Biology subjects

Sherman, M. A.

Publications and source records attributed to Sherman, M. A..

11 recordsLinked to original sources

bioRxiv2023

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 genomes 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.

genetics↗

bioRxiv2021

Developmental Dynamics of RNA Translation in the Human Brain

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity, and cognitive function. While several studies have deeply profiled mRNA dynamics in the developing human brain, there is a fundamental gap in our understanding of accompanying translational regulation. We perform ribosome profiling from more than 70 human prenatal and adult cortex samples across ontogeny and into adulthood, mapping translation events at nucleotide resolution. In addition to characterizing the translational regulation of annotated open reading frames (ORFs), we identify thousands of previously unknown translation events, including small open reading frames (sORFs) that give rise to human- and/or brain-specific microproteins, many of which we independently verify using size-selected proteomics. Ribosome profiling in stem cell-derived human neuronal cultures further corroborates these findings and shows that several neuronal activity-induced long non-coding RNAs (lncRNAs), including LINC00473, a primate-specific lncRNA implicated in depression, encode previously undescribed microproteins. Physicochemical analysis of these brain microproteinss identifies a large class harboring arginine-glycine-glycine (RGG) repeats as strong candidates for regulating RNA metabolism. Moreover, we find that, collectively, these previously unknown human brain sORFs are enriched for variants associated with schizophrenia. In addition to significantly expanding the translational landscape of the developing brain, this atlas will serve as a rich resource for the annotation and functional interrogation of thousands of previously unknown brain-specific protein products.

neuroscience↗

bioRxiv2021

Influences of rare copy number variation on human complex traits

The human genome contains hundreds of thousands of regions exhibiting copy number variation (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs (spanning tens of kilobases) have been ascertainable from the SNP-array data generated by large biobanks. We developed a new computational approach that leverages abundant haplotype-sharing in biobank cohorts to more sensitively detect CNVs co-inherited within extended SNP haplotypes. Applied to UK Biobank, this approach achieved 6-fold increased CNV detection sensitivity compared to previous analyses, accounting for approximately half of all rare gene inactivation events produced by genomic structural variation. This extensive CNV call set enabled the most comprehensive analysis to date of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (P < 5 x 10-8) - involving 97 loci - that rigorous statistical fine-mapping analyses indicated were likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling new insights into dosage-sensitivity of these genes and implicating several novel gene-trait relationships. CNVs at several loci created extended allelic series including deletions or duplications of distal enhancers that associated with much stronger phenotypic effects than SNPs within these regulatory elements. These results demonstrate the ability of haplotype-informed analysis to empower structural variant detection and provide insights into the genetic basis of human complex traits.

genetics↗

bioRxiv2021

Learning the mutational landscape of the cancer genome

An ongoing challenge to better understand and treat cancer is to distinguish neutral mutations, which do not affect tumor fitness, from those that provide a proliferative advantage. However, the variability of mutation rates has limited our ability to model patterns of neutral mutations and therefore identify cancer driver mutations. Here, we predict cancer-specific mutation rates genome-wide by leveraging deep neural networks to learn mutation rates within kilobase-scale regions and then refining these estimates to test for evidence of selection on combinations of mutations by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and used these maps to identify new putative drivers in understudied regions of the genome including cryptic alternative-splice sites, 5 untranslated regions and infrequently mutated genes. These results, available for exploration via web interface, indicate the potential for high-resolution neutral mutation models to empower further driver discovery as cancer sequencing cohorts grow.

cancer biology↗

bioRxiv2021

Sex-specific interactions between procedural and deliberative decision-making systems in a mouse model of Alzheimer's disease

A central question in aging and Alzheimers disease (AD) is when and how neural substrates underlying decision-making are altered. Here we show that while APP mice, a commonly used mouse model of AD, were able to learn Restaurant Row, a complex neuroeconomic decision-making task, they were significantly impaired in procedural, habit-forming, aspects of cognition and relied heavily on deliberation when making decisions. Surprisingly, these behavioral changes are associated with amyloid-beta (A{beta}) pathology and network remodeling in the striatum, a key brain region involved in procedural cognition. Furthermore, APP mice and control mice relied on distinct sex-specific strategies in this neuroeconomic task. These findings provide foundational pillars to examine how aging and age-related neurodegenerative diseases impact decision-making across sexes. They also highlight the need for complex behavioral tasks that allow for the dissociation of competing neurally-distinct decision-making circuits to get an accurate picture of changes in neurodegenerative models of human disease.

neuroscience↗

bioRxiv2021

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 proteincoding 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.

genetics↗

bioRxiv2020

Comprehensive identification of somatic nucleotide variants in human brain tissue

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. Here, the Brain Somatic Mosaicism Network conducted a coordinated, multi-institutional study to: (i) examine the ability of existing methods to detect simulated somatic single nucleotide variants (SNVs) in DNA mixing experiments; (ii) 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; (iii) devise strategies to discover somatic SNVs; and (iv) apply various approaches to validate somatic SNVs. These efforts led to the identification of 43 bona fide somatic SNVs that ranged in variant allele fractions from ~0.005 to ~0.28. Guided by these results, we devised best practices for calling mosaic SNVs from 250X whole genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrated that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. Thus, 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.

genomics↗

bioRxiv2020

The Landscape of Mutational Mosaicism in Autistic and Normal Human Cerebral Cortex

Although somatic mutations have well-established roles in cancer and certain focal epilepsies, the extent to which mutational mosaicism shapes the developing human brain is poorly understood. Here we characterize the landscape of somatic mutations in the human brain using ultra-deep (~250x) whole-genome sequencing of brains from 59 autism spectrum disorder (ASD) cases and 15 controls. We observe a mean of 26 ({+/-}10, range 10-60) somatic single nucleotide variants (sSNVs) 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 rate suggests that a typical individual possesses ~80 sSNVs 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. Although limited by sample size, ASD brains show an excess of somatic mutations in neural enhancer sequences compared to controls, suggesting that mosaic enhancer mutations may contribute to ASD risk.

neuroscience↗

bioRxiv2017

Aging and neurodegeneration are associated with increased mutations in single human neurons

It has long been hypothesized that aging and neurodegeneration are associated with somatic mutation in neurons; however, methodological hurdles have prevented testing this hypothesis directly. We used single-cell whole-genome sequencing to perform genome-wide somatic single-nucleotide variant (sSNV) identification on DNA from 161 single neurons from the prefrontal cortex and hippocampus of fifteen normal individuals (aged 4 months to 82 years) as well as nine individuals affected by early-onset neurodegeneration due to genetic disorders of DNA repair (Cockayne syndrome and Xeroderma pigmentosum). sSNVs increased approximately linearly with age in both areas (with a higher rate in hippocampus) and were more abundant in neurodegenerative disease. The accumulation of somatic mutations with age--which we term genosenium--shows age-related, region-related, and disease-related molecular signatures, and may be important in other human age-associated conditions.\n\nOne-Sentence SummarySomatic single-nucleotide variants accumulate in human neurons in aging with regional specificity and in progeroid diseases.

genomics↗

bioRxiv2017

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 the mutational heterogeneity in normal and disease tissues. A major difficulty, however, is distinguishing artifactual mutations that arise from DNA isolation and amplification from true mutations. Here, we describe linked-read analysis (LiRA), a method that utilizes phasing of somatic single nucleotide variants with nearby germline variants to identify true mutations, thereby allowing accurate estimation of somatic mutation rates at the single cell level.

bioinformatics↗

bioRxiv2017

PaSD-qc: Quality control for single cell whole-genome sequencing data using power spectral density estimation

Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, scWGS introduces DNA amplification-related biases that can confound downstream analysis. Here we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance, and inter-sample consistency as well as identifies aberrantly amplified chromosomes. We demonstrate the usefulness of this tool in evaluating scWGS protocols and in selecting high-quality libraries from low-coverage data for deep sequencing.

bioinformatics↗