The population frequency of human mitochondrial DNA variants is highly dependent upon mutational bias

Next-generation sequencing can quickly reveal genetic variation potentially linked to heritable disease. As databases encompassing human variation continue to expand, rare variants have been of high interest, since the frequency of a variant is expected to be low if the genetic change leads to a loss of fitness or fecundity. However, the use of variant frequency when seeking genomic changes linked to disease remains very challenging. Here, we explore the role of selection in controlling human variant frequency using the HelixMT database, which encompasses hundreds of thousands of mitochondrial DNA (mtDNA) samples. We find that a substantial number of synonymous substitutions, which have no effect on protein sequence, were never encountered in this large study, while many other synonymous changes are found at very low frequencies. Further analyses of human and mammalian mtDNA datasets indicate that the population frequency of synonymous variants is predominantly determined by mutational biases rather than by strong selection acting upon nucleotide choice. Our work has important implications that extend to the interpretation of variant frequency for non-synonymous substitutions.

Evaluation of population frequency for age-dependent multifactorial disorders

Aim. Description of the method to calculate the population incidence of age- and sex-dependent multifactorial diseases. Methods. For the analysis, we used statistical material of psychiatric hospitals in the Kharkiv region for 2016. Calculation of the population frequency was carrying out according to the methodology used in demographic studies. Results. In medical genetics, population frequency is mainly used for prognostic purposes to assess the genetic load of a population or to calculate the probability to inherit a disease. Evaluation of the population frequency of multifactorial disease is complicated by varying age of onset, differential survival, different thresholds of hereditary predisposition for men and women. Prevalence, which is often used instead population frequency, is not a gene pool characteristic and is not useful for genetic analysis and risk assessment. The population frequency, calculated for affective disorders by the proposed method (0.184%), is 1.33 times higher than the prevalence rate (0.138%), that is, a third of cases when using the prevalence turns out to be lost that distorts the derived genetic indicators. Conclusions. For the correct evaluation of the population frequency, the age-specific incidence for two sexes separately must be estimated, followed by the calculation of the cumulative frequencies. Keywords: multifactorial diseases, prevalence, morbidity, population frequency.

The Path to Conserved Extended Haplotypes: Megabase-Length Haplotypes at High Population Frequency

This minireview describes the history of the conceptual development of conserved extended haplotypes (CEHs): megabase-length haplotypes that exist at high (≥0.5%) population frequency. My career began in internal medicine, shifted to pediatrics, and clinical practice changed to research. My research interest was initially in hematology: on plasma proteins, their metabolism, synthesis, and function. This narrowed to a focus on proteins of the human complement system, their role in immunity and their genetics, beginning with polymorphism and deficiency of C3. My group identified genetic polymorphisms and/or inherited deficiencies of C2, C4, C6, and C8. After defining glycine-rich beta glycoprotein as factor B (Bf) in the properdin system, we found that the genes for Bf (CFB), C2, C4A, and C4B were inherited as a single haplotypic unit which we named the “complotype.” Complotypes are located within the major histocompatibility complex (MHC) between HLA-B and HLA-DRB1 and are designated (in arbitrary order) by their CFB, C2, C4A, and C4B types. Pedigree analysis revealed long stretches (several megabases) of apparently fixed DNA within the MHC that we referred to as “extended haplotypes” (later as “CEHs”). About 10 to 12 common CEHs constitute at least 25 – 30% of MHC haplotypes among European Caucasian populations. These CEHs contain virtually all the most common markers of MHC-associated diseases. In the case of type 1 diabetes, we have proposed a purely genetic and epigenetic model (with a small number of Mendelian recessive disease genes) that explains all the puzzling features of the disease, including its rising incidence.

Celiac disease and obesity: the possibility of comboridity of pathology in children

The article presents an analytical review of scientific research on celiac disease in children in different countries of the world. The prevalence of atypical manifestations of celiac disease has increased over the past two decades. A number of studies in children with celiac disease show that overweight / obesity at the onset of the disease is not uncommon. In addition, there is a tendency to develop overweight / obesity in patients with celiac disease who strictly adhere to a gluten-free diet. It has been shown that among obese children, the prevalence of celiac disease is comparable to the general population frequency. Thus, the diagnosis of celiac disease should be considered even in children with overweight / obesity, when this diagnosis can be easily missed.

Population and Individual Firing Behaviors in Sparsely Synchronized Rhythms in The Hippocampal Dentate Gyrus

We investigate population and individual firing behaviors in sparsely synchronized rhythms (SSRs) in a spiking neural network of the hippocampal dentate gyrus (DG). The main encoding granule cells (GCs) are grouped into lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell (BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all competition, leading to sparse activation of the GCs, occurs in each GC cluster. Such sparsity has been thought to enhance pattern separation performed in the DG. During the winner-take-all competition, SSRs are found to appear in each population of the GCs and the BCs through interaction of excitation of the GCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear successively with the population frequency fp (= 13 Hz) in the raster plots of spikes. We also note that excitatory hilar mossy cells (MCs) control the firing activity of the GC-BC loop by providing excitation to both the GCs and the BCs. SSR also appears in the population of MCs via interaction with the GCs (i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively characterized in terms of the synchronization measures. In addition, we investigate individual firing activity of GCs, BCs, and MCs in the SSRs. Individual GCs exhibit random spike skipping, leading to a multi-peaked inter-spike-interval histogram, which is well characterized in terms of the random phase-locking degree. In this case, population-averaged mean-firing-rate (MFR) <fi(GC)> is less than the population frequency fp. On the other hand, both BCs and MCs show "intrastripe" burstings within stripes, together with "interstripe" random spike skipping. Thus, the population-averaged MFR <fi(X)> (X= MC and BC) is larger than fp, in contrast to the case of the GCs. MC loss may occur during epileptogenesis. With decreasing the fraction of the MCs, changes in the population and individual firings in the SSRs are also studied. Finally, quantitative association between the population/individual firing behaviors in the SSRs and the winner-take-all competition is discussed.

Time-series trend of pandemic SARS-CoV-2 variants visualized using batch-learning self-organizing map for oligonucleotide compositions

To confront the global threat of coronavirus disease 2019, a massive number of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome sequences have been decoded, with the results promptly released through the GISAID database. Based on variant types, eight clades have already been defined in GISAID, but the diversity can be far greater. Owing to the explosive increase in available sequences, it is important to develop new technologies that can easily grasp the whole picture of the big-sequence data and support efficient knowledge discovery. An ability to efficiently clarify the detailed time-series changes in genome-wide mutation patterns will enable us to promptly identify and characterize dangerous variants that rapidly increase their population frequency. Here, we collectively analyzed over 150,000 SARS-CoV-2 genomes to understand their overall features and time-dependent changes using a batch-learning self-organizing map (BLSOM) for oligonucleotide composition, which is an unsupervised machine learning method. BLSOM can separate clades defined by GISAID with high precision, and each clade is subdivided into clusters, which shows a differential increase/decrease pattern based on geographic region and time. This allowed us to identify prevalent strains in each region and to show the commonality and diversity of the prevalent strains. Comprehensive characterization of the oligonucleotide composition of SARS-CoV-2 and elucidation of time-series trends of the population frequency of variants can clarify the viral adaptation processes after invasion into the human population and the time-dependent trend of prevalent epidemic strains across various regions, such as continents.

The impact of rare and low-frequency genetic variants in common variable immunodeficiency (CVID)

Next Generation Sequencing (NGS) has uncovered hundreds of common and rare genetic variants involved in complex and rare diseases including immune deficiencies in both an autosomal recessive and autosomal dominant pattern. These rare variants however, cannot be classified clinically, and common variants only marginally contribute to disease susceptibility. In this study, we evaluated the multi-gene panel results of Common Variable Immunodeficiency (CVID) patients and argue that rare variants located in different genes play a more prominent role in disease susceptibility and/or etiology. We performed NGS on DNA extracted from the peripheral blood leukocytes from 103 patients using a panel of 19 CVID-related genes: CARD11, CD19, CD81, ICOS, CTLA4, CXCR4, GATA2, CR2, IRF2BP2, MOGS, MS4A1, NFKB1, NFKB2, PLCG2, TNFRSF13B, TNFRSF13C, TNFSF12, TRNT1 and TTC37. Detected variants were evaluated and classified based on their impact, pathogenicity classification and population frequency as well as the frequency within our study group. NGS revealed 112 different (a total of 227) variants with under 10% population frequency in 103 patients of which 22(19.6%) were classified as benign, 29(25.9%) were classified as likely benign, 4(3.6%) were classified as likely pathogenic and 2(1.8%) were classified as pathogenic. Moreover, 55(49.1%) of the variants were classified as variants of uncertain significance. We also observed different variant frequencies when compared to population frequency databases. Case–control data is not sufficient to unravel the genetic etiology of immune deficiencies. Thus, it is important to understand the incidence of co-occurrence of two or more rare variants to aid in illuminating their potential roles in the pathogenesis of immune deficiencies.

Host genome analysis of structural variations by Optical Genome Mapping provides clinically valuable insights into genes implicated in critical immune, viral infection, and viral replication pathways in patients with severe COVID-19.

Background: The varied clinical manifestations and outcomes in patients with SARS-CoV-2 infections implicate a role of host-genetics in the predisposition to disease severity. This is supported by evidence that is now emerging, where initial reports identify common risk factors and rare genetic variants associated with high risk for severe/ life-threatening COVID-19. Impressive global efforts have focused on either identifying common genetic factors utilizing short-read sequencing data in Genome-Wide Association Studies (GWAS) or whole-exome and genome studies to interrogate the human genome at the level of detecting single nucleotide variants (SNVs) and short indels. However, these studies lack the sensitivity to accurately detect several classes of variants, especially large structural variants (SVs) including copy number variants (CNVs), which account for a substantial proportion of variation among individuals. Thus, we investigated the host genomes of individuals with severe/life-threatening COVID-19 at the level of large SVs (500bp-Mb level) to identify events that might provide insight into the inter-individual clinical variability in clinical course and outcomes of COVID-19 patients. Methods: Optical genome mapping using Bionano Saphyr system was performed on thirty-seven severely ill COVID-19 patients admitted to intensive care units (ICU). To extract candidate SVs, three distinct analyses were undertaken. First, an unbiased whole-genome analysis of SVs was performed to identify rare/unique genic SVs in these patients that did not appear in population datasets to determine candidate loci as decisive predisposing factors associated with severe COVID-19. Second, common SVs with a population frequency filter was interrogated for possible association with severe COVID-19 based on literature surveys. Third, genome-wide SV enrichment in severely ill patients versus the general population was investigated by calculating odds ratios to identify top-ranked genes/loci. Candidate SVs were confirmed using qPCR and an independent bioinformatics tool (FaNDOM). Results: Our patient-centric investigation identified 11 SVs involving 38 genes implicated in three key host-viral interaction pathways: (1) innate immunity and inflammatory response, (2) airway resistance to pathogens, and (3) viral replication, spread, and RNA editing. These included seven rare/unique SVs (not present in the control dataset), identified in 24.3% (9/37) of patients, impacting up to 31 genes, of which STK26 and DPP4 are the most promising candidates. A duplication partially overlapping STK26 was corroborated with data showing upregulation of this gene in severely ill patients. Further, using a population frequency filter of less than 20% in the Bionano control dataset, four SVs involving seven genes were identified in 56.7% (21/37) of patients. Conclusion: This study is the first to systematically assess and highlight SVs' potential role in the pathogenesis of COVID-19 severity. The genes implicated here identify novel SVs, especially STK26, and extend previous reports involving innate immunity and type I interferon response in the pathogenesis of COVID-19. Our study also shows that optical genome mapping can be a powerful tool to identify large SVs impacting disease outcomes with split survival and add valuable genomic information to the existing sequencing-based technology databases to understand the inter-individual variability associated with SARS-CoV-2 infections and COVID-19 mortality.