Estimation and predictors of the Omega-3 Index in the UK Biobank

Information on the Omega-3 Index (O3I) in the United Kingdom (UK) are scarce. The UK-Biobank (UKBB) contains data on total plasma omega-3 polyunsaturated fatty acids (n3-PUFA%) and DHA% measured by NMR. The aim of our study was to create an equation to estimate the O3I (eO3I) from these data. We first performed an interlaboratory experiment with 250 random blood samples in which the O3I was measured in erythrocytes by gas chromatography, and total n3% and DHA% were measured in plasma by NMR. The best predictor of eO3I included both DHA% and a derived metric, the total n3%-DHA%. Together these explained 65% of the variability (r=0.832, p<0.0001). We then estimated the O3I in 117, 108 UKBB subjects and correlated it with demographic and lifestyle variables in multivariable adjusted models. The mean (SD) eO3I was 5.58% (2.35%) this UKBB cohort. Several predictors were significantly correlated with eO3I (all p<0.0001). In general order of impact and with directionality (- = inverse, + = direct): oily-fish consumption (+), fish oil supplement use (+), female sex (+), older age (+), alcohol use (+), smoking (-), higher waist circumference and BMI (-), lower socioeconomic status and less education (-). Only 20.5% of eO3I variability could be explained by predictors investigated, and oily-fish consumption accounted for 7.0% of that. With the availability of the eO3I in the UKBB cohort we will be in a position to link risk for a variety of diseases with this commonly-used and well-documented marker of n3-PUFA biostatus

Analyzing Metabolomics Data for Association with Genotypes Using Two-Component Gaussian Mixture Distributions

Standard approaches to evaluate the impact of single nucleotide polymorphisms (SNP) on quantitative phenotypes use linear models. However, these normal-based approaches may not optimally model phenotypes which are better represented by Gaussian mixture distributions (e.g., some metabolomics data). We develop a likelihood ratio test on the mixing proportions of two-component Gaussian mixture distributions and consider more restrictive models to increase power in light of a priori biological knowledge. Data were simulated to validate the improved power of the likelihood ratio test and the restricted likelihood ratio test over a linear model and a log transformed linear model. Then, using real data from the Framingham Heart Study, we analyzed 20,315 SNPs on chromosome 11, demonstrating that the proposed likelihood ratio test identifies SNPs well known to participate in the desaturation of certain fatty acids. Our study both validates the approach of increasing power by using the likelihood ratio test that leverages Gaussian mixture models, and creates a model with improved sensitivity and interpretability

Analyzing Metabolomics Data for Association with Genotypes Using Two-Component Gaussian Mixture Distributions

Standard approaches to evaluate the impact of single nucleotide polymorphisms (SNP) on quantitative phenotypes use linear models. However, these normal-based approaches may not optimally model phenotypes which are better represented by Gaussian mixture distributions (e.g., some metabolomics data). We develop a likelihood ratio test on the mixing proportions of two-component Gaussian mixture distributions and consider more restrictive models to increase power in light of a priori biological knowledge. Data were simulated to validate the improved power of the likelihood ratio test and the restricted likelihood ratio test over a linear model and a log transformed linear model. Then, using real data from the Framingham Heart Study, we analyzed 20,315 SNPs on chromosome 11, demonstrating that the proposed likelihood ratio test identifies SNPs well known to participate in the desaturation of certain fatty acids. Our study both validates the approach of increasing power by using the likelihood ratio test that leverages Gaussian mixture models, and creates a model with improved sensitivity and interpretability

UMP, SCUN seal bilateral collaborations

Universiti Malaysia Pahang (UMP) fortified its international networking when it sealed a Memorandum of Understanding (MoU) with China’s South-Central University for Nationalities (SCUN) in Beijing, China, on June 1

Genome-Wide Interaction Study of Omega-3 PUFAs and Other Fatty Acids on Inflammatory Biomarkers of Cardiovascular Health in the Framingham Heart Study

Numerous genetic loci have been identified as being associated with circulating fatty acid (FA) levels and/or inflammatory biomarkers of cardiovascular health (e.g., C-reactive protein). Recently, using red blood cell (RBC) FA data from the Framingham Offspring Study, we conducted a genome-wide association study of over 2.5 million single nucleotide polymorphisms (SNPs) and 22 RBC FAs (and associated ratios), including the four Omega-3 FAs (ALA, DHA, DPA, and EPA). Our analyses identified numerous causal loci. In this manuscript, we investigate the extent to which polyunsaturated fatty acid (PUFA) levels moderate the relationship of genetics to cardiovascular health biomarkers using a genome-wide interaction study approach. In particular, we test for possible gene–FA interactions on 9 inflammatory biomarkers, with 2.5 million SNPs and 12 FAs, including all Omega-3 PUFAs. We identified eighteen novel loci, including loci which demonstrate strong evidence of modifying the impact of heritable genetics on biomarker levels, and subsequently cardiovascular health. The identified genes provide increased clarity on the biological functioning and role of Omega-3 PUFAs, as well as other common fatty acids, in cardiovascular health, and suggest numerous candidate loci for future replication and biological characterization

Leveraging Summary Statistics to Make Inferences about Complex Phenotypes in Large Biobanks

As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed. In this paper we explore the possibility of using summary statistics from simple linear models of phenotype on genotype in order to make inferences about more complex phenotypes (those that are derived from two or more simple phenotypes). We provide exact formulas for the slope, intercept, and standard error of the slope for linear regressions when combining phenotypes. Derived equations are validated via simulation and tested on a real data set exploring the genetics of fatty acids

A Genome-Wide Association Study of Red-Blood Cell Fatty Acids and Ratios Incorporating Dietary Covariates: Framingham Heart Study Offspring Cohort

Recent analyses have suggested a strong heritable component to circulating fatty acid (FA) levels; however, only a limited number of genes have been identified which associate with FA levels. In order to expand upon a previous genome wide association study done on participants in the Framingham Heart Study Offspring Cohort and FA levels, we used data from 2,400 of these individuals for whom red blood cell FA profiles, dietary information and genotypes are available, and then conducted a genome-wide evaluation of potential genetic variants associated with 22 FAs and 15 FA ratios, after adjusting for relevant dietary covariates. Our analysis found nine previously identified loci associated with FA levels (FADS, ELOVL2, PCOLCE2, LPCAT3, AGPAT4, NTAN1/PDXDC1, PKD2L1, HBS1L/MYB and RAB3GAP1/MCM6), while identifying four novel loci. The latter include an association between variants in CALN1 (Chromosome 7) and eicosapentaenoic acid (EPA), DHRS4L2(Chromosome 14) and a FA ratio measuring delta-9-desaturase activity, as well as two loci associated with less well understood proteins. Thus, the inclusion of dietary covariates had a modest impact, helping to uncover four additional loci. While genome-wide association studies continue to uncover additional genes associated with circulating FA levels, much of the heritable risk is yet to be explained, suggesting the potential role of rare genetic variation, epistasis and gene-environment interactions on FA levels as well. Further studies are needed to continue to understand the complex genetic picture of FA metabolism and synthesis

Natural hazards in Australia : floods

Floods are caused by a number of interacting factors, making it remarkably difficult to explain changes in flood hazard. This paper reviews the current understanding of historical trends and variability in flood hazard across Australia. Links between flood and rainfall trends cannot be made due to the influence of climate processes over a number of spatial and temporal scales as well as landscape changes that affect the catchment response. There are also still considerable uncertainties in future rainfall projections, particularly for sub-daily extreme rainfall events. This is in addition to the inherent uncertainty in hydrological modelling such as antecedent conditions and feedback mechanisms. Research questions are posed based on the current state of knowledge. These include a need for high-resolution climate modelling studies and efforts in compiling and analysing databases of sub-daily rainfall and flood records. Finally there is a need to develop modelling frameworks that can deal with the interaction between climate processes at different spatio-temporal scales, so that historical flood trends can be better explained and future flood behaviour understood

Genome-wide Association Study of Saturated, Mono- and Polyunsaturated Red Blood Cell Fatty Acids in the Framingham Heart Offspring Study

Most genome-wide association studies have explored relationships between genetic variants and plasma phospholipid fatty acid proportions, but few have examined apparent genetic influences on the membrane fatty acid profile of red blood cells (RBC). Using RBC fatty acid data from the Framingham Offspring Study, we analyzed over 2.5 million single nucleotide polymorphisms (SNPs) for association with 14 RBC fatty acids identifying 191 different SNPs associated with at least 1 fatty acid. Significant associations (p\u3c1×10−8) were located within five distinct 1 MB regions. Of particular interest were novel associations between (1) arachidonic acid and PCOLCE2 (regulates apoA-I maturation and modulates apoA-I levels), and (2) oleic and linoleic acid and LPCAT3 (mediates the transfer of fatty acids between glycerolipids). We also replicated previously identified strong associations between SNPs in the FADS (chromosome 11) and ELOVL (chromosome 6) regions. Multiple SNPs explained 8–14% of the variation in 3 high abundance (\u3e11%) fatty acids, but only 1–3% in 4 low abundance (\u3c3%) fatty acids, with the notable exception of dihomo-gamma linolenic acid with 53% of variance explained by SNPs. Further studies are needed to determine the extent to which variations in these genes influence tissue fatty acid content and pathways modulated by fatty acids

Stability of corn (\u3ci\u3eZea mays\u3c/i\u3e)- foxtail (\u3ci\u3eSetaria\u3c/i\u3e spp.) interference relationships

Variation in interference relationships have been shown for a number of crop-weed associations and may have an important effect on the implementation of decision support systems for weed management. Multiyear field experiments were conducted at eight locations to determine the stability of corn-foxtail interference relationships across years and locations. Two coefficients (I and A) of a rectangular hyperbola equation were estimated for each data set using nonlinear regression procedures. The I and A coefficients represent percent corn yield loss as foxtail density approaches zero and maximum percent corn yield loss, respectively. The coefficient I was stable across years at two locations and varied across years at four locations. Maximum yield loss (A) varied between years at one location. Both coefficients varied among locations. Although 3 to 4 foxtail plants m-1 row was a conservative estimate of the single-year economic threshold (Te) of foxtail density, variation in I and A resulted in a large variation in Te. Therefore, the utility of using common coefficient estimates to predict future crop yield loss from foxtail interference between years or among locations within a region is limited