<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li, Chang</style></author><author><style face="normal" font="default" size="100%">Grove, Megan L</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Jones, Barbara C</style></author><author><style face="normal" font="default" size="100%">Morrison, Alanna</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author><author><style face="normal" font="default" size="100%">Liu, Xiaoming</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic variants in microRNA genes and targets associated with cardiovascular disease risk factors in the African-American population.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3' Untranslated Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Black or African American</style></keyword><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotyping Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">137</style></volume><pages><style face="normal" font="default" size="100%">85-94</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The purpose of this study is to identify microRNA (miRNA) related polymorphism, including single nucleotide variants (SNVs) in mature miRNA-encoding sequences or in miRNA-target sites, and their association with cardiovascular disease (CVD) risk factors in African-American population. To achieve our objective, we examined 1900 African-Americans from the Atherosclerosis Risk in Communities study using SNVs identified from whole-genome sequencing data. A total of 971 SNVs found in 726 different mature miRNA-encoding sequences and 16,057 SNVs found in the three prime untranslated region (3'UTR) of 3647 protein-coding genes were identified and interrogated their associations with 17 CVD risk factors. Using single-variant-based approach, we found 5 SNVs in miRNA-encoding sequences to be associated with serum Lipoprotein(a) [Lp(a)], high-density lipoprotein (HDL) or triglycerides, and 2 SNVs in miRNA-target sites to be associated with Lp(a) and HDL, all with false discovery rates of 5%. Using a gene-based approach, we identified 3 pairs of associations between gene NSD1 and platelet count, gene HSPA4L and cardiac troponin T, and gene AHSA2 and magnesium. We successfully validated the association between a variant specific to African-American population, NR_039880.1:n.18A&gt;C, in mature hsa-miR-4727-5p encoding sequence and serum HDL level in an independent sample of 2135 African-Americans. Our study provided candidate miRNAs and their targets for further investigation of their potential contribution to ethnic disparities in CVD risk factors.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29264654?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Morrison, Alanna C</style></author><author><style face="normal" font="default" size="100%">Huang, Zhuoyi</style></author><author><style face="normal" font="default" size="100%">Yu, Bing</style></author><author><style face="normal" font="default" size="100%">Metcalf, Ginger</style></author><author><style face="normal" font="default" size="100%">Liu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Ballantyne, Christie</style></author><author><style face="normal" font="default" size="100%">Coresh, Josef</style></author><author><style face="normal" font="default" size="100%">Yu, Fuli</style></author><author><style face="normal" font="default" size="100%">Muzny, Donna</style></author><author><style face="normal" font="default" size="100%">Feofanova, Elena</style></author><author><style face="normal" font="default" size="100%">Rustagi, Navin</style></author><author><style face="normal" font="default" size="100%">Gibbs, Richard</style></author><author><style face="normal" font="default" size="100%">Boerwinkle, Eric</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Hum Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Black or African American</style></keyword><keyword><style  face="normal" font="default" size="100%">C-Reactive Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol, HDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol, LDL</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosomes, Human, Pair 9</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Hemoglobins</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Introns</style></keyword><keyword><style  face="normal" font="default" size="100%">Leukocyte Count</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipoprotein(a)</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnesium</style></keyword><keyword><style  face="normal" font="default" size="100%">Natriuretic Peptide, Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Neutrophils</style></keyword><keyword><style  face="normal" font="default" size="100%">Peptide Fragments</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphorus</style></keyword><keyword><style  face="normal" font="default" size="100%">Platelet Count</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Troponin T</style></keyword><keyword><style  face="normal" font="default" size="100%">White People</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Feb 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">100</style></volume><pages><style face="normal" font="default" size="100%">205-215</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28089252?dopt=Abstract</style></custom1></record></records></xml>