小柯机器人

澳大利亚昆士兰大学Loïc Yengo等研究人员合作绘制出一个与人类身高相关的常见遗传变体饱和图谱。这一研究成果于2022年10月12日在线发表在国际学术期刊《自然》上。

研究人员使用来自540万不同血统个体的全基因组关联研究的数据，表明与身高显著相关的12111个独立单核苷酸多态性（SNP）几乎占了所有基于SNP的遗传率。这些SNP聚集在7209个不重叠的基因组片段中，平均大小约为90kb，覆盖了基因组的21%。独立关联的密度在整个基因组中是不同的，密度增加的区域富含生物相关基因。在样本外估计和预测中，12111个SNP（或HapMap3中的所有SNP）在欧洲血统的人群中占40%（45%）的表型变异，但在其他血统的人群中只占约10-20%（14-24%）。不同血统的效应大小、相关区域和基因优先级相似，表明预测准确性的降低可能是由相关区域内的连锁不平衡和等位基因频率的差异造成的。

最后，研究人员表明，相关的生物途径可以用较小的样本量检测出来，而这是牵涉到因果基因和变体所需要的。总的来说，这项研究提供了一个包含绝大多数常见身高相关变体的特定基因组区域的综合图谱。虽然这个图谱在欧洲血统的人群中是饱和的，但要在其他血统中达到同等的饱和度，还需要进一步的研究。

据预测，常见的SNP共同解释了人类身高的40-50%的表型变异，但鉴定特定的变体和相关区域需要巨大的样本量。

附：英文原文

Title: A saturated map of common genetic variants associated with human height

Author: Yengo, Loc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U., Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D., Graham, Sarah E., Mukamel, Ronen E., Spracklen, Cassandra N., Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H., Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lll, Kreete, Malden, Deborah E., Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Reger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S., Akiyama, Masato, Allison, Matthew A., Alvarez, Marcus, Andersen, Mette K., Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F., Bollepalli, Sailalitha, Bonnycastle, Lori L., Bork-Jensen, Jette, Bradfield, Jonathan P., Bradford, Yuki, Braund, Peter S., Brody, Jennifer A., Burgdorf, Kristoffer S., Cade, Brian E., Cai, Hui, Cai, Qiuyin, Campbell, Archie, Caadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano

Issue&Volume: 2022-10-12

Abstract: Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

DOI: 10.1038/s41586-022-05275-y

Source: https://www.nature.com/articles/s41586-022-05275-y

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
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