Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Genetic diversity is a predictor of mortality in humans.

BACKGROUND: It has been well-established, both by population genetics theory and direct observation in many organisms, that increased genetic diversity provides a survival advantage. However, given the limitations of both sample size and genome-wide metrics, this hypothesis has not been comprehensively tested in human populations. Moreover, the presence of numerous segregating small effect alleles that influence traits that directly impact health directly raises the question as to whether global measures of genomic variation are themselves associated with human health and disease. RESULTS: We performed a meta-analysis of 17 cohorts followed prospectively, with a combined sample size of 46,716 individuals, including a total of 15,234 deaths. We find a significant association between increased heterozygosity and survival (P = 0.03). We estimate that within a single population, every standard deviation of heterozygosity an individual has over the mean decreases that person's risk of death by 1.57%. CONCLUSIONS: This effect was consistent between European and African ancestry cohorts, men and women, and major causes of death (cancer and cardiovascular disease), demonstrating the broad positive impact of genomic diversity on human survival.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.

Metabolic syndrome is associated with change in subclinical arterial stiffness: a community-based Taichung community health study.

BACKGROUND: The aim of this study was to evaluate the effect of MetS on arterial stiffness in a longitudinal study. METHODS: Brachial-ankle pulse wave velocity (baPWV), a measurement interpreted as arterial stiffness, was measured in 1518 community-dwelling persons at baseline and re-examined within a mean follow-up period of 3 years. Multivariate linear regression with generalized estimating equations (GEE) were used to examine the longitudinal relationship between MetS and its individual components and baPWV, while multivariate logistic regression with GEE was used to examine the longitudinal relationship between MetS and its individual components and the high risk group with arterial stiffness. RESULTS: Subjects with MetS showed significantly greater baPWV at the end point than those without MetS, after adjusting for age, gender, education, hypertension medication and mean arterial pressure (MAP). MetS was associated with the top quartile of baPWV (the high-risk group of arterial stiffness, adjusted odds ratio [95% confidence interval] 1.52 [1.21-1.90]), and a significant linear trend of risk for the number of components of MetS was found (p for trend < 0.05). In further considering the individual MetS component, elevated blood pressure and fasting glucose significantly predicted a high risk of arterial stiffness (adjusted OR [95% CI] 3.72 [2.81-4.93] and 1.35 [1.08-1.68], respectively). CONCLUSIONS: MetS affects the subject's progression to arterial stiffness. Arterial stiffness increased as the number of MetS components increased. Management of MetS is important for preventing the progression to advanced arterial stiffness.

Comparison of smoking-related DNA methylation between newborns from prenatal exposure and adults from personal smoking.

Aim: Cigarette smoking influences DNA methylation genome wide, in newborns from pregnancy exposure and in adults from personal smoking. Whether a unique methylation signature exists for in utero exposure in newborns is unknown. Materials & methods: We separately meta-analyzed newborn blood DNA methylation (assessed using Illumina450k Beadchip), in relation to sustained maternal smoking during pregnancy (9 cohorts, 5648 newborns, 897 exposed) and adult blood methylation and personal smoking (16 cohorts, 15907 participants, 2433 current smokers). Results & conclusion: Comparing meta-analyses, we identified numerous signatures specific to newborns along with many shared between newborns and adults. Unique smoking-associated genes in newborns were enriched in xenobiotic metabolism pathways. Our findings may provide insights into specific health impacts of prenatal exposure on offspring.

Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks.

With advances in high-throughput single-nucleotide polymorphism (SNP) genotyping, the amount of genotype data available for genetic studies is steadily increasing, and with it comes new abilities to study multigene interactions as well as to develop higher dimensional genetic models that more closely represent the polygenic nature of common disease risk. The combined impact of even small amounts of missing data on a multi-SNP analysis may be considerable. In this study, we present a neural network method for imputing missing SNP genotype data. We compared its imputation accuracy with fastPHASE and an expectation-maximization algorithm implemented in HelixTree. In a simulation data set of 1000 SNPs and 1000 subjects, 1, 5 and 10% of genotypes were randomly masked. Four levels of linkage disequilibrium (LD), LD R2<0.2, R2<0.5, R2<0.8 and no LD threshold, were examined to evaluate the impact of LD on imputation accuracy. All three methods are capable of imputing most missing genotypes accurately (accuracy >86%). The neural network method accurately predicted 92.0-95.9% of the missing genotypes. In a real data set comparison with 419 subjects and 126 SNPs from chromosome 2, the neural network method achieves the highest imputation accuracies >83.1% with missing rate from 1 to 5%. Using 90 HapMap subjects with 1962 SNPs, fastPHASE had the highest accuracy ( approximately 97%) while the other two methods had >95% accuracy. These results indicate that the neural network model is an accurate and convenient tool, requiring minimal parameter tuning for SNP data recovery, and provides a valuable alternative to usual complete-case analysis.

Associations between self-referral and health behavior responses to genetic risk information.

BACKGROUND: Studies examining whether genetic risk information about common, complex diseases can motivate individuals to improve health behaviors and advance planning have shown mixed results. Examining the influence of different study recruitment strategies may help reconcile inconsistencies. METHODS: Secondary analyses were conducted on data from the REVEAL study, a series of randomized clinical trials examining the impact of genetic susceptibility testing for Alzheimer's disease (AD). We tested whether self-referred participants (SRPs) were more likely than actively recruited participants (ARPs) to report health behavior and advance planning changes after AD risk and APOE genotype disclosure. RESULTS: Of 795 participants with known recruitment status, 546 (69%) were self-referred and 249 (31%) had been actively recruited. SRPs were younger, less likely to identify as African American, had higher household incomes, and were more attentive to AD than ARPs (all P < 0.01). They also dropped out of the study before genetic risk disclosure less frequently (26% versus 41%, P < 0.001). Cohorts did not differ in their likelihood of reporting a change to at least one health behavior 6 weeks and 12 months after genetic risk disclosure, nor in intentions to change at least one behavior in the future. However, interaction effects were observed where ε4-positive SRPs were more likely than ε4-negative SRPs to report changes specifically to mental activities (38% vs 19%, p < 0.001) and diets (21% vs 12%, p = 0.016) six weeks post-disclosure, whereas differences between ε4-positive and ε4-negative ARPs were not evident for mental activities (15% vs 21%, p = 0.413) or diets (8% versus 16%, P = 0.190). Similarly, ε4-positive participants were more likely than ε4-negative participants to report intentions to change long-term care insurance among SRPs (20% vs 5%, p < 0.001), but not ARPs (5% versus 9%, P = 0.365). CONCLUSIONS: Individuals who proactively seek AD genetic risk assessment are more likely to undergo testing and use results to inform behavior changes than those who respond to genetic testing offers. These results demonstrate how the behavioral impact of genetic risk information may vary according to the models by which services are provided, and suggest that how participants are recruited into translational genomics research can influence findings. TRIAL REGISTRATION: ClinicalTrials.gov NCT00089882 and NCT00462917.

Epigenomic Indicators of Age in African Americans.

Age is a well-established risk factor for chronic diseases. However, the cellular and molecular changes associated with aging processes that are related to chronic disease initiation and progression are not well-understood. Thus, there is an increased need to identify new markers of cellular and molecular changes that occur during aging processes. In this study, we use genome-wide DNA methylation from 26,428 CpG sites in 13,877 genes to investigate the relationship between age and epigenetic variation in the peripheral blood cells of 972 African American adults from the Genetic Epidemiology Network of Arteriopathy (GENOA) study (mean age=66.3 years, range=39-95). Age was significantly associated with 7,601 (28.8%) CpG sites after Bonferroni correction for α=0.05 (p<1.89×10-6). Due to the extraordinarily strong associations between age and many of the CpG sites (>7,000 sites with p-values ranging from 10-6 to 10-43), we investigated how well the DNA methylation markers predict age. We found that 2,095 (7.9%) CpG sites were significant predictors of age after Bonferroni correction. The top five principal components of the 2,095 age-associated CpG sites accounted for 69.3% of the variability in these CpG sites, and they explained 26.8% of the variation in age. The associations between methylation markers and adult age are so ubiquitous and strong that we hypothesize that DNA methylation patterns may be an important measure of cellular aging processes. Given the highly correlated nature of the age-associated epigenome (as evidenced by the principal components analysis), whole pathways may be regulated as a consequence of aging.

Identification of genes associated with complex traits by testing the genetic dissimilarity between individuals.

Using the exome sequencing data from 697 unrelated individuals and their simulated disease phenotypes from Genetic Analysis Workshop 17, we develop and apply a gene-based method to identify the relationship between a gene with multiple rare genetic variants and a phenotype. The method is based on the Mantel test, which assesses the correlation between two distance matrices using a permutation procedure. Using up to 100,000 permutations to estimate the statistical significance in 200 replicate data sets, we found that the method had 5.1% type I error at an α level of 0.05 and had various power to detect genes with simulated genetic associations. FLT1 and KDR had the most significant correlations with Q1 and were replicated 170 and 24 times, respectively, in 200 simulated data sets using a Bonferroni corrected p-value of 0.05 as a threshold. These results suggest that the distance correlation method can be used to identify genotype-phenotype association when multiple rare genetic variants in a gene are involved.

Identification of correlated genetic variants jointly associated with rheumatoid arthritis using ridge regression.

Using the North American Rheumatoid Arthritis Consortium genome-wide association dataset, we applied ridged, multiple least-squares regression to identify genetic variants with apparent unique contributions to variation of anti-cyclic citrullinated peptide (anti-CCP), a newly identified clinical risk factor for development of rheumatoid arthritis. Within a 2.7-Mbp region on chromosome 6 around the well studied HLA-DRB1 locus, ridge regression identified a single-nucleotide polymorphism that was associated with anti-CCP variation when including the additive effects of other single-nucleotide polymorphisms in a multivariable analysis, but that showed only a weak direct association with anti-CCP. This suggests that multivariable methods can be used to identify potentially relevant genetic variants in regions of interest that would be difficult to detect based on direct associations.

Investigating the complex genetic architecture of ankle-brachial index, a measure of peripheral arterial disease, in non-Hispanic whites.

BACKGROUND: Atherosclerotic peripheral arterial disease (PAD) affects 8-10 million people in the United States and is associated with a marked impairment in quality of life and an increased risk of cardiovascular events. Noninvasive assessment of PAD is performed by measuring the ankle-brachial index (ABI). Complex traits, such as ABI, are influenced by a large array of genetic and environmental factors and their interactions. We attempted to characterize the genetic architecture of ABI by examining the main and interactive effects of individual single nucleotide polymorphisms (SNPs) and conventional risk factors. METHODS: We applied linear regression analysis to investigate the association of 435 SNPs in 112 positional and biological candidate genes with ABI and related physiological and biochemical traits in 1046 non-Hispanic white, hypertensive participants from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. The main effects of each SNP, as well as SNP-covariate and SNP-SNP interactions, were assessed to investigate how they contribute to the inter-individual variation in ABI. Multivariable linear regression models were then used to assess the joint contributions of the top SNP associations and interactions to ABI after adjustment for covariates. We reduced the chance of false positives by 1) correcting for multiple testing using the false discovery rate, 2) internal replication, and 3) four-fold cross-validation. RESULTS: When the results from these three procedures were combined, only two SNP main effects in NOS3, three SNP-covariate interactions (ADRB2 Gly 16 - lipoprotein(a) and SLC4A5 - diabetes interactions), and 25 SNP-SNP interactions (involving SNPs from 29 different genes) were significant, replicated, and cross-validated. Combining the top SNPs, risk factors, and their interactions into a model explained nearly 18% of variation in ABI in the sample. SNPs in six genes (ADD2, ATP6V1B1, PRKAR2B, SLC17A2, SLC22A3, and TGFB3) were also influencing triglycerides, C-reactive protein, homocysteine, and lipoprotein(a) levels. CONCLUSION: We found that candidate gene SNP main effects, SNP-covariate and SNP-SNP interactions contribute to the inter-individual variation in ABI, a marker of PAD. Our findings underscore the importance of conducting systematic investigations that consider context-dependent frameworks for developing a deeper understanding of the multidimensional genetic and environmental factors that contribute to complex diseases.

Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests.

Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.