Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis in Populus deltoides.

Despite the growing resources and tools for high-throughput characterization and analysis of genomic information, the discovery of the genetic elements that regulate complex traits remains a challenge. Systems genetics is an emerging field that aims to understand the flow of biological information that underlies complex traits from genotype to phenotype. In this study, we used a systems genetics approach to identify and evaluate regulators of the lignin biosynthesis pathway in Populus deltoides by combining genome, transcriptome, and phenotype data from a population of 268 unrelated individuals of P. deltoides The discovery of lignin regulators began with the quantitative genetic analysis of the xylem transcriptome and resulted in the detection of 6706 and 4628 significant local- and distant-eQTL associations, respectively. Among the locally regulated genes, we identified the R2R3-MYB transcription factor MYB125 (Potri.003G114100) as a putative trans-regulator of the majority of genes in the lignin biosynthesis pathway. The expression of MYB125 in a diverse population positively correlated with lignin content. Furthermore, overexpression of MYB125 in transgenic poplar resulted in increased lignin content, as well as altered expression of genes in the lignin biosynthesis pathway. Altogether, our findings indicate that MYB125 is involved in the control of a transcriptional coexpression network of lignin biosynthesis genes during secondary cell wall formation in P. deltoides.

MOSS: multi-omic integration with sparse value decomposition.

SUMMARY: This article presents multi-omic integration with sparse value decomposition (MOSS), a free and open-source R package for integration and feature selection in multiple large omics datasets. This package is computationally efficient and offers biological insight through capabilities, such as cluster analysis and identification of informative omic features. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=MOSS. SUPPLEMENTARY INFORMATION: Supplementary information can be found at https://github.com/agugonrey/GonzalezReymundez2021.

DNA Methylation and Gene Expression with Clinical Covariates Explain Variation in Aggressiveness and Survival of Pancreatic Cancer Patients.

Pancreatic cancer (PC) is associated with a high mortality rate. We explored the interindividual variation of cancer outcomes, attributable to DNA methylation, gene expression, and clinical factors among PC patients. We aim to determine whether we could differentiate subjects with greater nodal involvement, higher cancer staging, and subsequent survival. We modeled every response variable as a function of a linear predictor involving the effects of clinical variables, methylation, and gene expression in a Bayesian framework. Our results highlight the overall importance of wide-spread alterations in methylation and gene expression patterns associated with survival, nodal metastasis, and staging.

The influence of 15-week exercise training on dietary patterns among young adults.

BACKGROUND/OBJECTIVES: Little is currently known about how exercise may influence dietary patterns and/or food preferences. The present study aimed to examine the effect of a 15-week exercise training program on overall dietary patterns among young adults. SUBJECTS/METHODS: This study consisted of 2680 young adults drawn from the Training Intervention and Genetics of Exercise Response (TIGER) study. Subjects underwent 15 weeks of aerobic exercise training, and exercise duration, intensity, and dose were recorded for each session using computerized heart rate monitors. In total, 4355 dietary observations with 102 food items were collected using a self-administered food frequency questionnaire before and after exercise training (n = 2476 at baseline; n = 1859 at 15 weeks). Dietary patterns were identified using a Bayesian sparse latent factor model. Changes in dietary pattern preferences were evaluated based on the pre/post-training differences in dietary pattern scores, accounting for the effects of gender, race/ethnicity, and BMI. RESULTS: Within each of the seven dietary patterns identified, most dietary pattern scores were decreased following exercise training, consistent with increased voluntary regulation of food intake. A longer duration of exercise was associated with decreased preferences for the western (ß: -0.0793; 95% credible interval: -0.1568, -0.0017) and snacking (ß: -0.1280; 95% credible interval: -0.1877, -0.0637) patterns, while a higher intensity of exercise was linked to an increased preference for the prudent pattern (ß: 0.0623; 95% credible interval: 0.0159, 0.1111). Consequently, a higher dose of exercise was related to a decreased preference for the snacking pattern (ß: -0.0023; 95% credible interval: -0.0042, -0.0004) and an increased preference for the prudent pattern (ß: 0.0029; 95% credible interval: 0.0009, 0.0048). CONCLUSIONS: The 15-week exercise training appeared to motivate young adults to pursue healthier dietary preferences and to regulate their food intake.

Advanced Dietary Patterns Analysis Using Sparse Latent Factor Models in Young Adults.

Background: Principal components analysis (PCA) has been the most widely used method for deriving dietary patterns to date. However, PCA requires arbitrary ad hoc decisions for selecting food variables in interpreting dietary patterns and does not easily accommodate covariates. Sparse latent factor models can be utilized to address these issues. Objective: The objective of this study was to compare Bayesian sparse latent factor models with PCA for identifying dietary patterns among young adults. Methods: Habitual food intake was estimated in 2730 sedentary young adults from the Training Interventions and Genetics of Exercise Response (TIGER) Study [aged 18-35 y; body mass index (BMI; in kg/m2): 26.5 ± 6.1] who exercised <30 min/wk during the previous 30 d without restricting caloric intake before study enrollment. A food-frequency questionnaire was used to generate the frequency intakes of 102 food items. Sparse latent factor modeling was applied to the standardized food intakes to derive dietary patterns, incorporating additional covariates (sex, race/ethnicity, and BMI). The identified dietary patterns via sparse latent factor modeling were compared with the PCA derived dietary patterns. Results: Seven dietary patterns were identified in both PCA and sparse latent factor analysis. In contrast to PCA, the sparse latent factor analysis allowed the covariate information to be jointly accounted for in the estimation of dietary patterns in the model and offered probabilistic criteria to determine the foods relevant to each dietary pattern. The derived patterns from both methods generally described common dietary behaviors. Dietary patterns 1-4 had similar food subsets using both statistical approaches, but PCA had smaller sets of foods with more cross-loading elements between the 2 factors. Overall, the sparse latent factor analysis produced more interpretable dietary patterns, with fewer of the food items excluded from all patterns. Conclusion: Sparse latent factor models can be useful in future studies of dietary patterns by reducing the intrinsic arbitrariness involving the choice of food variables in interpreting dietary patterns and incorporating covariates in the assessment of dietary patterns.

Changes in milk characteristics and fatty acid profile during the estrous cycle in dairy cows.

The relationship of the estrous cycle to milk composition and milk physical properties was assessed on Holstein (n = 10,696), Brown Swiss (n = 20,501), Simmental (n = 17,837), and Alpine Grey (n = 8,595) cows reared in northeastern Italy. The first insemination after calving for each cow was chosen to be the day of estrus and insemination. Test days surrounding the insemination date (from 10 d before to 10 d after the day of the estrus) were selected and categorized in phases relative to estrus as diestrus high-progesterone, proestrus, estrus, metestrus, and diestrus increasing-progesterone phases. Milk components and physical properties were predicted on the basis of Fourier-transform infrared spectra of milk samples and were analyzed using a linear mixed model, which included the random effects of herd, the fixed classification effects of year-month, parity number, breed, estrous cycle phase, day nested within the estrous cycle phase, conception, partial regressions on linear and quadratic effects of days in milk nested within parity number, as well as the interactions between conception outcome with estrous cycle phase and breed with estrous cycle phase. Milk composition, particularly fat, protein, and lactose, showed clear differences among the estrous cycle phases. Fat increased by 0.14% from diestrus high-progesterone to estrous phase, whereas protein concomitantly decreased by 0.03%. Lactose appeared to remain relatively constant over diestrus high-progesterone, rising 1 d before the day of estrus followed by a gradual reduction over the subsequent phases. Specific fatty acids were also affected across the estrous cycle phases: C14:0 and C16:0 decreased (-0.34 and -0.48%) from proestrus to estrus with a concomitant increase in C18:0 and C18:1 cis-9 (0.40 and 0.73%). More general categories of fatty acids showed a similar behavior; that is, unsaturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, trans fatty acids, and long-chain fatty acids increased, whereas the saturated fatty acids, medium-chain fatty acids, and short-chain fatty acids decreased during the estrous phase. Finally, urea, somatic cell score, freezing point, pH, and homogenization index were also affected indicating variation associated with the hormonal and behavioral changes of cows in standing estrus. Hence, the variation in milk profiles of cows showing estrus should potentially be taken into account for precision dairy farming management.

Diagnosing pregnancy status using infrared spectra and milk composition in dairy cows.

Data on Holstein (16,890), Brown Swiss (31,441), Simmental (25,845), and Alpine Grey (12,535) cows reared in northeastern Italy were used to assess the ability of milk components (fat, protein, casein, and lactose) and Fourier transform infrared (FTIR) spectral data to diagnose pregnancy. Pregnancy status was defined as whether a pregnancy was confirmed by a subsequent calving and no other subsequent inseminations within 90 d of the breeding of specific interest. Milk samples were analyzed for components and FTIR full-spectrum data using a MilkoScan FT+ 6000 (Foss Electric, Hillerød, Denmark). The spectrum covered 1,060 wavenumbers (wn) from 5,010 to 925 cm-1. Pregnancy status was predicted using generalized linear models with fat, protein, lactose, casein, and individual FTIR spectral bands or wavelengths as predictors. We also fitted a generalized linear model as a simultaneous function of all wavelengths (1,060 wn) with a Bayesian variable selection model using the BGLR R-package (https://r-forge.r-project.org/projects/bglr/). Prediction accuracy was determined using the area under a receiver operating characteristic curve based on a 10-fold cross-validation (CV-AUC) assessment based on sensitivities and specificities of phenotypic predictions. Overall, the best prediction accuracies were obtained for the model that included the complete FTIR spectral data. We observed similar patterns across breeds with small differences in prediction accuracy. The highest CV-AUC value was obtained for Alpine Grey cows (CV-AUC = 0.645), whereas Brown Swiss and Simmental cows had similar performance (CV-AUC = 0.630 and 0.628, respectively), followed by Holsteins (CV-AUC = 0.607). For single-wavelength analyses, important peaks were detected at wn 2,973 to 2,872 cm-1 where Fat-B (C-H stretch) is usually filtered, wn 1,773 cm-1 where Fat-A (C=O stretch) is filtered, wn 1,546 cm-1 where protein is filtered, wn 1,468 cm-1 associated with urea and fat, wn 1,399 and 1,245 cm-1 associated with acetone, and wn 1,025 to 1,013 cm-1 where lactose is filtered. In conclusion, this research provides new insight into alternative strategies for pregnancy screening of dairy cows.

Genome-wide association study reveals putative regulators of bioenergy traits in Populus deltoides.

Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. These polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.

Serum urate gene associations with incident gout, measured in the Framingham Heart Study, are modified by renal disease and not by body mass index.

We hypothesized that serum urate-associated SNPs, individually or collectively, interact with BMI and renal disease to contribute to risk of incident gout. We measured the incidence of gout and associated comorbidities using the original and offspring cohorts of the Framingham Heart Study. We used direct and imputed genotypes for eight validated serum urate loci. We fit binomial regression models of gout incidence as a function of the covariates, age, type 2 diabetes, sex, and all main and interaction effects of the eight serum urate SNPs with BMI and renal disease. Models were also fit with a genetic risk score for serum urate levels which corresponds to the sum of risk alleles at the eight SNPs. Model covariates, age (P = 5.95E-06), sex (P = 2.46E-39), diabetes (P = 2.34E-07), BMI (P = 1.14E-11) and the SNPs, rs1967017 (P = 9.54E-03), rs13129697 (P = 4.34E-07), rs2199936 (P = 7.28E-03) and rs675209 (P = 4.84E-02) were all associated with incident gout. No BMI by SNP or BMI by serum urate genetic risk score interactions were statistically significant, but renal disease by rs1106766 was statistically significant (P = 6.12E-03). We demonstrated that minor alleles of rs1106766 (intergenic, INHBC) were negatively associated with the risk of incident gout in subjects without renal disease, but not for individuals with renal disease. These analyses demonstrate that a significant component of the risk of gout may involve complex interplay between genes and environment.

Prediction of complex human traits using the genomic best linear unbiased predictor.

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R(2)) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R(2) based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1-b)(2), where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R(2). However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R(2). Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R(2) may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects.

Pharmacogenetic effects of 'candidate gene complexes' on stroke in the GenHAT study.

OBJECTIVE: The aim of this study was to investigate whether there is a genotype-by-treatment interaction in patients experiencing stroke and treated with one of three antihypertensive drugs, that is chlorthalidone, amlodipine, or lisinopril. PARTICIPANTS AND METHODS: A population of 436 African Americans and 539 whites who had experienced stroke in the GenHAT study were genotyped for 768 single nucleotide polymorphisms (SNPs) in 280 candidate genes. To detect a genotype-by-treatment interaction, we used the Pearson's χ-test to assess whether the genotype frequencies differed at the single SNP level for the three drug treatment groups. From these single SNP analyses, we derived a summary statistic for the degree of association at the gene and gene complex levels. This was done by grouping SNPs using information on gene locations and defining gene complexes on the basis of protein-protein interactions. To assess the statistical significance of the observed test statistic, we derived an empirical P-value by simulating data under the null hypothesis. RESULTS: We found that, in patients who have experienced stroke, there is a significant genetic difference between hypertension drug treatment groups. In African Americans, SNP rs12143842 showed a significant association (P<0.001) with drug treatment. At the gene level, HNRNPA1P4 and NOS1AP in African Americans and PRICKLE1 and NINJ2 in non-Hispanic whites were significantly associated (P<0.01) with drug treatment, whereas none of the gene complexes tested showed significance. CONCLUSION: On the basis of the genetic differences between drug treatment groups, we conclude that there may be an interaction between certain genotypes and antihypertensive treatment in stroke patients. This needs to be replicated in other studies.

Beyond missing heritability: prediction of complex traits.

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the "missing heritability" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼ 0.80), substantial room for improvement remains.

Propagation of obesity across generations: the roles of differential realized fertility and assortative mating by body mass index.

BACKGROUND/AIMS: To quantify the extent to which the increase in obesity observed across recent generations of the American population is associated with the individual or combined effects of assortative mating (AM) for body mass index (BMI) and differential realized fertility by BMI. METHODS: A Monte Carlo framework is formed and informed using data collected from the National Longitudinal Survey of Youth (NLSY). The model has 2 portions: one that generates childbirth events on an annual basis and another that produces a BMI for each child. Once the model is informed using the data, a reference distribution of offspring BMIs is simulated. We quantify the effects of our factors of interest by removing them from the model and comparing the resulting offspring BMI distributions with that of the baseline scenario. RESULTS: An association between maternal BMI and number of offspring is evidenced in the NLSY data as well as the presence of AM. These 2 factors combined are associated with an increased mean BMI (+0.067, 95% CI: 0.056; 0.078), an increased BMI variance (+0.578, 95% CI: 0.418; 0.736) and an increased prevalence of obesity (RR 1.032, 95% CI: 1.023; 1.041) and BMIs >40 (RR 1.083, 95% CI: 1.053; 1.118) among offspring. CONCLUSION: Our investigation suggests that both differential realized fertility and AM by BMI appear to play a role in the increasing prevalence of obesity in America.

Subgroups of pelvic pain are differentially associated with endometriosis and inflammatory comorbidities: a latent class analysis.

ABSTRACT: Chronic pelvic pain is heterogeneous with potentially clinically informative subgroups. We aimed to identify subgroups of pelvic pain based on symptom patterns and investigate their associations with inflammatory and chronic pain-related comorbidities. Latent class analysis (LCA) identified subgroups of participants (n = 1255) from the Adolescence to Adulthood (A2A) cohort. Six participant characteristics were included in the LCA: severity, frequency, and impact on daily activities of both menstruation-associated (cyclic) and non-menstruation-associated (acyclic) pelvic pain. Three-step LCA quantified associations between LC subgroups, demographic and clinical variables, and 18 comorbidities (10 with prevalence ≥10%). Five subgroups were identified: none or minimal (23%), moderate cyclic only (28%), severe cyclic only (20%), moderate or severe acyclic plus moderate cyclic (9%), and severe acyclic plus severe cyclic (21%). Endometriosis prevalence within these 5 LCA-pelvic pain-defined subgroups ranged in size from 4% in "none or minimal pelvic pain" to 24%, 72%, 70%, and 94%, respectively, in the 4 pain subgroups, with statistically significant odds of membership only for the latter 3 subgroups. Migraines were associated with significant odds of membership in all 4 pelvic pain subgroups relative to those with no pelvic pain (adjusted odds ratios = 2.92-7.78), whereas back, joint, or leg pain each had significantly greater odds of membership in the latter 3 subgroups. Asthma or allergies had three times the odds of membership in the most severe pain group. Subgroups with elevated levels of cyclic or acyclic pain are associated with greater frequency of chronic overlapping pain conditions, suggesting an important role for central inflammatory and immunological mechanisms.

Breastfeeding history and adenomyosis risk using a novel case-control study design.

OBJECTIVE: To evaluate the association between breastfeeding history, including lifetime exclusive breastfeeding, and risk of adenomyosis. DESIGN: We used data from a case-control study designed with 2 control groups to address the challenge of selecting noncases for a valid epidemiologic study when cases are identified by hysterectomy. The case-control study was conducted among premenopausal and postmenopausal enrollees aged 18-59 years in a large, integrated health care system in western Washington state. PATIENT(S): Cases were enrollees with incident, pathology-confirmed adenomyosis diagnosed during 2001-2006 (n = 386). The 2 control groups were as follows: (1) randomly selected age-matched enrollees with intact uteri ("population controls," n = 323) and (2) hysterectomy controls (n = 233). INTERVENTION(S): Data on breastfeeding history were collected by in-person interviews. For each reported live birth, participants were asked whether they breastfed, along with infant age at supplemental feeding introduction and breastfeeding discontinuation. MAIN OUTCOME MEASURE(S): Among participants with at least 1 live birth (330 cases, 246 population controls, and 198 hysterectomy controls), we used unconditional logistic regression to estimate adjusted odds ratios and 95% confidence intervals (CIs) for the associations between the following: (1) ever breastfeeding, (2) ever breastfeeding for ≥8 weeks, (3) lifetime breastfeeding, and (4) lifetime exclusive breastfeeding and risk of adenomyosis. Analyses were adjusted for age, reference year, smoking, education, and parity. RESULT(S): In analyses comparing cases with population controls, we observed a 40% decreased odds of adenomyosis with a history of ever breastfeeding (adjusted odds ratio, 0.6; 95% CI, 0.3-1.0) and breastfeeding for ≥8 weeks (adjusted odds ratio, 0.6; 95% CI, 0.4-0.8). The strongest associations, 60%-70% decreased odds of adenomyosis, were observed with ≥12 months of lifetime breastfeeding (vs. <3 months) (adjusted odds ratio, 0.4; 95% CI, 0.2-0.6) and 9 to <12 months of lifetime exclusive breastfeeding (vs. <3 months) (adjusted odds ratio, 0.3; 95% CI, 0.2-0.6), comparing cases to population controls. In analyses using hysterectomy controls, we observed similar patterns of associations slightly attenuated in magnitude. CONCLUSION(S): Breastfeeding history was associated with a 40% decreased odds of adenomyosis, a condition that can confer substantial morbidity and requires hysterectomy for definitive treatment. The consistency of our findings with that of a previous study lends support that breastfeeding may modify risk of adenomyosis.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research.

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

Association of Gout Polygenic Risk Score With Age at Disease Onset and Tophaceous Disease in European and Polynesian Men With Gout.

OBJECTIVE: To determine whether a gout polygenic risk score (PRS) is associated with age at gout onset and tophaceous disease in European, East Polynesian, and West Polynesian men and women with gout. METHODS: A 19-variant gout PRS was produced in 7 European gout cohorts (N = 4,016), 2 East Polynesian gout cohorts (N = 682), and 1 West Polynesian gout cohort (N = 490). Sex-stratified regression models were used to estimate the relationship between the PRS and age at gout onset and tophaceous disease. RESULTS: The PRS was associated with earlier age at gout onset in men (ß = -3.61 in years per unit PRS [95% confidence interval (95% CI) -4.32, -2.90] in European men; ß = -6.35 [95% CI -8.91, -3.80] in East Polynesian men; ß = -3.51 [95% CI -5.46, -1.57] in West Polynesian men) but not in women (ß = 0.07 [95% CI -2.32, 2.45] in European women; ß = 0.20 [95% CI -7.21, 7.62] in East Polynesian women; ß -3.33 [95% CI -9.28, 2.62] in West Polynesian women). The PRS showed a positive association with tophaceous disease in men (odds ratio [OR] for the association 1.15 [95% CI 1.00, 1.31] in European men; OR 2.60 [95% CI 1.66, 4.06] in East Polynesian men; OR 1.53 [95% CI 1.07, 2.19] in West Polynesian men) but not in women (OR for the association 0.68 [95% CI 0.42, 1.10] in European women; OR 1.45 [95% CI 0.39, 5.36] in East Polynesian women). The PRS association with age at gout onset was robust to the removal of ABCG2 variants from the PRS in European and East Polynesian men (ß = -2.42 [95% CI -3.37, -1.46] and ß = -6.80 [95% CI -10.06, -3.55], respectively) but not in West Polynesian men (ß = -1.79 [95% CI -4.74, 1.16]). CONCLUSION: Genetic risk variants for gout also harbor risk for earlier age at gout onset and tophaceous disease in European and Polynesian men. Our findings suggest that earlier gout onset involves the accumulation of gout risk alleles in men but perhaps not in women, and that this genetic risk is shared across multiple ancestral groups.

Local genetic covariance between serum urate and kidney function estimated with Bayesian multitrait models.

Hyperuricemia (serum urate >6.8 mg/dl) is associated with several cardiometabolic and renal diseases, such as gout and chronic kidney disease. Previous studies have examined the shared genetic basis of chronic kidney disease and hyperuricemia in humans either using single-variant tests or estimating whole-genome genetic correlations between the traits. Individual variants typically explain a small fraction of the genetic correlation between traits, thus the ability to map pleiotropic loci is lacking power for available sample sizes. Alternatively, whole-genome estimates of genetic correlation indicate a moderate correlation between these traits. While useful to explain the comorbidity of these traits, whole-genome genetic correlation estimates do not shed light on what regions may be implicated in the shared genetic basis of traits. Therefore, to fill the gap between these two approaches, we used local Bayesian multitrait models to estimate the genetic covariance between a marker for chronic kidney disease (estimated glomerular filtration rate) and serum urate in specific genomic regions. We identified 134 overlapping linkage disequilibrium windows with statistically significant covariance estimates, 49 of which had positive directionalities, and 85 negative directionalities, the latter being consistent with that of the overall genetic covariance. The 134 significant windows condensed to 64 genetically distinct shared loci which validate 17 previously identified shared loci with consistent directionality and revealed 22 novel pleiotropic genes. Finally, to examine potential biological mechanisms for these shared loci, we have identified a subset of the genomic windows that are associated with gene expression using colocalization analyses. The regions identified by our local Bayesian multitrait model approach may help explain the association between chronic kidney disease and hyperuricemia.

Mapping pleiotropic loci using a fast-sequential testing algorithm.

Pleiotropy (i.e., genes with effects on multiple traits) leads to genetic correlations between traits and contributes to the development of many syndromes. Identifying variants with pleiotropic effects on multiple health-related traits can improve the biological understanding of gene action and disease etiology, and can help to advance disease-risk prediction. Sequential testing is a powerful approach for mapping genes with pleiotropic effects. However, the existing methods and the available software do not scale to analyses involving millions of SNPs and large datasets. This has limited the adoption of sequential testing for pleiotropy mapping at large scale. In this study, we present a sequential test and software that can be used to test pleiotropy in large systems of traits with biobank-sized data. Using simulations, we show that the methods implemented in the software are powerful and have adequate type-I error rate control. To demonstrate the use of the methods and software, we present a whole-genome scan in search of loci with pleiotropic effects on seven traits related to metabolic syndrome (MetS) using UK-Biobank data (n~300 K distantly related white European participants). We found abundant pleiotropy and report 170, 44, and 18 genomic regions harboring SNPs with pleiotropic effects in at least two, three, and four of the seven traits, respectively. We validate our results using previous studies documented in the GWAS-catalog and using data from GTEx. Our results confirm previously reported loci and lead to several novel discoveries that link MetS-related traits through plausible biological pathways.

Genetic correlations between traits associated with hyperuricemia, gout, and comorbidities.

Hypertension, obesity, chronic kidney disease and type 2 diabetes are comorbidities that have very high prevalence among persons with hyperuricemia (serum urate > 6.8 mg/dL) and gout. Here we use multivariate genetic models to test the hypothesis that the co-association of traits representing hyperuricemia and its comorbidities is genetically based. Using Bayesian whole-genome regression models, we estimated the genetic marker-based variance and the covariance between serum urate, serum creatinine, systolic blood pressure (SBP), blood glucose and body mass index (BMI) from two independent family-based studies: The Framingham Heart Study-FHS and the Hypertension Genetic Epidemiology Network study-HyperGEN. The main genetic findings that replicated in both FHS and HyperGEN, were (1) creatinine was genetically correlated only with urate and (2) BMI was genetically correlated with urate, SBP, and glucose. The environmental covariance among the traits was generally highest for trait pairs involving BMI. The genetic overlap of traits representing the comorbidities of hyperuricemia and gout appears to cluster in two separate axes of genetic covariance. Because creatinine is genetically correlated with urate but not with metabolic traits, this suggests there is one genetic module of shared loci associated with hyperuricemia and chronic kidney disease. Another module of shared loci may account for the association of hyperuricemia and metabolic syndrome. This study provides a clear quantitative genetic basis for the clustering of comorbidities with hyperuricemia.