Differential metabolic and multi-tissue transcriptomic responses to fructose consumption among genetically diverse mice

Understanding how individuals react differently to the same treatment is a major concern in precision medicine. Metabolic challenges such as the one posed by high fructose intake are important determinants of disease mechanisms. We embarked on studies to determine how fructose affects differential metabolic dysfunctions across genetically dissimilar mice, namely, C57BL/6 J (B6), DBA/2 J (DBA) and FVB/NJ (FVB), by integrating physiological and gene regulatory mechanisms. We report that fructose has strain-specific effects, involving tissue-specific gene regulatory cascades in hypothalamus, liver, and white adipose tissues. DBA mice showed the largest numbers of genes associated with adiposity, congruent with their highest susceptibility to adiposity gain and glucose intolerance across the three tissues. In contrast, B6 and FVB mainly exhibited cholesterol phenotypes, accompanying the largest number of adipose genes correlating with total cholesterol in B6, and liver genes correlating with LDL in FVB mice. Tissue-specific network modeling predicted strain-and tissue-specific regulators such as Fgf21 (DBA) and Lss (B6), which were subsequently validated in primary hepatocytes. Strain-specific fructose-responsive genes revealed susceptibility for human diseases such that genes in liver and adipose tissue in DBA showed strong enrichment for human type 2 diabetes and obesity traits. Liver and adipose genes in FVB were mostly related to lipid traits, and liver and adipose genes in B6 showed relevance to most cardiometabolic traits tested. Our results show that fructose induces gene regulatory pathways that are tissue specific and dependent on the genetic make-up, which may underlie interindividual variability in cardiometabolic responses to high fructose consumption.     

Genetic predisposition for Type 2 diabetes, but not for overweight/obesity, is associated with a restricted adipogenesis

Background

Development of Type 2 diabetes, like obesity, is promoted by a genetic predisposition. Although several genetic variants have been identified they only account for a small proportion of risk. We have asked if genetic risk is associated with abnormalities in storing excess lipids in the abdominal subcutaneous adipose tissue.

Methodology/Principal Findings

We recruited 164 lean and 500 overweight/obese individuals with or without a genetic predisposition for Type 2 diabetes or obesity. Adipose cell size was measured in biopsies from the abdominal adipose tissue as well as insulin sensitivity (HOMA index), HDL-cholesterol and Apo AI and Apo B. 166 additional non-obese individuals with a genetic predisposition for Type 2 diabetes underwent a euglycemic hyperinsulinemic clamp to measure insulin sensitivity. Genetic predisposition for Type 2 diabetes, but not for overweight/obesity, was associated with inappropriate expansion of the adipose cells, reduced insulin sensitivity and a more proatherogenic lipid profile in non-obese individuals. However, obesity per se induced a similar expansion of adipose cells and dysmetabolic state irrespective of genetic predisposition.

Conclusions/Significance

Genetic predisposition for Type 2 diabetes, but not obesity, is associated with an impaired ability to recruit new adipose cells to store excess lipids in the subcutaneous adipose tissue, thereby promoting ectopic lipid deposition. This becomes particularly evident in non-obese individuals since obesity per se promotes a dysmetabolic state irrespective of genetic predisposition. These results identify a novel susceptibility factor making individuals with a genetic predisposition for Type 2 diabetes particularly sensitive to the environment and caloric excess.     

Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits

The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs.     

Gene-SCOUT: identifying genes with similar continuous trait fingerprints from phenome-wide association analyses

Large-scale phenome-wide association studies performed using densely-phenotyped cohorts such as the UK Biobank (UKB), reveal many statistically robust gene-phenotype relationships for both clinical and continuous traits. Here, we present Gene-SCOUT, a tool used to identify genes with similar continuous trait fingerprints to a gene of interest. A fingerprint reflects the continuous traits identified to be statistically associated with a gene of interest based on multiple underlying rare variant genetic architectures. Similarities between genes are evaluated by the cosine similarity measure, to capture concordant effect directionality, elucidating clusters of genes in a high dimensional space. The underlying gene-biomarker population-scale association statistics were obtained from a gene-level rare variant collapsing analysis performed on over 1500 continuous traits using 394 692 UKB participant exomes, with additional metabolomic trait associations provided through Nightingale Health''s recent study of 121 394 of these participants. We demonstrate that gene similarity estimates from Gene-SCOUT provide stronger enrichments for clinical traits compared to existing methods. Furthermore, we provide a fully interactive web-resource (http://genescout.public.cgr.astrazeneca.com) to explore the pre-calculated exome-wide similarities. This resource enables a user to examine the biological relevance of the most similar genes for Gene Ontology (GO) enrichment and UKB clinical trait enrichment statistics, as well as a detailed breakdown of the traits underpinning a given fingerprint.     

Identification of differentially expressed genes in subcutaneous adipose tissue from subjects with familial combined hyperlipidemia

Subjects with familial combined hyperlipidemia (FCHL) are characterized by a complex metabolic phenotype with hyperlipidemia, insulin resistance, and central obesity. FCHL is due to impaired adipose tissue function superimposed on hepatic overproduction of lipoproteins. We investigated adipose tissue as an interesting target tissue for differential gene expression in FCHL. Human cDNA expression array analyses, in which adipose tissue from five FCHL patients was compared with that from four age, gender, and BMI matched controls, resulted in the identification of 22 up-regulated and three down-regulated genes. The genes differentially expressed imply activation of the adipocyte cell cycle genes. Furthermore, the differential expression of the genes coding for tumor necrosis factor alpha, interleukin 6, and intracellular adhesion molecule 1 support a role for adipose tissue in insulin resistance in FCHL subjects. The observed changes represent a primary genetic defect, an adaptive response, or a contribution of both.     

DISENTANGLING THE ROLE OF PHENOTYPIC PLASTICITY AND GENETIC DIVERGENCE IN CONTEMPORARY ECOTYPE FORMATION DURING A BIOLOGICAL INVASION

The occurrence of contemporary ecotype formation through adaptive divergence of populations within the range of an invasive species typically requires standing genetic variation but can be facilitated by phenotypic plasticity. The relative contributions of both of these to adaptive trait differentiation have rarely been simultaneously quantified in recently diverging vertebrate populations. Here we study a case of intraspecific divergence into distinct lake and stream ecotypes of threespine stickleback that evolved in the past 140 years within the invasive range in Switzerland. Using a controlled laboratory experiment with full‐sib crosses and treatments mimicking a key feature of ecotypic niche divergence, we test if the phenotypic divergence that we observe in the wild results from phenotypic plasticity or divergent genetic predisposition. Our experimental groups show qualitatively similar phenotypic divergence as those observed among wild adults. The relative contribution of plasticity and divergent genetic predisposition differs among the traits studied, with traits related to the biomechanics of feeding showing a stronger genetic predisposition, whereas traits related to locomotion are mainly plastic. These results implicate that phenotypic plasticity and standing genetic variation interacted during contemporary ecotype formation in this case.     

Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA

For many complex traits, genetic variants have been found associated. However, it is still mostly unclear through which downstream mechanism these variants cause these phenotypes. Knowledge of these intermediate steps is crucial to understand pathogenesis, while also providing leads for potential pharmacological intervention. Here we relied upon natural human genetic variation to identify effects of these variants on trans-gene expression (expression quantitative trait locus mapping, eQTL) in whole peripheral blood from 1,469 unrelated individuals. We looked at 1,167 published trait- or disease-associated SNPs and observed trans-eQTL effects on 113 different genes, of which we replicated 46 in monocytes of 1,490 different individuals and 18 in a smaller dataset that comprised subcutaneous adipose, visceral adipose, liver tissue, and muscle tissue. HLA single-nucleotide polymorphisms (SNPs) were 10-fold enriched for trans-eQTLs: 48% of the trans-acting SNPs map within the HLA, including ulcerative colitis susceptibility variants that affect plausible candidate genes AOAH and TRBV18 in trans. We identified 18 pairs of unlinked SNPs associated with the same phenotype and affecting expression of the same trans-gene (21 times more than expected, P<10(-16)). This was particularly pronounced for mean platelet volume (MPV): Two independent SNPs significantly affect the well-known blood coagulation genes GP9 and F13A1 but also C19orf33, SAMD14, VCL, and GNG11. Several of these SNPs have a substantially higher effect on the downstream trans-genes than on the eventual phenotypes, supporting the concept that the effects of these SNPs on expression seems to be much less multifactorial. Therefore, these trans-eQTLs could well represent some of the intermediate genes that connect genetic variants with their eventual complex phenotypic outcomes.     

Significant sparse polygenic risk scores across 813 traits in UK Biobank

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10⁻⁵) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman’s ⍴ = 0.61, p = 2.2 x 10⁻⁵⁹ for quantitative traits, ⍴ = 0.21, p = 9.6 x 10⁻⁴ for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).     

A Method to Prioritize Quantitative Traits and Individuals for Sequencing in Family-Based Studies

Owing to recent advances in DNA sequencing, it is now technically feasible to evaluate the contribution of rare variation to complex traits and diseases. However, it is still cost prohibitive to sequence the whole genome (or exome) of all individuals in each study. For quantitative traits, one strategy to reduce cost is to sequence individuals in the tails of the trait distribution. However, the next challenge becomes how to prioritize traits and individuals for sequencing since individuals are often characterized for dozens of medically relevant traits. In this article, we describe a new method, the Rare Variant Kinship Test (RVKT), which leverages relationship information in family-based studies to identify quantitative traits that are likely influenced by rare variants. Conditional on nuclear families and extended pedigrees, we evaluate the power of the RVKT via simulation. Not unexpectedly, the power of our method depends strongly on effect size, and to a lesser extent, on the frequency of the rare variant and the number and type of relationships in the sample. As an illustration, we also apply our method to data from two genetic studies in the Old Order Amish, a founder population with extensive genealogical records. Remarkably, we implicate the presence of a rare variant that lowers fasting triglyceride levels in the Heredity and Phenotype Intervention (HAPI) Heart study (p = 0.044), consistent with the presence of a previously identified null mutation in the APOC3 gene that lowers fasting triglyceride levels in HAPI Heart study participants.     

Adipocyte Hypertrophy,Inflammation and Fibrosis Characterize Subcutaneous Adipose Tissue of Healthy,Non-Obese Subjects Predisposed to Type 2 Diabetes

Background

The adipose tissue is important for development of insulin resistance and type 2 diabetes and adipose tissue dysfunction has been proposed as an underlying cause. In the present study we investigated presence of adipocyte hypertrophy, and gene expression pattern of adipose tissue dysfunction in the subcutaneous adipose tissue of healthy, non-obese subjects predisposed to type 2 diabetes compared to matched control subjects with no known genetic predisposition for type 2 diabetes.

Method

Seventeen healthy and non-obese subjects with known genetic predisposition for type 2 diabetes (first-degree relatives, FDRs) and 17 control subjects were recruited. The groups were matched for gender and BMI and had similar age. Glucose tolerance was determined by an oral glucose tolerance test and insulin sensitivity was calculated using HOMA-index. Blood samples were collected and subcutaneous abdominal adipose tissue biopsies obtained for gene expression analysis and adipocyte cell size measurement.

Results

Our findings show that, in spite of similar age, BMI and percent body fat, FDRs displayed adipocyte hypertrophy, as well as higher waist/hip ratio, fasting insulin levels, HOMA-IR and serum triglycerides. Adipocyte hypertrophy in the FDR group, but not among controls, was associated with measures of impaired insulin sensitivity. The adipocyte hypertrophy was accompanied by increased inflammation and Wnt-signal activation. In addition, signs of tissue remodeling and fibrosis were observed indicating presence of early alterations associated with adipose tissue dysfunction in the FDRs.

Conclusion

Genetic predisposition for type 2 diabetes is associated with impaired insulin sensitivity, adipocyte hypertrophy and other markers of adipose tissue dysfunction. A dysregulated subcutaneous adipose tissue may be a major susceptibility factor for later development of type 2 diabetes.     

Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis

BackgroundEpidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses.Methods and findingsWe selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10⁻⁸ from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m²; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10⁻⁵; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m², p = 2.1 × 10⁻⁵). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null.ConclusionsIn this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.

Aaron Leong and co-workers investigate causal risk factors for COVID-10 illness and severity.     

Genome-wide association study for feed efficiency in collective cage-raised rabbits under full and restricted feeding

Feed efficiency (FE) is one of the most economically and environmentally relevant traits in the animal production sector. The objective of this study was to gain knowledge about the genetic control of FE in rabbits. To this end, GWASs were conducted for individual growth under two feeding regimes (full feeding and restricted) and FE traits collected from cage groups, using 114 604 autosome SNPs segregating in 438 rabbits. Two different models were implemented: (1) an animal model with a linear regression on each SNP allele for growth trait; and (2) a two-trait animal model, jointly fitting the performance trait and each SNP allele content, for FE traits. This last modeling strategy is a new tool applied to GWAS and allows information to be considered from non-genotyped individuals whose contribution is relevant in the group average traits. A total of 189 SNPs in 17 chromosomal regions were declared to be significantly associated with any of the five analyzed traits at a chromosome-wide level. In 12 of these regions, 20 candidate genes were proposed to explain the variation of the analyzed traits, including genes such as FTO, NDUFAF6 and CEBPA previously associated with growth and FE traits in monogastric species. Candidate genes associated with behavioral patterns were also identified. Overall, our results can be considered as the foundation for future functional research to unravel the actual causal mutations regulating growth and FE in rabbits.     

Estimates of direct and indirect effects for early juvenile survival in captive populations maintained for conservation purposes: the case of Cuvier's gazelle

Together with the avoidance of any negative impact of inbreeding, preservation of genetic variability for life‐history traits that could undergo future selective pressure is a major issue in endangered species management programmes. However, most of these programmes ignore that, apart from the direct action of genes on such traits, parents, as contributors of offspring environment, can influence offspring performance through indirect parental effects (when parental genotype and phenotype exerts environmental influences on offspring phenotype independently of additive genetic effects). Using quantitative genetic models, we estimated the additive genetic variance for juvenile survival in a population of the endangered Cuvier's gazelle kept in captivity since 1975. The dataset analyzed included performance recording for 700 calves and a total pedigree of 740 individuals. Results indicated that in this population juvenile survival harbors significant additive genetic variance. The estimates of heritability obtained were in general moderate (0.115–0.457) and not affected by the inclusion of inbreeding in the models. Maternal genetic contribution to juvenile survival seems to be of major importance in this gazelle's population as well. Indirect genetic and indirect environmental effects assigned to mothers (i.e., maternal genetic and maternal permanent environmental effects) roughly explain a quarter of the total variance estimated for the trait analyzed. These findings have major evolutionary consequences for the species as show that offspring phenotypes can evolve strictly through changes in the environment provided by mothers. They are also relevant for the captive breeding programme of the species. To take into account, the contribution that mothers have on offspring phenotype through indirect genetic effects when designing pairing strategies might serve to identify those females with better ability to recruit, and, additionally, to predict reliable responses to selection in the captive population.     

Epistatic interactions of genes influence within-individual variation of physical activity traits in mice

A number of quantitative trait loci (QTLs) recently have been discovered that affect various activity traits in mice, but their collective impact does not appear to explain the consistently moderate to high heritabilities for these traits. We previously suggested interactions of genes, or epistasis, might account for additional genetic variability of activity, and tested this for the average distance, duration and speed run by mice during a 3 week period. We found abundant evidence for epistasis affecting these traits, although, recognized that epistatic effects may well vary within individuals over time. We therefore conducted a full genome scan for epistatic interactions affecting these traits in each of seven three-day intervals. Our intent was to assess the extent and trends in epistasis affecting these traits in each of the intervals. We discovered a number of epistatic interactions of QTLs that influenced the activity traits in the mice, the majority of which were not previously found and appeared to affect the activity traits (especially distance and speed) primarily in the early or in the late age intervals. The overall impact of epistasis was considerable, its contribution to the total phenotypic variance varying from an average of 22–35% in the three traits across all age intervals. It was concluded that epistasis is more important than single-locus effects of genes on activity traits at specific ages and it is therefore an essential component of the genetic architecture of physical activity.     

REDUCED GENETIC VARIANCE AMONG HIGH FITNESS INDIVIDUALS: INFERRING STABILIZING SELECTION ON MALE SEXUAL DISPLAYS IN DROSOPHILA SERRATA

Directional selection is prevalent in nature, yet phenotypes tend to remain relatively constant, suggesting a limit to trait evolution. However, the genetic basis of this limit is unresolved. Given widespread pleiotropy, opposing selection on a trait may arise from the effects of the underlying alleles on other traits under selection, generating net stabilizing selection on trait genetic variance. These pleiotropic costs of trait exaggeration may arise through any number of other traits, making them hard to detect in phenotypic analyses. Stabilizing selection can be inferred, however, if genetic variance is greater among low‐ compared to high‐fitness individuals. We extend a recently suggested approach to provide a direct test of a difference in genetic variance for a suite of cuticular hydrocarbons (CHCs) in Drosophila serrata. Despite strong directional sexual selection on these traits, genetic variance differed between high‐ and low‐fitness individuals and was greater among the low‐fitness males for seven of eight CHCs, significantly more than expected by chance. Univariate tests of a difference in genetic variance were nonsignificant but likely have low power. Our results suggest that further CHC exaggeration in D. serrata in response to sexual selection is limited by pleiotropic costs mediated through other traits.     

Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.