Coronary collateralization shows sex and racial-ethnic differences in obstructive artery disease patients

<div><p>Background</p><p>Coronary collateral circulation protects cardiac tissues from myocardial infarction damage and decreases sudden cardiac death. So far, it is unclear how coronary collateralization varies by race-ethnicity groups and by sex.</p><p>Methods</p><p>We assessed 868 patients with obstructive CAD. Patients were assessed for collateral grades based on Rentrop grading system, as well as other covariates. DNA samples were genotyped using the Affymetrix 6.0 genotyping array. To evaluate genetic contributions to collaterals, we performed admixture mapping using logistic regression with estimated local and global ancestry.</p><p>Results</p><p>Overall, 53% of participants had collaterals. We found difference between sex and racial-ethnic groups. Men had higher rates of collaterals than women (P-value = 0.000175). White Hispanics/Latinos showed overall higher rates of collaterals than African Americans and non-Hispanic Whites (59%, 50% and 48%, respectively, P-value = 0.017), and especially higher rates in grade 1 and grade 3 collateralization than the other two populations (P-value = 0.0257). Admixture mapping showed Native American ancestry was associated with the presence of collaterals at a region on chromosome 17 (chr17:35,243,142-41,251,931, β = 0.55, P-value = 0.000127). African ancestry also showed association with collaterals at a different region on chromosome 17 (chr17: 32,266,966-34,463,323, β = 0.38, P-value = 0.00072).</p><p>Conclusions</p><p>In our study, collateralization showed sex and racial-ethnic differences in obstructive CAD patients. We identified two regions on chromosome 17 that were likely to harbor genetic variations that influenced collateralization.</p></div

Population demographics and clinical characteristics based on collateral grades.

<p>Population demographics and clinical characteristics based on collateral grades.</p

Chromosome 17 regional plot showed peak association between local African/Native American ancestries and collateralization.

<p>Local Native American ancestry was highly associated with the presence of collaterals at a region on chromosome 17 (35,243,142-41,251,931 (hg19), min P-value = 0.000127, −log10 (P-value) = 3.90). Local African ancestry also showed association with collaterals at a different region on chromosome 17 (32,266,966-34,463,323 (hg19), min P-value = 0.00072, -log10 (P-value) = 3.14). X-axis represented the base pair location on chromosome 17; Y-axis represented −log10 P-values.</p

Manhattan plots showed associations between collateralization and local ancestry.

<p>(A) local Native American ancestry; (B) local African ancestry in admixture mapping. The peak regions of local Native American ancestry and local African ancestry were both located on chromosome 17. X-axis indicated chromosomes 1 to 22. Y-axis indicated −log10 of local Native American/African ancestry P-values.</p

One for all and all for One: Improving replication of genetic studies through network diffusion

<div><p>Improving accuracy in genetic studies would greatly accelerate understanding the genetic basis of complex diseases. One approach to achieve such an improvement for risk variants identified by the genome wide association study (GWAS) approach is to incorporate previously known biology when screening variants across the genome. We developed a simple approach for improving the prioritization of candidate disease genes that incorporates a network diffusion of scores from known disease genes using a protein network and a novel integration with GWAS risk scores, and tested this approach on a large Alzheimer disease (AD) GWAS dataset. Using a statistical bootstrap approach, we cross-validated the method and for the first time showed that a network approach improves the expected replication rates in GWAS studies. Several novel AD genes were predicted including <i>CR2</i>, <i>SHARPIN</i>, <i>and PTPN2</i>. Our re-prioritized results are enriched for established known AD-associated biological pathways including inflammation, immune response, and metabolism, whereas standard non-prioritized results were not. Our findings support a strategy of considering network information when investigating genetic risk factors.</p></div

Proximity between RAD genes in PPI network.

<p>Each RAD gene was ranked (in comparison to the other 19,972 genes in the network) based upon its degree (number of interactions in network), its ASP distance to the RAD genes, and total diffusion distance from the RAD genes. The average ranking of the RAD genes was 7,949 using ASP (60th percentile, t-test p = 0.015) and 6,959 for diffusion (65th percentile, t-test p = 0.00054).</p

Comparison of GWAS and network Z-scores.

<p><b>A.</b> Transformed Z-scores are uncorrelated. <b>B.</b> Genes with high network scores had higher replication rates compared to those with low network scores, as further visualized and confirmed statistically as shown in <b><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007306#pgen.1007306.g004" target="_blank">Fig 4</a></b>. Reprate = replication rate.</p

RAD genes and the type of study that identified them.

<p>RAD genes and the type of study that identified them.</p

GSEA results after ranking genes by combined Z-scores.

<p>GSEA results after ranking genes by combined Z-scores.</p

Proximity of non-RAD hub genes to RAD genes.

<p>Proximity of non-RAD hub genes to RAD genes.</p