, there is a gap in understanding of accompanying translational regulation. In this study, we performed ribosome profiling on 73 human prenatal and adult cortex samples. We characterized the translational regulation of annotated open reading frames (ORFs) and identified thousands of previously unknown translation events, including small ORFs that give rise to human-specific and/or brain-specific microproteins, many of which we independently verified using proteomics. Ribosome profiling in stem-cell-derived human neuronal cultures corroborated these findings and revealed that several neuronal activity-induced non-coding RNAs encode previously undescribed microproteins. Physicochemical analysis of brain microproteins identified a class of proteins that contain arginine-glycine-glycine (RGG) repeats and, thus, may be regulators of RNA metabolism. This resource expands the known translational landscape of the human brain and illuminates previously unknown brain-specific protein products.

Genome-wide mapping of somatic mutation rates uncovers drivers of cancer.

Identification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these maps to identify putative drivers within intronic cryptic splice regions, 5' untranslated regions and infrequently mutated genes. Our high-resolution mutation rate maps, available for web-based exploration, are a resource to enable driver discovery genome-wide.

A spectrum of recessiveness among Mendelian disease variants in UK Biobank.

Recent work has found increasing evidence of mitigated, incompletely penetrant phenotypes in heterozygous carriers of recessive Mendelian disease variants. We leveraged whole-exome imputation within the full UK Biobank cohort (n ∼ 500K) to extend such analyses to 3,475 rare variants curated from ClinVar and OMIM. Testing these variants for association with 58 quantitative traits yielded 102 significant associations involving variants previously implicated in 34 different diseases. Notable examples included a POR missense variant implicated in Antley-Bixler syndrome that associated with a 1.76 (SE 0.27) cm increase in height and an ABCA3 missense variant implicated in interstitial lung disease that associated with reduced FEV1/FVC ratio. Association analyses with 1,134 disease traits yielded five additional variant-disease associations. We also observed contrasting levels of recessiveness between two more-common, classical Mendelian diseases. Carriers of cystic fibrosis variants exhibited increased risk of several mitigated disease phenotypes, whereas carriers of spinal muscular atrophy alleles showed no evidence of altered phenotypes. Incomplete penetrance of cystic fibrosis carrier phenotypes did not appear to be mediated by common allelic variation on the functional haplotype. Our results show that many disease-associated recessive variants can produce mitigated phenotypes in heterozygous carriers and motivate further work exploring penetrance mechanisms.

Paving the path toward genomic privacy with secure imputation.

Ensuring the privacy of participants in genomic studies is a critical responsibility of the biomedical community. Accurate and efficient implementations of secure genotype imputation highlight practical approaches to safeguard sensitive genomic data that can be adapted for numerous bioinformatics applications.

Protein-coding repeat polymorphisms strongly shape diverse human phenotypes.

Many human proteins contain domains that vary in size or copy number because of variable numbers of tandem repeats (VNTRs) in protein-coding exons. However, the relationships of VNTRs to most phenotypes are unknown because of difficulties in measuring such repetitive elements. We developed methods to estimate VNTR lengths from whole-exome sequencing data and impute VNTR alleles into single-nucleotide polymorphism haplotypes. Analyzing 118 protein-altering VNTRs in 415,280 UK Biobank participants for association with 786 phenotypes identified some of the strongest associations of common variants with human phenotypes, including height, hair morphology, and biomarkers of health. Accounting for large-effect VNTRs further enabled fine-mapping of associations to many more protein-coding mutations in the same genes. These results point to cryptic effects of highly polymorphic common structural variants that have eluded molecular analyses to date.

Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses.

Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total n ≈ 500,000) to impute exome-wide variants with accuracy R2 > 0.5 down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 significant associations (P < 5 × 10-8) involving 675 distinct rare protein-altering variants (MAF < 0.01) that passed stringent filters for likely causality. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified allelic series containing up to 45 distinct 'likely-causal' variants. Our results demonstrate the utility of within-cohort imputation in population-scale genome-wide association studies, provide a catalog of likely-causal, large-effect coding variant associations and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.

Comprehensive identification of somatic nucleotide variants in human brain tissue.

BACKGROUND: Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. RESULTS: Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. CONCLUSIONS: This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

Activity-dependent regulome of human GABAergic neurons reveals new patterns of gene regulation and neurological disease heritability.

Neuronal activity-dependent gene expression is essential for brain development. Although transcriptional and epigenetic effects of neuronal activity have been explored in mice, such an investigation is lacking in humans. Because alterations in GABAergic neuronal circuits are implicated in neurological disorders, we conducted a comprehensive activity-dependent transcriptional and epigenetic profiling of human induced pluripotent stem cell-derived GABAergic neurons similar to those of the early developing striatum. We identified genes whose expression is inducible after membrane depolarization, some of which have specifically evolved in primates and/or are associated with neurological diseases, including schizophrenia and autism spectrum disorder (ASD). We define the genome-wide profile of human neuronal activity-dependent enhancers, promoters and the transcription factors CREB and CRTC1. We found significant heritability enrichment for ASD in the inducible promoters. Our results suggest that sequence variation within activity-inducible promoters of developing human forebrain GABAergic neurons contributes to ASD risk.

Large mosaic copy number variations confer autism risk.

Although germline de novo copy number variants (CNVs) are known causes of autism spectrum disorder (ASD), the contribution of mosaic (early-developmental) copy number variants (mCNVs) has not been explored. In this study, we assessed the contribution of mCNVs to ASD by ascertaining mCNVs in genotype array intensity data from 12,077 probands with ASD and 5,500 unaffected siblings. We detected 46 mCNVs in probands and 19 mCNVs in siblings, affecting 2.8-73.8% of cells. Probands carried a significant burden of large (>4-Mb) mCNVs, which were detected in 25 probands but only one sibling (odds ratio = 11.4, 95% confidence interval = 1.5-84.2, P = 7.4 × 10-4). Event size positively correlated with severity of ASD symptoms (P = 0.016). Surprisingly, we did not observe mosaic analogues of the short de novo CNVs recurrently observed in ASD (eg, 16p11.2). We further experimentally validated two mCNVs in postmortem brain tissue from 59 additional probands. These results indicate that mCNVs contribute a previously unexplained component of ASD risk.

The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing.

We characterize the landscape of somatic mutations-mutations occurring after fertilization-in the human brain using ultra-deep (~250×) whole-genome sequencing of prefrontal cortex from 59 donors with autism spectrum disorder (ASD) and 15 control donors. We observe a mean of 26 somatic single-nucleotide variants per brain present in ≥4% of cells, with enrichment of mutations in coding and putative regulatory regions. Our analysis reveals that the first cell division after fertilization produces ~3.4 mutations, followed by 2-3 mutations in subsequent generations. This suggests that a typical individual possesses ~80 somatic single-nucleotide variants present in ≥2% of cells-comparable to the number of de novo germline mutations per generation-with about half of individuals having at least one potentially function-altering somatic mutation somewhere in the cortex. ASD brains show an excess of somatic mutations in neural enhancer sequences compared with controls, suggesting that mosaic enhancer mutations may contribute to ASD risk.

Protein-coding repeat polymorphisms strongly shape diverse human phenotypes.

Hundreds of the proteins encoded in human genomes contain domains that vary in size or copy number due to variable numbers of tandem repeats (VNTRs) in protein-coding exons. VNTRs have eluded analysis by the molecular methods-SNP arrays and high-throughput sequencing-used in large-scale human genetic studies to date; thus, the relationships of VNTRs to most human phenotypes are unknown. We developed ways to estimate VNTR lengths from whole-exome sequencing data, identify the SNP haplotypes on which VNTR alleles reside, and use imputation to project these haplotypes into abundant SNP data. We analyzed 118 protein-altering VNTRs in 415,280 UK Biobank participants for association with 791 phenotypes. Analysis revealed some of the strongest associations of common variants with human phenotypes including height, hair morphology, and biomarkers of human health; for example, a VNTR encoding 13-44 copies of a 19-amino-acid repeat in the chondroitin sulfate domain of aggrecan (ACAN) associated with height variation of 3.4 centimeters (s.e. 0.3 cm). Incorporating large-effect VNTRs into analysis also made it possible to map many additional effects at the same loci: for the blood biomarker lipoprotein(a), for example, analysis of the kringle IV-2 VNTR within the LPA gene revealed that 18 coding SNPs and the VNTR in LPA explained 90% of lipoprotein(a) heritability in Europeans, enabling insights about population differences and epidemiological significance of this clinical biomarker. These results point to strong, cryptic effects of highly polymorphic common structural variants that have largely eluded molecular analyses to date.

An Activity-Mediated Transition in Transcription in Early Postnatal Neurons.

The maturation of the mammalian brain occurs after birth, and this stage of neuronal development is frequently impaired in neurological disorders, such as autism and schizophrenia. However, the mechanisms that regulate postnatal brain maturation are poorly defined. By purifying neuronal subpopulations across brain development in mice, we identify a postnatal switch in the transcriptional regulatory circuits that operates in the maturing mammalian brain. We show that this developmental transition includes the formation of hundreds of cell-type-specific neuronal enhancers that appear to be modulated by neuronal activity. Once selected, these enhancers are active throughout adulthood, suggesting that their formation in early life shapes neuronal identity and regulates mature brain function.

Linked-read analysis identifies mutations in single-cell DNA-sequencing data.

Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.