Whole genome association studies in complex diseases: where do we stand?

Yield of genetic association signals from genomes, exomes and imputation in the UK Biobank

Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES + IMP) have the largest association yield. Although WGS results in an approximately fivefold increase in the total number of assayed variants over WES + IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Given that WES + IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES + IMP to larger samples. When we extend our WES + IMP analyses to 468,169 UK Biobank individuals, we observe an approximately fourfold increase in association signals with the threefold increase in sample size. We conclude that prioritizing WES + IMP and large sample sizes rather than contemporary short-read WGS alternatives will maximize the number of discoveries in genetic association studies.

A Frameshift Variant of GluN2A Identified in an Epilepsy Patient Results in NMDA Receptor Mistargeting

N-methyl-D-aspartate receptors (NMDARs) are crucial for neuronal development and synaptic plasticity. Dysfunction of NMDARs is associated with multiple neurodevelopmental disorders, including epilepsy, autism spectrum disorder, and intellectual disability. Understanding the impact of genetic variants of NMDAR subunits can shed light on the mechanisms of disease. Here, we characterized the functional implications of a de novo mutation of the GluN2A subunit (P1199Rfs*32) resulting in the truncation of the C-terminal domain. The variant was identified in a male patient with epileptic encephalopathy, multiple seizure types, severe aphasia, and neurobehavioral changes. Given the known role of the CTD in NMDAR trafficking, we examined changes in receptor localization and abundance at the postsynaptic membrane using a combination of molecular assays in heterologous cells and rat primary neuronal cultures. We observed that the GluN2A P1199Rfs*32-containing receptors traffic efficiently to the postsynaptic membrane but have increased extra-synaptic expression relative to WT GluN2A-containing NMDARs. Using in silico predictions, we hypothesized that the mutant would lose all PDZ interactions, except for the recycling protein Scribble1. Indeed, we observed impaired binding to the scaffolding protein postsynaptic protein-95 (PSD-95); however, we found the mutant interacts with Scribble1, which facilitates the recycling of both the mutant and the WT GluN2A. Finally, we found that neurons expressing GluN2A P1199Rfs*32 have fewer synapses and decreased spine density, indicating compromised synaptic transmission in these neurons. Overall, our data show that GluN2A P1199Rfs*32 is a loss-of-function variant with altered membrane localization in neurons and provide mechanistic insight into disease etiology.

Investigation of pathogenic germline variants in gastric cancer and development of "GasCanBase" database

BACKGROUND

Gastric cancer, which is also known as stomach cancer, can be influenced by both germline and somatic mutations. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in germline have long been reported to play a pivotal role in cancer progression.

AIM

The aim of this study is to examine the nsSNP in GC-associated genes. The study also aims to develop a database with extensive information regarding the nsSNPs in the GC-associated genes and their impacts.

METHODS AND RESULTS

A total of 34,588 nsSNPs from 1,493,460 SNPs of the 40 genes were extracted from the available SNP database. Drug binding and energy minimization were examined by molecular docking and YASARA. To validate the existence of the germline CDH1 gene mutation (rs34466743) in the isolated blood DNA of gastric cancer (GC) patients, polymerase chain reaction (PCR) and DNA sequencing were performed. According to the results of the gene network analysis, 17 genes may interact with other types of cancer. A total of 11,363 nsSNPs were detected within the 40 GC genes. Among these, 474 nsSNPs were predicted to be damaging and 40 to be the most damaging. The SNPs in domain regions were thought to be strong candidates that alter protein functions. Our findings proposed that most of the selected nsSNPs were within the domains or motif regions. Free Energy Deviation calculation of protein structure pointed toward noteworthy changes in the structure of each protein that can demolish its natural function. Subsequently, drug binding confirmed the structural variation and the ineffectiveness of the drug against the mutant model in individuals with these germline variants. Furthermore, in vitro analysis of the rs34466743 germline variant from the CDH1 gene confirmed the strength and robustness of the pipeline that could expand the somatic alteration for causing cancer. In addition, a comprehensive gastric cancer polymorphism database named "GasCanBase" was developed to make data available to researchers.

CONCLUSION

The findings of this study and the "GasCanBase" database may greatly contribute to our understanding of molecular epidemiology and the development of precise therapeutics for gastric cancer. GasCanBase is available at: https://www.gascanbase.com/.

Evaluation of Anti-Alzheimer Activity of Synthetic Coumarins by Combination of in Vitro and in Silico Approaches

Series of synthetic coumarin derivatives (1-16) were tested against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), two enzymes linked to the pathology of Alzheimer's disease (AD). Compound 16 was the most active AChE inhibitor with IC50 32.23±2.91 μM, while the reference (galantamine) had IC50 =1.85±0.12 μM. Compounds 9 (IC50 75.14±1.82 μM), 13 (IC50 =16.14±0.43 μM), were determined to be stronger BChE inhibitors than the reference galantamine (IC50 =93.53±2.23 μM). The IC50 value of compound 16 for BChE inhibition (IC50 =126.56±11.96 μM) was slightly higher than galantamine. The atomic interactions between the ligands and the key amino acids inside the binding cavities were simulated to determine their ligand-binding positions and free energies. The three inhibitory coumarins (9, 13, 16) were next tested for their effects on the genes associated with AD using human neuroblastoma (SH-SY5Y) cell lines. Our data indicate that they could be considered for further evaluation as new anti-Alzheimer drug candidates.

Association of the Sp1 binding site and -1997 promoter variations in COL1A1 with osteoporosis risk: The application of meta-analysis and bioinformatics approaches offers a new perspective for future research

As a complex disease, osteoporosis is influenced by several genetic markers. Many studies have examined the link between the Sp1 binding site +1245 G > T (rs1800012) and -1997 G > T (rs1107946) variations in the COL1A1 gene with osteoporosis risk. However, the findings of these studies have been contradictory; therefore, we performed a meta-analysis to aggregate additional information and obtain increased statistical power to more efficiently estimate this correlation. A meta-analysis was conducted with studies published between 1991-2020 that were identified by a systematic electronic search of the Scopus and Clarivate Analytics databases. Studies with bone mineral density (BMD) data and complete genotypes of the single-nucleotide variations (SNVs) for the overall and postmenopausal female population were included in this meta-analysis and analyzed using the R metaphor package. A relationship between rs1800012 and significantly decreased BMD values at the lumbar spine and femoral neck was found in individuals carrying the "ss" versus the "SS" genotype in the overall population according to a random effects model (p < 0.0001). Similar results were also found in the postmenopausal female population (p = 0.003 and 0.0002, respectively). Such findings might be an indication of increased osteoporosis risk in both studied groups in individuals with the "ss" genotype. Although no association was identified between the -1997 G > T and low BMD in the overall population, those individuals with the "GT" genotype showed a higher level of BMD than those with "GG" in the subgroup analysis (p = 0.007). To determine which transcription factor (TF) might bind to the -1997 G > T in COL1A1, 45 TFs were identified based on bioinformatics predictions. According to the GSE35958 microarray dataset, 16 of 45 TFs showed differential expression profiles in osteoporotic human mesenchymal stem cells relative to normal samples from elderly donors. By identifying candidate TFs for the -1997 G > T site, our study offers a new perspective for future research.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

The future of human cerebral cartography: a novel approach

Cerebral cartography can be understood in a limited, static, neuroanatomical sense. Temporal information from electrical recordings contributes information on regional interactions adding a functional dimension. Selective tagging and imaging of molecules adds biochemical contributions. Cartographic detail can also be correlated with normal or abnormal psychological or behavioural data. Modern cerebral cartography is assimilating all these elements. Cartographers continue to collect ever more precise data in the hope that general principles of organization will emerge. However, even detailed cartographic data cannot generate knowledge without a multi-scale framework making it possible to relate individual observations and discoveries. We propose that, in the next quarter century, advances in cartography will result in progressively more accurate drafts of a data-led, multi-scale model of human brain structure and function. These blueprints will result from analysis of large volumes of neuroscientific and clinical data, by a process of reconstruction, modelling and simulation. This strategy will capitalize on remarkable recent developments in informatics and computer science and on the existence of much existing, addressable data and prior, though fragmented, knowledge. The models will instantiate principles that govern how the brain is organized at different levels and how different spatio-temporal scales relate to each other in an organ-centred context.

Genome-wide association studies in pharmacogenomics of antidepressants

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Doctors must prescribe antidepressants based on educated guesses due to the fact that it is unmanageable to predict the effectiveness of any particular antidepressant in an individual patient. With the recent advent of scientific research, the genome-wide association study (GWAS) is extensively employed to analyze hundreds of thousands of single nucleotide polymorphisms by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been utilized to investigate the determinants of antidepressant response to therapy. In this study, we reviewed GWAS studies, their limitations and future directions with respect to the pharmacogenomics of antidepressants in MDD.

Genome-wide association studies of suicidal behaviors: a review

Suicidal behaviors represent a fatal dimension of mental ill-health, involving both environmental and heritable (genetic) influences. The putative genetic components of suicidal behaviors have until recent years been mainly investigated by hypothesis-driven research (of "candidate genes"). But technological progress in genotyping has opened the possibilities towards (hypothesis-generating) genomic screens and novel opportunities to explore polygenetic perspectives, now spanning a wide array of possible analyses falling under the term Genome-Wide Association Study (GWAS). Here we introduce and discuss broadly some apparent limitations but also certain developing opportunities of GWAS. We summarize the results from all the eight GWAS conducted up to date focused on suicidality outcomes; treatment emergent suicidal ideation (3 studies), suicide attempts (4 studies) and completed suicides (1 study). Clearly, there are few (if any) genome-wide significant and reproducible findings yet to be demonstrated. We then discuss and pinpoint certain future considerations in relation to sample sizes, the units of genetic associations used, study designs and outcome definitions, psychiatric diagnoses or biological measures, as well as the use of genomic sequencing. We conclude that GWAS should have a lot more potential to show in the case of suicidal outcomes, than what has yet been realized.

Nine things to remember about human genome diversity

Understanding how and why humans are biologically different is indispensable to get oriented in the ever-growing body of genomic data. Here we discuss the evidence based on which we can confidently state that humans are the least genetically variable primate, both when individuals and when populations are compared, and that each individual genome can be regarded as a mosaic of fragments of different origins. Each population is somewhat different from any other population, and there are geographical patterns in that variation. These patterns clearly indicate an African origin for our species, and keep a record of the main demographic changes accompanying the peopling of the whole planet. However, only a minimal fraction of alleles, and a small fraction of combinations of alleles along the chromosome, is restricted to a single geographical region (and even less so to a single population), and diversity between members of the same population is very large. The small genomic differences between populations and the extensive allele sharing across continents explain why historical attempts to identify, once and for good, major biological groups in humans have always failed. Nevertheless, racial categorization is all but gone, especially in clinical studies. We argue that racial labels may not only obscure important differences between patients but also that they have become positively useless now that cheap and reliable methods for genotyping are making it possible to pursue the development of truly personalized medicine.

Comparison of statistics in association tests of genetic markers for survival outcomes

Computationally efficient statistical tests are needed in association testing of large scale genetic markers for survival outcomes. In this study, we explore several test statistics based on the Cox proportional hazards model for survival data. First, we consider the classical partial likelihood-based Wald and score tests. A revised way to compute the score statistics is explored to improve the computational efficiency. Next, we propose a Cox-Snell residual-based score test, which allows us to handle the controlling variables more conveniently. We also illustrated the incorporation of these three tests into a permutation procedure to adjust for the multiple testing. In addition, we examine a simulation-based approach proposed by Lin (2005) to adjust for multiple testing. We presented the comparison of these four statistics in terms of type I error, power, family-wise error rate, and computational efficiency under various scenarios via extensive simulation.

A novel method, the Variant Impact On Linkage Effect Test (VIOLET), leads to improved identification of causal variants in linkage regions

The Human Genome Project was expected to individualize medicine by rapidly advancing knowledge of common complex disease through discovery of disease-causing genetic variants. However, this has proved challenging. Although linkage analysis has identified replicated chromosomal regions, subsequent detection of causal variants for complex traits has been limited. One explanation for this difficulty is that utilization of association to follow up linkage is problematic given that linkage and association are not required to co-occur. Indeed, co-occurrence is likely to occur only in special circumstances, such as Mendelian inheritance, but cannot be universally expected. To overcome this problem, we propose a novel method, the Variant Impact On Linkage Effect Test (VIOLET), which differs from other quantitative methods in that it is designed to follow up linkage by identifying variants that influence the variance explained by a quantitative trait locus. VIOLET's performance was compared with measured genotype and combined linkage association in two data sets with quantitative traits. Using simulated data, VIOLET had high power to detect the causal variant and reduced false positives compared with standard methods. Using real data, VIOLET identified a single variant, which explained 24% of linkage; this variant exhibited only nominal association (P=0.04) using measured genotype and was not identified by combined linkage association. These results demonstrate that VIOLET is highly specific while retaining low false-negative results. In summary, VIOLET overcomes a barrier to gene discovery and thus may be broadly applicable to identify underlying genetic etiology for traits exhibiting linkage.

Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications

Polycystic ovary syndrome (PCOS) is now recognized as an important metabolic as well as reproductive disorder conferring substantially increased risk for type 2 diabetes. Affected women have marked insulin resistance, independent of obesity. This article summarizes the state of the science since we last reviewed the field in the Endocrine Reviews in 1997. There is general agreement that obese women with PCOS are insulin resistant, but some groups of lean affected women may have normal insulin sensitivity. There is a post-binding defect in receptor signaling likely due to increased receptor and insulin receptor substrate-1 serine phosphorylation that selectively affects metabolic but not mitogenic pathways in classic insulin target tissues and in the ovary. Constitutive activation of serine kinases in the MAPK-ERK pathway may contribute to resistance to insulin's metabolic actions in skeletal muscle. Insulin functions as a co-gonadotropin through its cognate receptor to modulate ovarian steroidogenesis. Genetic disruption of insulin signaling in the brain has indicated that this pathway is important for ovulation and body weight regulation. These insights have been directly translated into a novel therapy for PCOS with insulin-sensitizing drugs. Furthermore, androgens contribute to insulin resistance in PCOS. PCOS may also have developmental origins due to androgen exposure at critical periods or to intrauterine growth restriction. PCOS is a complex genetic disease, and first-degree relatives have reproductive and metabolic phenotypes. Several PCOS genetic susceptibility loci have been mapped and replicated. Some of the same susceptibility genes contribute to disease risk in Chinese and European PCOS populations, suggesting that PCOS is an ancient trait.

Behavior genetics and postgenomics

The science of genetics is undergoing a paradigm shift. Recent discoveries, including the activity of retrotransposons, the extent of copy number variations, somatic and chromosomal mosaicism, and the nature of the epigenome as a regulator of DNA expressivity, are challenging a series of dogmas concerning the nature of the genome and the relationship between genotype and phenotype. According to three widely held dogmas, DNA is the unchanging template of heredity, is identical in all the cells and tissues of the body, and is the sole agent of inheritance. Rather than being an unchanging template, DNA appears subject to a good deal of environmentally induced change. Instead of identical DNA in all the cells of the body, somatic mosaicism appears to be the normal human condition. And DNA can no longer be considered the sole agent of inheritance. We now know that the epigenome, which regulates gene expressivity, can be inherited via the germline. These developments are particularly significant for behavior genetics for at least three reasons: First, epigenetic regulation, DNA variability, and somatic mosaicism appear to be particularly prevalent in the human brain and probably are involved in much of human behavior; second, they have important implications for the validity of heritability and gene association studies, the methodologies that largely define the discipline of behavior genetics; and third, they appear to play a critical role in development during the perinatal period and, in particular, in enabling phenotypic plasticity in offspring. I examine one of the central claims to emerge from the use of heritability studies in the behavioral sciences, the principle of minimal shared maternal effects, in light of the growing awareness that the maternal perinatal environment is a critical venue for the exercise of adaptive phenotypic plasticity. This consideration has important implications for both developmental and evolutionary biology.

Predicting risk and the emergence of schizophrenia

This article gives an overview of genetic and environmental risk factors for schizophrenia. The presence of certain molecular, biological, and psychosocial factors at certain points in the life span, has been linked to later development of schizophrenia. All need to be considered in the context of schizophrenia as a lifelong brain disorder. Research interest in schizophrenia is shifting to late childhood/early adolescence for screening and preventative measures. This article discusses those environmental risk factors for schizophrenia for which there is the largest evidence base.

Exome sequencing followed by large-scale genotyping fails to identify single rare variants of large effect in idiopathic generalized epilepsy

Idiopathic generalized epilepsy (IGE) is a complex disease with high heritability, but little is known about its genetic architecture. Rare copy-number variants have been found to explain nearly 3% of individuals with IGE; however, it remains unclear whether variants with moderate effect size and frequencies below what are reliably detected with genome-wide association studies contribute significantly to disease risk. In this study, we compare the exome sequences of 118 individuals with IGE and 242 controls of European ancestry by using next-generation sequencing. The exome-sequenced epilepsy cases include study subjects with two forms of IGE, including juvenile myoclonic epilepsy (n = 93) and absence epilepsy (n = 25). However, our discovery strategy did not assume common genetic control between the subtypes of IGE considered. In the sequence data, as expected, no variants were significantly associated with the IGE phenotype or more specific IGE diagnoses. We then selected 3,897 candidate epilepsy-susceptibility variants from the sequence data and genotyped them in a larger set of 878 individuals with IGE and 1,830 controls. Again, no variant achieved statistical significance. However, 1,935 variants were observed exclusively in cases either as heterozygous or homozygous genotypes. It is likely that this set of variants includes real risk factors. The lack of significant association evidence of single variants with disease in this two-stage approach emphasizes the high genetic heterogeneity of epilepsy disorders, suggests that the impact of any individual single-nucleotide variant in this disease is small, and indicates that gene-based approaches might be more successful for future sequencing studies of epilepsy predisposition.

Epi4K: gene discovery in 4,000 genomes

A major challenge in epilepsy research is to unravel the complex genetic mechanisms underlying both common and rare forms of epilepsy, as well as the genetic determinants of response to treatment. To accelerate progress in this area, the National Institute of Neurological Disorders and Stroke (NINDS) recently offered funding for the creation of a “Center without Walls” to focus on the genetics of human epilepsy. This article describes Epi4K, the collaborative study supported through this grant mechanism and having the aim of analyzing the genomes of a minimum 4,000 subjects with highly selected and well-characterized epilepsy.

Assessing gene-gene interactions in pharmacogenomics

In pharmacogenomics studies, gene-gene interactions play an important role in characterizing a trait that involves complex pharmacokinetic and pharmacodynamic mechanisms, particularly when each involved feature only demonstrates a minor effect. In addition to the candidate gene approach, genome-wide association studies (GWAS) are widely utilized to identify common variants that are associated with treatment response. In the wake of recent advances in scientific research, a paradigm shift from GWAS to whole-genome sequencing is expected, because of the reduced cost and the increased throughput of next-generation sequencing technologies. This review first outlines several promising methods for addressing gene-gene interactions in pharmacogenomics studies. We then summarize some candidate gene studies for various treatments with consideration of gene-gene interactions. Furthermore, we give a brief overview for the pharmacogenomics studies with the GWAS approach and describe the limitations of these GWAS in terms of gene-gene interactions. Future research in translational medicine promises to lead to mechanistic findings related to drug responsiveness in light of complex gene-gene interactions and will probably make major contributions to individualized medicine and therapeutic decision-making.

Mouse models of aneuploidy

Abnormalities of chromosome copy number are called aneuploidies and make up a large health load on the human population. Many aneuploidies are lethal because the resulting abnormal gene dosage is highly deleterious. Nevertheless, some whole chromosome aneuploidies can lead to live births. Alterations in the copy number of sections of chromosomes, which are also known as segmental aneuploidies, are also associated with deleterious effects. Here we examine how aneuploidy of whole chromosomes and segmental aneuploidy of chromosomal regions are modeled in the mouse. These models provide a whole animal system in which we aim to investigate the complex phenotype-genotype interactions that arise from alteration in the copy number of genes. Although our understanding of this subject is still in its infancy, already research in mouse models is highlighting possible therapies that might help alleviate the cognitive effects associated with changes in gene number. Thus, creating and studying mouse models of aneuploidy and copy number variation is important for understanding what it is to be human, in both the normal and genomically altered states.

Evolutionary evidence of the effect of rare variants on disease etiology

The common disease/common variant hypothesis has been popular for describing the genetic architecture of common human diseases for several years. According to the originally stated hypothesis, one or a few common genetic variants with a large effect size control the risk of common diseases. A growing body of evidence, however, suggests that rare single-nucleotide polymorphisms (SNPs), i.e. those with a minor allele frequency of less than 5%, are also an important component of the genetic architecture of common human diseases. In this study, we analyzed the relevance of rare SNPs to the risk of common diseases from an evolutionary perspective and found that rare SNPs are more likely than common SNPs to be functional and tend to have a stronger effect size than do common SNPs. This observation, and the fact that most of the SNPs in the human genome are rare, suggests that rare SNPs are a crucial element of the genetic architecture of common human diseases. We propose that the next generation of genomic studies should focus on analyzing rare SNPs. Further, targeting patients with a family history of the disease, an extreme phenotype, or early disease onset may facilitate the detection of risk-associated rare SNPs.