Causal inference at the population level

Three elements are needed to formalize a causal quantity at the population level: response, treatment, and the causal element, which are introduced here by notation. Inclusion of two essential causal assumptions, the monitoring and illumination assumptions, in a function distinguishes causal from association analyses. The discussion provides insight into causal inference

Risk factor correlates of platelet and leukocyte markers assessed by flow cytometry in a population-based sample

Background—Platelet and leukocyte products are involved in atherothrombosis. However, the determinants of of platelet and leukocyte markers assessed by flow cytometry have not been documented in a population-based sample. Methods and results—We performed flow cytometry on blood from participants (n=1,894) in the Atherosclerosis Risk in Communities (ARIC) Carotid MRI Study. Cellular aggregates and multiple platelet and leukocyte markers, such as myeloperoxidase in granulocytes and toll-like receptor-4, CD14, and CD45 in monocytes, were quantified. Their cross-sectional associations with demographic and risk factors were assessed using multiple linear regression. Mean values of most cellular markers and aggregates were considerably higher in blacks than whites (p<0.01). There were some differences in cellular markers between men and women, but little association with age. LDL-cholesterol was associated positively with several markers (toll-like receptor-4 and myeloperoxidase in granulocytes and CD162 in lymphocytes). Lipid lowering therapy tended to show opposite associations. Smokers had much higher granulocyte myeloperoxidase than nonsmokers. However, most other correlations between risk factors and cellular markers were nonsignificant. Conclusions—Race/ethnicity, sex, and to a lesser degree LDL-cholesterol and lipid-lowering therapy, but few other risk factors, were correlated with markers of cellular activation in this population-based study

Associations Between the Serum Metabolome and All-Cause Mortality Among African Americans in the Atherosclerosis Risk in Communities (ARIC) Study

Early and accurate identification of people at high risk of premature death may assist in the targeting of preventive therapies in order to improve overall health. To identify novel biomarkers for all-cause mortality, we performed untargeted metabolomics in the Atherosclerosis Risk in Communities (ARIC) Study. We included 1,887 eligible ARIC African Americans, and 671 deaths occurred during a median follow-up period of 22.5 years (1987–2011). Chromatography and mass spectroscopy identified and quantitated 204 serum metabolites, and Cox proportional hazards models were used to analyze the longitudinal associations with all-cause and cardiovascular mortality. Nine metabolites, including cotinine, mannose, glycocholate, pregnendiol disulfate, α-hydroxyisovalerate, N-acetylalanine, andro-steroid monosulfate 2, uridine, and γ-glutamyl-leucine, showed independent associations with all-cause mortality, with an average risk change of 18% per standard-deviation increase in metabolite level (P < 1.23 × 10−4). A metabolite risk score, created on the basis of the weighted levels of the identified metabolites, improved the predictive ability of all-cause mortality over traditional risk factors (bias-corrected Harrell's C statistic 0.752 vs. 0.730). Mannose and glycocholate were associated with cardiovascular mortality (P < 1.23 × 10−4), but predictive ability was not improved beyond the traditional risk factors. This metabolomic analysis revealed potential novel biomarkers for all-cause mortality beyond the traditional risk factors

Epistasis analysis for quantitative traits by functional regression model

The critical barrier in interaction analysis for rare variants is that most traditional statistical methods for testing interactions were originally designed for testing the interaction between common variants and are difficult to apply to rare variants because of their prohibitive computational time and poor ability. The great challenges for successful detection of interactions with next-generation sequencing (NGS) data are (1) lack of methods for interaction analysis with rare variants, (2) severe multiple testing, and (3) time-consuming computations. To meet these challenges, we shift the paradigm of interaction analysis between two loci to interaction analysis between two sets of loci or genomic regions and collectively test interactions between all possible pairs of SNPs within two genomic regions. In other words, we take a genome region as a basic unit of interaction analysis and use high-dimensional data reduction and functional data analysis techniques to develop a novel functional regression model to collectively test interactions between all possible pairs of single nucleotide polymorphisms (SNPs) within two genome regions. By intensive simulations, we demonstrate that the functional regression models for interaction analysis of the quantitative trait have the correct type 1 error rates and a much better ability to detect interactions than the current pairwise interaction analysis. The proposed method was applied to exome sequence data from the NHLBI's Exome Sequencing Project (ESP) and CHARGE-S study. We discovered 27 pairs of genes showing significant interactions after applying the Bonferroni correction (P-values < 4.58 × 10) in the ESP, and 11 were replicated in the CHARGE-S study

Risk of Type 2 Diabetes and Obesity Is Differentially Associated with Variation in FTO in Whites and African-Americans in the ARIC Study

Single nucleotide polymorphisms (SNPs) in the fat mass and obesity associated (FTO) gene are associated with body mass index (BMI) in populations of European descent. The FTO rs9939609 variant, first detected in a genome-wide association study of diabetes, conferred an increased disease risk that was abolished after adjustment for BMI, suggesting that the association may be due to variation in adiposity. The relationship between diabetes, four previously identified FTO polymorphisms that span a 19.6-kb genomic region, and obesity was therefore evaluated in the biracial population-based Atherosclerosis Risk in Communities Study with the goal of further refining the association by comparing results between the two ethnic groups. The prevalence of diabetes and obesity (BMI ≥30 kg/m2) was established at baseline, and diabetes was determined by either self-report, a fasting glucose level ≥126 mg/dL, or non-fasting glucose ≥200 mg/dL. There were 1,004 diabetes cases and 10,038 non-cases in whites, and 670 cases and 2,780 non-cases in African-Americans. Differences in mean BMI were assessed by a general linear model, and multivariable logistic regression was used to predict the risk of diabetes and obesity. For white participants, the FTO rs9939609 A allele was associated with an increased risk of diabetes (odds ratio (OR)  = 1.19, p<0.001) and obesity (OR = 1.22, p<0.001) under an additive genetic model that was similar for all of the SNPs analyzed. In African-Americans, only the rs1421085 C allele was a determinant of obesity risk (OR = 1.17, p = 0.05), but was found to be protective against diabetes (OR = 0.79, p = 0.03). Adjustment for BMI did not eliminate any of the observed associations with diabetes. Significant statistical interaction between race and the FTO variants suggests that the effect on diabetes susceptibility may be context dependent

A scan statistic for identifying chromosomal patterns of SNP association

We have developed a single nucleotide polymorphism (SNP) association scan statistic that takes into account the complex distribution of the human genome variation in the identification of chromosomal regions with significant SNP associations. This scan statistic has wide applicability for genetic analysis, whether to identify important chromosomal regions associated with common diseases based on whole-genome SNP association studies or to identify disease susceptibility genes based on dense SNP positional candidate studies. To illustrate this method, we analyzed patterns of SNP associations on chromosome 19 in a large cohort study. Among 2,944 SNPs, we found seven regions that contained clusters of significantly associated SNPs. The average width of these regions was 35 kb with a range of 10–72 kb. We compared the scan statistic results to Fisher's product method using a sliding window approach, and detected 22 regions with significant clusters of SNP associations. The average width of these regions was 131 kb with a range of 10.1–615 kb. Given that the distances between SNPs are not taken into consideration in the sliding window approach, it is likely that a large fraction of these regions represents false positives. However, all seven regions detected by the scan statistic were also detected by the sliding window approach. The linkage disequilibrium (LD) patterns within the seven regions were highly variable indicating that the clusters of SNP associations were not due to LD alone. The scan statistic developed here can be used to make gene-based or region-based SNP inferences about disease association. Genet. Epidemiol . 2006. © 2006 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55838/1/20173_ftp.pd

Contrasting multi-site genotypic distributions among discordant quantitative phenotypes: the APOA1/C3/A4/A5 gene cluster and cardiovascular disease risk factors

Most tests of association between DNA sequence variation and quantitative phenotypes in samples of randomly chosen individuals rely on specification of genotypic strata followed by comparison of phenotypes across these strata. This strategy often succeeds when phenotypic differences are caused by one or two single nucleotide polymorphisms (SNPs) among the surveyed markers. However, when multiple-SNP haplotypes account for observed phenotypic variation, identification of the best partitioning requires examination of an inordinate number of SNP combinations. An alternative approach is to rank individuals by their phenotypic measures and ask whether attributes of the genotypic variation show a non-random distribution along this phenotypic ranking. One simple version of this strategy selects the top and bottom tails of the distribution, and then tests whether genotypes from these two samples are drawn from a single population. This framework does not require the recovery of phased haplotypes and allows contrasts between large numbers of sites at once. We use a method based on this approach to identify associations between plasma triglyceride level, a risk factor for cardiovascular disease, and multi-site genotypes located in the APOA1/C3/A4/A5 cluster of apolipoprotein genes in unrelated individuals (1,071 African-American females, 780 African-American males, 1,036 European-American females, and 930 European-American males) sampled from four US cities as part of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Method performance is investigated using simulations that model genealogical variation and different genetic architectures. Results indicate that this multi-site test can identify genotype-phenotype associations with reasonable power, including those generated by some simple epistatic models. Genet. Epidemiol . 2006. © 2006 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55790/1/20163_ftp.pd

Moderate mutation rate in the SARS coronavirus genome and its implications

BACKGROUND: The outbreak of severe acute respiratory syndrome (SARS) caused a severe global epidemic in 2003 which led to hundreds of deaths and many thousands of hospitalizations. The virus causing SARS was identified as a novel coronavirus (SARS-CoV) and multiple genomic sequences have been revealed since mid-April, 2003. After a quiet summer and fall in 2003, the newly emerged SARS cases in Asia, particularly the latest cases in China, are reinforcing a wide-spread belief that the SARS epidemic would strike back. With the understanding that SARS-CoV might be with humans for years to come, knowledge of the evolutionary mechanism of the SARS-CoV, including its mutation rate and emergence time, is fundamental to battle this deadly pathogen. To date, the speed at which the deadly virus evolved in nature and the elapsed time before it was transmitted to humans remains poorly understood. RESULTS: Sixteen complete genomic sequences with available clinical histories during the SARS outbreak were analyzed. After careful examination of multiple-sequence alignment, 114 single nucleotide variations were identified. To minimize the effects of sequencing errors and additional mutations during the cell culture, three strategies were applied to estimate the mutation rate by 1) using the closely related sequences as background controls; 2) adjusting the divergence time for cell culture; or 3) using the common variants only. The mutation rate in the SARS-CoV genome was estimated to be 0.80 – 2.38 × 10(-3 )nucleotide substitution per site per year which is in the same order of magnitude as other RNA viruses. The non-synonymous and synonymous substitution rates were estimated to be 1.16 – 3.30 × 10(-3 )and 1.67 – 4.67 × 10(-3 )per site per year, respectively. The most recent common ancestor of the 16 sequences was inferred to be present as early as the spring of 2002. CONCLUSIONS: The estimated mutation rates in the SARS-CoV using multiple strategies were not unusual among coronaviruses and moderate compared to those in other RNA viruses. All estimates of mutation rates led to the inference that the SARS-CoV could have been with humans in the spring of 2002 without causing a severe epidemic