STRUCTURED ABSTRACT Objectives We evaluated whether carotid intima-media thickness (C-IMT) and the presence or absence of plaque improved coronary heart disease (CHD) risk prediction when added to traditional risk factors (TRF). Background Traditional CHD risk prediction schemes need further improvement as the majority of the CHD events occur in the “low” and “intermediate” risk groups. C-IMT and presence of plaque on an ultrasound are associated with CHD and therefore could potentially help improve CHD risk prediction. Methods Risk prediction models (overall, in men and women) considered included TRF-only, TRF+C-IMT, TRF+plaque, and TRF+C-IMT+ plaque. Model predictivity was determined by calculating the area under the receiver operating characteristic curve (AUC) adjusted for optimism. Cox-proportional hazards models were used to estimate 10-year CHD risk for each model, and the number of individuals reclassified determined. Observed events were compared with expected events; and, the net reclassification index (NRI) was calculated. Results Of 13,145 eligible individuals (5,682 men; 7,463 women), ~23% were reclassified by adding C-IMT+plaque information. Overall, the addition of C-IMT and plaque separately or together to the TRF model improved the AUC which increased from 0.742 to 0.750, 0.751 and 0.755 for the TRF-only, TRF+C-IMT, TRF+plaque and TRF+C-IMT+plaque model respectively. The C-IMT+TRF+plaque model had a NRI of 9.9% when compared to TRF-only in the overall population. However, comparison of TRF+C-IMT+plaque with TRF+C-IMT or TRF+plaque only resulted in non-significant or modestly significant changes of the various statistical tests. Sex-specific analyses are presented in the manuscript. Conclusion Adding plaque and C-IMT to TRF improves CHD risk prediction in the ARIC study.

Approaches exploiting extremes of the trait distribution may reveal novel loci for common traits, but it is unknown whether such loci are generalizable to the general population. In a genome-wide search for loci associated with upper vs. lower 5th percentiles of body mass index, height and waist-hip ratio, as well as clinical classes of obesity including up to 263,407 European individuals, we identified four new loci (IGFBP4, H6PD, RSRC1, PPP2R2A) influencing height detected in the tails and seven new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3, ZZZ3) for clinical classes of obesity. Further, we show that there is large overlap in terms of genetic structure and distribution of variants between traits based on extremes and the general population and little etiologic heterogeneity between obesity subgroups.

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (e.g. transcriptomics for RNA transcripts). However, a single layer of ‘omics’ can only provide limited insights into the biological mechanisms of a disease. In the case of GWAS, although thousands of SNPs have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life-course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

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Motivation Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR). Results We introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g. clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based P-values that can control for sample correlation. The LDM can also be applied to transformed data, and an ‘omnibus’ test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations. Availability and implementation The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows. Supplementary information Supplementary data are available at Bioinformatics online.