Elements of 'missing heritability'

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Neural tube defects and epigenetics: role of histone post-translational histone modifications

Neural tube defects (NTDs) are the most common congenital anomalies of the CNS. It is widely appreciated that both genetic and environmental factors contribute to their etiology. The inability to ascribe clear genetic patterns of inheritance to various NTD phenotypes suggests it is possible that epigenetic mechanisms are involved in the etiology of NTDs. In this context, the contribution of DNA methylation as an underlying contributing factor to the etiology of NTDs has been extensively reviewed. Here, an updated accounting of the evidence linking post-translational histone modifications to these birth defects, relying heavily upon studies in humans, and the possible molecular implications inferred from reports based on cellular and animal models, are presented.

Genetic Basis of Inflammatory Demyelinating Diseases of the Central Nervous System: Multiple Sclerosis and Neuromyelitis Optica Spectrum

Demyelinating diseases alter myelin or the coating surrounding most nerve fibers in the central and peripheral nervous systems. The grouping of human central nervous system demyelinating disorders today includes multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) as distinct disease categories. Each disease is caused by a complex combination of genetic and environmental variables, many involving an autoimmune response. Even though these conditions are fundamentally similar, research into genetic factors, their unique clinical manifestations, and lesion pathology has helped with differential diagnosis and disease pathogenesis knowledge. This review aims to synthesize the genetic approaches that explain the differential susceptibility between these diseases, explore the overlapping clinical features, and pathological findings, discuss existing and emerging hypotheses on the etiology of demyelination, and assess recent pathogenicity studies and their implications for human demyelination. This review presents critical information from previous studies on the disease, which asks several questions to understand the gaps in research in this field.

Evolutionary Origins of Metabolic Reprogramming in Cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.

The missing heritability in type 1 diabetes

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Identification of FAT3 as a new candidate gene for adolescent idiopathic scoliosis

In an effort to identify rare alleles associated with adolescent idiopathic scoliosis (AIS) whole-exome sequencing was performed on a discovery cohort of 73 unrelated patients and 70 age-and sex matched controls, all of French-Canadian ancestry. A collapsing gene burden test was performed to analyze rare protein-altering variants using case–control statistics. Since no single gene achieved statistical significance, targeted exon sequencing was performed for 24 genes with the smallest p values, in an independent replication cohort of unrelated severely affected females with AIS and sex-matched controls (N = 96 each). An excess of rare, potentially protein-altering variants was noted in one particular gene, FAT3, although it did not achieve statistical significance. Independently, we sequenced the exomes of all members of a rare multiplex family of three affected sisters and unaffected parents. All three sisters were compound heterozygous for two rare protein-altering variants in FAT3. The parents were single heterozygotes for each variant. The two variants in the family were also present in our discovery cohort. A second validation step was done, using another independent replication cohort of 258 unrelated AIS patients having reach their skeletal maturity and 143 healthy controls to genotype nine FAT3 gene variants, including the two variants previously identified in the multiplex family: p.L517S (rs139595720) and p.L4544F (rs187159256). Interestingly, two FAT3 variants, rs139595720 (genotype A/G) and rs80293525 (genotype C/T), were enriched in severe scoliosis cases (4.5% and 2.7% respectively) compared to milder cases (1.4% and 0.7%) and healthy controls (1.6% and 0.8%). Our results implicate FAT3 as a new candidate gene in the etiology of AIS.

Genomic Regions Associated With Salinity Stress Tolerance in Tropical Maize (Zea Mays L.)

Being a widely cultivated crop globally under diverse climatic conditions and soil types, maize is often exposed to an array of biotic and abiotic stresses. Soil salinity is one of the challenges for maize cultivation in many parts of lowland tropics that significantly affects crop growth and reduces economic yields. Breeding strategies integrated with molecular approach might accelerate the process of identifying and developing salinity-tolerant maize cultivars. In this study, an association mapping panel consisting of 305 diverse maize inbred lines was phenotyped in a managed salinity stress phenotyping facility at International Center for Biosaline Agriculture (ICBA), Dubai, United Arab Emirates (UAE). Wide genotypic variability was observed in the panel under salinity stress for key phenotypic traits viz., grain yield, days to anthesis, anthesis-silking interval, plant height, cob length, cob girth, and kernel number. The panel was genotyped following the genome-based sequencing approach to generate 955,690 SNPs. Total SNPs were filtered to 213,043 at a call rate of 0.85 and minor allele frequency of 0.05 for association analysis. A total of 259 highly significant (P ≤ 1 × 10^-5) marker-trait associations (MTAs) were identified for seven phenotypic traits. The phenotypic variance for MTAs ranged between 5.2 and 9%. A total of 64 associations were found in 19 unique putative gene expression regions. Among them, 12 associations were found in gene models with stress-related biological functions.

Determining population stratification and subgroup effects in association studies of rare genetic variants for nicotine dependence

Supplemental Digital Content is available in the text.

Background

Rare variants (minor allele frequency < 1% or 5 %) can help researchers to deal with the confounding issue of ‘missing heritability’ and have a proven role in dissecting the etiology for human diseases and complex traits.

Methods

We extended the combined multivariate and collapsing (CMC) and weighted sum statistic (WSS) methods and accounted for the effects of population stratification and subgroup effects using stratified analyses by the principal component analysis, named here as ‘str-CMC’ and ‘str-WSS’. To evaluate the validity of the extended methods, we analyzed the Genetic Architecture of Smoking and Smoking Cessation database, which includes African Americans and European Americans genotyped on Illumina Human Omni2.5, and we compared the results with those obtained with the sequence kernel association test (SKAT) and its modification, SKAT-O that included population stratification and subgroup effect as covariates. We utilized the Cochran–Mantel–Haenszel test to check for possible differences in single nucleotide polymorphism allele frequency between subgroups within a gene. We aimed to detect rare variants and considered population stratification and subgroup effects in the genomic region containing 39 acetylcholine receptor-related genes.

Results

The Cochran–Mantel–Haenszel test as applied to GABRG2 (P = 0.001) was significant. However, GABRG2 was detected both by str-CMC (P= 8.04E-06) and str-WSS (P= 0.046) in African Americans but not by SKAT or SKAT-O.

Conclusions

Our results imply that if associated rare variants are only specific to a subgroup, a stratified analysis might be a better approach than a combined analysis.

Establishing a three-generation prospective study: Bogalusa daughters

OBJECTIVE

The Developmental Origins of Disease hypothesis has spurred increased interest in how prenatal exposures affect lifelong health, while mechanisms such as epigenetics may explain the multigenerational influences on health. Such factors are not well captured within conventional epidemiologic study designs. We explored the feasibility of collecting information on the offspring and grand-offspring of participants in a long-running study.

DESIGN

The Bogalusa Heart Study is a study, begun in 1973, of life-course cardiovascular health in a semirural population (65% white and 35% black).

MAIN MEASURES

Female participants who had previously provided information on their pregnancies were contacted to obtain contact information for their daughters aged 12 and older. Daughters were then contacted to obtain reproductive histories, and invited for a clinic or lab visit to measure cardiovascular risk factors.

RESULTS

Two hundred seventy-four daughters of 208 mothers were recruited; 81% (223) had a full clinic visit and 19% (51) a phone interview only. Forty-five percent of the daughters were black, and 55% white. Mean and median age at interview was 27, with 15% under the age of 18. The strongest predictors of participation were black race, recent maternal participation in the parent study, and living in or near Bogalusa. Simple correlations for cardiovascular risk factors across generations were between r = 0.19 (systolic blood pressure) and r = 0.39 (BMI, LDL).

CONCLUSION

It is feasible to contact the children of study participants even when participants are adults, and initial information on the grandchildren can also be determined in this manner.

The interaction effect of angiogenesis and endothelial dysfunction-related gene variants increases the susceptibility of recurrent pregnancy loss

PURPOSE

The role of genetic polymorphisms in the pathogenesis of recurrent pregnancy loss (RPL) has been studied intensively. Complex diseases, including miscarriage, are believed to have a polygenic basis, and gene-gene interactions can play a significant role in the etiology of the disease. This study was conducted to investigate the association of gene-gene interactions with angiogenesis, endothelial dysfunction-related gene polymorphisms, and RPL.

METHODS

A case-control study was conducted with 253 unrelated RPL patients with 2 or more spontaneous pregnancy losses and 339 healthy women with no history of pregnancy complications. Genotyping of single-nucleotide polymorphisms (SNPs) was performed using real-time polymerase chain reaction (real-time PCR), restriction fragment length polymorphism (RFLP), or allele-specific polymerase chain reaction methods.

RESULTS

The genotypes 677TT of the MTHFR gene, 936TT, 936CT, and 634CC, 634GC of the VEGF gene, and allele 894T of the NOS3 gene were associated with a predisposition to RPL in the Russian population. A significant role of additive and epistatic effects in the gene-gene interactions of the SNPs of SERPINE-1, ACE, NOS3, MTHFR, and VEGF genes in RPL was demonstrated.

CONCLUSIONS

The results showed that gene-gene interactions are important for RPL susceptibility. Additionally, analysis of the genotype combinations of several allelic variants provides more information on RPL risk than analysis of independent polymorphic markers.

Host and Microbiome Genome-Wide Association Studies: Current State and Challenges

The involvement of the microbiome in health and disease is well established. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome. The emergence of this relatively new field has been facilitated by the advent of next generation sequencing technologies that enable the investigation of the complex interaction between host genetics and microbial communities. In this paper, we review recent studies investigating host-microbiome interactions using mGWAS. Additionally, we highlight the marked disparity in the sampling population of mGWAS carried out to date and draw attention to the critical need for inclusion of diverse populations.

Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival

Characterization of gene-environment interactions (GEIs) in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from 747 rectal cancer cases and 956 controls from the Diet, Activity and Lifestyle as a Risk Factor for Rectal Cancer study. We applied a 3-step analysis approach: first, we searched for interactions among single nucleotide polymorphisms on the pathway genes; second, we searched for interactions among the genes, both steps using Logic regression; third, we examined the GEIs significant at the 5% level using logistic regression for cancer risk and Cox proportional hazards models for survival. Permutation-based test was used for multiple testing adjustment. We identified 8 significant GEIs associated with risk among 6 genes adjusting for multiple testing: TNF (OR = 1.85, 95% CI: 1.10, 3.11), TLR4 (OR = 2.34, 95% CI: 1.38, 3.98), and EGR2 (OR = 2.23, 95% CI: 1.04, 4.78) with smoking; IGF1R (OR = 1.69, 95% CI: 1.04, 2.72), TLR4 (OR = 2.10, 95% CI: 1.22, 3.60) and EGR2 (OR = 2.12, 95% CI: 1.01, 4.46) with alcohol; and PDGFB (OR = 1.75, 95% CI: 1.04, 2.92) and MMP1 (OR = 2.44, 95% CI: 1.24, 4.81) with protein. Five GEIs were associated with survival at the 5% significance level but not after multiple testing adjustment: CXCR1 (HR = 2.06, 95% CI: 1.13, 3.75) with smoking; and KDR (HR = 4.36, 95% CI: 1.62, 11.73), TLR2 (HR = 9.06, 95% CI: 1.14, 72.11), EGR2 (HR = 2.45, 95% CI: 1.42, 4.22), and EGFR (HR = 6.33, 95% CI: 1.95, 20.54) with protein. GEIs between angiogenesis genes and smoking, alcohol, and animal protein impact rectal cancer risk. Our results support the importance of considering the biologic hypothesis to characterize GEIs associated with cancer outcomes.

Assessing the Heritability of Complex Traits in Humans: Methodological Challenges and Opportunities

The goal of this review article is to provide a conceptual based summary of how heritability estimates for complex traits such as obesity are determined and to explore the future directions of research in the heritability field. The target audience are researchers who use heritability data rather than those conducting heritability studies. The article provides an introduction to key concepts critical to understanding heritability studies including: i) definitions of heritability: broad sense versus narrow sense heritability; ii) how data for heritability studies are collected: twin, adoption, family and population-based studies; and iii) analytical techniques: path analysis, structural equations and mixed or regressive models of complex segregation analysis. For each section, a discussion of how the different definitions and methodologies influence heritability estimates is provided. The general limitations of heritability studies are discussed including the issue of “missing heritability” in which heritability estimates are significantly higher than the variance explained by known genetic variants. Potential causes of missing heritability include restriction of many genetic association studies to single nucleotide polymorphisms, gene by gene interactions, epigenetics, and gene by environment interactions. Innovative strategies of accounting for missing heritability including modeling techniques and improved software are discussed.

The Human Microbiome and the Missing Heritability Problem

The "missing heritability" problem states that genetic variants in Genome-Wide Association Studies (GWAS) cannot completely explain the heritability of complex traits. Traditionally, the heritability of a phenotype is measured through familial studies using twins, siblings and other close relatives, making assumptions on the genetic similarities between them. When this heritability is compared to the one obtained through GWAS for the same traits, a substantial gap between both measurements arise with genome wide studies reporting significantly smaller values. Several mechanisms for this "missing heritability" have been proposed, such as epigenetics, epistasis, and sequencing depth. However, none of them are able to fully account for this gap in heritability. In this paper we provide evidence that suggests that in order for the phenotypic heritability of human traits to be broadly understood and accounted for, the compositional and functional diversity of the human microbiome must be taken into account. This hypothesis is based on several observations: (A) The composition of the human microbiome is associated with many important traits, including obesity, cancer, and neurological disorders. (B) Our microbiome encodes a second genome with nearly a 100 times more genes than the human genome, and this second genome may act as a rich source of genetic variation and phenotypic plasticity. (C) Human genotypes interact with the composition and structure of our microbiome, but cannot by themselves explain microbial variation. (D) Microbial genetic composition can be strongly influenced by the host's behavior, its environment or by vertical and horizontal transmissions from other hosts. Therefore, genetic similarities assumed in familial studies may cause overestimations of heritability values. We also propose a method that allows the compositional and functional diversity of our microbiome to be incorporated to genome wide association studies.

Statistical Identification of Gene-gene Interactions Triggered By Nonlinear Environmental Modulation

Complex diseases are often caused by the function of multiple genes, gene-gene (G×G) interactions as well as gene-environment (G×E) interactions. G×G and G×E interactions are ubiquitous in nature. Empirical evidences have shown that the effect of G×G interaction on disease risk could be largely modified by environmental changes. Such a G×G×E triple interaction could be a potential contributing factor to phenotypic plasticity. Although the role of environmental factors moderating genetic influences on disease risk has been broadly recognized, no statistical method has been developed to rigorously assess how environmental changes modify G×G interactions to affect disease risk. To address this issue, we developed a G×G×E triple interaction model in this work. We modeled the environmental modification effect via a varying-coefficient model where the structure of the varying effect is determined by data. Thus the model has the flexibility to assess nonlinear environmental moderation effect on G×G interaction. Simulation and real data analysis were conducted to show the utility of the method. Our approach provides a quantitative framework to assess triple interactions hypothesized in literature.

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data

BACKGROUND AND OBJECTIVE

To clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of the researchers. Most detected genetic associations have not reached a genome-wide level of statistical significance, and suffered from the lack of replication in the studies of independent populations. The reasons for slow progress in this research area include low efficiency of statistical methods used in data analyses, genetic heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g., age dependent) effects of genetic variants on such traits, underestimation of the effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external factors and differences in genetic backgrounds of individuals in the populations under study, the weakness of conceptual biological framework that does not fully account for above mentioned factors. One more limitation of conducted studies is that they did not fully realize the potential of longitudinal data that allow for evaluating how genetic influences on life span are mediated by physiological variables and other biomarkers during the life course. The objective of this paper is to address these issues.

DATA AND METHODS

We performed GWAS of human life span using different subsets of data from the original Framingham Heart Study cohort corresponding to different quality control (QC) procedures and used one subset of selected genetic variants for further analyses. We used simulation study to show that approach to combining data improves the quality of GWAS. We used FHS longitudinal data to compare average age trajectories of physiological variables in carriers and non-carriers of selected genetic variants. We used stochastic process model of human mortality and aging to investigate genetic influence on hidden biomarkers of aging and on dynamic interaction between aging and longevity. We investigated properties of genes related to selected variants and their roles in signaling and metabolic pathways.

RESULTS

We showed that the use of different QC procedures results in different sets of genetic variants associated with life span. We selected 24 genetic variants negatively associated with life span. We showed that the joint analyses of genetic data at the time of bio-specimen collection and follow up data substantially improved significance of associations of selected 24 SNPs with life span. We also showed that aging related changes in physiological variables and in hidden biomarkers of aging differ for the groups of carriers and non-carriers of selected variants.

CONCLUSIONS

. The results of these analyses demonstrated benefits of using biodemographic models and methods in genetic association studies of these traits. Our findings showed that the absence of a large number of genetic variants with deleterious effects may make substantial contribution to exceptional longevity. These effects are dynamically mediated by a number of physiological variables and hidden biomarkers of aging. The results of these research demonstrated benefits of using integrative statistical models of mortality risks in genetic studies of human aging and longevity.

Missing heritability of complex diseases: Enlightenment by genetic variants from intermediate phenotypes

Diseases of complex origin have a component of quantitative genetics that contributes to their susceptibility and phenotypic variability. However, after several studies, a major part of the genetic component of complex phenotypes has still not been found, a situation known as "missing heritability." Although there have been many hypotheses put forward to explain the reasons for the missing heritability, its definitive causes remain unknown. Complex diseases are caused by multiple intermediate phenotypes involved in their pathogenesis and, very often, each one of these intermediate phenotypes also has a component of quantitative inheritance. Here we propose that at least part of the missing heritability can be explained by the genetic component of intermediate phenotypes that is not detectable at the level of the main complex trait. At the same time, the identification of the genetic component of intermediate phenotypes provides an opportunity to identify part of the missing heritability of complex diseases.

Proceedings: consideration of genetics in the design of induced pluripotent stem cell-based models of complex disease

The goal of exploiting induced pluripotent stem cell (iPSC) technology for the discovery of new mechanisms and treatments of disease is being pursued by many laboratories, and analyses of rare monogenic diseases have already provided ample evidence that this approach has merit. Considering the enormous medical burden imposed by common chronic diseases, successful implementation of iPSC-based models has the potential for major impact on these diseases as well. Since common diseases represent complex traits with varying genetic and environmental contributions to disease manifestation, the use of iPSC technology poses unique challenges. In this perspective, we will consider how the genetics of complex disease and mechanisms underlying phenotypic variation affect experimental design.

Heritabilities, proportions of heritabilities explained by GWAS findings, and implications of cross-phenotype effects on PR interval

Electrocardiogram (ECG) measurements are a powerful tool for evaluating cardiac function and are widely used for the diagnosis and prediction of a variety of conditions, including myocardial infarction, cardiac arrhythmias, and sudden cardiac death. Recently, genome-wide association studies (GWASs) identified a large number of genes related to ECG parameter variability, specifically for the QT, QRS, and PR intervals. The aims of this study were to establish the heritability of ECG traits, including indices of left ventricular hypertrophy, and to directly assess the proportion of those heritabilities explained by GWAS variants. These analyses were conducted in a large, Dutch family-based cohort study, the Erasmus Rucphen Family study using variance component methods implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) software package. Heritability estimates ranged from 34 % for QRS and Cornell voltage product to 49 % for 12-lead sum. Trait-specific GWAS findings for each trait explained a fraction of their heritability (17 % for QRS, 4 % for QT, 2 % for PR, 3 % for Sokolow–Lyon index, and 4 % for 12-lead sum). The inclusion of all ECG-associated single nucleotide polymorphisms explained an additional 6 % of the heritability of PR. In conclusion, this study shows that, although GWAS explain a portion of ECG trait variability, a large amount of heritability remains to be explained. In addition, larger GWAS for PR are likely to detect loci already identified, particularly those observed for QRS and 12-lead sum.

Epigenetic inheritance and the missing heritability

Genome-wide association studies of complex physiological traits and diseases consistently found that associated genetic factors, such as allelic polymorphisms or DNA mutations, only explained a minority of the expected heritable fraction. This discrepancy is known as “missing heritability”, and its underlying factors and molecular mechanisms are not established. Epigenetic programs may account for a significant fraction of the “missing heritability.” Epigenetic modifications, such as DNA methylation and chromatin assembly states, reflect the high plasticity of the genome and contribute to stably alter gene expression without modifying genomic DNA sequences. Consistent components of complex traits, such as those linked to human stature/height, fertility, and food metabolism or to hereditary defects, have been shown to respond to environmental or nutritional condition and to be epigenetically inherited. The knowledge acquired from epigenetic genome reprogramming during development, stem cell differentiation/de-differentiation, and model organisms is today shedding light on the mechanisms of (a) mitotic inheritance of epigenetic traits from cell to cell, (b) meiotic epigenetic inheritance from generation to generation, and (c) true transgenerational inheritance. Such mechanisms have been shown to include incomplete erasure of DNA methylation, parental effects, transmission of distinct RNA types (mRNA, non-coding RNA, miRNA, siRNA, piRNA), and persistence of subsets of histone marks.

Obesity and genomics: role of technology in unraveling the complex genetic architecture of obesity

Obesity is a complex and multifactorial disease that occurs as a result of the interaction between "obesogenic" environmental factors and genetic components. Although the genetic component of obesity is clear from the heritability studies, the genetic basis remains largely elusive. Successes have been achieved in identifying the causal genes for monogenic obesity using animal models and linkage studies, but these approaches are not fruitful for polygenic obesity. The developments of genome-wide association approach have brought breakthrough discovery of genetic variants for polygenic obesity where tens of new susceptibility loci were identified. However, the common SNPs only accounted for a proportion of heritability. The arrival of NGS technologies and completion of 1000 Genomes Project have brought other new methods to dissect the genetic architecture of obesity, for example, the use of exome genotyping arrays and deep sequencing of candidate loci identified from GWAS to study rare variants. In this review, we summarize and discuss the developments of these genetic approaches in human obesity.

Twin and family studies reveal strong environmental and weaker genetic cues explaining heritability of eosinophilic esophagitis

BACKGROUND

Eosinophilic esophagitis (EoE) is a chronic antigen-driven allergic inflammatory disease, likely involving the interplay of genetic and environmental factors, yet their respective contributions to heritability are unknown.

OBJECTIVE

To quantify the risk associated with genes and environment on familial clustering of EoE.

METHODS

Family history was obtained from a hospital-based cohort of 914 EoE probands (n = 2192 first-degree "Nuclear-Family" relatives) and an international registry of monozygotic and dizygotic twins/triplets (n = 63 EoE "Twins" probands). Frequencies, recurrence risk ratios (RRRs), heritability, and twin concordance were estimated. Environmental exposures were preliminarily examined.

RESULTS

Analysis of the Nuclear-Family-based cohort revealed that the rate of EoE, in first-degree relatives of a proband, was 1.8% (unadjusted) and 2.3% (sex-adjusted). RRRs ranged from 10 to 64, depending on the family relationship, and were higher in brothers (64.0; P = .04), fathers (42.9; P = .004), and males (50.7; P < .001) than in sisters, mothers, and females, respectively. The risk of EoE for other siblings was 2.4%. In the Nuclear-Family cohort, combined gene and common environment heritability was 72.0% ± 2.7% (P < .001). In the Twins cohort, genetic heritability was 14.5% ± 4.0% (P < .001), and common family environment contributed 81.0% ± 4% (P < .001) to phenotypic variance. Probandwise concordance in monozygotic co-twins was 57.9% ± 9.5% compared with 36.4% ± 9.3% in dizygotic co-twins (P = .11). Greater birth weight difference between twins (P = .01), breast-feeding (P = .15), and fall birth season (P = .02) were associated with twin discordance in disease status.

CONCLUSIONS

EoE RRRs are increased 10- to 64-fold compared with the general population. EoE in relatives is 1.8% to 2.4%, depending on relationship and sex. Nuclear-Family heritability appeared to be high (72.0%). However, the Twins cohort analysis revealed a powerful role for common environment (81.0%) compared with additive genetic heritability (14.5%).

The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study

Background

Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear.

This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach.

Results

Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e.g. additive dominant model P_uncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with P_uncorrected = 6.95E-5 with odds ratio (OR estimated from β3) value = 4.99) the area under the curve (AUC) estimates were low (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 0.15).

Conclusions

We conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously unsuspected effects that could provide novel insights into MDD risk, but much larger sample sizes are needed before this strategy can be powerfully applied.

Human leukocyte antigen haplotype phasing by allele-specific enrichment with peptide nucleic acid probes

Targeted capture of large fragments of genomic DNA that enrich for human leukocyte antigen (HLA) system haplotypes has utility in haematopoietic stem cell transplantation. Current methods of HLA matching are based on inference or familial studies of inheritance; and each approach has its own inherent limitations. We have designed and tested a probe–target-extraction method for capturing specific HLA haplotypes by hybridization of peptide nucleic acid (PNA) probes to alleles of the HLA-DRB1 gene. Short target fragments contained in plasmids were initially used to optimize the method followed by testing samples of genomic DNA from human subjects with preselected HLA haplotypes and obtained approximately 10% enrichment for the specific haplotype. When performed with high-molecular-weight genomic DNA, 99.0% versus 84.0% alignment match was obtained for the specific haplotype probed. The allele-specific target enrichment that we obtained can facilitate the elucidation of haplotypes between the 65 kb separating the HLA-DRB1 and the HLA-DQA1 genes, potentially spanning a total distance of at least 130 kb. Allele-specific target enrichment with PNA probes is a straightforward technique that has the capability to improve the resolution of DNA and whole genome sequencing technologies by allowing haplotyping of enriched DNA and crucially, retaining the DNA methylation profile.

A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility

Genome-wide association studies (GWASs) perform per-SNP association tests to identify variants involved in disease or trait susceptibility. However, such an approach is not powerful enough to unravel genes that are not individually contributing to the disease/trait, but that may have a role in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted of ITGAL, ICAM1 and ICAM3 genes, could be of interest to develop novel therapeutic targets.

Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

BACKGROUND

Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear.

METHODS

This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups).

RESULTS

The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance.

CONCLUSIONS

This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.

Hereditary vulnerabilities to post-operative cognitive dysfunction and dementia

In view of multiple prospective investigations reporting an incidence of 10% or greater in elderly patients after cardiac and non-cardiac procedures, it is surprising that no families, twins or even individual cases have been reported with persistent post-operative cognitive dysfunction (POCD) or post-operative dementia (POD) that is otherwise unexplained. As POCD and POD research has shifted in recent years from surgical and anesthetic variables to predictors of intrinsic, patient-specific susceptibility, a number of markers based on DNA sequence variation have been investigated. Nevertheless, no heritable, genomic indices of persistent POCD or post-operative dementia lasting 3 months or longer after surgery have been identified to date. The present manuscript surveys challenges confronting the search for markers of heritable vulnerability to POCD and POD, and proposes steps forward to be taken now, including the addition of surgical and anesthetic descriptors to ongoing longitudinal dementia protocols and randomized clinical trials (RCTs) comprising serial psychometric testing, and a fresh focus on phenotypes and genotypes shared between outliers with "extreme" POCD and POD traits.

Genetics of nonsyndromic obesity

PURPOSE OF REVIEW

Common obesity is widely regarded as a complex, multifactorial trait influenced by the 'obesogenic' environment, sedentary behavior, and genetic susceptibility contributed by common and rare genetic variants. This review describes the recent advances in understanding the role of genetics in obesity.

RECENT FINDINGS

New susceptibility loci and genetic variants are being uncovered, but the collective effect is relatively small and could not explain most of the BMI heritability. Yet-to-be identified common and rare variants, epistasis, and heritable epigenetic changes may account for part of the 'missing heritability'. Evidence is emerging about the role of epigenetics in determining obesity susceptibility, mediating developmental plasticity, which confers obesity risk from early life experiences. Genetic prediction scores derived from selected genetic variants, and also differential DNA methylation levels and methylation scores, have been shown to correlate with measures of obesity and response to weight loss intervention. Genetic variants, which confer susceptibility to obesity-related morbidities like nonalcoholic fatty liver disease, were also discovered recently.

SUMMARY

We can expect discovery of more rare genetic variants with the advent of whole exome and genome sequencing, and also greater understanding of epigenetic mechanisms by which environment influences genetic expression and which mediate the gene-environment interaction.

Folate and epigenetic mechanisms in neural tube development and defects

INTRODUCTION

Multiple genetic and epigenetic factors involved in central nervous system (CNS) development influence the incidence of neural tube defects (NTDs).

DISCUSSION

The beneficial effect of periconceptional folic acid on NTD prevention denotes a vital role for the single-carbon biochemical pathway in NTD genesis. Indeed, NTDs are associated with polymorphisms in a diversity of genes that encode folate pathway enzymes. Recent evidence suggests that CNS development and function, and consequently NTDs, are also associated with epigenetic mechanisms, many of which participate in the folate cycle and its input and output pathways. We provide an overview with select examples drawn from the authors' research.

Advances in blood pressure genomics

The elucidation of genes implicated in Mendelian forms of hypertension demonstrates rare variants with substantial effects are responsible, and often these genes lie within pathways managing sodium homeostasis. More recently with advances in affordable high-throughput genotyping strategies, multiple common genetic variants with modest effects on blood pressure (<1 mmHg systolic) have been discovered in the population. In aggregate, these common variants explain <3% of the variance of blood pressure. Although these findings may offer new mechanistic insights into the biology of blood pressure, a key question is can these findings translate into patient benefit? It is timely to reflect on recent advances in genomics, and the use of new resources, such as the 1000 Genomes Project and the Encyclopedia of DNA Elements, to annotate likely causal variants, and their relevance to cardiovascular disease. In this review, we discuss the advances in relation to our knowledge of the genetic architecture of blood pressure, and whether gene discoveries might influence cardiovascular risk assessment, help to stratify patient response to medicine, or identify new biological pathways for novel therapeutic targets.

The enigma of genetics etiology of atherosclerosis in the post-GWAS era

Coronary atherosclerosis is a complex heritable trait with an enigmatic genetic etiology. Genome-wide association studies (GWAS) have successfully led to identification of over 100 different loci for susceptibility to coronary atherosclerosis. Most identified single nucleotide polymorphisms (SNP)s and genes have not been previously implicated in the pathogenesis of atherosclerosis and hence, have modest biological plausibility. The novel discoveries, however, might provide the opportunity for identification of new pathways and consequently novel preventive and therapeutic targets. A notable outcome of GWAS is relatively modest effect sizes of the associated SNPs. Collectively, the identified SNPs account for a relatively small fraction of heritability of coronary atherosclerosis, which raises the question of "missing heritability". Because GWAS test the common disease-comment variant hypothesis, a plausible explanation might be the presence of uncommon and rare variants in the genome that are untested in GWAS but that might exert large effect sizes on the risk of atherosclerosis. The latter, however, remains an empiric question pending validation through experimentation. Alternative mechanisms, such as transgenerational epigenetics including microRNAs, might in part account for the heritability of coronary atherosclerosis. Collectively, the recent findings are indicative of the etiological complexity of coronary atherosclerosis. Hence, it is expected that genetic etiology of coronary atherosclerosis will remain enigmatic in the foreseeable future.

Challenges in medical applications of whole exome/genome sequencing discoveries

Despite the well-documented influence of genetics on susceptibility to cardiovascular diseases, delineation of the full spectrum of the risk alleles had to await the development of modern next-generation sequencing technologies. The techniques provide unbiased approaches for identification of the DNA sequence variants (DSVs) in the entire genome (whole genome sequencing [WGS]) or the protein-coding exons (whole exome sequencing [WES]). Each genome contains approximately 4 million DSVs and each exome approximately 13,000 single nucleotide variants. The challenge facing researchers and clinicians alike is to decipher the biological and clinical significance of these variants and harness the information for the practice of medicine. The common DSVs typically exert modest effect sizes, as evidenced by the results of genome-wide association studies, and hence have modest or negligible clinical implications. The focus is on the rare variants with large effect sizes, which are expected to have stronger clinical implications, as in single gene disorders with Mendelian patterns of inheritance. However, the clinical implications of the rare variants for common complex cardiovascular diseases remain to be established. The most important contribution of WES or WGS is in delineation of the novel molecular pathways involved in the pathogenesis of the phenotype, which would be expected to provide for preventive and therapeutic opportunities.