{'fraction_non_alphanumeric': 0.046338436077782375, 'fraction_numerical': 0.05213074058750517, 'mean_word_length': 5.014925373134329, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Abstract 1804: Pathway-Analysis of Kinesin Protein Family (KIF) using Genome-wide SNP Data in Patients with Myocardial Infarction

Objectives. Kinesins constitute a large protein family involved in ATP-binding and intracellular transport. They facilitate transport of mitochondria, lysosomes and tubulin oligomers. Kinesin genes, especially KIF 6 (rs20455), have been described to be associated with 24% increased risk of coronary heart disease (CHD) and 34% higher risk of myocardial infarction (MI). Aim of our study was to perform a hypothesis-based pathway analysis of SNPs in genes encoding KIF. Methods. Identification of relevant genes was undertaken by means of medical literature [pubmed] and internet databases[Entrez Genes, HapMap]. AvailableSNPs were investigated in silico in two independent genome wide SNP data sets (GerMIFSI [875 MI Cases, 1644 Controls] and GerMIFSII [1221 MI Cases, 1298 Controls]). GerMIFSI was genotyped with 500K Affymetrix SNP Array and subsequently imputed using MACH algorithm, GerMIFSII was genotyped with Affymetrix 6.0 Array. Pathway coverage in GWA-data was carried out by generating linkage disequilibrium unit (LDU) maps of HapMap data. GWA studies received quality control (minor allele frequency ≥ 1%, p-value of deviation from HWD in controls ≥ 0.001, Armitage’s p-trend test ≤ 0.05). Signal intensity plots were visually inspected by two independent reviewers. Haplotype analysis was carried out. Results. Twenty one genes were identified in KIF pathway. Among them, 4 genes encoding for KIF2B (chr17q22), KIFC3 (chr16q13), KIF6 (chr6q12) and KIF 9 (chr17p13) carried several SNPs significantly associated with MI in either GerMIFSI or GerMIFSII. In KIF2B 9/11 SNPs (p-value range 0,00024 to 0,04), in KIFC3 20/31 SNPs (p-value range 1,2 x 10 −6 to 0,05) were significant for MI. 33/230 KIF6 SNPs revealed p-values between 1,66 x 10 −8 and 0,04. In KIF9, 15/235 SNPs were significant with a p-value between 4,9 x 10 −7 and 0,04. Haplotype analysis confirmed these results. Conclusions. Several SNPs of four KIF encoding genes revealed highly significant results for MI either in GerMIFSI or GerMIFSII data sets. However, precise in silico replication in GerMIFSII was reached exclusively for rs 12929142 in KIFC3. Therefore, our preliminary results support a significant relation between KIF SNPs and MI risk. A subsequent replication analysis is currently underway.