Post-Mendelian genetic model in COVID-19

Genetic counseling in direct-to-consumer exome sequencing: a case report.

Genome-wide associations studies (GWAS) have been very successful in identifying common genetic variation associated to numerous complex diseases [1]. However, most of the identified common genetic variants appear to confer modest risk and few causal alleles have been identified [2]. Furthermore, these associations account for a small portion of the total heritability of inherited disease variation [1]. This has led to the reexamination of the contribution of environment, gene-gene and gene-environment interactions, and rare genetic variants in complex diseases [1, 3, 4]. There is strong evidence that rare variants play an important role in complex disease etiology and may have larger genetic effects than common variants [2]. Currently, much of what we know regarding the contribution of rare genetic variants to disease risk is based on a limited number of phenotypes and candidate genes. However, rapid advancement of second generation sequencing technologies will invariably lead to widespread association studies comparing whole exome and eventually whole genome sequencing of cases and controls. A tremendous challenge for enabling these "next generation" medical genomic studies is developing statistical approaches for correlating rare genetic variants with disease outcome. The analysis of rare variants is challenging since methods used for common variants are woefully underpowered. Therefore, methods that can deal with genetic heterogeneity at the trait-associated locus have been developed to analyze rare variants. These methods instead analyzing individual variants analyze variants within a region/gene as a group and usually rely on collapsing. They can be applied to both in cases vs. controls and quantitative trait studies are needed. The paper of Bansal et al. in this volume describes the application of a number of statistical methods for testing associations between rare variants in two genes to obesity. The authors considered the relative merits of the different methods as well as important implementation details, such as the leveraging of genomic annotations and determining p-values. Knowledge of haplotypes can increase the power of GWAS studies and also highlight associations that are impossible to detect without haplotype phase (e.g. loss of heterozygosity). Even more complicated phase-dependent interactions of variants in linkage equilibrium have also been suggested as possible causes of missing heritability. In their work, Hallsorsson et al. formulate algorithmic strategies for haplotype phasing by multi-assembly of shared haplotypes from next-generation sequencing data. These methods would allow testing haplotypes harboring rare variants for association and potentially increase their explanatory power. Since single SNP tests are often underpowered in rare variant association analysis, Zeggini and Asimit propose a locus-based method that has high power in the presence of rare variants and that incorporate base quality scores available for sequencing data. Their results suggest that this multi-marker approach may be best suited for smaller regions, or after some filtering to reduce the number of SNPs that are jointly tested to reduce loss of power due to multiple-testing adjustments. Finally, the paper of Zhou et al., presents a penalized regression framework for association testing on sequence data, in the presence of both common and rare variants. This method also introduces the use of weights to incorporate available biological information on the variants. Although these tactics improve both false positive and false negative rates, they represent an incremental development and there is still significant room for improvement. With the development of sequencing technologies and methods to detect complex trait rare variant associations many new and exciting discovery are imminent. The analysis of rare variants is still in its infancy and the next few years promises to produce many new methods to meet the special demands of analyzing this type of data. Note from Publisher: This article contains the abstract and references.

RUNX2 is a transcription factor essential for osteoblast differentiation and skeletal morphogenesis. Its mutation creates cleidocranial dysplasia (CCD), a disorder characterized by skeletal abnormalities and bone mineral density (BMD) alterations. The purpose of the present study has been to clarify whether polymorphisms affecting this gene could be associated with changes in BMD in women. To that end, we performed an association study of BMD values from 776 women with two single nucleotide polymorphisms (SNPs) located at P2 promoter (-1025 T>C) and at exon 2 (+198 G>A), and with a deletion polymorphism (17Ala>11Ala), also located at exon 2. We found an association of -1025 T>C SNP with femoral neck BMD (FN-BMD), being the women of TC/CC genotype who have higher BMD than women of TT genotype (P=0.006). This association was independent of age, weight, menopausal status, or hormone replacement therapy (HRT) use as shown by regression analysis. When women of highest versus lowest quartile of BMD were compared, this association became more evident (P=0.002), extending also to +198 G>A SNP (GA/AA women with higher FN-BMD; P<0.05). In addition, we describe herein three novel rare variants in the polyglutamine domain of RUNX2 protein: an in-frame insertion and two deletions in exon 2, resulting in the insertions of 7 and deletions of 7 and 5 glutamines, respectively. These variants do not produce CCD, increased frequency of bone fracture, or BMD alterations. In conclusion, common polymorphisms in Runx2 are associated with FN-BMD. Nevertheless, rare variants that modify the polyglutamine domain of RUNX2 neither have any effect on BMD nor produce the CCD phenotype. These results underscore the significance of polymorphisms in the 5'-region of Runx2 in the determination of FN-BMD.

SLCO1B1 Variation and Methotrexate Disposition in Children with Acute Lymphoblastic Leukemia: The Importance of Rare Variants in Pharmacogenetics

Candidate gene approach to identifying rare genetic variants associated with lone atrial fibrillation

Genetics play an important role in systemic lupus erythematosus (SLE) pathogenesis. We calculated the prevalence of rare variants in known monogenic lupus genes among children suspected of monogenic lupus. We completed paired-end genome-wide sequencing (whole genome sequencing [WGS] or whole exome sequencing) in patients suspected of monogenic lupus, and focused on 36 monogenic lupus genes. We prioritized rare (minor allele frequency < 1%) exonic, nonsynonymous, and splice variants with predicted pathogenicity classified as deleterious variants (Combined Annotation Dependent Depletion [CADD], PolyPhen2, and Sorting Intolerant From Tolerant [SIFT] scores). Additional filtering restricted to predicted damaging variants by considering reported zygosity. In those with WGS (n = 69), we examined copy number variants (CNVs) > 1 kb in size. We created additive non-HLA and HLA SLE genetic risk scores (GRSs) using common SLE-risk single-nucleotide polymorphisms. We tested the relationship between SLE GRSs and the number of rare variants with multivariate logistic models, adjusted for sex, ancestry, and age of diagnosis. The cohort included 71 patients, 80% female, with a mean age at diagnosis of 8.9 (SD 3.2) years. We identified predicted damaging variants in 9 (13%) patients who were significantly younger at diagnosis compared to those without a predicted damaging variant (6.8 [SD 2.1] years vs 9.2 [SD 3.2] years, P = 0.01). We did not identify damaging CNVs. There was no significant association between non-HLA or HLA SLE GRSs and the odds of carrying ≥ 1 rare variant in multivariate analyses. In a cohort of patients with suspected monogenic lupus who underwent genome-wide sequencing, 13% carried rare predicted damaging variants for monogenic lupus. Additional studies are needed to validate our findings.

Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.

Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.

PTEN (phosphatase and tensin homolog deleted on chromosome 10) functions as a major tumor suppressor gene and is frequently deleted in different types of tumors including prostate cancer (PCa). It was hypothesized that germ-line genetic changes of PTEN affect susceptibility to PCa. Both common (with a minor allele frequency 5%) and rare (with a minor allele frequency <5%) germ-line variants of PTEN were comprehensively evaluated. A total of 15 germ-line variants were identified by re-sequencing the PTEN gene, including 5' untranslated region, all nine exons, exon-intron junctions and 3' untranslated region, in 188 probands of hereditary prostate cancer (HPC) families recruited from Johns Hopkins Hospital. Two microsatellite markers surrounding PTEN were used to test the co-segregation of 10 rare variants, which may give rise to highly penetrance in HPC. Two common single nucleotide polymorphisms (SNPs) were evaluated in the 188 HPC families using a family-based association study approach. To study low penetrant SNPs in PCa susceptibility, 33 SNPs covering PTEN were selected from the whole genome-wide association studies (GWAS) from our available case-control studies in Sweden (Cancer of the Prostate in Sweden (CAPS)) and the publicly available cancer genetic markers of susceptibility (CGEMS) study. Germ-line copy-number variations (CNVs) in PTEN were assessed in CAPS. Co-segregation of germ-line variants and PCa was not observed among HPC families and no significant differences in the allele frequencies were observed in sporadic cases and controls, aggressive and non-aggressive PCa (P>0.05). These results suggest that germ-line variants in PTEN do not have an important role in PCa susceptibility.

In humans, most of the genetic variation is rare and often population-specific. Whereas the role of rare genetic variants in familial monogenic diseases is firmly established, we are only now...

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with a myriad of diseases, offering critical insights into the biological pathways involved. However, the field is moving beyond single-marker analyses towards more integrative approaches and gene set-based associations. Polygenic risk scores (PRS) represent a significant leap, allowing for the aggregation of multiple genetic variants to predict individual risk profiles more accurately. Passero et al investigate various longitudinal modelling approaches to determine how these methods compare when analysing trajectory changes of a continuous, repeatedly measured phenotype. The authors evaluate the power, false positive rate, estimation accuracy, and precision of five models alongside naïve linear regression with a simulation study. By incorporating PRS in the longitudinal model, they study post-traumatic stress disorder over time in individuals of African and European ancestry. The advent of whole-genome sequencing and whole-exome sequencing has unveiled the intricate landscape of rare genetic variants. These technologies enable researchers to explore the full spectrum of genetic variation. On the rare variant association space, Falk et al extend the convex-optimised sequence kernel association test for rare variant studies that learns from data the optimal convex combination of kernels, to the full Generalised Linear Model setting with arbitrary non-genetic covariates. They show that the resulting optimisation problem is a quadratic programming problem that can be solved with no additional cost. A modified objective is related to an upper bound for the p-value through a decreasing exponential term in the objective function, indicating that optimising this objective function is a principled way of learning the combination of kernels. The proposed new method enables correcting for important confounders in association studies such as age, sex or population structure for both quantitative and binary traits. The authors show that this new approach can recover sensible weights and achieve higher power across different sample sizes and misspecification settings.Genetic epidemiology also seeks to unravel the complex interplay between genetic variations and environmental factors in the context of human health and disease. Gene and environment interactions are often cited as a potential source for the "missing heritability". Recent advances in multi-omics data technology have revolutionised genetic epidemiology research. Highthroughput technologies now allow comprehensive analysis of genomes, epigenomes, and transcriptomes at unprecedented scale and resolution. The integration of these multi-omic data sets provides a holistic view of biological processes, enabling researchers to uncover complex gene-environment interactions and regulatory mechanisms underlying disease susceptibility. These advancements hold immense potential for elucidating the genetic architecture of complex diseases and improving health outcomes globally. Majarian et al. conduct a study to examine whether lifestyle exposures can alter the association between genetic variants and cardiometabolic outcomes in diverse populations. They leverage publicly available genetic, epigenomic and transcriptomic summary association data to identify a subset of interactions among which multiple forms of evidence point toward potential mechanistic explanations. They also observed an overrepresentation of prioritised interaction loci drawn from meta-analyses of African-ancestry populations. Liu et al. report a study aiming to detect the associations of SNPs in a collection of genes with Cardiovascular disease and Ischemic Stroke as well as gene and gene/environment interactions in Chinese Han population. They explore the possible pathogenic mechanisms of these genes and provide a theoretical basis for early Cardiovascular disease and Ischemic Stroke prevention and treatment.A significant breakthrough in the field is the integration of genetic testing in both research and clinical settings. Genetic testing has become a vital tool in identifying hereditary conditions and assessing individual disease risk. By combining genetic testing data with epidemiological methods, researchers can more precisely determine the genetic underpinnings of various conditions and tailor preventive and therapeutic strategies. Jiang and colleagues use multiple genetic methods to identify rare thalassemia variants for prenatal diagnosis. It is important to identify the genotypes of parents before prenatal thalassemia diagnosis, especially in cases with phenotypes not matching genotypes. They conclude that accurate gene diagnosis for probands with combined molecular biology techniques is the key to prenatal diagnosis of rare thalassemia. Multiple techniques for α globin cluster should be performed on cases with severe β-thalassemia who only carry a mutation in the HBB gene. Compared to other methods, singlemolecule real-time sequencing offers advantages to find complicated genotypes of thalassemia.The field of genetic epidemiology is rapidly advancing, driven by technological innovations and increased data availability. The small collection of articles showcased in this Research Topic not only present several innovative techniques and methodology advancement but also highlight the importance of increasing the representation of diverse ethnic and demographic populations in genetic studies.

An Excess of Deleterious Variants in VEGF-A Pathway Genes in Down-Syndrome-Associated Atrioventricular Septal Defects

Rare Germline Variant in NFATC4 Associated with Familial Chronic Lymphocytic Leukemia

For decades, the heritable contribution of cancer has been the subject of intense study, first pursued in families with multiple affected individuals and more recently investigated in unrelated population-based studies. For example, family studies have yielded important genes for breast cancer (BRCA1 and BRCA2), colon cancer (MSH2 and MLH1), and families with a spectrum of less common cancers (TP53). The pursuit of candidate genes yielded only a handful of conclusive associations, such as NAT2 and GSTM1 in bladder cancer or alcohol dehydrogenase genes (ADH1B and ADH7) in aerodigestive cancers. New genotyping technologies together with a comprehensive map of human haplotypes (International HapMap Project) have enabled investigators to scan across the genome in a sufficiently large set of cases and controls without a set of prior hypotheses in search of common susceptibility alleles with low effect sizes. Genome-wide association studies (GWAS) have emerged as an important tool for discovering regions of the genome using hundreds of thousands of common single nucleotide polymorphism (SNP) markers. This ‘agnostic’ approach has conclusively discovered more than 125 regions for nearly two dozen types of cancer. At least eight regions have been associated with more than one distinct cancer type. For instance, the region flanking the MYC oncogene on 8q24 harbors at least five independent loci associated with prostate cancer as well as loci associated with cancers of the breast, colon, bladder, ovaries, and chronic lymphocytic leukemia. On 5p15.33, there are at least seven cancers, namely, basal cell carcinoma, bladder, brain, lung (adenocarcinoma), melanoma, pancreas, and testicular that map to the TERT-CLPTM1L locus. This locus is notable for the telomerase gene (TERT) in which rare mutations have been associated with dyskeratosis congenital (an inherited bone marrow failure syndrome), idiopathic pulmonary fibrosis, acute myelogenous leukemia, and chronic lymphocytic leukemia. The number of variants identified by GWAS per cancer varies greatly, suggesting different underlying genetic architectures, namely genetic contributions to common and uncommon cancers. Of the 125 regions associated with cancer risk, none of the regions have been conclusively associated with prognosis. In prostate cancer, there are at least 35 distinct loci harboring common susceptibility alleles identified by GWAS yet not a single one clearly distinguishes between aggressive and nonaggressive disease. This observation suggests that there could be regions that distinctly contribute to cancer risk and cancer outcomes (e.g., aggressive or metastatic disease). For lung cancer, a disease strongly driven by exposure to tobacco products, so far, only three or four regions have been conclusively established. Of these regions, the signal on chromosome 15 could be related to smoking. For nearly all common SNP markers (namely those with a minor allele frequency of greater than 5%), the estimated effect sizes are below 1.5. A notable exception is for testicular cancer in which variants on chromosome 12 near the KITLG gene have estimated effect sizes of greater than 2.5 for heterozygotes; this is not surprising since the hereditary component of testicular cancer is very strong. Overall, each region confers a small contribution to the risk for cancer, which suggests that it is daunting to consider any single SNP as a clinical test. GWAS regions are undergoing fine mapping to nominate the optimal candidates for functional studies designed to explain the direct association. Nearly all GWAS signals have mapped to noncoding regions, underscoring the contribution of regulatory mechanisms in neighboring genes (e.g., FGFR2 and breast cancer, MSMB/NCOA4 and prostate cancer). The plausibility of the underlying association can form the foundation for developing new strategies to intervene or diagnose cancer at an earlier stage by providing new insights into biological mechanisms that become targets. The application of GWAS to therapeutic outcomes and toxicities is beginning to discover new regions worthy of follow-up studies. However, for pharmacogenomics to eventually have an impact in the clinic, there will have to be a transition from discovery to characterization of the regions that ultimately yields targets for therapy and chemoprevention. Since we are still early in the discovery of common genetic variants associated with risk for specific cancers, it is not surprising that the utility of applying common SNPs to disease prediction is premature. Common genetic variants represent only a proportion of genetic variants that contribute to disease risk; uncommon, rare and copy number variants will undoubtedly contribute to risk in both familial and unrelated settings, and their relative contributions are expected to vary by cancer site. The addition of common SNP markers to classical epidemiological risk factors for breast cancer has minimally shifted the receiver operator curve (ROC) from 50% to approximately 60%. For prostate cancer, a disease in which epidemiological studies have identified age, ethnic background, and family history as the only conclusive risk factors, it is not surprising that more than 30 common SNPs shift the ROC curve to roughly 65%. Discovery of additional genetic variants should improve the risk prediction but each will be limited by the overall heredity of the cancer. New approaches towards applying common genetic variants could be beneficial in the reclassification of individuals based on genetic risk profiles or alternatively, the identification of subsets of individuals at high risk who might undergo a diagnostic or therapeutic intervention. In conclusion, the fruits of GWAS have important implications for investigating the etiology of different cancers, particularly as it relates to gene-environment and gene-gene interactions. The complex genomic architecture of disease susceptibility will require further discovery of uncommon and rare variants using next-generation sequence technologies. It will be critical to assess the applicability of genetic tests in specific clinical settings, such as when to perform screening tests with calculable risks (e.g., biopsies or chemoprevention) before incorporating SNPs into clinical practice. To incorporate the fruits of current genomic observations, new studies will need to be designed to validate the utility of known genetic variants in assessing risk for cancer as well as its outcomes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr PL01-02. doi:10.1158/1538-7445.AM2011-PL01-02

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with phenotypic and genetic heterogeneity. Recent studies have suggested an oligogenic basis of ALS, in which the co-occurrence of two or more genetic variants has additive or synergistic deleterious effects. To assess the contribution of possible oligogenic inheritance, we profiled a panel of 43 relevant genes in 57 sporadic ALS (sALS) patients and eight familial ALS (fALS) patients from five pedigrees in east China. We filtered rare variants using the combination of the Exome Aggregation Consortium, the1000 Genomes and theHuaBiao Project. We analyzed patients with multiple rare variants in 43 known ALS causative genes and the genotype-phenotype correlation. Overall, we detected 30 rare variants in 16 different genes and found that 16 of the sALS patients and all the fALS patients examined harbored at least one variant in the investigated genes, among which two sALS and four fALS patients harbored two or more variants. Of note, the sALS patients with one or more variants in ALS genes had worse survival than the patients with no variants. Typically, in one fALS pedigree with three variants, the family member with three variants (Superoxide dismutase 1(SOD1)p.V48A, Optineurin (OPTN)p.A433V and TANK binding kinase 1 (TBK1) p.R573H) exhibited much more severe disease phenotype than the member carrying one variant (TBK1 p.R573H). Our findings suggest that rare variants could exert a negative prognostic effect, thereby supporting the oligogenic inheritance of ALS.