Genetic distance and ancestry proportion modify the association between maternal genetic risk score of type 2 diabetes and fetal growth.

IF 3.8 3区医学 Q2 GENETICS & HEREDITY

Human Genomics Pub Date : 2024-07-19 DOI:10.1186/s40246-024-00645-1

Tesfa Dejenie Habtewold, Prabhavi Wijesiriwardhana, Richard J Biedrzycki, Fasil Tekola-Ayele

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引用次数: 0

Abstract

Background: Maternal genetic risk of type 2 diabetes (T2D) has been associated with fetal growth, but the influence of genetic ancestry is not yet fully understood. We aimed to investigate the influence of genetic distance (GD) and genetic ancestry proportion (GAP) on the association of maternal genetic risk score of T2D (GRS_T2D) with fetal weight and birthweight.

Methods: Multi-ancestral pregnant women (n = 1,837) from the NICHD Fetal Growth Studies - Singletons cohort were included in the current analyses. Fetal weight (in grams, g) was estimated from ultrasound measurements of fetal biometry, and birthweight (g) was measured at delivery. GRS_T2D was calculated using T2D-associated variants identified in the latest trans-ancestral genome-wide association study and was categorized into quartiles. GD and GAP were estimated using genotype data of four reference populations. GD was categorized into closest, middle, and farthest tertiles, and GAP was categorized as highest, medium, and lowest. Linear regression analyses were performed to test the association of GRS_T2D with fetal weight and birthweight, adjusted for covariates, in each GD and GAP category.

Results: Among women with the closest GD from African and Amerindigenous ancestries, the fourth and third GRS_T2D quartile was significantly associated with 5.18 to 7.48 g (weeks 17-20) and 6.83 to 25.44 g (weeks 19-27) larger fetal weight compared to the first quartile, respectively. Among women with middle GD from European ancestry, the fourth GRS_T2D quartile was significantly associated with 5.73 to 21.21 g (weeks 18-26) larger fetal weight. Furthermore, among women with middle GD from European and African ancestries, the fourth and second GRS_T2D quartiles were significantly associated with 117.04 g (95% CI = 23.88-210.20, p = 0.014) and 95.05 g (95% CI = 4.73-185.36, p = 0.039) larger birthweight compared to the first quartile, respectively. The absence of significant association among women with the closest GD from East Asian ancestry was complemented by a positive significant association among women with the highest East Asian GAP.

Conclusions: The association between maternal GRS_T2D and fetal growth began in early-second trimester and was influenced by GD and GAP. The results suggest the use of genetic GD and GAP could improve the generalizability of GRS.

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遗传距离和祖先比例会改变母体 2 型糖尿病遗传风险评分与胎儿生长之间的关联。

背景：2型糖尿病（T2D）的母体遗传风险与胎儿生长有关，但遗传祖先的影响尚未完全明了。我们旨在研究遗传距离（GD）和遗传祖先比例（GAP）对母体 2 型糖尿病遗传风险评分（GRST2D）与胎儿体重和出生体重相关性的影响：本次分析纳入了 NICHD 胎儿生长研究 - 单胎队列中的多祖先孕妇（n = 1,837）。胎儿体重（以克为单位）根据胎儿生物测量的超声波测量结果估算，出生体重（以克为单位）则在分娩时测量。GRST2D使用最新的跨祖先全基因组关联研究中发现的T2D相关变异进行计算，并分为四等分。GD和GAP是利用四个参考人群的基因型数据估算的。GD分为最近、中间和最远三等分，GAP分为最高、中等和最低三等分。在对各GD和GAP类别的协变量进行调整后，对GRST2D与胎儿体重和出生体重的关系进行了线性回归分析：在GD最接近的非洲裔和美洲土著妇女中，GRST2D的第四和第三四分位数与第一四分位数相比，分别与胎儿体重增加5.18至7.48克（第17至20周）和6.83至25.44克（第19至27周）显著相关。在欧洲血统的中等 GD 孕妇中，GRST2D 四分位数的第四位与胎儿体重增加 5.73 至 21.21 克（第 18-26 周）显著相关。此外，在欧洲血统和非洲血统的中等 GD 孕妇中，与第一四分位数相比，第四和第二 GRST2D 四分位数分别与 117.04 克（95% CI = 23.88-210.20，p = 0.014）和 95.05 克（95% CI = 4.73-185.36，p = 0.039）较大的出生体重显著相关。与东亚血统最接近的 GD 妇女之间没有显着关联，而与东亚 GAP 最高的妇女之间存在正向显着关联：结论：母体 GRST2D 与胎儿生长的关系始于妊娠早期的第二个三个月，并受 GD 和 GAP 的影响。结果表明，使用遗传 GD 和 GAP 可以提高 GRS 的普适性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Human Genomics GENETICS & HEREDITY-

CiteScore

6.00

自引率

2.20%

发文量

审稿时长

11 weeks

期刊介绍： Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.