Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

A novel application of data-consistent inversion to overcome spurious inference in genome-wide association studies

The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption-free model built upon data-consistent inversion (DCI), which is a recently developed measure-theoretic framework utilized for uncertainty quantification. This proposed DCI-derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI-derived model to cover all genetic variants without emphasizing on additivity of the classic-GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type-I error rate at least as well as the classic-GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.

A Multibreed Genome-Wide Association Study for Cattle Leukocyte Telomere Length

Telomeres are terminal DNA regions of chromosomes that prevent chromosomal fusion and degradation during cell division. In cattle, leukocyte telomere length (LTL) is associated with longevity, productive lifespan, and disease susceptibility. However, the genetic basis of LTL in this species is less studied than in humans. In this study, we utilized the whole-genome resequencing data of 239 animals from 17 cattle breeds for computational leukocyte telomere length estimation and subsequent genome-wide association study of LTL. As a result, we identified 42 significant SNPs, of which eight were found in seven genes (EXOC6B, PTPRD, RPS6KC1, NSL1, AGBL1, ENSBTAG00000052188, and GPC1) when using covariates for two major breed groups (Turano-Mongolian and European). Association analysis with covariates for breed effect detected 63 SNPs, including 13 in five genes (EXOC6B, PTPRD, RPS6KC1, ENSBTAG00000040318, and NELL1). The PTPRD gene, demonstrating the top signal in analysis with breed effect, was previously associated with leukocyte telomere length in cattle and likely is involved in the mechanism of alternative lengthening of telomeres. The single nucleotide variants found could be tested for marker-assisted selection to improve telomere-length-associated traits.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Significance of linkage disequilibrium and epistasis on genetic variances in noninbred and inbred populations

Background

The influence of linkage disequilibrium (LD), epistasis, and inbreeding on genotypic variance continues to be an important area of investigation in genetics and evolution. Although the current knowledge about biological pathways and gene networks indicates that epistasis is important in determining quantitative traits, the empirical evidence for a range of species and traits is that the genotypic variance is most additive. This has been confirmed by some recent theoretical studies. However, because these investigations assumed linkage equilibrium, considered only additive effects, or used simplified assumptions for two- and higher-order epistatic effects, the objective of this investigation was to provide additional information about the impact of LD and epistasis on genetic variances in noninbred and inbred populations, using a simulated dataset.

Results

In general, the most important component of the genotypic variance was additive variance. Because of positive LD values, after 10 generations of random crosses there was generally a decrease in all genetic variances and covariances, especially the nonepistatic variances. Thus, the epistatic variance/genotypic variance ratio is inversely proportional to the LD level. Increasing inbreeding increased the magnitude of the additive, additive x additive, additive x dominance, and dominance x additive variances, and decreased the dominance and dominance x dominance variances. Except for duplicate epistasis with 100% interacting genes, the epistatic variance/genotypic variance ratio was proportional to the inbreeding level. In general, the additive x additive variance was the most important component of the epistatic variance. Concerning the genetic covariances, in general, they showed lower magnitudes relative to the genetic variances and positive and negative signs. The epistatic variance/genotypic variance ratio was maximized under duplicate and dominant epistasis and minimized assuming recessive and complementary epistasis. Increasing the percentage of epistatic genes from 30 to 100% increased the epistatic variance/genotypic variance ratio by a rate of 1.3 to 12.6, especially in inbred populations. The epistatic variance/genotypic variance ratio was maximized in the noninbred and inbred populations with intermediate LD and an average allelic frequency of the dominant genes of 0.3 and in the noninbred and inbred populations with low LD and an average allelic frequency of 0.5.

Conclusions

Additive variance is in general the most important component of genotypic variance. LD and inbreeding have a significant effect on the magnitude of the genetic variances and covariances. In general, the additive x additive variance is the most important component of epistatic variance. The maximization of the epistatic variance/genotypic variance ratio depends on the LD level, degree of inbreeding, epistasis type, percentage of interacting genes, and average allelic frequency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08335-9.

Accurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations

Genetic association studies seek to uncover the link between genotype and phenotype, and often utilize inbred reference panels as a replicable source of genetic variation. However, inbred reference panels can differ substantially from wild populations in their genotypic distribution, patterns of linkage-disequilibrium, and nucleotide diversity. As a result, associations discovered using inbred reference panels may not reflect the genetic basis of phenotypic variation in natural populations. To address this problem, we evaluated a mapping population design where dozens to hundreds of inbred lines are outbred for few generations, which we call the Hybrid Swarm. The Hybrid Swarm approach has likely remained underutilized relative to pre-sequenced inbred lines due to the costs of genome-wide genotyping. To reduce sequencing costs and make the Hybrid Swarm approach feasible, we developed a computational pipeline that reconstructs accurate whole genomes from ultra-low-coverage (0.05X) sequence data in Hybrid Swarm populations derived from ancestors with phased haplotypes. We evaluate reconstructions using genetic variation from the Drosophila Genetic Reference Panel as well as variation from neutral simulations. We compared the power and precision of Genome-Wide Association Studies using the Hybrid Swarm, inbred lines, recombinant inbred lines (RILs), and highly outbred populations across a range of allele frequencies, effect sizes, and genetic architectures. Our simulations show that these different mapping panels vary in their power and precision, largely depending on the architecture of the trait. The Hybrid Swam and RILs outperform inbred lines for quantitative traits, but not for monogenic ones. Taken together, our results demonstrate the feasibility of the Hybrid Swarm as a cost-effective method of fine-scale genetic mapping.

Genome-wide conditional association study reveals the influences of lifestyle cofactors on genetic regulation of body surface area in MESA population

Body surface area (BSA) is an important trait used for many clinical purposes. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). GWAS of BSA was conducted on 5,324 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from the Multi-Ethnic Study of Atherocloris (MESA) data using unconditional and conditional full genetic models. In this study, fifteen SNPs were identified (Experiment-wise PEW < 1×10-5) using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. By comparing association analysis results from unconditional and cofactor conditional models, we observed three different scenarios: (i) genetic effects of several SNPs did not affected by cofactors, e.g., additive effect of gene CREB5 (a≙ -0.013 for T/T and 0.013 for G/G, -Log10 PEW = 8.240) did not change in the cofactor models; (ii) genetic effects of several SNPs affected by cofactors, e.g., the genetic additive effect (a≙ 0.012 for A/A and -0.012 for G/G, -Log10 PEW = 7.185) of SNP of the gene GRIN2A was not significant in transportation cofactor model; and (iii) genetic effects of several SNPs suppressed by cofactors, e.g., additive (a≙ -0.018 for G/G and 0.018 for C/C, -Log10 PEW = 19.737) and dominance (d≙ -0.038 for G/C, -Log10 PEW = 27.734) effects of SNP of gene ERBB4 was identified using only transportation cofactor model. Gene ontology analysis showed that several genes are related to the metabolic pathway of calcium compounds, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer. This study revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes.

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding.

Genetic variation in P2RX7 and pain tolerance

P2X7 is a nonselective cation channel activated by extracellular ATP. P2X7 activation contributes to the proinflammatory response to injury or bacterial invasion and mediates apoptosis. Recently, P2X7 function has been linked to chronic inflammatory and neuropathic pain. P2X7 may contribute to pain modulation both by effects on peripheral tissue injury underlying clinical pain states, and through alterations in central nervous system processing, as suggested by animal models. To further test its role in pain sensitivity, we examined whether variation within the P2RX7 gene, which encodes the P2X7 receptor, was associated with experimentally induced pain in human patients. Experimental pain was assessed in Tromsø 6, a longitudinal and cross-sectional population-based study (N = 3016), and the BrePainGen cohort, consisting of patients who underwent breast cancer surgery (N = 831). For both cohorts, experimental pain intensity and tolerance were assessed with the cold-pressor test. In addition, multisite chronic pain was assessed in Tromsø 6 and pain intensity 1 week after surgery was assessed in BrePainGen. We tested whether the single-nucleotide polymorphism rs7958311, previously implicated in clinical pain, was associated with experimental and clinical pain phenotypes. In addition, we examined effects of single-nucleotide polymorphisms rs208294 and rs208296, for which previous results have been equivocal. Rs7958311 was associated with experimental pain intensity in the meta-analysis of both cohorts. Significant associations were also found for multisite pain and postoperative pain. Our results strengthen the existing evidence and suggest that P2X7 and genetic variation in the P2RX7-gene may be involved in the modulation of human pain sensitivity.

Conditional GWAS revealing genetic impacts of lifestyle behaviors on low-density lipoprotein (LDL)

BACKGROUND

Accumulation of LDL cholesterol (LDL-c) within artery walls is strongly associated with the initiation and progression of atherosclerosis development. This complex trait is affected by multifactor involving polygenes, environments, and their interactions. Uncovering genetic architecture of LDL may help to increase the understanding of the genetic mechanism of cardiovascular diseases.

METHODS

We used a genetic model to analyze genetic effects including additive, dominance, epistasis, and ethnic interactions for data from the Multi-Ethnic Study of Atherosclerosis (MESA). Three lifestyle behaviors (reading, intentional exercising, smoking) were used as cofactor in conditional models.

RESULTS

We identified 156 genetic effects of 10 quantitative trait SNPs (QTSs) in base model and three conditional models. The total estimated heritability of these genetic effects was approximately 72.88% in the base model. Five genes (CELSR2, MARK2, ADAMTS12, PFDN4, and MAGI2) have biological functions related to LDL.

CONCLUSIONS

Compared with the based model LDL, the results in three conditional models revealed that intentional exercising and smoking could have impacts for causing and suppressing some of genetic effects and influence the levels of LDL. Furthermore, these two lifestyles could have different genetic effects for each ethnic group on a specific QTS. As most of the heritability in based model LDL and conditional model LDL|Smk was contributed from epistasis effects, our result indicated that epistasis effects played important roles in determining LDL levels. Our study provided useful insight into the biological mechanisms underlying regulation of LDL and might help in the discovery of novel therapeutic targets for cardiovascular disease.

Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population

Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log₁₀P_EW > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability ( hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.

Full genetic analysis for genome-wide association study of Fangji: a powerful approach for effectively dissecting the molecular architecture of personalized traditional Chinese medicine

Elucidation of the systems biology foundation underlying the effect of Fangji, which are multi-herbal traditional Chinese medicine (TCM) formulas, is one of the major aims in the field. The numerous bioactive ingredients of a Fangji deal with the multiple targets of a complex disease, which is influenced by a number of genes and their interactions with the environment. Genome-wide association study (GWAS) is an unbiased approach for dissecting the genetic variants underlying complex diseases and individual response to a given treatment. GWAS has great potential for the study of systems biology from the point of view of genomics, but the capacity using current analysis models is largely handicapped, as evidenced by missing heritability. Recent development of a full genetic model, in which gene-gene interactions (dominance and epistasis) and gene-environment interactions are all considered, has addressed these problems. This approach has been demonstrated to substantially increase model power, remarkably improving the detection of association of GWAS and the construction of the molecular architecture. This analysis does not require a very large sample size, which is often difficult to meet for a GWAS of treatment response. Furthermore, this analysis can integrate other omic information and allow for variations of Fangji, which is very promising for Fangjiomic study and detection of the sophisticated molecular architecture of the function of Fangji, as well as for the delineation of the systems biology of personalized medicine in TCM in an unbiased and comprehensive manner.

The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation

One of the primary goals in this era of precision medicine is to understand the biology of human diseases and their treatment, such that each individual patient receives the best possible treatment for their disease based on their genetic and environmental exposures. One way to work towards achieving this goal is to identify the environmental exposures and genetic variants that are relevant to each disease in question, as well as the complex interplay between genes and environment. Genome-wide association studies (GWAS) have allowed for a greater understanding of the genetic component of many complex traits. However, these genetic effects are largely small and thus, our ability to use these GWAS finding for precision medicine is limited. As more and more GWAS have been performed, rather than focusing only on common single nucleotide polymorphisms (SNPs) and additive genetic models, many researchers have begun to explore alternative heritable components of complex traits including rare variants, structural variants, epigenetics, and genetic interactions. While genetic interactions are a plausible reality that could explain some of the heritabliy that has not yet been identified, especially when one considers the identification of genetic interactions in model organisms as well as our understanding of biological complexity, still there are significant challenges and considerations in identifying these genetic interactions. Broadly, these can be summarized in three categories: abundance of methods, practical considerations, and biological interpretation. In this review, we will discuss these important elements in the search for genetic interactions along with some potential solutions. While genetic interactions are theoretically understood to be important for complex human disease, the body of evidence is still building to support this component of the underlying genetic architecture of complex human traits. Our hope is that more sophisticated modeling approaches and more robust computational techniques will enable the community to identify these important genetic interactions and improve our ability to implement precision medicine in the future.

Potential influence of κ-casein and β-lactoglobulin genes in genetic association studies of milk quality traits

OBJECTIVE

From a review of published information on genetic association studies, a meta-analysis was conducted to determine the influence of the genes κ-casein (CSN3) and β-lactoglobulin (LGB) on milk yield traits in Holstein, Jersey, Brown Swiss, and Fleckvieh.

METHODS

The GLIMMIX procedure was used to analyze milk production and percentage of protein and fat in milk. Models included the main effects and all their possible two-way interactions; not estimable effects and non-significant (p>0.05) two-way interactions were dropped from the models. The three traits analyzed used Poisson distribution and a log link function and were determined with the Interactive Data Analysis of SAS software. Least square means and multiple mean comparisons were obtained and performed for significant main effects and their interactions (p<0.0255).

RESULTS

Interaction of breed by gene showed that Holstein and Fleckvieh were the breeds on which CSN3 (6.01%±0.19% and 5.98%±0.22%), and LGB (6.02%±0.19% and 5.70%±0.22%) have the greatest influence. Interaction of breed by genotype nested in the analyzed gene indicated that Holstein and Jersey showed greater influence of the CSN3 AA genotype, 6.04%±0.22% and 5.59%±0.31% than the other genotypes, while LGB AA genotype had the largest influence on the traits analyzed, 6.05%±0.20% and 5.60%±0.19%, respectively. Furthermore, interaction of type of statistical model by genotype nested in the analyzed gene indicated that CSN3 and LGB genes had similar behavior, maintaining a difference of more than 7% across analyzed genotypes. These results could indicate that both Holstein and Jersey have had lower substitution allele effect in selection programs that include CSN3 and LGB genes than Brown Swiss and Fleckvieh.

CONCLUSION

Breed determined which genotypes had the greatest association with analyzed traits. The mixed model based in Bayesian or Ridge Regression was the best alternative to analyze CSN3 and LGB gene effects on milk yield and protein and fat percentages.