Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development

Somatic mutations in autoinflammatory and autoimmune disease

Somatic mutations (also known as acquired mutations) are emerging as common, age-related processes that occur in all cells throughout the body. Somatic mutations are canonically linked to malignant processes but over the past decade have been increasingly causally connected to benign diseases including rheumatic conditions. Here we outline the contribution of somatic mutations to complex and monogenic immunological diseases with a detailed review of unique aspects associated with such causes. Somatic mutations can cause early- or late-onset rheumatic monogenic diseases but also contribute to the pathogenesis of complex inflammatory and immune-mediated diseases, affect disease progression and define new clinical subtypes. Although even variants with a low variant allele fraction can be pathogenic, clonal dynamics could lead to changes over time in the proportion of mutant cells, with possible phenotypic consequences for the individual. Thus, somatic mutagenesis and clonal expansion have relevant implications in genetic testing and counselling. On the basis of both increased recognition of somatic diseases in clinical practice and improved technical and bioinformatic processes, we hypothesize that there will be an ever-expanding list of somatic mutations in various genes leading to inflammatory conditions, particularly in late-onset disease.

Human embryonic genetic mosaicism and its effects on development and disease

Nearly every mammalian cell division is accompanied by a mutational event that becomes fixed in a daughter cell. When carried forward to additional cell progeny, a clone of variant cells can emerge. As a result, mammals are complex mosaics of clones that are genetically distinct from one another. Recent high-throughput sequencing studies have revealed that mosaicism is common, clone sizes often increase with age and specific variants can affect tissue function and disease development. Variants that are acquired during early embryogenesis are shared by multiple cell types and can affect numerous tissues. Within tissues, variant clones compete, which can result in their expansion or elimination. Embryonic mosaicism has clinical implications for genetic disease severity and transmission but is likely an under-recognized phenomenon. To better understand its implications for mosaic individuals, it is essential to leverage research tools that can elucidate the mechanisms by which expanded embryonic variants influence development and disease.

CLEMENT: genomic decomposition and reconstruction of non-tumor subclones

Genome-level clonal decomposition of a single specimen has been widely studied; however, it is mostly limited to cancer research. In this study, we developed a new algorithm CLEMENT, which conducts accurate decomposition and reconstruction of multiple subclones in genome sequencing of non-tumor (normal) samples. CLEMENT employs the Expectation-Maximization (EM) algorithm with optimization strategies specific to non-tumor subclones, including false variant call identification, non-disparate clone fuzzy clustering, and clonal allele fraction confinement. In the simulation and in vitro cell line mixture data, CLEMENT outperformed current cancer decomposition algorithms in estimating the number of clones (root-mean-square-error = 0.58-0.78 versus 1.43-3.34) and in the variant-clone membership agreement (∼85.5% versus 70.1-76.7%). Additional testing on human multi-clonal normal tissue sequencing confirmed the accurate identification of subclones that originated from different cell types. Clone-level analysis, including mutational burden and signatures, provided a new understanding of normal-tissue composition. We expect that CLEMENT will serve as a crucial tool in the currently emerging field of non-tumor genome analysis.

Low-level brain somatic mutations in exonic regions are collectively implicated in autism with germline mutations in autism risk genes

Low-level somatic mutations in the human brain are implicated in various neurological disorders. The contribution of low-level brain somatic mutations to autism spectrum disorder (ASD), however, remains poorly understood. Here, we performed high-depth exome sequencing with an average read depth of 559.3x in 181 cortical, cerebellar, and peripheral tissue samples to identify brain somatic single nucleotide variants (SNVs) in 24 ASD subjects and 31 controls. We detected ~2.4 brain somatic SNVs per exome per single brain region, with a variant allele frequency (VAF) as low as 0.3%. The mutational profiles, including the number, signature, and type, were not significantly different between the ASD patients and controls. Intriguingly, when considering genes with low-level brain somatic SNVs and ASD risk genes with damaging germline SNVs together, the merged set of genes carrying either somatic or germline SNVs in ASD patients was significantly involved in ASD-associated pathophysiology, including dendrite spine morphogenesis (p = 0.025), mental retardation (p = 0.012), and intrauterine growth retardation (p = 0.012). Additionally, the merged gene set showed ASD-associated spatiotemporal expression in the early and mid-fetal cortex, striatum, and thalamus (all p < 0.05). Patients with damaging mutations in the merged gene set had a greater ASD risk than did controls (odds ratio = 3.92, p = 0.025, 95% confidence interval = 1.12-14.79). The findings of this study suggest that brain somatic SNVs and germline SNVs may collectively contribute to ASD-associated pathophysiology.

Comprehensive benchmarking and guidelines of mosaic variant calling strategies

Rapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants represented as heterozygous, homozygous and mosaic mutations. However, the best practices for mosaic variant calling remain disorganized owing to the technical and conceptual difficulties faced in evaluation. Here we present our benchmark of 11 feasible mosaic variant detection approaches based on a systematically designed whole-exome-level reference standard that mimics mosaic samples, supported by 354,258 control positive mosaic single-nucleotide variants and insertion-deletion mutations and 33,111,725 control negatives. We identified not only the best practice for mosaic variant detection but also the condition-dependent strengths and weaknesses of the current methods. Furthermore, feature-level evaluation and their combinatorial usage across multiple algorithms direct the way for immediate to prolonged improvements in mosaic variant detection. Our results will guide researchers in selecting suitable calling algorithms and suggest future strategies for developers.