Garza Ariana L, Blangero John, Lee Miryoung, Bauer Cici X, Czerwinski Stefan A, Choh Audrey C
School of Public Health, UT Health Science Center, Brownsville, TX 78520, USA.
School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.
Int J Mol Sci. 2025 May 17;26(10):4812. doi: 10.3390/ijms26104812.
The identification of causal genomic regions for liver fat accumulation in the context of metabolic dysfunction remains a challenging goal. This study aimed to identify potential endophenotypes for liver fat content and employ them in bivariate linkage searches for pleiotropic genetic regions where targeted association analysis is more likely to reveal significant variants. Multiple metabolic risk and adiposity distribution traits were assessed using the endophenotype ranking value. The top-ranked endophenotypes were then used in a bivariate linkage analysis, paired with liver fat content. Quantitative trait loci (QTLs) identified as significant or suggestive were targeted for measured genotype association analyses. The highest-ranked endophenotypes for liver fat accumulation were insulin resistance (IR), visceral adipose tissue (VAT), and high-density lipoprotein cholesterol (HDL-C). The univariate linkage analysis for liver fat content identified one significant QTL at chromosome 17p13.2 (Logarithm of odds score (LOD) = 2.90, = 1.29 × 10). The bivariate linkage analysis pairing liver fat with IR and VAT improved the localization of two suggestive QTLs at 13q21.31 (LOD = 2.11, = 9.03 × 10), and 6q21 (LOD = 2.35, = 5.07 × 10), respectively. Targeted association analyses within the -1-LOD score regions of these QTLs revealed 17 marginally significant single nucleotide polymorphisms (SNPs) associated with liver fat content or its combination with the selected endophenotypes. The endophenotype-informed linkage analysis successfully identified regions suitable for the targeted association analysis of liver fat content, either alone or in combination with IR or VAT, leading to the discovery of marginally significant variants with potential for future functional studies.
在代谢功能障碍背景下识别导致肝脏脂肪堆积的因果基因组区域仍然是一个具有挑战性的目标。本研究旨在识别肝脏脂肪含量的潜在内表型，并将其用于双变量连锁搜索，以寻找多效性遗传区域，在这些区域进行靶向关联分析更有可能揭示显著的变异。使用内表型排名值评估多种代谢风险和肥胖分布特征。然后将排名靠前的内表型用于双变量连锁分析，并与肝脏脂肪含量配对。被确定为显著或提示性的数量性状位点（QTL）用于测量基因型关联分析。肝脏脂肪堆积排名最高的内表型是胰岛素抵抗（IR）、内脏脂肪组织（VAT）和高密度脂蛋白胆固醇（HDL-C）。肝脏脂肪含量的单变量连锁分析在17号染色体p13.2区域发现了一个显著的QTL（优势对数评分（LOD）=2.90，=1.29×10）。将肝脏脂肪与IR和VAT配对的双变量连锁分析分别改善了两个提示性QTL在13q21.31（LOD = 2.11，= 9.03×10）和6q21（LOD = 2.35，= 5.07×10）区域的定位。在这些QTL的-1-LOD评分区域内进行的靶向关联分析揭示了17个与肝脏脂肪含量或其与选定内表型组合相关的边缘显著单核苷酸多态性（SNP）。基于内表型的连锁分析成功识别了适合单独或与IR或VAT联合进行肝脏脂肪含量靶向关联分析的区域，从而发现了具有潜在功能研究价值的边缘显著变异。
