Gunn-Helen Moen, Liang-Dar Hwang, Caroline Brito Nunes, Nicole M. Warrington, David M. Evans
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The aim of this study was to investigate genetic variation associated within the tail ends of the birthweight distribution, to: (1) see whether the genetic factors operating in these regions were different from those that explained variation in birthweight within the normal range; (2) explore the genetic correlation between extremes of birthweight and cardiometabolic disease; and (3) investigate whether analysing the full distribution of birthweight values, including the extremes, improved the ability to detect genuine loci in GWAS.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We performed case–control GWAS analysis of low (<2.5 kg) and high (>4.5 kg) birthweight in the UK Biobank using REGENIE software (<i>N</i><sub>low</sub>=20,947; <i>N</i><sub>high</sub>=12,715; <i>N</i><sub>controls</sub>=207,506) and conducted three continuous GWAS of birthweight, one including the full range of birthweights, one involving a truncated GWAS including only individuals with birthweights between 2.5 and 4.5 kg and a third GWAS that winsorised birthweight values <2.5 kg and >4.5 kg. Additionally, we performed bivariate linkage disequilibrium (LD) score regression to estimate the genetic correlation between low/normal/high birthweight and cardiometabolic traits.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Bivariate LD score regression analyses suggested that high birthweight had a mostly similar genetic aetiology to birthweight within the normal range (genetic correlation coefficient [<i>r</i><sub>G</sub>]=0.91, 95% CI 0.83, 0.99), whereas there was more evidence for a separate set of genes underlying low birthweight (<i>r</i><sub>G</sub>=−0.74, 95% CI 0.66, 0.82). Low birthweight was also significantly positively genetically correlated with most cardiometabolic traits and diseases we examined, whereas high birthweight was mostly positively genetically correlated with adiposity and anthropometric-related traits. The winsorisation strategy performed best in terms of locus detection, with the number of independent genome-wide significant associations (<i>p</i><5×10<sup>−8</sup>) increasing from 120 genetic variants at 94 loci in the truncated GWAS to 270 genetic variants at 178 loci, including 27 variants at 25 loci that had not been identified in previous birthweight GWAS. This included a novel low-frequency missense variant in the <i>ABCC8</i> gene, a gene known to be involved in congenital hyperinsulinism, neonatal diabetes mellitus and MODY, that was estimated to be responsible for a 170 g increase in birthweight amongst carriers.</p><h3 data-test=\"abstract-sub-heading\">Conclusions/interpretation</h3><p>Our results underscore the importance of genetic factors in the genesis of the phenotypic correlation between birthweight and cardiometabolic traits and diseases.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\n","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":"183 1","pages":""},"PeriodicalIF":8.4000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The genetics of low and high birthweight and their relationship with cardiometabolic disease\",\"authors\":\"Gunn-Helen Moen, Liang-Dar Hwang, Caroline Brito Nunes, Nicole M. 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The aim of this study was to investigate genetic variation associated within the tail ends of the birthweight distribution, to: (1) see whether the genetic factors operating in these regions were different from those that explained variation in birthweight within the normal range; (2) explore the genetic correlation between extremes of birthweight and cardiometabolic disease; and (3) investigate whether analysing the full distribution of birthweight values, including the extremes, improved the ability to detect genuine loci in GWAS.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>We performed case–control GWAS analysis of low (<2.5 kg) and high (>4.5 kg) birthweight in the UK Biobank using REGENIE software (<i>N</i><sub>low</sub>=20,947; <i>N</i><sub>high</sub>=12,715; <i>N</i><sub>controls</sub>=207,506) and conducted three continuous GWAS of birthweight, one including the full range of birthweights, one involving a truncated GWAS including only individuals with birthweights between 2.5 and 4.5 kg and a third GWAS that winsorised birthweight values <2.5 kg and >4.5 kg. 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引用次数: 0
摘要
目的/假设低出生体重婴儿不仅死亡风险增加,而且在以后的生活中患2型糖尿病和心血管疾病的风险也增加。另一方面,高出生体重婴儿出现出生并发症的风险增加,如肩难产、新生儿低血糖和肥胖,同样也增加了患2型糖尿病和心血管疾病的风险。然而,英国生物银行之前的全基因组关联研究(GWAS)主要集中在“正常”范围内的个体,并排除了高出生体重和低出生体重(2.5公斤或4.5公斤)的个体。本研究旨在探讨与出生体重分布尾部相关的遗传变异,以:(1)了解在这些区域起作用的遗传因素是否与解释正常范围内出生体重变化的遗传因素不同;(2)探讨出生体重极值与心脏代谢疾病的遗传相关性;(3)研究分析出生体重值的完整分布(包括极端分布)是否提高了检测GWAS中真正基因位点的能力。方法采用REGENIE软件对英国生物银行低出生体重(2.5 kg)和高出生体重(4.5 kg)的新生儿进行病例对照GWAS分析(Nlow=20,947;Nhigh = 12715;Ncontrols=207,506),并进行了三次连续的出生体重GWAS,一次包括出生体重的全部范围,一次涉及截断的出生体重GWAS,仅包括出生体重在2.5至4.5 kg之间的个体,第三次GWAS包括出生体重值为2.5 kg和4.5 kg的个体。此外,我们进行了双变量连锁不平衡(LD)评分回归,以估计低/正常/高出生体重与心脏代谢性状之间的遗传相关性。结果双变量LD评分回归分析表明,高出生体重与正常范围内的出生体重具有基本相似的遗传病因(遗传相关系数[rG]=0.91, 95% CI 0.83, 0.99),而低出生体重有更多的证据表明存在一组单独的基因(rG= - 0.74, 95% CI 0.66, 0.82)。低出生体重也与我们研究的大多数心脏代谢特征和疾病显著正相关,而高出生体重与肥胖和人体测量相关特征主要呈正相关。winsorisation策略在基因座检测方面表现最好,独立的全基因组显著关联(p<5×10−8)的数量从截断GWAS中94个位点的120个遗传变异增加到178个位点的270个遗传变异,包括以前出生体重GWAS中未发现的25个位点的27个变异。其中包括ABCC8基因的一种新型低频错义变异,这种基因已知与先天性高胰岛素症、新生儿糖尿病和MODY有关,据估计,携带者的出生体重增加了170克。结论/解释我们的研究结果强调了遗传因素在出生体重与心脏代谢特征和疾病之间表型相关性的发生中的重要性。图形抽象
The genetics of low and high birthweight and their relationship with cardiometabolic disease
Aims/hypothesis
Low birthweight infants are at increased risk not only of mortality, but also of type 2 diabetes mellitus and CVD in later life. At the opposite end of the spectrum, high birthweight infants have increased risk of birth complications, such as shoulder dystocia, neonatal hypoglycaemia and obesity, and similarly increased risk of type 2 diabetes mellitus and CVD. However, previous genome-wide association studies (GWAS) of birthweight in the UK Biobank have primarily focused on individuals within the ‘normal’ range and have excluded individuals with high and low birthweight (<2.5 kg or >4.5 kg). The aim of this study was to investigate genetic variation associated within the tail ends of the birthweight distribution, to: (1) see whether the genetic factors operating in these regions were different from those that explained variation in birthweight within the normal range; (2) explore the genetic correlation between extremes of birthweight and cardiometabolic disease; and (3) investigate whether analysing the full distribution of birthweight values, including the extremes, improved the ability to detect genuine loci in GWAS.
Methods
We performed case–control GWAS analysis of low (<2.5 kg) and high (>4.5 kg) birthweight in the UK Biobank using REGENIE software (Nlow=20,947; Nhigh=12,715; Ncontrols=207,506) and conducted three continuous GWAS of birthweight, one including the full range of birthweights, one involving a truncated GWAS including only individuals with birthweights between 2.5 and 4.5 kg and a third GWAS that winsorised birthweight values <2.5 kg and >4.5 kg. Additionally, we performed bivariate linkage disequilibrium (LD) score regression to estimate the genetic correlation between low/normal/high birthweight and cardiometabolic traits.
Results
Bivariate LD score regression analyses suggested that high birthweight had a mostly similar genetic aetiology to birthweight within the normal range (genetic correlation coefficient [rG]=0.91, 95% CI 0.83, 0.99), whereas there was more evidence for a separate set of genes underlying low birthweight (rG=−0.74, 95% CI 0.66, 0.82). Low birthweight was also significantly positively genetically correlated with most cardiometabolic traits and diseases we examined, whereas high birthweight was mostly positively genetically correlated with adiposity and anthropometric-related traits. The winsorisation strategy performed best in terms of locus detection, with the number of independent genome-wide significant associations (p<5×10−8) increasing from 120 genetic variants at 94 loci in the truncated GWAS to 270 genetic variants at 178 loci, including 27 variants at 25 loci that had not been identified in previous birthweight GWAS. This included a novel low-frequency missense variant in the ABCC8 gene, a gene known to be involved in congenital hyperinsulinism, neonatal diabetes mellitus and MODY, that was estimated to be responsible for a 170 g increase in birthweight amongst carriers.
Conclusions/interpretation
Our results underscore the importance of genetic factors in the genesis of the phenotypic correlation between birthweight and cardiometabolic traits and diseases.
期刊介绍:
Diabetologia, the authoritative journal dedicated to diabetes research, holds high visibility through society membership, libraries, and social media. As the official journal of the European Association for the Study of Diabetes, it is ranked in the top quartile of the 2019 JCR Impact Factors in the Endocrinology & Metabolism category. The journal boasts dedicated and expert editorial teams committed to supporting authors throughout the peer review process.