Biology subjects

Collins, R. L.

Publications and source records attributed to Collins, R. L..

16 recordsLinked to original sources

bioRxiv2025

Simulation and empirical evaluation of biologically-informed neural network performance

Biologically-informed neural networks (BiNNs) offer interpretable deep learning models for biological data, but the dataset characteristics required for strong performance remain poorly understood. For instance, we previously developed P-NET, a BiNN with an architecture based on the Reactome pathway database, and applied this model to predict metastatic status of patients with prostate cancer using somatic mutation and copy number information. It seems likely that including additional relevant signal - e.g., germline variation in this context - should improve model performance, but we currently lack a principled approach to assess whether BiNNs will successfully detect this signal. Here, we developed two simulation frameworks to evaluate the factors that influence BiNN performance - including signal type, signal strength, feature sparsity, and sample size - and empirically tested how integrating germline and somatic data affects the models ability to predict prostate cancer metastatic status. Simulations revealed that small sample size, weak signal strength, and especially extreme feature sparsity limit BiNN performance, and that the model preferentially uses linear over nonlinear signal. Empirically, P-NET performed poorly on sparse germline data, and while adding germline to somatic data did not improve prediction, it improved gene prioritization and model interpretation. Broadly, our simulation frameworks enable systematic evaluation of how dataset-level characteristics affect BiNN performance and provide a principled framework for benchmarking novel methods.

bioinformatics↗

bioRxiv2025

Aberrant recursive splicing in a human disease locus

Recursive splice sites are rare motifs postulated to facilitate splicing across massive introns and shape isoform diversity, especially for long, brain-expressed genes. The necessity of this unique mechanism remains unsubstantiated, as does the role of recursive splicing (RS) in human disease. From analyses of rare copy number variants (CNVs) from almost one million individuals, we previously identified large, heterozygous deletions eliminating an RS site (RS1) in the first intron of CADM2 that conferred substantial risk for attention deficit hyperactivity disorder (ADHD) and other neurobehavioral traits. CADM2 encodes a neuronally expressed cell adhesion molecule that has repeatedly been associated with ADHD and numerous similar traits. To explore the molecular impact of RS ablation in CADM2, we used CRISPR to model patient deletions and to target a smaller region ([~]500 base pairs) containing RS1 in both human induced neurons (iNs) and rats. Transcriptome analyses in unedited iNs provided a catalog of CADM2 transcripts, including novel transcripts that retained RS exons. Intriguingly, ablating RS1 altered the gradient of RNA abundance across the first intron of CADM2, decreased the level of CADM2 expression, and impacted transcript usage. Decreased CADM2 expression was reflected in reduced exon usage downstream of the RS1 site and global alteration to genes involved in neuronal processes including synapse and axon development. Given the scale of our analyses and the widespread association of CADM2 with neurobehavioral traits, we sought to validate these findings using in vivo models and found that rodent models harboring Cadm2 RS1 deletions exhibited significant changes in relevant behaviors and functional brain connectivity. In summary, our analyses demonstrate a functional role for RS as a noncoding regulatory mechanism in a gene associated with a spectrum of neuropsychiatric and behavioral traits. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/666599v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@171156borg.highwire.dtl.DTLVardef@13553baorg.highwire.dtl.DTLVardef@bee7forg.highwire.dtl.DTLVardef@156f19e_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

genomics↗

bioRxiv2024

Rare germline structural variants increase risk for pediatric solid tumors

AO_SCPLOWBSTRACTC_SCPLOWPediatric solid tumors are rare malignancies that represent a leading cause of death by disease among children in developed countries. The early age-of-onset of these tumors suggests that germline genetic factors are involved, yet conventional germline testing for short coding variants in established predisposition genes only identifies pathogenic events in 10-15% of patients. Here, we examined the role of germline structural variants (SVs)--an underexplored form of germline variation--in pediatric extracranial solid tumors using germline genome sequencing of 1,766 affected children, their 943 unaffected relatives, and 6,665 adult controls. We discovered a sex-biased association between very large (>1 megabase) germline chromosomal abnormalities and a four-fold increased risk of solid tumors in male children. The overall impact of germline SVs was greatest in neuroblastoma, where we revealed burdens of ultra-rare SVs that cause loss-of-function of highly expressed, mutationally intolerant, neurodevelopmental genes, as well as noncoding SVs predicted to disrupt three-dimensional chromatin domains in neural crest-derived tissues. Collectively, our results implicate rare germline SVs as a predisposing factor to pediatric solid tumors that may guide future studies and clinical practice.

genomics↗

bioRxiv2022

GATK-gCNV: A Rare Copy Number Variant Discovery Algorithm and Its Application to Exome Sequencing in the UK Biobank

Copy number variants (CNVs) are major contributors to genetic diversity and disease. To date, exome sequencing (ES) has been generated for millions of individuals in international biobanks, human disease studies, and clinical diagnostic screening. While standardized methods exist for detecting short variants (single nucleotide and insertion/deletion variants) using tools such as the Genome Analysis ToolKit (GATK), technical challenges have confounded similarly uniform large-scale CNV analyses from ES data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, the lack of widely-adopted and robustly benchmarked rare CNV discovery tools has presented a barrier to routine exome-wide assessment of this critical class of variation. Here, we introduce GATK-gCNV, a flexible algorithm to discover rare CNVs from genome sequencing read-depth information, which we distribute as an open-source tool packaged in GATK. GATK-gCNV uses a probabilistic model and inference framework that accounts for technical biases while simultaneously predicting CNVs, which enables self-consistency between technical read-depth normalization and variant calling. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data. These analyses demonstrated 97% recall of rare ([≤]1% site frequency) coding CNVs detected by microarrays and 95% recall of rare coding CNVs discovered by genome sequencing at a resolution of more than two exons. We applied GATK-gCNV to generate a reference catalog of rare coding CNVs in 197,306 individuals with ES from the UK Biobank. We observed strong correlations between CNV rates per gene and measures of mutational constraint, as well as rare CNV associations with multiple traits. In summary, GATK-gCNV is a tunable approach for sensitive and specific CNV discovery in ES, which can easily be applied across trait association and clinical screening.

genomics↗

bioRxiv2022

Genome-wide analysis of Structural Variants in Parkinson's Disease using Short-Read Sequencing data

Parkinsons disease is a complex neurodegenerative disorder, affecting approximately one million individuals in the USA alone. A significant proportion of risk for Parkinsons disease is driven by genetics. Despite this, the majority of the common genetic variation that contributes to disease risk is unknown, in-part because previous genetic studies have focussed solely on the contribution of single nucleotide variants. Structural variants represent a significant source of genetic variation in the human genome. However, because assay of this variability is challenging, structural variants have not been cataloged on a genome-wide scale, and their contribution to the risk of Parkinsons disease remains unknown. In this study, we 1) leveraged the GATK-SV pipeline to detect and genotype structural variants in 7,772 short-read sequencing data and 2) generated a subset of matched whole-genome Oxford Nanopore Technologies long-read sequencing data from the PPMI cohort to allow for comprehensive structural variant confirmation. We detected, genotyped, and tested 3,154 "high-confidence" common structural variant loci, representing over 412 million nucleotides of non-reference genetic variation. Using the long-read sequencing data, we validated three structural variants that may drive the association signals at known Parkinsons disease risk loci, including a 2kb intronic deletion within the gene LRRN4. Further, we confirm that the majority of structural variants in the human genome cannot be detected using short-read sequencing alone, encompassing on average around 4 million nucleotides of inaccessible sequence per genome. Therefore, although these data provide the most comprehensive survey of the contribution of structural variants to the genetic risk of Parkinsons disease to date, this study highlights the need for large-scale long-read datasets to fully elucidate the role of structural variants in Parkinsons disease.

genetics↗

bioRxiv2022

Tissue and cell-type specific molecular and functional signatures of 16p11.2 reciprocal genomic disorder across mouse brain and human neuronal models

Recurrent deletion and duplication of [~]743 kilobases of unique genomic sequence and segmental duplications at chromosome 16p11.2 underlie a reciprocal genomic disorder (RGD; OMIM 611913 and 614671) associated with neurodevelopmental and psychiatric phenotypes, including intellectual disability, autism spectrum disorder (ASD), and schizophrenia (SCZ). To define molecular alterations associated with the 16p11.2 RGD, we performed transcriptome analyses of mice with reciprocal copy number variants (CNVs) of the syntenic chromosome 7qF3 region and human neuronal models derived from isogenic human induced pluripotent stem cells (hiPSCs) carrying CRISPR-engineered CNVs at 16p11.2. Analysis of differentially expressed genes (DEGs) in mouse cortex, striatum, cerebellum and three non-brain tissues, as well as in human neural stem cells and induced glutamatergic neurons revealed that the strongest and most consistent effects occurred within the CNV sequence, with notable instances of differential expression of genes in the immediate vicinity that could reflect position effect. While differential expression of genes outside of chromosome 16p11.2 was largely region, tissue, and cell type-specific, a small but significant minority of such DEGs was shared between brain regions or human cell types. Gene Ontology (GO) enrichment analyses to identify cellular processes dysregulated due to these CNVs found support in select circumstances for terms related to energy metabolism, RNA metabolism, and translation but did not reveal a single universally affected process. Weighted gene co-expression network analysis identified modules that showed significant correlation with reciprocal or individual CNV genotype and better captured shared effects, indicating that energy metabolism, RNA metabolism, translation and protein targeting were disrupted across all three brain regions. The first two of these processes also emerged in the human neural stem cell (NSC) data. A subset of co-expression modules that correlated with CNV genotype revealed significant enrichments for known neurodevelopmental disorder genes, loss-of-function constrained genes, FMRP targets, and chromatin modifiers. Intriguingly, neuronal differentiation of the hiPSCs revealed that both the deletion and duplication CNV resulted in similar deficits in neurite extension and branching and alterations in electrical activity. Finally, generation of cerebral organoid derivatives indicated that the CNVs reciprocally altered the ratio of excitatory and inhibitory GABAergic neurons generated during in vitro neurodevelopment, consistent with a major mechanistic hypothesis for ASD. Collectively, our data suggest that the 16p11.2 RGD involves disruption of multiple biological processes, with a relative impact that is context-specific. Perturbation of individual and multiple genes within the CNV region will be required to dissect single-gene effects, uncover regulatory interactions, and define how each contributes to abnormal neurodevelopment.

genomics↗

bioRxiv2020

Genome-wide maps of enhancer regulation connect risk variants to disease genes

Genome-wide association studies have now identified tens of thousands of noncoding loci associated with human diseases and complex traits, each of which could reveal insights into biological mechanisms of disease. Many of the underlying causal variants are thought to affect enhancers, but we have lacked genome-wide maps of enhancer-gene regulation to interpret such variants. We previously developed the Activity-by-Contact (ABC) Model to predict enhancer-gene connections and demonstrated that it can accurately predict the results of CRISPR perturbations across several cell types. Here, we apply this ABC Model to create enhancer-gene maps in 131 cell types and tissues, and use these maps to interpret the functions of fine-mapped GWAS variants. For inflammatory bowel disease (IBD), causal variants are >20-fold enriched in enhancers in particular cell types, and ABC outperforms other regulatory methods at connecting noncoding variants to target genes. Across 72 diseases and complex traits, ABC links 5,036 GWAS signals to 2,249 unique genes, including a class of 577 genes that appear to influence multiple phenotypes via variants in enhancers that act in different cell types. Guided by these variant-to-function maps, we show that an enhancer containing an IBD risk variant regulates the expression of PPIF to tune mitochondrial membrane potential. Together, our study reveals insights into principles of genome regulation, illuminates mechanisms that influence IBD, and demonstrates a generalizable strategy to connect common disease risk variants to their molecular and cellular functions.

genetics↗

bioRxiv2020

Systematic evaluation of genome sequencing as a first-tier diagnostic test for prenatal and pediatric disorders

Current clinical guidelines recommend three genetic tests for the assessment of fetal structural anomalies: karyotype to detect microscopically-visible balanced and unbalanced chromosomal rearrangements, chromosomal microarray (CMA) to detect sub-microscopic copy number variants (CNVs), and exome sequencing (ES) to identify individual nucleotide changes in coding sequence. Advances in genome sequencing (GS) analysis suggest that it is poised to displace the sequential application of all three conventional tests to become a single diagnostic approach for the assessment of fetal structural anomalies. However, systematic benchmarking is required to assure that GS can capture the full mutational spectrum associated with fetal structural anomalies and to accurately quantify the added diagnostic yield of GS. We applied a novel GS analytic framework that included the discovery, filtration, and interpretation of nine classes of genomic variation to 7,195 individuals. We assessed the sensitivity of GS to detect diagnostic variants (pathogenic or likely pathogenic) from three standard-of-care tests using 1,612 autism spectrum disorder quartet families (ASD; n=6,448) with matched GS, ES, and CMA data, and validated these findings in 46 fetuses with a clinically reportable variant originally identified by karyotype, CMA, or ES. We then assessed the added diagnostic yield of GS in 249 trios (n=747) comprising a fetus with a structural anomaly detected by ultrasound and two unaffected parents that were pre-screened with a combination of all three standard-of-care tests. Across both cohorts, our GS analytic framework identified 98.2% of all diagnostic variants detected by standard-of-care tests, including 100% of those originally detected by CMA (n=88) and ES (n=61), as well as 78.6% (n=11/14) of the chromosomal rearrangements identified by karyotype. The diagnostic yield from GS was 7.8% across all 1,612 ASD probands, almost two-fold more than CMA (4.4%) and three-fold more than ES (3.0%). We also demonstrated that the yield of ES can approach that of GS when CNVs are captured with high sensitivity from exome data (7.4% vs. 7.8%, respectively). In 249 pre-screened fetuses with structural anomalies, GS provided an additional diagnostic yield of 0.4% beyond the combination of all three tests (karyotype, CMA, and ES). Applying our benchmarking results to existing data indicates that GS can achieve an overall diagnostic yield of 46.1% in unselected fetuses with fetal structural anomalies, providing an estimated 17.2% increase in diagnostic yield over karyotype, 14.1% over CMA, and 36.1% over ES when sequence variants are assessed, and 4.1% when CNVs are also identified from exome data. In this study we demonstrate that GS is sensitive to the detection of almost all pathogenic variation captured by karyotype, CMA, and ES, provides a superior diagnostic yield than any individual test by a wide margin, and contributes a modest increase in diagnostic yield beyond the combination of all three tests. We also outline several strategies to aid the interpretation of GS variants that are cryptic to conventional technologies, which we anticipate will be increasingly encountered as comprehensive variant identification from GS is performed. Taken together, these data suggest GS warrants consideration as a first-tier diagnostic approach for fetal structural anomalies.

genomics↗

bioRxiv2020

Expectations and blind spots for structural variation detection from short-read alignment and long-read assembly

Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and emerging clinical diagnostic approaches utilize short-reads (srWGS), which present constraints for genome-wide discovery of structural variants (SVs). Alternative long-read single molecule technologies (lrWGS) offer significant advantages for genome assembly and SV detection, while these technologies are currently cost prohibitive for large-scale disease studies and clinical diagnostics (∼5-12X higher cost than comparable coverage srWGS). Moreover, only dozens of such genomes are currently publicly accessible by comparison to millions of srWGS genomes that have been commissioned for international initiatives. Given this ubiquitous reliance on srWGS in human genetics and genomics, we sought to characterize and quantify the properties of SVs accessible to both srWGS and lrWGS to establish benchmarks and expectations in ongoing medical and population genetic studies, and to project the added value of SVs uniquely accessible to each technology. In analyses of three trios with matched srWGS and lrWGS from the Human Genome Structural Variation Consortium (HGSVC), srWGS captured ∼11,000 SVs per genome using reference-based algorithms, while haplotype-resolved assembly from lrWGS identified ∼25,000 SVs per genome. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplications (SD) and simple repeats (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of the human reference, we observed extremely high concordance (93.8%) for deletions discovered by srWGS and lrWGS after error correction using the raw lrWGS reads. Conversely, lrWGS was superior for detection of insertions across all genomic contexts. Given that the non-SD/SR sequences span 90.3% of the GRCh38 reference, and encompass 95.9% of coding exons in currently annotated disease associated genes, improved sensitivity from lrWGS to discover novel and interpretable pathogenic deletions not already accessible to srWGS is likely to be incremental. However, these analyses highlight the added value of assembly-based lrWGS to create new catalogues of functional insertions and transposable elements, as well as disease associated repeat expansions in genomic regions previously recalcitrant to routine assessment.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2019

An open resource of structural variation for medical and population genetics

Structural variants (SVs) rearrange large segments of the genome and can have profound consequences for evolution and human diseases. As national biobanks, disease association studies, and clinical genetic testing grow increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral for interpreting genetic variation. To date, no large-scale reference maps of SVs exist from high-coverage sequencing comparable to those available for point mutations in protein-coding genes. Here, we constructed a reference atlas of SVs across 14,891 genomes from diverse global populations (54% non-European) as a component of gnomAD. We discovered a rich landscape of 433,371 distinct SVs, including 5,295 multi-breakpoint complex SVs across 11 mutational subclasses, and examples of localized chromosome shattering, as in chromothripsis. The average individual harbored 7,439 SVs, which accounted for 25-29% of all rare protein-truncating events per genome. We found strong correlations between constraint against damaging point mutations and rare SVs that both disrupt and duplicate protein-coding sequence, suggesting intolerance to reciprocal dosage alterations for a subset of tightly regulated genes. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than any effect on noncoding SVs. Finally, we benchmarked carrier rates for medically relevant SVs, finding very large ([≥]1Mb) rare SVs in 3.8% of genomes (~1:26 individuals) and clinically reportable incidental SVs in 0.18% of genomes (~1:556 individuals). These data have been integrated directly into the gnomAD browser (https://gnomad.broadinstitute.org) and will have broad utility for population genetics, disease association, and diagnostic screening.

genomics↗

bioRxiv2019

Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organisms function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

genomics↗

bioRxiv2018

Novel genes for autism implicate both excitatory and inhibitory cell lineages in risk

We present the largest exome sequencing study to date focused on rare variation in autism spectrum disorder (ASD) (n=35,584). Integrating de novo and case-control variation with an enhanced Bayesian framework incorporating evolutionary constraint against mutation, we implicate 99 genes in ASD risk at a false discovery rate (FDR) [≤] 0.1. Of these 99 risk genes, 46 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 50 show higher frequencies in individuals ascertained for ASD, and comparing ASD cases with disruptive mutations in the two groups shows differences in phenotypic presentation. Expressed early in brain development, most of the risk genes have roles in neuronal communication or regulation of gene expression, and 12 fall within recurrent copy number variant loci. In human cortex single-cell gene expression data, expression of the 99 risk genes is also enriched in both excitatory and inhibitory neuronal lineages, implying that disruption of these genes alters the development of both neuron types. Together, these insights broaden our understanding of the neurobiology of ASD.

genetics↗

bioRxiv2017

Multi-platform discovery of haplotype-resolved structural variation in human genomes

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent-child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs ([≥]50 bp) per human genome. We also discover 156 inversions per genome--most of which previously escaped detection. Fifty-eight of the inversions we discovered intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The method and the dataset serve as a gold standard for the scientific community and we make specific recommendations for maximizing structural variation sensitivity for future large-scale genome sequencing studies.

genomics↗

bioRxiv2017

Dissecting the Causal Mechanism of X-Linked Dystonia-Parkinsonism by Integrating Genome and Transcriptome Assembly

X-linked Dystonia-Parkinsonism (XDP) is a Mendelian neurodegenerative disease endemic to the Philippines. We integrated genome and transcriptome assembly with induced pluripotent stem cell-based modeling to identify the XDP causal locus and potential pathogenic mechanism. Genome sequencing identified novel variation that was shared by all probands and three recombination events that narrowed the causal locus to a genomic segment including TAF1. Transcriptome assembly in neural derivative cells discovered novel TAF1 transcripts, including a truncated transcript exclusively observed in probands that involved aberrant splicing and intron retention (IR) associated with a SINE-VNTR-Alu (SVA)-type retrotransposon insertion. This IR correlated with decreased expression of the predominant TAF1 transcript and altered expression of neurodevelopmental genes; both the IR and aberrant TAF1 expression patterns were rescued by CRISPR/Cas9 excision of the SVA. These data suggest a unique genomic cause of XDP and may provide a roadmap for integrative genomic studies in other unsolved Mendelian disorders.\n\nHighlights O_LIGenome assembly narrows the XDP causal locus to a segment including TAF1\nC_LIO_LIXDP-specific SVA insertion induces intron retention and down-regulation of TAF1\nC_LIO_LICRISPR/Cas9 excision of SVA rescues aberrant splicing and cTAF1 expression in XDP\nC_LIO_LIGene networks perturbed in proband cells associate to synapse and neurodevelopment\nC_LI

genomics↗

bioRxiv2017

Indexcov: fast coverage quality control for whole-genome sequencing

The BAM1 and CRAM2 formats provide a supplementary linear index that facilitates rapid access to sequence alignments in arbitrary genomic regions. Comparing consecutive entries in a BAM or CRAM index allows one to infer the number of alignment records per genomic region for use as an effective proxy of sequence depth in each genomic region. Based on these properties, we have developed indexcov, an efficient estimator of whole-genome sequencing coverage to rapidly identify samples with aberrant coverage profiles, reveal large scale chromosomal anomalies, recognize potential batch effects, and infer the sex of a sample. Indexcov is available at: https://github.com/brentp/goleft under the MIT license.

genomics↗

bioRxiv2017

Limited contribution of rare, noncoding variation to autism spectrum disorder from sequencing of 2,076 genomes in quartet families

Genomic studies to date in autism spectrum disorder (ASD) have largely focused on newly arising mutations that disrupt protein coding sequence and strongly influence risk. We evaluate the contribution of noncoding regulatory variation across the size and frequency spectrum through whole genome sequencing of 519 ASD cases, their unaffected sibling controls, and parents. Cases carry a small excess of de novo (1.02-fold) noncoding variants, which is not significant after correcting for paternal age. Assessing 51,801 regulatory classes, no category is significantly associated with ASD after correction for multiple testing. The strongest signals are observed in coding regions, including structural variation not detected by previous technologies and missense variation. While rare noncoding variation likely contributes to risk in neurodevelopmental disorders, no category of variation has impact equivalent to loss-of-function mutations. Average effect sizes are likely to be smaller than that for coding variation, requiring substantially larger samples to quantify this risk.

genomics↗