Variance explained by large region joint associations in HRS.

<p>¹ 28 SNPs from [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005103#pgen.1005103.ref018" target="_blank">18</a>] were tested for BMI, 16 SNPs from [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005103#pgen.1005103.ref020" target="_blank">20</a>] were tested for CRP, 67 SNPs from [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005103#pgen.1005103.ref019" target="_blank">19</a>] were tested for HDLc, and 180 SNPs from [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005103#pgen.1005103.ref017" target="_blank">17</a>] were tested for height.</p><p>² In the unadjusted analysis, traits were not adjusted for candidate SNPs.</p><p>³ In the adjusted analysis, traits were first adjusted for candidate SNPs associations.</p><p>⁴<i>P</i>-values were obtained from 1,000 permutations.</p><p>Variance explained by large region joint associations in HRS.</p

Large region joint association at known height loci.

<p>Adjusting height for age, sex and 180 known loci, we tested for large region joint association using the previously defined additive model, setting window size at 100 SNPs with steps of 1 SNP. Windows were initially centered on known height loci and distance (x-axis) was defined as the number of SNPs between the center of a window and a known height SNP. Genomic distance (in Kb) covered by windows is illustrated in (A), with red lines representing the median minimum and maximum distances between window boundaries and known height loci. Median distance between window center and known height loci is shown as the black line. In (B),—log10 <i>p</i>-value for area under the receiver operating characteristic curve is illustrated, where windows at each given distance from known height loci are compared to all 9,648 windows (the red line represents <i>p</i> = 0.05). In (C), corresponding area under the receiver operating characteristic curve is illustrated.</p

Estimated variance explained and power as a function of non-additivity measure τ<i>´</i> in regions of 100 SNPs simulated from 1000 genome data.

<p>Regions of 100 SNPs were simulated from phased 1000 Genomes data in 5,000 individuals, excluding SNPs with minor allele frequency higher than 0.01 and further pruning SNPs such that maximal pairwise linkage disequilibrium was <i>r</i>² = 0.80. Assuming two SNPs defining 4 haplotypes that are truly associated with a quantitative trait, the additive and variance component models were tested for their abilities to capture genetic variance and statistical power. The proportion of variance explained by haplotypes was fixed at 0.006 while haplotype effects varied such that the non-additivity parameter (τ<i>´</i>) ranged from 0 to 0.8. Pairwise <i>r</i>² between the 2 causal SNPs and the 98 nuisance SNPs varied from 0 to 0.25. Each scenario was simulated 10,000 times, and mean variance explained and power calculated. The frequency of haplotypes was fixed such that pairwise linkage disequilibrium between the two truly associated SNPs was either <i>r</i>^<i>2</i> = 0 (A and D), <i>r</i>^<i>2</i> = 0.2 (B and E) or <i>r</i>^<i>2</i> = 0.8 (C and F). In figures (A), (B) and (C) the two causal SNPs were assumed to be directly genotyped along with the 98 nuisance SNPs. In figures (D), (E) and (F) the two causal SNPs were masked and only the 98 nuisance SNPs tested for association. The black line represents variance explained by the underlying haplotype model while the additive model is represented in green, and the variance-component model in dashed purple. The upper panel of each figure illustrates the estimated proportion of phenotypic variance explained by joint association as a function of non-additivity τ<i>´</i>. The lower panel illustrates the power to detect such joint association at a <i>p</i>-value threshold of 0.0001.</p

Estimated variance explained and power as a function of non-additivity measure τ<i>´</i>.

<p>A quantitative trait was assumed to be genetically determined according to the underlying (unobserved) haplotype model <b><i>Y</i></b><i>=</i><b><i>Dβ</i></b><i>+</i><b><i>ε</i></b>, where 2 SNPs define 4 possible haplotypes. The proportion of variance explained by haplotypes was fixed at 0.006 while haplotype effects varied such that the non-additivity parameter (τ<i>´</i>) ranged from 0 to 1. Two non-associated nuisance SNPs were added in (B), (E) and (H), bringing the total number of SNPs to <i>m</i> = 4, and four non-associated nuisance SNPs were added in (C), (F) and (I), bringing the total number of SNPs to <i>m</i> = 6. In (A), (B) and (C), the frequency of haplotypes was fixed such that pairwise SNP linkage disequilibrium <i>r</i>^<i>2</i> = 0, in (D), (E) and (F) frequencies were fixed such that <i>r</i>^<i>2</i> = 0.2, and in (G), (H) and (I) frequencies were fixed such that <i>r</i>^<i>2</i> = 0.8. Each line corresponds to a genetic association model, with the underlying haplotype model in black, the additive model in green, the interaction effects model in orange, the genotypic model in dashed brown, the haplotype probability model in grey, and the variance-component model in dashed purple. The upper panel of each figure illustrates the estimated proportion of phenotypic variance explained by joint association as a function of non-additivity τ<i>´</i>. The lower panel illustrates the power to detect such joint association with a <i>p</i>-value threshold of 5x10^-5.</p

Contribution of Large Region Joint Associations to Complex Traits Genetics

<div><p>A polygenic model of inheritance, whereby hundreds or thousands of weakly associated variants contribute to a trait’s heritability, has been proposed to underlie the genetic architecture of complex traits. However, relatively few genetic variants have been positively identified so far and they collectively explain only a small fraction of the predicted heritability. We hypothesized that joint association of multiple weakly associated variants over large chromosomal regions contributes to complex traits variance. Confirmation of such regional associations can help identify new loci and lead to a better understanding of known ones. To test this hypothesis, we first characterized the ability of commonly used genetic association models to identify large region joint associations. Through theoretical derivation and simulation, we showed that multivariate linear models where multiple SNPs are included as independent predictors have the most favorable association profile. Based on these results, we tested for large region association with height in 3,740 European participants from the Health and Retirement Study (HRS) study. Adjusting for SNPs with known association with height, we demonstrated clustering of weak associations (<i>p</i> = 2x10^-4) in regions extending up to 433.0 Kb from known height loci. The contribution of regional associations to phenotypic variance was estimated at 0.172 (95% CI 0.063-0.279; <i>p</i> < 0.001), which compared favorably to 0.129 explained by known height variants. Conversely, we showed that suggestively associated regions are enriched for known height loci. To extend our findings to other traits, we also tested BMI, HDLc and CRP for large region associations, with consistent results for CRP. Our results demonstrate the presence of large region joint associations and suggest these can be used to pinpoint weakly associated SNPs.</p></div

Large region joint association with height in HRS.

<p>First adjusting height for age, sex and 180 known loci, we tested for large region joint association using the previously defined additive model, setting window size at 100 SNPs with steps of 50 SNPs for a total of 9,648 windows. The quantile-quantile plot of joint association <i>p</i>-values for all windows is illustrated in (A), with 95% confidence interval. Windows encompassing each one of the 180 known loci (only) are presented in (B). Considering windows encompassing one of the 180 known height loci as true positives and all other windows as true negatives, a receiver-operating curve was constructed based on window <i>p</i>-values (C). Numbers in red represent specific window <i>p</i>-value thresholds.</p

Top genes associated with stable sinus rhythm after electrical cardioversion.

<p>Fold Change (FC). Confidence Interval (CI).</p

Participant demographics for biomarker discovery cohort.

<p>Participant demographics for biomarker discovery cohort.</p

Top genes associated with stable sinus rhythm after electrical cardioversion.

<p>Fold Change (FC). Confidence Interval (CI).</p

Volcano plot of gene expression changes pre- and post-cardioversion.

<p>Each point represents one of the RNA transcripts tested and the ten most significant genes have been labeled. The x-axis represents the effect of each gene, reported as log2 fold change, and a positive log2 fold change is indicative of increased expression in post-cardioversion samples. The y-axis represents the–log10(P-value). Triangle points represent genes that have significant differential expressed after Bonferroni correction (P-value <3.6x10^-6).</p