Publications

Although oncogenic mutations have been found in nondiseased, proliferative nonneural tissues, their prevalence in the human brain is unknown. Targeted sequencing of genes implicated in brain tumors in 418 samples derived from 110 individuals of varying ages, without tumor diagnoses, detected oncogenic somatic single-nucleotide variants (sSNV) in 5.4% of the brains, including IDH1 R132H. These mutations were largely present in subcortical white matter and enriched in glial cells and, surprisingly, were less common in older individuals. A depletion of high-allele frequency sSNVs representing macroscopic clones with age was replicated by analysis of bulk RNA sequencing data from 1,816 nondiseased brain samples ranging from fetal to old age. We also describe large clonal copy number variants and that sSNVs show mutational signatures resembling those found in gliomas, suggesting that mutational processes of the normal brain drive early glial oncogenesis. This study helps understand the origin and early evolution of brain tumors. SIGNIFICANCE: In the nondiseased brain, clonal oncogenic mutations are enriched in white matter and are less common in older individuals. We revealed early steps in acquiring oncogenic variants, which are essential to understanding brain tumor origins and building new mutational baselines for diagnostics.This article is highlighted in the In This Issue feature, p. 1.

Somatic mutations are DNA variants that occur after the fertilization of zygotes and accumulate during the developmental and aging processes in the human lifespan. Somatic mutations have long been known to cause cancer, and more recently have been implicated in a variety of non-cancer diseases. The patterns of somatic mutations, or mutational signatures, also shed light on the underlying mechanisms of the mutational process. Advances in next-generation sequencing over the decades have enabled genome-wide profiling of DNA variants in a high-throughput manner; however, unlike germline mutations, somatic mutations are carried only by a subset of the cell population. Thus, sensitive bioinformatic methods are required to distinguish mutant alleles from sequencing and base calling errors in bulk tissue samples. An alternative way to study somatic mutations, especially those present in an extremely small number of cells or even in a single cell, is to sequence single-cell genomes after whole-genome amplification (WGA); however, it is critical and technically challenging to exclude numerous technical artifacts arising during error-prone and uneven genome amplification in current WGA methods. To address these challenges, multiple bioinformatic tools have been developed. In this review, we summarize the latest progress in methods for identification of somatic mutations and the challenges that remain to be addressed in the future.

BACKGROUND: Retrotransposons have been implicated as causes of Mendelian disease, but their role in autism spectrum disorder (ASD) has not been systematically defined, because they are only called with adequate sensitivity from whole genome sequencing (WGS) data and a large enough cohort for this analysis has only recently become available.

RESULTS: We analyzed WGS data from a cohort of 2288 ASD families from the Simons Simplex Collection by establishing a scalable computational pipeline for retrotransposon insertion detection. We report 86,154 polymorphic retrotransposon insertions-including > 60% not previously reported-and 158 de novo retrotransposition events. The overall burden of de novo events was similar between ASD individuals and unaffected siblings, with 1 de novo insertion per 29, 117, and 206 births for Alu, L1, and SVA respectively, and 1 de novo insertion per 21 births total. However, ASD cases showed more de novo L1 insertions than expected in ASD genes. Additionally, we observed exonic insertions in loss-of-function intolerant genes, including a likely pathogenic exonic insertion in CSDE1, only in ASD individuals.

CONCLUSIONS: These findings suggest a modest, but important, impact of intronic and exonic retrotransposon insertions in ASD, show the importance of WGS for their analysis, and highlight the utility of specific bioinformatic tools for high-throughput detection of retrotransposon insertions.

Thyroid cancer is the most common endocrine malignancy. However, the cytological diagnosis of follicular thyroid carcinoma (FTC), Hürthle cell carcinoma (HCC), and follicular variant of papillary thyroid carcinoma (FVPTC) and their benign counterparts is a challenge for preoperative diagnosis. Nearly 20-30% of biopsied thyroid nodules are classified as having indeterminate risk of malignancy and incur costs to the health care system. Based on that, 120 patients were screened for the main driver mutations previously described in thyroid cancer. Subsequently, 14 mutation-negative cases that are the main source of diagnostic errors (FTC, HCC, or FVPTC) underwent RNA-Sequencing analysis. Somatic variants in candidate driver genes (ECD, NUP98,LRP1B, NCOR1, ATM, SOS1, and SPOP) and fusions were described. NCOR1 and SPOP variants underwent validation. Moreover, expression profiling of driver-negative samples was compared to 16 BRAF V600E, RAS, or PAX8-PPARg positive samples. Negative samples were separated in two clusters, following the expression pattern of the RAS/PAX8-PPARg or BRAF V600E positive samples. Both negative groups showed distinct BRS, ERK, and TDS scores, tumor mutation burden, signaling pathways and immune cell profile. Altogether, here we report novel gene variants and describe cancer-related pathways that might impact preoperative diagnosis and provide insights into thyroid tumor biology.

Transposable elements (TEs) significantly contribute to shaping the diversity of the human genome, and lines of evidence suggest TEs as one of driving forces of human brain evolution. Existing computational approaches, including cross-species comparative genomics and population genetic modeling, can be adapted for the study of the role of TEs in evolution. In particular, diverse ancient and archaic human genome sequences are increasingly available, allowing reconstruction of past human migration events and holding the promise of identifying and tracking TEs among other evolutionarily important genetic variants at an unprecedented spatiotemporal resolution. However, highly degraded short DNA templates and other unique challenges presented by ancient human DNA call for major changes in current experimental and computational procedures to enable the identification of evolutionarily important TEs. Ancient human genomes are valuable resources for investigating TEs in the evolutionary context, and efforts to explore ancient human genomes will potentially provide a novel perspective on the genetic mechanism of human brain evolution and inspire a variety of technological and methodological advances. In this review, we summarize computational and experimental approaches that can be adapted to identify and validate evolutionarily important TEs, especially for human brain evolution. We also highlight strategies that leverage ancient genomic data and discuss unique challenges in ancient transposon genomics.

Transposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at https://github.com/parklab/xTea .

Thyroid cancer incidences have been steadily increasing worldwide and are projected to become the fourth leading cancer diagnosis by 2030. Improved diagnosis and prognosis predictions for this type of cancer depend on understanding its genetic bases and disease biology. RAS mutations have been found in a wide range of thyroid tumors, from benign to aggressive thyroid carcinomas. Based on that and in vivo studies, it has been suggested that RAS cooperates with other driver mutations to induce tumorigenesis. This study aims to identify genetic alterations or pathways that cooperate with the RAS mutation in the pathogenesis of thyroid cancer. From a cohort of 120 thyroid carcinomas, 11 RAS-mutated samples were identified. The samples were subjected to RNA-Sequencing analyses. The mutation analysis in our eleven RAS-positive cases uncovered that four genes that belong to the Hippo pathway were mutated. The gene expression analysis revealed that this pathway was dysregulated in the RAS-positive samples. We additionally explored the mutational status and expression profiling of 60 RAS-positive papillary thyroid carcinomas (PTC) from The Cancer Genome Atlas (TCGA) cohort. Altogether, the mutational landscape and pathway enrichment analysis (gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genome (KEGG)) detected the Hippo pathway as dysregulated in RAS-positive thyroid carcinomas. Finally, we suggest a crosstalk between the Hippo and other signaling pathways, such as Wnt and BMP.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with a complex genetic etiology. Besides the apolipoprotein E ε4 (APOE ε4) allele, a few dozen other genetic loci associated with AD have been identified through genome-wide association studies (GWAS) conducted mainly in individuals of European ancestry. Recently, several GWAS performed in other ethnic groups have shown the importance of replicating studies that identify previously established risk loci and searching for novel risk loci. APOE-stratified GWAS have yielded novel AD risk loci that might be masked by, or be dependent on, APOE alleles. We performed whole-genome sequencing (WGS) on DNA from blood samples of 331 AD patients and 169 elderly controls of Korean ethnicity who were APOE ε4 carriers. Based on WGS data, we designed a customized AD chip (cAD chip) for further analysis on an independent set of 543 AD patients and 894 elderly controls of the same ethnicity, regardless of their APOE ε4 allele status. Combined analysis of WGS and cAD chip data revealed that SNPs rs1890078 (P = 6.64E-07) and rs12594991 (P = 2.03E-07) in SORCS1 and CHD2 genes, respectively, are novel genetic variants among APOE ε4 carriers in the Korean population. In addition, nine possible novel variants that were rare in individuals of European ancestry but common in East Asia were identified. This study demonstrates that APOE-stratified analysis is important for understanding the genetic background of AD in different populations.

UNLABELLED: Hi-C is a common technique for assessing 3D chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline Hi-C-based TE analyzer (HiTea) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole-genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE-insertion landscape. We employ the pipeline to identify TE-insertions from human cell-line Hi-C samples.

AVAILABILITY AND IMPLEMENTATION: HiTea is available at https://github.com/parklab/HiTea and as a Docker image.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.