The mystery of missing heritability: Genetic interactions create phantom heritability

Association of soluble epoxide hydrolase 2 gene with the risk of non-syndromic cleft lip with or without cleft palate in western Han Chinese population

OBJECTIVES

This study aimed to explore the associations between soluble epoxide hydrolase 2 gene (EPHX2) variants and non-syndromic cleft lip with or without cleft palate (NSCL/P) in Chinese Han population.

METHODS

We recruited 159 NSCL/P cases from Chinese Han population and carried out targeted resequencing using the whole genome sequencing data of 542 healthy Chinese individuals from Novegene internal database as controls. We classified EPHX2 variants as common or rare according to their minor allele frequency and performed an association analysis for common variations and a burden analysis for rare variations.

RESULTS

The lowest P-value in NSCL/P was observed at rs57699806 (P=0.000 13, OR=2.849 and 95% CI: 1.691-4.800), followed by rs4732723 (P=0.006 50, OR=0.662 and 95%CI: 0.491-0.892), rs7829267 (P=0.009 20, OR=1.496 and 95%CI: 1.117-2.005), rs721619 (P=0.011 00, OR=1.474 and 95%CI: 1.098-1.980), and rs7816586 (P=0.040 00, OR=1.310 and 95%CI: 1.015-1.691). The odds ratios suggested the C allele at rs4732723 as a protective factor for NSCL/P and the reference alleles at other single nucleotide polymorphisms (SNPs) as the risk factors for NSCL/P. Burden analysis showed no statistical significance (P>0.05).

CONCLUSIONS

Through targeted resequencing, this study identified five SNPs named rs57699806, rs4732723, rs7829267, rs721619, and rs7816586 around the region of EPHX2 gene associated with NSCL/P in Chinese Han population. Four SNPs of rs57699806, rs4732723, rs7829267, and rs7816586 were first identified.

Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis

Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e⁻¹⁴) and myometrial relaxation and contraction (p = 3e⁻⁰⁸) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.

Challenging the utility of polygenic scores for social science: Environmental confounding, downward causation, and unknown biology

The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as "genetic propensities" for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene-environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d'être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.

Biomarker interaction selection and disease detection based on multivariate gain ratio

Background

Disease detection is an important aspect of biotherapy. With the development of biotechnology and computer technology, there are many methods to detect disease based on single biomarker. However, biomarker does not influence disease alone in some cases. It’s the interaction between biomarkers that determines disease status. The existing influence measure I-score is used to evaluate the importance of interaction in determining disease status, but there is a deviation about the number of variables in interaction when applying I-score. To solve the problem, we propose a new influence measure Multivariate Gain Ratio (MGR) based on Gain Ratio (GR) of single-variate, which provides us with multivariate combination called interaction.

Results

We propose a preprocessing verification algorithm based on partial predictor variables to select an appropriate preprocessing method. In this paper, an algorithm for selecting key interactions of biomarkers and applying key interactions to construct a disease detection model is provided. MGR is more credible than I-score in the case of interaction containing small number of variables. Our method behaves better with average accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93.13\%$$\end{document}93.13% than I-score of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$91.73\%$$\end{document}91.73% in Breast Cancer Wisconsin (Diagnostic) Dataset. Compared to the classification results \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.80\%$$\end{document}89.80% based on all predictor variables, MGR identifies the true main biomarkers and realizes the dimension reduction. In Leukemia Dataset, the experiment results show the effectiveness of MGR with the accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.32\%$$\end{document}97.32% compared to I-score with accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.11\%$$\end{document}89.11%. The results can be explained by the nature of MGR and I-score mentioned above because every key interaction contains a small number of variables in Leukemia Dataset.

Conclusions

MGR is effective for selecting important biomarkers and biomarker interactions even in high-dimension feature space in which the interaction could contain more than two biomarkers. The prediction ability of interactions selected by MGR is better than I-score in the case of interaction containing small number of variables. MGR is generally applicable to various types of biomarker datasets including cell nuclei, gene, SNPs and protein datasets.

Genetically Adjusted Propensity Score Matching: A Comparison to Discordant MZ Twin Models

Discordant monozygotic (MZ) twin methodologies are considered one of the foremost statistical approaches for estimating the influence of environmental factors on phenotypic variance. Limitations associated with the discordant MZ twin approach generates an inability to estimate particular relationships and adjust estimates for the confounding influence of gene-nonshared environment interactions. Recent advancements in molecular genetics, however, can provide the opportunity to address these limitations. The current study reviews an alternative technique, genetically adjusted propensity scores (GAPS) matching, that integrates observed genetic and environmental information to adjust for the confounding of these factors in nonkin individuals. Simulations and a real data example were used to compare the GAPS matching approach to the discordant MZ twin method. Although the results of the simulated comparisons demonstrated that the discordant MZ twin approach remains the more robust statistical technique to adjust for shared environmental and genetic factors, GAPS matching - under certain conditions - could represent a viable alternative when MZ twin samples are unavailable. Overall, the findings suggest that GAPS matching can potentially provide an alternative to the discordant MZ twin approach when limited variation exists between identical twin pairs. Moreover, the ability to adjust for gene-nonshared environment interactions represents a potential advancement associated with the GAPS approach. The limitations of the approach, as well as polygenic risk scores, are also discussed.

SNP-based heritability and selection analyses: Improved models and new results

Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ.

Decisive gene strategy on osteoporosis: a comprehensive whole-literature-based approach for conclusive candidate gene targets

Purpose: Previous meta-analyses only examined the association between single gene polymorphisms and osteoporosis; there is no compilation of all gene loci that correlate with osteoporosis in the literature. In this study, we develop a new literature-based approach, a decisive gene strategy (DGS), to examine the sufficiency of the cumulative sample size for each gene locus and to assess whether a definite conclusion of the association between the gene locus and osteoporosis can be drawn.

Methods: The DGS was used to search PubMed, Embase, and Cochrane databases for all meta-analyses that correlated gene polymorphisms with osteoporosis. Trial sequential analysis was employed to examine the sufficiency of the cumulative sample size. Finally, we assessed the importance of gene loci in osteoporosis based on whether there were enough sample sizes and the heterogeneity of the literature with the I² value.

Results: After excluding 169 irrelevant publications, 39 meta-analysis papers were obtained. Among Caucasians, in 17 gene loci, there were eight gene loci (e.g., vitamin D Receptor ApaI rs7975232) with sufficient cumulative sample size to confirm that they were unrelated to the disease. Among Asians, in 15 gene loci, four gene loci that had sufficient sample sizes were risk factors: VDR FokI rs2228570 (odds ratio (OR) = 1.44, 95% confidence interval (CI) = 1.22–1.70), TGF β1 rs1800470 (OR = 1.35, 95% CI = 1.10–1.65), IGF1 rs2288377 (OR = 1.44, 95% CI = 1.28–1.62), and IGF1 rs35767 (OR = 1.20, 95% CI = 1.06–1.36), respectively, whereas one gene locus, ESR2 RsaI rs1256049 (OR = 0.69, 95% CI = 0.59–0.81), was a protective factor.

Conclusions: The DGS successfully identified five gene loci in osteoporosis that will apply to other diseases to find causal genes, which may contribute to further genetic therapy.

Fast heritability estimation based on MINQUE and batch training

Heritability, the proportion of phenotypic variance explained by genome-wide single nucleotide polymorphisms (SNPs) in unrelated individuals, is an important measure of the genetic contribution to human diseases and plays a critical role in studying the genetic architecture of human diseases. Linear mixed model (LMM) has been widely used for SNP heritability estimation, where variance component parameters are commonly estimated by using a restricted maximum likelihood (REML) method. REML is an iterative optimization algorithm, which is computationally intensive when applied to large-scale datasets (e.g. UK Biobank). To facilitate the heritability analysis of large-scale genetic datasets, we develop a fast approach, minimum norm quadratic unbiased estimator (MINQUE) with batch training, to estimate variance components from LMM (LMM.MNQ.BCH). In LMM.MNQ.BCH, the parameters are estimated by MINQUE, which has a closed-form solution for fast computation and has no convergence issue. Batch training has also been adopted in LMM.MNQ.BCH to accelerate the computation for large-scale genetic datasets. Through simulations and real data analysis, we demonstrate that LMM.MNQ.BCH is much faster than two existing approaches, GCTA and BOLT-REML.

Deltapine 15 contributes to the genomic architecture of modern upland cotton cultivars

Foundation parents play a critical role in the genetic constituents of the derived genotypes. Deltapine-15 (DLP-15), introduced to China in 1950, is one of the most commonly used parents for early breeding programs in China. However, the formation and inheritance patterns of genomic constituents have not been studied. Therefore, this study aimed at understanding and exploring the genomic architecture of 146 DLP-15 derived cultivars with a common foundation parent DLP-15. Population structure based on sequencing data clustered genotypes into two groups (G1 and G2) supported by principal component analysis. Further exploration led to the identification of Chr-A08 with significantly differentiated regions between two groups. Moreover, we identified genome-wide identity by descent (IBD) segments (840 segments) to understand the genomic inheritance pattern in DLP-15 derived cultivars, spanning the 20-95 Mb region on Chr-A08. Interestingly, Chr-A08 depicted a unique inheritance pattern from DLP-15 to its derived cultivars. IBD-segment-based haplotype analysis suggested significant differences among the two groups. Phenotypic trait association with DLP-derived haplotypes concerning Chr-A08 suggested a significant increase in yield and fiber quality. Furthermore, distinguished IBD segments overlapped with previously reported QTLs concerning fiber yield and quality. Our results systematically identified genomic signatures transmitted from the foundation parent DLP-15 to its derived cultivars and provided a basis for further exploiting excellent haplotypes associated with DLP-15.

Omnibus testing approach for gene-based gene-gene interaction

Genetic interaction is considered as one of the main heritable component of complex traits. With the emergence of genome-wide association studies (GWAS), a collection of statistical methods dedicated to the identification of interaction at the SNP level have been proposed. More recently, gene-based gene-gene interaction testing has emerged as an attractive alternative as they confer advantage in both statistical power and biological interpretation. Most of the gene-based interaction methods rely on a multidimensional modeling of the interaction, thus facing a lack of robustness against the huge space of interaction patterns. In this paper, we study a global testing approaches to address the issue of gene-based gene-gene interaction. Based on a logistic regression modeling framework, all SNP-SNP interaction tests are combined to produce a gene-level test for interaction. We propose an omnibus test that takes advantage of (1) the heterogeneity between existing global tests and (2) the complementarity between allele-based and genotype-based coding of SNPs. Through an extensive simulation study, it is demonstrated that the proposed omnibus test has the ability to detect with high power the most common interaction genetic models with one causal pair as well as more complex genetic models where more than one causal pair is involved. On the other hand, the flexibility of the proposed approach is shown to be robust and improves power compared to single global tests in replication studies. Furthermore, the application of our procedure to real datasets confirms the adaptability of our approach to replicate various gene-gene interactions.

IPRS: Leveraging Gene-Environment Interaction to Reconstruct Polygenic Risk Score

Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising.

Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer.

Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205).

Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations.

Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms

Due to epistasis, the same mutation can have drastically different phenotypic consequences in different individuals. This phenomenon is pertinent to precision medicine as well as antimicrobial drug development, but its general characteristics are largely unknown. We approach this question by genome-wide assessment of gene essentiality polymorphism in 16 Saccharomyces cerevisiae strains using transposon insertional mutagenesis. Essentiality polymorphism is observed for 9.8% of genes, most of which have had repeated essentiality switches in evolution. Genes exhibiting essentiality polymorphism lean toward having intermediate numbers of genetic and protein interactions. Gene essentiality changes tend to occur concordantly among components of the same protein complex or metabolic pathway and among a group of over 100 mitochondrial proteins, revealing molecular machines or functional modules as units of gene essentiality variation. Most essential genes tolerate transposon insertions consistently among strains in one or more coding segments, delineating nonessential regions within essential genes.

Cis-epistasis at the LPA locus and risk of cardiovascular diseases

AIMS

Coronary artery disease (CAD) has a strong genetic predisposition. However, despite substantial discoveries made by genome-wide association studies (GWAS), a large proportion of heritability awaits identification. Non-additive genetic effects might be responsible for part of the unaccounted genetic variance. Here, we attempted a proof-of-concept study to identify non-additive genetic effects, namely epistatic interactions, associated with CAD.

METHODS AND RESULTS

We tested for epistatic interactions in 10 CAD case-control studies and UK Biobank with focus on 8068 SNPs at 56 loci with known associations with CAD risk. We identified a SNP pair located in cis at the LPA locus, rs1800769 and rs9458001, to be jointly associated with risk for CAD [odds ratio (OR) = 1.37, P = 1.07 × 10-11], peripheral arterial disease (OR = 1.22, P = 2.32 × 10-4), aortic stenosis (OR = 1.47, P = 6.95 × 10-7), hepatic lipoprotein(a) (Lp(a)) transcript levels (beta = 0.39, P = 1.41 × 10-8), and Lp(a) serum levels (beta = 0.58, P = 8.7 × 10-32), while individual SNPs displayed no association. Further exploration of the LPA locus revealed a strong dependency of these associations on a rare variant, rs140570886, that was previously associated with Lp(a) levels. We confirmed increased CAD risk for heterozygous (relative OR = 1.46, P = 9.97 × 10-32) and individuals homozygous for the minor allele (relative OR = 1.77, P = 0.09) of rs140570886. Using forward model selection, we also show that epistatic interactions between rs140570886, rs9458001, and rs1800769 modulate the effects of the rs140570886 risk allele.

CONCLUSIONS

These results demonstrate the feasibility of a large-scale knowledge-based epistasis scan and provide rare evidence of an epistatic interaction in a complex human disease. We were directed to a variant (rs140570886) influencing risk through additive genetic as well as epistatic effects. In summary, this study provides deeper insights into the genetic architecture of a locus important for cardiovascular diseases.

Discrimination of Geographical Origins of Wolfberry (Lycium barbarum L.) Fruits Using Stable Isotopes, Earth Elements, Free Amino Acids, and Saccharides

To develop sophisticated approaches for distinguishing goji origins, 325 wolfberry fruit samples of a certain cultivar, plant age, drying method, and collection season were gathered from 26 producing areas across Northwest China in 2017 and 2018. We employed 49 indices, including stable isotopes, earth elements, soluble amino acids, and saccharides, to identify the regions of origin of these goji fruits. Analysis of variance (ANOVA) and heritability analysis were used to assess the effects of the environment (producing areas), cultivar, plant age, drying process, and collection season. Samples from the same place can be classified and partially discriminated using principal component analysis (PCA). We were able to distinguish fruits produced in Zhongning County from those produced in the other five producing provinces using orthogonal projection to latent structure-discriminant analysis (OPLS-DA). Calcium (Ca), manganese (Mn), ornithine (Orn), cystine (Cys-Cys), glutamate (Glu), phenylalanine (Phe), phosphoserine (Ps), serine (Ser), lysine (Lys), taurine (Tau), proline (Pro), and tyrosine (Tyr) indices were chosen using S-plots and heritability analysis, and their repeatability was established with samples collected in 2018. The indices selected in this study can distinguish goji berries produced in Zhongning County from fruits originating from five other Provinces with high repeatability, which was validated with various cultivars, drying methods, harvest seasons, and plant ages and with heritability analysis.

Genetic susceptibility to hereditary non-medullary thyroid cancer

Non-medullary thyroid cancer (NMTC) is the most common type of thyroid cancer. With the increasing incidence of NMTC in recent years, the familial form of the disease has also become more common than previously reported, accounting for 5-15% of NMTC cases. Familial NMTC is further classified as non-syndromic and the less common syndromic FNMTC. Although syndromic NMTC has well-known genetic risk factors, the gene(s) responsible for the vast majority of non-syndromic FNMTC cases are yet to be identified. To date, several candidate genes have been identified as susceptibility genes in hereditary NMTC. This review summarizes genetic predisposition to non-medullary thyroid cancer and expands on the role of genetic variants in thyroid cancer tumorigenesis and the level of penetrance of NMTC-susceptibility genes.

Systemic lupus erythematosus as a genetic disease

Systemic lupus erythematosus is the prototypical systemic autoimmune disease, as it is characterized both by protean multi-organ system manifestations and by the uniform presence of pathogenic autoantibodies directed against components of the nucleus. Prior to the modern genetic era, the diverse clinical manifestations of SLE suggested to many that SLE patients were unlikely to share a common genetic risk basis. However, modern genetic studies have revealed that SLE usually arises when an environmental exposure occurs in an individual with a collection of genetic risk variants passing a liability threshold. Here, we summarize the current state of the field aimed at: (1) understanding the genetic architecture of this complex disease, (2) synthesizing how this genetic risk architecture impacts cellular and molecular disease pathophysiology, (3) providing illustrative examples that highlight the rich complexity of the pathobiology of this prototypical autoimmune disease and (4) communicating this complex etiopathogenesis to patients.

Association of mental health with the risk of coronary artery disease in patients with diabetes: A mendelian randomization study

BACKGROUND AND AIMS

Observational studies have shown an association between mental health and coronary artery disease (CAD) in patients with diabetes. Nevertheless, whether these associations are causal is still unknown. In this two-sample Mendelian randomization (MR) study, we aimed to assess the causality between mental health and CAD in patients with diabetes.

METHODS AND RESULTS

Single-nucleotide polymorphisms (SNPs) associated with: depression (807,553 individuals), anxiety (83,556 individuals) and neuroticism (329,821 individuals) were identified from the largest genome-wide association studies (GWAS). Summary-level data for CAD were extracted from the recently published GWAS of 15,666 diabetic patients (3968 CAD cases and 11,696 controls). The inverse-variance weighted (IVW) method was used for the main analysis. Sensitivity analyses included weighted median, maximum likelihood, and the MR-Egger method. Genetic liability to depression was significantly associated with a higher risk of CAD in patients with diabetes (odds ratio [OR], 1.286; 95%CI，1.018-1.621；p = 0.035). For anxiety and neuroticism, no causal association with CAD in patients with diabetes was observed. Consistent results were obtained in most sensitivity analyses.

CONCLUSIONS

This MR study provides genetic evidence that depression is a potential risk factor for CAD in patients with diabetes. However, anxiety and neuroticism were not causally associated with CAD in patients with diabetes. Mental health treatments should be enhanced to prevent CAD in patients with diabetes.

Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data

Analyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.

Dominance can increase genetic variance after a population bottleneck: a synthesis of the theoretical and empirical evidence

Drastic reductions in population size, or population bottlenecks, can lead to a reduction in additive genetic variance and adaptive potential. Genetic variance for some quantitative genetic traits, however, can increase after a population reduction. Empirical evaluations of quantitative traits following experimental bottlenecks indicate that non-additive genetic effects, including both allelic dominance at a given locus and epistatic interactions among loci, may impact the additive variance contributed by alleles that ultimately influences phenotypic expression and fitness. The dramatic effects of bottlenecks on overall genetic diversity have been well studied, but relatively little is known about how dominance and demographic events like bottlenecks can impact additive genetic variance. Herein, we critically examine how the degree of dominance among alleles affects additive genetic variance after a bottleneck. We first review and synthesize studies that document the impact of empirical bottlenecks on dominance variance. We then extend earlier work by elaborating on two theoretical models that illustrate the relationship between dominance and the potential increase in additive genetic variance immediately following a bottleneck. Furthermore, we investigate the parameters that influence the maximum level of genetic variation (associated with adaptive potential) after a bottleneck, including the number of founding individuals. Finally, we validated our methods using forward-time population genetic simulations of loci with varying dominance and selection levels. The fate of non-additive genetic variation following bottlenecks could have important implications for conservation and management efforts in a wide variety of taxa, and our work should help contextualize future studies (e.g., epistatic variance) in population genomics.

Unifying heritability in evolutionary theory

Despite being widely used in both biology and psychology as if it were a single notion, heritability is not a unified concept. This is also true in evolutionary theory, in which the word 'heritability' has at least two technical definitions that only partly overlap. These yield two approaches to heritability: the 'variance approach' and the 'regression approach.' In this paper, I aim to unify these two approaches. After presenting them, I argue that a general notion of heritability ought to satisfy two desiderata-'general applicability' and 'separability of the causes of resemblance.' I argue that neither the variance nor the regression approach satisfies these two desiderata concomitantly. From there, I develop a general definition of heritability that relies on the distinction between intrinsic and extrinsic properties. I show that this general definition satisfies the two desiderata. I then illustrate the potential usefulness of this general definition in the context of microbiome research.

Rare diseases: human genome research is coming home

After a long and largely disappointing detour, Genome Research has reidentified Rare Diseases as a major opportunity for improving health care and a clue to understanding gene and genome function. In this Special Issue of CSH Molecular Case Studies on Rare Diseases, several invited Perspectives, numerous Case Reports, and this Editorial itself address recent breakthroughs as well as unsolved problems in this wide field. These range from exciting prospects for gap-free diagnostic whole-genome sequencing to persisting problems related to identifying and distinguishing pathogenic and benign variants; and from the good news that soon, the United Kingdom will no longer be the only country to have introduced whole-genome sequencing into health care to the sobering conclusion that in many countries the clinical infrastructure for bringing Genome Medicine to the patient is still lacking. With less than 5000 genes firmly implicated in disease, the identification of at least twice as many disease genes is a major challenge, and the elucidation of their function is an even larger task. But given the renewed interest in rare diseases, their importance for health care, and the vast and growing spectrum of concepts and methods for studying them, the future of Human Genome Research is bright.

The mutation effect reaction norm (mu-rn) highlights environmentally dependent mutation effects and epistatic interactions

Since the modern synthesis, the fitness effects of mutations and epistasis have been central yet provocative concepts in evolutionary and population genetics. Studies of how the interactions between parcels of genetic information can change as a function of environmental context have added a layer of complexity to these discussions. Here I introduce the "mutation effect reaction norm" (Mu-RN), a new instrument through which one can analyze the phenotypic consequences of mutations and interactions across environmental contexts. It embodies the fusion of measurements of genetic interactions with the reaction norm, a classic depiction of the performance of genotypes across environments. I demonstrate the utility of the Mu-RN through the signature of a "compensatory ratchet" mutation that undermines reverse evolution of antimicrobial resistance. More broadly, I argue that the mutation effect reaction norm may help us resolve the dynamism and unpredictability of evolution, with implications for theoretical biology, genetic modification technology, and public health. This article is protected by copyright. All rights reserved.

The selfish environment meets the selfish gene: Coevolution and inheritance of RNA and DNA pools: A model for organismal life incorporating coevolution, horizontal transfer, and inheritance of internal and external RNA and DNA pools.: A model for organismal life incorporating coevolution, horizontal transfer, and inheritance of internal and external RNA and DNA pools

Throughout evolution, there has been interaction and exchange between RNA pools in the environment, and DNA and RNA pools of eukaryotic organisms. Metagenomic and metatranscriptomic sequencing of invertebrate hosts and their microbiota has revealed a rich evolutionary history of RNA virus shuttling between species. Horizontal transfer adapted the RNA pool for successful future interactions which lead to zoonotic transmission and detrimental RNA viral pandemics like SARS-CoV2. In eukaryotes, noncoding RNA (ncRNA) is an established mechanism derived from prokaryotes to defend against viral attack through innate immunity and regulation of host-derived mRNA. Transgenerational inheritance of ncRNA is evidence for feedforward adaptive immunity and epigenetically encoded environmental change across generations, which may explain the ''missing heritability'' of common disease. Causal graph theory and the Price Equation can model epigenetic inheritance involving dynamic internal and external RNA pools. Experimental designs should include metatranscriptomic analyses to understand how ncRNA responds to rapidly changing environmental conditions, within and between organisms, and across generations.

Transmission distortion and genetic incompatibilities between alleles in a multigenerational mouse advanced intercross line

While direct additive and dominance effects on complex traits have been mapped repeatedly, additional genetic factors contributing to the heterogeneity of complex traits have been scarcely investigated. To assess genetic background effects, we investigated transmission ratio distortions (TRDs) of alleles from parent to offspring using an advanced intercross line (AIL) of an initial cross between the mouse inbred strains C57BL/6NCrl (B6N) and BFMI860-12 [Berlin Fat Mouse Inbred (BFMI)]. A total of 341 males of generation 28 and their respective 61 parents and 66 grandparents were genotyped using Mega Mouse Universal Genotyping Arrays. TRDs were investigated using allele transmission asymmetry tests, and pathway overrepresentation analysis was performed. Sequencing data were used to test for overrepresentation of nonsynonymous SNPs (nsSNPs) in TRD regions. Genetic incompatibilities were tested using the Bateson–Dobzhansky–Muller two-locus model. A total of 62 TRD regions were detected, many in close proximity to the telocentric centromere. TRD regions contained 44.5% more nsSNPs than randomly selected regions (182 vs 125.9 ± 17.0, P < 1 × 10⁻⁴). Testing for genetic incompatibilities between TRD regions identified 29 genome-wide significant incompatibilities between TRD regions [P_(BF) < 0.05]. Pathway overrepresentation analysis of genes in TRD regions showed that DNA methylation, epigenetic regulation of RNA, and meiotic/meiosis regulation pathways were affected independent of the parental origin of the TRD. Paternal BFMI TRD regions showed overrepresentation in the small interfering RNA biogenesis and in the metabolism of lipids and lipoproteins. Maternal B6N TRD regions harbored genes involved in meiotic recombination, cell death, and apoptosis pathways. The analysis of genes in TRD regions suggests the potential distortion of protein–protein interactions influencing obesity and diabetic retinopathy as a result of disadvantageous combinations of allelic variants in Aass, Pgx6, and Nme8. Using an AIL significantly improves the resolution at which we can investigate TRD. Our analysis implicates distortion of protein–protein interactions as well as meiotic drive as the underlying mechanisms leading to the observed TRD in our AIL. Furthermore, genes with large amounts of nsSNPs located in TRD regions are more likely to be involved in pathways that are related to the phenotypic differences between the parental strains. Genes in these TRD regions provide new targets for investigating genetic adaptation, protein–protein interactions, and determinants of complex traits such as obesity.

Targeted re-sequencing and genome-wide association analysis for wood property traits in breeding population of Eucalyptus tereticornis × E. grandis

Globally, Eucalyptus plantations occupy 22 million ha area and is one of the preferred hardwood species due to their short rotation, rapid growth, adaptability and wood properties. In this study, we present results of GWAS in parents and 100 hybrids of Eucalyptus tereticornis × E. grandis using 762 genes presumably involved in wood formation. Comparative analysis between parents predicted 32,202 polymorphic SNPs with high average read depth of 269-562× per individual per nucleotide. Seventeen wood related traits were phenotyped across three diverse environments and GWAS was conducted using 13,610 SNPs. A total of 45 SNP-trait associations were predicted across two locations. Seven large effect markers were identified which explained more than 80% of phenotypic variation for fibre area. This study has provided an array of candidate genes which may govern fibre morphology in this genus and has predicted potential SNPs which can guide future breeding programs in tropical Eucalyptus.

Further illusions: On key evolutionary mechanisms that could never fit with Modern Synthesis

In the light of illusions of the Modern Synthesis (MS) listed by Noble (2021a), its key concept, that gradual accumulation of gene mutations within microevolution leads to macroevolution, requires reexamination. In this article, additional illusions of the MS are identified as being caused by the absence of system information and correct history. First, the MS lacks distinction among the two basic types of information: genome-defined system and gene-defined parts-information, as its treatment was based mostly on gene information. In contrast, it is argued here that system information is maintained by species-specific karyotype code, and macroevolution is based on the whole genome information package rather than on specific genes. Linking the origin of species with system information shows that the creation and accumulation of the latter in evolution is the fundamental question omitted from the MS. Second, modern evidence eliminates the MS's preferred theory that present evolutionary events can be linearly extrapolated to the past to reconstruct Life's history, wrongly assuming that most of the fossil record supports the gradual change while ignoring the true karyotype/genome patterns. Furthermore, stasis, as the most prominent pattern of the deep history of Life, remains a puzzle to the MS, but can be explained by the mechanism of karyotype-preservation-via-sex. Consequently, the concept of system-information is smoothly integrated into the two-phased evolutionary model that paleontology requires (Eldredge and Gould, 1972). Finally, research on genome-level causation of evolution, which does not fit the MS, is summarized. The availability of alternative concepts further illustrates that it is time to depart from the MS.

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut (Arachis hypogaea L.)

The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect-increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanut, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold (α = 1%). We detected 7,682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (p-value < 1.0E-16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil.

Pairwise effects between lipid GWAS genes modulate lipid plasma levels and cellular uptake

Complex traits are characterized by multiple genes and variants acting simultaneously on a phenotype. However, studying the contribution of individual pairs of genes to complex traits has been challenging since human genetics necessitates very large population sizes, while findings from model systems do not always translate to humans. Here, we combine genetics with combinatorial RNAi (coRNAi) to systematically test for pairwise additive effects (AEs) and genetic interactions (GIs) between 30 lipid genome-wide association studies (GWAS) genes. Gene-based burden tests from 240,970 exomes show that in carriers with truncating mutations in both, APOB and either PCSK9 or LPL ("human double knock-outs") plasma lipid levels change additively. Genetics and coRNAi identify overlapping AEs for 12 additional gene pairs. Overlapping GIs are observed for TOMM40/APOE with SORT1 and NCAN. Our study identifies distinct gene pairs that modulate plasma and cellular lipid levels primarily via AEs and nominates putative drug target pairs for improved lipid-lowering combination therapies.

Evolvability: A Quantitative-Genetics Perspective

The concept of evolvability emerged in the early 1990s and soon became fashionable as a label for different streams of research in evolutionary biology. In evolutionary quantitative genetics, evolvability is defined as the ability of a population to respond to directional selection. This differs from other fields by treating evolvability as a property of populations rather than organisms or lineages and in being focused on quantification and short-term prediction rather than on macroevolution. While the term evolvability is new to quantitative genetics, many of the associated ideas and research questions have been with the field from its inception as biometry. Recent research on evolvability is more than a relabeling of old questions, however. New operational measures of evolvability have opened possibilities for understanding adaptation to rapid environmental change, assessing genetic constraints, and linking micro- and macroevolution.

Coral adaptation to climate change: Meta-analysis reveals high heritability across multiple traits

Anthropogenic climate change is a rapidly intensifying selection pressure on biodiversity across the globe and, particularly, on the world's coral reefs. The rate of adaptation to climate change is proportional to the amount of phenotypic variation that can be inherited by subsequent generations (i.e., narrow-sense heritability, h² ). Thus, traits that have higher heritability (e.g., h²  > 0.5) are likely to adapt to future conditions faster than traits with lower heritability (e.g., h²  < 0.1). Here, we synthesize 95 heritability estimates across 19 species of reef-building corals. Our meta-analysis reveals low heritability (h² < 0.25) of gene expression metrics, intermediate heritability (h²  = 0.25-0.50) of photochemistry, growth, and bleaching, and high heritability (h²  > 0.50) for metrics related to survival and immune responses. Some of these values are higher than typically observed in other taxa, such as survival and growth, while others were more comparable, such as gene expression and photochemistry. There was no detectable effect of temperature on heritability, but narrow-sense heritability estimates were generally lower than broad-sense estimates, indicative of significant non-additive genetic variation across traits. Trait heritability also varied depending on coral life stage, with bleaching and growth in juveniles generally having lower heritability compared to bleaching and growth in larvae and adults. These differences may be the result of previous stabilizing selection on juveniles or may be due to constrained evolution resulting from genetic trade-offs or genetic correlations between growth and thermotolerance. While we find no evidence that heritability decreases under temperature stress, explicit tests of the heritability of thermal tolerance itself-such as coral thermal reaction norm shape-are lacking. Nevertheless, our findings overall reveal high trait heritability for the majority of coral traits, suggesting corals may have a greater potential to adapt to climate change than has been assumed in recent evolutionary models.

Associating complex traits with genetic variants: polygenic risk scores, pleiotropy and endophenotypes

Genotype-phenotype causal modeling has evolved significantly since Johannsen's and Wright's original designs were published. The development of genomewide assays to interrogate and detect possible causal variants associated with complex traits has expanded the scope of genotype-phenotype research considerably. Clusters of causal variants discovered by genomewide assays and associated with complex traits have been used to develop polygenic risk scores to predict clinical diagnoses of multidimensional human disorders. However, genomewide investigations have met with many challenges to their research designs and statistical complexities which have hindered the reliability and validity of their predictions. Findings linked to differences in heritability estimates between causal clusters and complex traits among unrelated individuals remain a research area of some controversy. Causal models developed from case-control studies as opposed to experiments, as well as other issues concerning the genotype-phenotype causal model and the extent to which various forms of pleiotropy and the concept of the endophenotype add to its complexity, will be reviewed.

Sequencing-based genome-wide association studies reporting standards

Genome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics.

Metabolomics for Crop Breeding: General Considerations

The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 with altered metabolic enzyme activities have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.

Decoding 'Unnecessary Complexity': A Law of Complexity and a Concept of Hidden Variation Behind "Missing Heritability" in Precision Medicine

The high hopes for the Human Genome Project and personalized medicine were not met because the relationship between genotypes and phenotypes turned out to be more complex than expected. In a previous study we laid the foundation of a theory of complexity and showed that because of the blind nature of evolution, and molecular and historical contingency, cells have accumulated unnecessary complexity, complexity beyond what is necessary and sufficient to describe an organism. Here we provide empirical evidence and show that unnecessary complexity has become integrated into the genome in the form of redundancy and is relevant to molecular evolution of phenotypic complexity. Unnecessary complexity creates uncertainty between molecular and phenotypic complexity, such that phenotypic complexity (CP) is higher than molecular complexity (CM), which is higher than DNA complexity (CD). The qualitative inequality in complexity is based on the following hierarchy: CP > CM > CD. This law-like relationship holds true for all complex traits, including complex diseases. We present a hypothesis of two types of variation, namely open and closed (hidden) systems, show that hidden variation provides a hitherto undiscovered "third source" of phenotypic variation, beside genotype and environment, and argue that "missing heritability" for some complex diseases is likely to be a case of "diluted heritability". There is a need for radically new ways of thinking about the principles of genotype-phenotype relationship. Understanding how cells use hidden, pathway variation to respond to stress can shed light on why two individuals who share the same risk factors may not develop the same disease, or how cancer cells escape death.

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks

Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

Discovery and implications of polygenicity of common diseases

The sequencing of the human genome has allowed the study of the genetic architecture of common diseases: the number of genomic variants that contribute to risk of disease and their joint frequency and effect size distribution. Common diseases are polygenic, with many loci contributing to phenotype, and the cumulative burden of risk alleles determines individual risk in conjunction with environmental factors. Most risk loci occur in noncoding regions of the genome regulating cell- and context-specific gene expression. Although the effect sizes of most risk alleles are small, their cumulative effects in individuals, quantified as a polygenic (risk) score, can identify people at increased risk of disease, thereby facilitating prevention or early intervention.

Nasopharyngeal cancer in monozygotic twins: A case report and review of the role of genetics and the environment

Introduction and importance

Nasopharyngeal carcinoma (NPC) is considered a rare malignant head and neck tumour. However, the importance of genetics and environmental factors in the epidemiology of NPC is still unclear. Twins represent an excellent study population for genetic epidemiology; this is especially true of monozygotic-type twins because they are genetically identical. The difference in cancer occurrence between monozygotic twins is typically interpreted as a result of possible environmental factors.

Case presentation and clinical discussion

We present the first case report of monozygotic twins with NPC. The twins' significant features are homogenous presentation, tumour location (both left-sided) and identical histology; therefore, the prognoses may be similar. Environmental factors could not be addressed in these twins because they shared the same background, and at the same time, they had no potential known contributing factors.

Conclusion

Having one of the twins affected is a strong and easily recognisable risk factor for developing NPC in the other. This strong association suggests the need for regular screening of the second twin for early diagnosis of NPC.

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The importance of genetics and environmental factors in the epidemiology of NPC is still unclear.

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Having one of the twins affected is a strong and easily recognisable risk factor for developing Nasopharyngeal carcinoma in the other.

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This strong association suggests the need for regular screening of the second twin for early diagnosis of Nasopharyngeal carcinoma.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.

Inferring multilayer interactome networks shaping phenotypic plasticity and evolution

Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.

Meta-analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems

Linking genotype to phenotype is a primary goal for understanding the genomic underpinnings of evolution. However, little work has explored whether patterns of linked genomic and phenotypic differentiation are congruent across natural study systems and traits. Here, we investigate such patterns with a meta-analysis of studies examining population-level differentiation at subsets of loci and traits putatively responding to divergent selection. We show that across the 31 studies (88 natural population-level comparisons) we examined, there was a moderate (R 2 = 0.39) relationship between genomic differentiation (F ST ) and phenotypic differentiation (P ST ) for loci and traits putatively under selection. This quantitative relationship between P ST and F ST for loci under selection in diverse taxa provides broad context and cross-system predictions for genomic and phenotypic adaptation by natural selection in natural populations. This context may eventually allow for more precise ideas of what constitutes "strong" differentiation, predictions about the effect size of loci, comparisons of taxa evolving in nonparallel ways, and more. On the other hand, links between P ST and F ST within studies were very weak, suggesting that much work remains in linking genomic differentiation to phenotypic differentiation at specific phenotypes. We suggest that linking genotypes to specific phenotypes can be improved by correlating genomic and phenotypic differentiation across a spectrum of diverging populations within a taxon and including wide coverage of both genomes and phenomes.

Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes

Escherichia coli is an important cause of bacterial infections worldwide, with multidrug-resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in health care settings, the presence of subinhibitory antimicrobial residues in the environment and in clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. Here, we used machine learning to predict the population doubling time and cell growth yield of 1,407 genetically diverse E. coli strains expanding under exposure to three subinhibitory concentrations of six classes of antimicrobials from single-nucleotide genetic variants, accessory gene variation, and the presence of known AMR genes. We predicted cell growth yields in the held-out test data with an average correlation (Spearman's ρ) of 0.63 (0.36 to 0.81 across concentrations) and cell doubling times with an average correlation of 0.59 (0.32 to 0.92 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This finding points to the remaining missing heritability of growth under antimicrobial exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual single-nucleotide polymorphisms (SNPs) and genes. Predictions based on whole-genome information were generally superior to those based only on known AMR genes and were accurate for AMR resistance at therapeutic concentrations. We pinpointed genes and SNPs determining the predicted growth and thereby recapitulated many known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing the growth effects for known resistance genes in each individual strain. Our results underscore the potential of predictive modeling of growth patterns from genomic data under subinhibitory concentrations of antimicrobials, although the remaining missing heritability poses a challenge for achieving the accuracy and precision required for clinical use. IMPORTANCE Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains. In this study, we used a high-throughput phenotypic assay to measure the growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under nontherapeutic subinhibitory concentrations of antimicrobials that are common in nonclinical settings. We found a moderate to strong correlation between predicted and actual values for the different collected data sets. Moreover, we observed that the known resistance genes are still effective at sublethal concentrations, pointing to clinical implications of these concentrations.

Moderators of gene-outcome associations following traumatic brain injury

The field of genomics is the principal avenue in the ongoing development of precision/personalised medicine for a variety of health conditions. However, relating genes to outcomes is notoriously complex, especially when considering that other variables can change, or moderate, gene-outcome associations. Here, we comprehensively discuss moderation of gene-outcome associations in the context of traumatic brain injury (TBI), a common, chronically debilitating, and costly neurological condition that is under complex polygenic influence. We focus our narrative review on single nucleotide polymorphisms (SNPs) of three of the most studied genes (apolipoprotein E, brain-derived neurotrophic factor, and catechol-O-methyltransferase) and on three demographic variables believed to moderate associations between these SNPs and TBI outcomes (age, biological sex, and ethnicity). We speculate on the mechanisms which may underlie these moderating effects, drawing widely from biomolecular and behavioural research (n = 175 scientific reports) within the TBI population (n = 72) and other neurological, healthy, ageing, and psychiatric populations (n = 103). We conclude with methodological recommendations for improved exploration of moderators in future genetics research in TBI and other populations.

Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach

Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits.

PRICKLE1 × FOCAD Interaction Revealed by Genome-Wide vQTL Analysis of Human Facial Traits

The human face is a highly complex and variable structure resulting from the intricate coordination of numerous genetic and non-genetic factors. Hundreds of genomic loci impacting quantitative facial features have been identified. While these associations have been shown to influence morphology by altering the mean size and shape of facial measures, their effect on trait variance remains unclear. We conducted a genome-wide association analysis for the variance of 20 quantitative facial measurements in 2,447 European individuals and identified several suggestive variance quantitative trait loci (vQTLs). These vQTLs guided us to conduct an efficient search for gene-by-gene (G × G) interactions, which uncovered an interaction between PRICKLE1 and FOCAD affecting cranial base width. We replicated this G × G interaction signal at the locus level in an additional 5,128 Korean individuals. We used the hypomorphic Prickle1 Beetlejuice (Prickle1 Bj ) mouse line to directly test the function of Prickle1 on the cranial base and observed wider cranial bases in Prickle1 Bj/Bj . Importantly, we observed that the Prickle1 and Focadhesin proteins co-localize in murine cranial base chondrocytes, and this co-localization is abnormal in the Prickle1 Bj/Bj mutants. Taken together, our findings uncovered a novel G × G interaction effect in humans with strong support from both epidemiological and molecular studies. These results highlight the potential of studying measures of phenotypic variability in gene mapping studies of facial morphology.

Genetic and shared couple environmental contributions to smoking and alcohol use in the UK population

Alcohol use and smoking are leading causes of death and disability worldwide. Both genetic and environmental factors have been shown to influence individual differences in the use of these substances. In the present study we tested whether genetic factors, modelled alongside common family environment, explained phenotypic variance in alcohol use and smoking behaviour in the Generation Scotland (GS) family sample of up to 19,377 individuals. SNP and pedigree-associated effects combined explained between 18 and 41% of the variance in substance use. Shared couple effects explained a significant amount of variance across all substance use traits, particularly alcohol intake, for which 38% of the phenotypic variance was explained. We tested whether the within-couple substance use associations were due to assortative mating by testing the association between partner polygenic risk scores in 34,987 couple pairs from the UK Biobank (UKB). No significant association between partner polygenic risk scores were observed. Associations between an individual's alcohol PRS (b = 0.05, S.E. = 0.006, p < 2 × 10-16) and smoking status PRS (b = 0.05, S.E. = 0.005, p < 2 × 10-16) were found with their partner's phenotype. In support of this, G carriers of a functional ADH1B polymorphism (rs1229984), known to be associated with greater alcohol intake, were found to consume less alcohol if they had a partner who carried an A allele at this SNP. Together these results show that the shared couple environment contributes significantly to patterns of substance use. It is unclear whether this is due to shared environmental factors, assortative mating, or indirect genetic effects. Future studies would benefit from longitudinal data and larger sample sizes to assess this further.

Host Genetics and Gut Microbiome: Perspectives for Multiple Sclerosis

As a complex disease, Multiple Sclerosis (MS)'s etiology is determined by both genetic and environmental factors. In the last decade, the gut microbiome has emerged as an important environmental factor, but its interaction with host genetics is still unknown. In this review, we focus on these dual aspects of MS pathogenesis: we describe the current knowledge on genetic factors related to MS, based on genome-wide association studies, and then illustrate the interactions between the immune system, gut microbiome and central nervous system in MS, summarizing the evidence available from Experimental Autoimmune Encephalomyelitis mouse models and studies in patients. Finally, as the understanding of influence of host genetics on the gut microbiome composition in MS is in its infancy, we explore this issue based on the evidence currently available from other autoimmune diseases that share with MS the interplay of genetic with environmental factors (Inflammatory Bowel Disease, Rheumatoid Arthritis and Systemic Lupus Erythematosus), and discuss avenues for future research.