With the advance of sequencing technology, an increasing number of populations have been sequenced to study the histories of worldwide populations, including their divergence, admixtures, migration, and effective sizes. The variants detected in sequencing studies are largely rare and mostly population specific. Population-specific variants are often recent mutations and are informative for revealing substructures and admixtures in populations; however, computational methods and tools to analyze them are still lacking. In this work, we propose using reference populations and single nucleotide polymorphisms (SNPs) specific to the reference populations. Ancestral information, the best linear unbiased estimator (BLUE) of the ancestral proportion, is proposed, which can be used to infer ancestral proportions in recently admixed target populations and measure the extent to which reference populations serve as good proxies for the admixing sources. Based on the same panel of SNPs, the ancestral information is comparable across samples from different studies and is not affected by genetic outliers, related samples, or the sample sizes of the admixed target populations. In addition, ancestral spectrum is useful for detecting genetic outliers or exploring co-ancestry between study samples and the reference populations. The methods are implemented in a program, Ancestral Spectrum Analyzer (ASA), and are applied in analyzing high-coverage sequencing data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP). In the analyses of American populations from the 1000 Genomes Project, we demonstrate that recent admixtures can be dissected from ancient admixtures by comparing ancestral spectra with and without indigenous Americans being included in the reference populations.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) defined by impairments in social communication and social interactions, accompanied by repetitive behavior and restricted interests. ASD is characterized by its clinical and etiological heterogeneity, which makes it difficult to elucidate the neurobiological mechanisms underlying its pathogenesis. Recently, de novo mutations (DNMs) have been recognized as strong source of genetic causality. Here, we review different aspects of the DNMs associated with ASD, including their functional annotation and classification. In addition, we also focus on the most recent advances in this area, such as the detection of PZMs (post-zygotic mutations), and we outline the main bioinformatics tools commonly employed to study these. Some of these approaches available allow DNMs to be analyzed in the context of gene networks and pathways, helping to shed light on the biological processes underlying ASD. To end this review, a brief insight into the future perspectives for genetic studies into ASD will be provided.

Genetic and environmental factors, alone or in combination, contribute to the pathogenesis of autism spectrum disorder (ASD). Although many protein-coding genes have now been identified as disease risk genes for ASD, a detailed illustration of long non-coding RNAs (lncRNAs) associated with ASD remains elusive. In this study, we first identified ASD-related lncRNAs based on genomic variant data of individuals with ASD from a twin study. In total, 532 ASD-related lncRNAs were identified, and 86.7% of these ASD-related lncRNAs were further validated by an independent copy number variant (CNV) dataset. Then, the functions and associated biological pathways of ASD-related lncRNAs were explored by enrichment analysis of their three different types of functional neighbor genes (i.e., genomic neighbors, competing endogenous RNA (ceRNA) neighbors, and gene co-expression neighbors in the cortex). The results have shown that most of the functional neighbor genes of ASD-related lncRNAs were enriched in nervous system development, inflammatory responses, and transcriptional regulation. Moreover, we explored the differential functions of ASD-related lncRNAs in distinct brain regions by using gene co-expression network analysis based on tissue-specific gene expression profiles. As a set, ASD-related lncRNAs were mainly associated with nervous system development and dopaminergic synapse in the cortex, but associated with transcriptional regulation in the cerebellum. In addition, a functional network analysis was conducted for the highly reliable functional neighbor genes of ASD-related lncRNAs. We found that all the highly reliable functional neighbor genes were connected in a single functional network, which provided additional clues for the action mechanisms of ASD-related lncRNAs. Finally, we predicted several potential drugs based on the enrichment of drug-induced pathway sets in the ASD-altered biological pathway list. Among these drugs, several (e.g., amoxapine, piperine, and diflunisal) were partly supported by the previous reports. In conclusion, ASD-related lncRNAs participated in the pathogenesis of ASD through various known biological pathways, which may be differential in distinct brain regions. Detailed investigation into ASD-related lncRNAs can provide clues for developing potential ASD diagnosis biomarkers and therapy.

Placenta accreta spectrum (PAS) is a pathological condition of the placenta with abnormal adhesion or invasion of the placental villi to the uterine wall, which is associated with a variety of adverse maternal and fetal outcomes. Although some PAS-related molecules have been reported, the underlying regulatory mechanism is still unclear. Compared with the study of single gene or pathway, omics study, using advanced sequencing technology and bioinformatics methods, can increase our systematic understanding of diseases. In this study, placenta tissues from 5 patients with PAS and 5 healthy pregnant women were collected for transcriptomic and proteomic sequencing and integrated analysis. A total of 728 messenger RNAs and 439 proteins were found to be significantly different between PAS group and non-PAS group, in which 23 hub genes were differentially expressed in both transcriptome and proteome. Functional enrichment analysis showed that the differentially expressed genes were mainly related to cell proliferation, migration and vascular development. Totally 18 long non-coding RNA were found that might regulate the expression of hub genes. Many kinds of single nucleotide polymorphism, alternative splicing and gene fusion of hub genes were detected. This is the first time to systematically explore the hub genes and gene structure variations of PAS through integrated omics analysis, which provided a genetic basis for further in-depth study on the underlying regulatory mechanism of PAS.

Autism spectrum disorder (ASD) is genetically and phenotypically heterogeneous. Former genetic studies suggested that both common and rare genetic variants play a role in the etiology. In this study, we aimed to analyze rare variants detected by next generation sequencing (NGS) in an autism cohort from Hungary.

Hemifacial microsomia (HM) is a craniofacial congenital defect involving the first and second branchial arch, mainly characterized by ocular, ear, maxilla-zygoma complex, mandible, and facial nerve malformation. HM follows autosomal dominant inheritance. Whole-exome sequencing of a family revealed a missense mutation in a highly conserved domain of ITPR1. ITPR1 is a calcium ion channel. By studying ITPR1's expression pattern, we found that ITPR1 participated in craniofacial development, especially the organs that corresponded to the phenotype of HM. In zebrafish, itpr1b, which is homologous to human ITPR1, is closely related to craniofacial bone formation. The knocking down of itpr1b in zebrafish could lead to a remarkable decrease in craniofacial skeleton formation. qRT-PCR suggested that knockdown of itpr1b could increase the expression of plcb4 while decreasing the mRNA level of Dlx5/6. Our findings highlighted ITPR1's role in craniofacial formation for the first time and suggested that ITPR1 mutation contributes to human HM.

Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine-based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.

Background: Hearing loss (HL) is an impairment of auditory function with identified genetic forms that can be syndromic (30%) or non-syndromic (70%). HL is genetically heterogeneous, with more than 1,000 variants across 150 causative genes identified to date. The genetic diagnostic rate varies significantly depending on the population being tested. Countries with a considerably high rate of consanguinity provide a unique resource for studying rare forms of recessive HL. In this study, we identified genetic variants associated with bilateral sensorineural HL (SNHL) using whole-exome sequencing (WES) in 11 families residing in the United Arab Emirates (UAE). Results: We established the molecular diagnosis in six probands, with six different pathogenic or likely pathogenic variants in the genes MYO15A, SLC26A4, and GJB2. One novel nonsense variant, MYO15A:p.Tyr1962Ter*, was identified in a homozygous state in one family, which has not been reported in any public database. SLC26A4 and GJB2 were found to be the most frequently associated genes in this study. In addition, six variants of uncertain significance (VUS) were detected in five probands in the genes CDH23, COL11A1, ADGRV1, NLRP3, and GDF6. In total, 12 variants were observed in eight genes. Among these variants, eight missense variants (66.7%), three nonsense variants (25.0%), and one frameshift (8.3%) were identified. The overall diagnostic rate of this study was 54.5%. Approximately 45.5% of the patients in this study came from consanguineous families. Conclusion: Understanding the genetic basis of HL provides insight for the clinical diagnosis of hearing impairment cases through the utilization of next-generation sequencing (NGS). Our findings contribute to the knowledge of the heterogeneous genetic profile of HL, especially in a population with a high rate of consanguineous marriage in the Arab population.

To describe the clinical and molecular spectrum of Stargardt disease (STGD) in a cohort of Argentinean patients.

Variants in TTN are associated with a broad range of clinical phenotypes, from dominant adult-onset dilated cardiomyopathy to recessive infantile-onset myopathy. However, few foetal cases have been reported for multiple reasons. Next-generation sequencing has facilitated the prenatal identification of a growing number of suspected titinopathy variants. We investigated six affected foetuses from three families, completed the intrauterine course of the serial phenotypic spectrum of TTN, and discussed the genotype-phenotype correlations from a broader perspective. The recognizable prenatal feature onset at the second trimester was started with reduced movement, then contracture 3-6 weeks later, followed with/without hydrops, finally at late pregnancy was accompanied with polyhydramnio (major) or oligohydramnios. Two cases with typical arthrogryposis-hydrops sequences identified a meta-only transcript variant c.36203-1G>T. Deleterious transcriptional consequences of the substitution were verified by minigene splicing analysis. Case 3 identified a homozygous splicing variant in the constitutively expressed Z-disc. It presented a milder phenotype than expected, which was presumably saved by the isoform of corons. A summary of the foetal-onset titinopathy cases implied that variants in TTN present with a series of signs and a spectrum of clinical severity, which followed the dosage/positional effect; the meta-only transcript allele involvement may be a prerequisite for the development of fatal hydrops.

In March 2020, the World Health Organization declared that an infectious respiratory disease caused by a new severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2, causing coronavirus disease 2019 (COVID-19)] became a pandemic. In our study, we have analyzed a large publicly available dataset, the Genome Aggregation Database (gnomAD), as well as a cohort of 37 Russian patients with COVID-19 to assess the influence of different classes of genetic variants in the angiotensin-converting enzyme-2 (ACE2) gene on the susceptibility to COVID-19 and the severity of disease outcome.

Disorders of the mitochondrial energy metabolism are clinically and genetically heterogeneous. An increasingly recognized subgroup is caused by defective mitochondrial iron-sulfur (Fe-S) cluster biosynthesis, with defects in 13 genes being linked to human disease to date. Mutations in three of them, NFU1, BOLA3, and IBA57, affect the assembly of mitochondrial [4Fe-4S] proteins leading to an impairment of diverse mitochondrial metabolic pathways and ATP production. Patients with defects in these three genes present with lactic acidosis, hyperglycinemia, and reduced activities of respiratory chain complexes I and II, the four lipoic acid-dependent 2-oxoacid dehydrogenases and the glycine cleavage system (GCS). To date, five different NFU1 pathogenic variants have been reported in 15 patients from 12 families. We report on seven new patients from five families carrying compound heterozygous or homozygous pathogenic NFU1 mutations identified by candidate gene screening and exome sequencing. Six out of eight different disease alleles were novel and functional studies were performed to support the pathogenicity of five of them. Characteristic clinical features included fatal infantile encephalopathy and pulmonary hypertension leading to death within the first 6 months of life in six out of seven patients. Laboratory investigations revealed combined defects of pyruvate dehydrogenase complex (five out of five) and respiratory chain complexes I and II+III (four out of five) in skeletal muscle and/or cultured skin fibroblasts as well as increased lactate (five out of six) and glycine concentration (seven out of seven). Our study contributes to a better definition of the phenotypic spectrum associated with NFU1 mutations and to the diagnostic workup of future patients.

Background: The diagnosis of autism spectrum disorder (ASD) relies on behavioral assessment. Efforts to define biomarkers of ASD have not resulted in an objective, reliable test. Studies of RNA levels in ASD have demonstrated potential utility, but have been limited by a focus on single RNA types, small sample sizes, and lack of developmental delay controls. We hypothesized that a saliva-based poly-"omic" RNA panel could objectively distinguish children with ASD from their neurotypical peers and children with non-ASD developmental delay. Methods: This multi-center cross-sectional study included 456 children, ages 19-83 months. Children were either neurotypical (n = 134) or had a diagnosis of ASD (n = 238), or non-ASD developmental delay (n = 84). Comprehensive human and microbial RNA abundance was measured in the saliva of all participants using unbiased next generation sequencing. Prior to analysis, the sample was randomly divided into a training set (82% of subjects) and an independent validation test set (18% of subjects). The training set was used to develop an RNA-based algorithm that distinguished ASD and non-ASD children. The validation set was not used in model development (feature selection or training) but served only to validate empirical accuracy. Results: In the training set (n = 372; mean age 51 months; 75% male; 51% ASD), a set of 32 RNA features (controlled for demographic and medical characteristics), identified ASD status with a cross-validated area under the curve (AUC) of 0.87 (95% CI: 0.86-0.88). In the completely separate validation test set (n = 84; mean age 50 months; 85% male; 60% ASD), the algorithm maintained an AUC of 0.88 (82% sensitivity and 88% specificity). Notably, the RNA features were implicated in physiologic processes related to ASD (axon guidance, neurotrophic signaling). Conclusion: Salivary poly-omic RNA measurement represents a novel, non-invasive approach that can accurately identify children with ASD. This technology could improve the specificity of referrals for ASD evaluation or provide objective support for ASD diagnoses.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

TP53 mutations are among the most common mutations found in lung cancers, identified as an independent prognostic factor in many types of cancers. The purpose of this study was to investigate the frequency and prognostic impact of TP53 mutations in never-smokers and in different histological subtypes of lung cancer.

Autism spectrum disorders (ASD) comprise a number of underlying sub-types with various symptoms and presumably different genetic causes. One important difference between these sub-phenotypes is IQ. Some forms of ASD such as Asperger's have relatively intact intelligence while the majority does not. In this study, we explored the role of genetic factors that might account for this difference. Using a case-control study based on IQ status in 1657 ASD probands, we analyzed both common and rare variants provided by the Autism Genome Project (AGP) consortium via dbGaP (database of Genotypes and Phenotypes). We identified a set of genes, among them HLA-DRB1 and KIAA0319L, which are strongly associated with IQ within a population of ASD patients.

Autism spectrum disorder (ASD) is heterogeneous in symptom and etiology. Rare copy number variations (CNVs) are important genetic factors contributing to ASD. Currently chromosomal microarray (CMA) detecting CNVs is recommended as a first-tier diagnostic assay, largely based on research in North America and Europe. The feature of rare CNVs has not been well characterized in ASD cohorts from non-European ancestry. In this study, high resolution CMA was utilized to investigate rare CNVs in a Chinese cohort of ASD (n = 401, including 177 mildly/moderately and 224 severely affected individuals), together with an ancestry-matched control cohort (n = 197). Diagnostic yield was about 4.2%, with 17 clinically significant CNVs identified in ASD individuals, of which 12 CNVs overlapped with recurrent autism risk loci or genes. Autosomal rare CNV burden analysis showed an overrepresentation of rare loss events in ASD cohort, whereas the rate of rare gain events correlated with the phenotypic severity. Further analysis showed rare losses disrupting genes highly intolerant of loss-of-function variants were enriched in the ASD cohort. Among these highly constrained genes disrupted by rare losses, RIMS2 is a promising candidate contributing to ASD risk. This pilot study evaluated clinical utility of CMA and the feature of rare CNVs in Chinese ASD, with candidate genes identified as potential risk factors.

Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.

Renal hypodysplasia and cystic kidney diseases, the common non-glomerular causes of pediatric chronic kidney disease (CKD), are usually diagnosed by their clinical and imaging characteristics. The high degree of phenotypic heterogeneity, in both conditions, makes the correct final diagnosis dependent on genetic testing. It is not clear, however, whether the frequencies of damaged alleles vary among different ethnicities in children with non-glomerular CKD, and this will influence the strategy used for genetic testing. In this study, 69 unrelated children (40 boys, 29 girls) of predominantly Han Chinese ethnicity with stage 2-5 non-glomerular CKD caused by suspected renal hypodysplasia or cystic kidney diseases were enrolled and assessed by molecular analysis using proband-only targeted exome sequencing and array-comparative genomic hybridization. Targeted exome sequencing discovered genetic etiologies in 33 patients (47.8%) covering 10 distinct genetic disorders. The clinical diagnoses in 13/48 patients (27.1%) with suspected renal hypodysplasia were confirmed, and two patients were reclassified carrying mutations in nephronophthisis (NPHP) genes. The clinical diagnoses in 16/20 patients (80%) with suspected cystic kidney diseases were confirmed, and one patient was reclassified as carrying a deletion in the hepatocyte nuclear factor-1-beta gene (HNF1B). The diagnosis of one patient with unknown non-glomerular disease was elucidated. No copy number variations were identified in the 20 patients with negative targeted exome sequencing results. NPHP genes were the most common disease-causing genes in the patients with disease onsets above 6 years of age (14/45, 31.1%). The children with stage 2 and 3 CKD at onset were found to carry causative mutations in paired box gene 2 (PAX2) and HNF1B gene (11/24, 45.8%), whereas those with stage 4 and 5 CKD mostly carried causative mutations in NPHP genes (19/45, 42.2%). The causative genes were not suspected by the kidney imaging patterns at disease onset. Thus, our data show that in Chinese children with non-glomerular renal dysfunction caused by renal hypodysplasia and cystic kidney diseases, the common causative genes vary with age and CKD stage at disease onset. These findings have the potential to improve management and genetic counseling of these diseases in clinical practice.

MED12, a subunit of mediator complex genes is known to harbor genetic mutations, (mostly in exon 2), causal to the genesis of uterine leiomyomas among Caucasian, African American, and Asian women. However, the precise relationship between genetic mutations vs. protein or disease phenotype is not well-explained. Therefore, we sought to replicate the MED12 mutation frequency in leiomyomas of Saudi Arabian women, who represents ethnically and culturally distinct population. We performed molecular screening of MED12 gene (in 308 chromosomes belonging to 154 uterine biopsies), analyzed the genotype-disease phenotype correlations and determined the biophysical characteristics of mutated protein through diverse computational approaches. We discovered that >44% (34/77) leiomyomas of Arab women carry a spectrum of MED12 mutations (30 missense, 1 splice site, and 3 indels). In addition to known codon 44, we observed novel somatic mutations in codons 36, 38, and 55. Most genetically mutated tumors (27/30; 90%) demonstrated only one type of genetic change, highlighting that even single allele change in MED12 can have profound impact in transforming the normal uterine myometrium to leiomyomas. An interesting inverse correlation between tumor size and LH is observed when tumor is positive to MED12 mutation (p < 0.05). Our computational investigations suggest that amino acid substitution mutations in exon-2 region of MED12 might contribute to potential alterations in phenotype as well as the stability of MED12 protein. Our study, being the first one from Arab world, confirms the previous findings that somatic MED12 mutations are critical to development and progression of uterine leiomyomas irrespective of the ethnic background. We recommend that mutation screening, particularly codon 44 of MED12 can assist in molecular diagnostics of uterine leiomyomas in majority of the patients.