整合mRNA转录本与基因组信息的基因组选择方法研究

doi:10.16288/j.yczz.24-096

遗传 ›› 2024, Vol. 46 ›› Issue (7): 560-569.doi: 10.16288/j.yczz.24-096

整合mRNA转录本与基因组信息的基因组选择方法研究

胡玉龙¹(), 杨芳¹, 陈彦潼¹, 谌烁楷¹, 闫煜博¹, 张跃博¹, 吴晓林²^,³, 汪加明⁴, 何俊¹(), 高宁¹()

1.湖南农业大学动物科学技术学院，长沙 410128
2.美国奶牛育种委员会，马里兰州鲍伊市 20716
3.威斯康星大学动物与奶业科学系，威斯康星州麦迪逊市 53706
4.湖南新五丰股份有限公司，长沙 410005

收稿日期:2024-04-08 修回日期:2024-05-24 出版日期:2024-07-20 发布日期:2024-06-03
通讯作者: 何俊，教授，博士生导师，研究方向：猪遗传育种。E-mail: hejun@hunau.edu.cn；高宁，助理研究员，研究方向：分子数量遗传与动物育种。E-mail: gaon@hunau.edu.cn
作者简介:胡玉龙，硕士研究生，专业方向：分子数量遗传与动物育种。E-mail: huyulong1024864@163.com
基金资助:
国家自然科学基金项目(32002148);湖南省教育厅资助科研项目(22B0219);湖南省自然科学基金项目(2022JJ30286);湖南省科协科技人才托举工程项目(2022TJ-Q15);湖南省企业科技创新创业团队项目资助

Integrating mRNA transcripts and genomic information into genomic prediction

Yulong Hu¹(), Fang Yang¹, Yantong Chen¹, Shuokai Shen¹, Yubo Yan¹, Yuebo Zhang¹, Xiaolin Wu²^,³, Jiaming Wang⁴, Jun He¹(), Ning Gao¹()

1. College of Animal Science and Technology, Hunan Agricultural University, Changsha 410125, China
2. Council on Dairy Cattle Breeding, Bowie, MD 20716, USA
3. Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706, USA
4. Hunan Xinwufeng Co., Ltd, Changsha 410005, China

Received:2024-04-08 Revised:2024-05-24 Published:2024-07-20 Online:2024-06-03
Supported by:
National Natural Science Foundation of China(32002148);Scientific Research Fund of Hunan Provincial Education Department(22B0219);Natural Science Foundation of Hunan Province(2022JJ30286);Hunan Association for Science and Technology Talent Support Project(2022TJ-Q15);Hunan Province Enterprise Technology Innovation and Entrepreneurship Team Project]

摘要/Abstract

摘要：

基因组预测已成为畜禽、作物遗传评估和人类疾病风险预测的主要技术，但经典的基因组预测方法在性状遗传调控机制等生物学先验信息的整合方面有一定的不足。本研究提出一种将mRNA转录本信息整合应用于复杂性状表型预测的方法。基于国际上广泛应用于数量遗传学研究的果蝇群体，对本研究提出的新方法进行准确性评估。结果显示，整合mRNA转录本，可有效提高部分性状基因组预测准确性，但对部分性状的表型预测准确性没有改善。与GBLUP相比，雄性果蝇D-香芹酮嗅觉反应(dCarvone)准确性由0.256提高到0.274，提高幅度7%。雄性果蝇咖啡因耐受反应(cafe)准确性由0.355提高到0.401，提高幅度13%。雄性果蝇百草枯耐受反应(survival_paraquat)准确性由0.101提高到0.138，提高幅度36%。雌性果蝇1-已醇嗅觉反应(1hexanol)准确性由0.147提高到0.210，提高幅度43%。综上所述，对于部分性状，通过整合mRNA转录本可有效提高基因组预测准确性(提高幅度为7%~43%)。对于部分性状，整合mRNA转录本并考虑互作效应可进一步提高预测准确性。

关键词: 基因组选择, 基因组预测, mRNA转录本, 整合组学

Abstract:

Genomic prediction has emerged as a pivotal technology for the genetic evaluation of livestock, crops, and for predicting human disease risks. However, classical genomic prediction methods face challenges in incorporating biological prior information such as the genetic regulation mechanisms of traits. This study introduces a novel approach that integrates mRNA transcript information to predict complex trait phenotypes. To evaluate the accuracy of the new method, we utilized a Drosophila population that is widely employed in quantitative genetics researches globally. Results indicate that integrating mRNA transcript data can significantly enhance the genomic prediction accuracy for certain traits, though it does not improve phenotype prediction accuracy for all traits. Compared with GBLUP, the prediction accuracy for olfactory response to dCarvone in male Drosophila increased from 0.256 to 0.274. Similarly, the accuracy for cafe in male Drosophila rose from 0.355 to 0.401. The prediction accuracy for survival_paraquat in male Drosophila is improved from 0.101 to 0.138. In female Drosophila, the accuracy of olfactory response to 1hexanol increased from 0.147 to 0.210. In conclusion, integrating mRNA transcripts can substantially improve genomic prediction accuracy of certain traits by up to 43%, with range of 7% to 43%. Furthermore, for some traits, considering interaction effects along with mRNA transcript integration can lead to even higher prediction accuracy.

Key words: genomic selection, genomic prediction, mRNA transcripts, integrative omics

胡玉龙, 杨芳, 陈彦潼, 谌烁楷, 闫煜博, 张跃博, 吴晓林, 汪加明, 何俊, 高宁. 整合mRNA转录本与基因组信息的基因组选择方法研究[J]. 遗传, 2024, 46(7): 560-569.

Yulong Hu, Fang Yang, Yantong Chen, Shuokai Shen, Yubo Yan, Yuebo Zhang, Xiaolin Wu, Jiaming Wang, Jun He, Ning Gao. Integrating mRNA transcripts and genomic information into genomic prediction[J]. Hereditas(Beijing), 2024, 46(7): 560-569.

图/表 6

图1

图2

图3

表1

表2

表3

不同基因组预测模型对果蝇多个性状基因组预测的准确性"

性状	GBLUP	GmBLUP	GmBLUP*	GHBLUP\|T	GHBLUP\|T*	CHM\|T	CHM\|T*	CHE\|T	CHE\|T*
startle(F)	0.347±0.009	0.361±0.009	0.358±0.010	0.355±0.009	0.341±0.009	0.364±0.009	0.357±0.009	0.321±0.009	0.343±0.009
startle(M)	0.315±0.010	0.329±0.010	0.327±0.010	0.324±0.010	0.314±0.010	0.321±0.010	0.313±0.009	0.284±0.010	0.310±0.010
starvation(F)	0.344±0.008	0.329±0.008	0.334±0.008	0.324±0.008	0.338±0.007	0.325±0.008	0.341±0.008	0.282±0.008	0.344±0.008
starvation(M)	0.362±0.007	0.346±0.007	0.351±0.007	0.361±0.006	0.353±0.006	0.350±0.007	0.360±0.007	0.299±0.007	0.362±0.007
1hexanol(F)	0.147±0.009	0.152±0.010	0.136±0.010	0.194±0.010	0.189±0.010	0.210±0.010	0.208±0.010	0.206±0.009	0.199±0.010
1hexanol(M)	0.198±0.010	0.181±0.010	0.190±0.010	0.203±0.009	0.192±0.009	0.202±0.009	0.189±0.010	0.205±0.009	0.197±0.009
2heptanone(F)	0.284±0.007	0.270±0.007	0.277±0.007	0.261±0.007	0.284±0.007	0.228±0.007	0.285±0.007	0.247±0.006	0.284±0.007
2heptanone(M)	0.137±0.009	0.140±0.010	0.126±0.008	0.131±0.009	0.125±0.009	0.106±0.009	0.132±0.009	0.134±0.010	0.124±0.010
2phenylEthylAlcohol(F)	0.332±0.005	0.327±0.005	0.317±0.005	0.328±0.005	0.324±0.005	0.333±0.005	0.325±0.005	0.301±0.006	0.330±0.004
2phenylEthylAlcohol(M)	0.143±0.008	0.140±0.008	0.126±0.008	0.132±0.009	0.128±0.009	0.123±0.009	0.133±0.010	0.114±0.010	0.128±0.010
benz(F)	0.291±0.011	0.302±0.010	0.298±0.010	0.280±0.010	0.280±0.010	0.299±0.010	0.285±0.010	0.266±0.011	0.288±0.011
benz(M)	0.198±0.014	0.210±0.013	0.205±0.013	0.182±0.015	0.192±0.015	0.205±0.014	0.192±0.014	0.161±0.017	0.195±0.015
benzaldehyde(F)	0.392±0.007	0.399±0.008	0.399±0.008	0.361±0.008	0.387±0.007	0.370±0.007	0.386±0.007	0.338±0.007	0.389±0.007
benzaldehyde(M)	0.307±0.008	0.318±0.007	0.313±0.007	0.288±0.008	0.299±0.008	0.293±0.007	0.300±0.008	0.251±0.006	0.303±0.008
cafe(F)	0.255±0.010	0.259±0.010	0.248±0.011	0.261±0.011	0.247±0.011	0.253±0.010	0.240±0.011	0.256±0.009	0.249±0.010
cafe(M)	0.355±0.007	0.372±0.007	0.370±0.007	0.401±0.007	0.400±0.007	0.401±0.007	0.401±0.007	0.378±0.006	0.349±0.008
dCarvone(F)	0.211±0.012	0.221±0.011	0.209±0.011	0.222±0.012	0.212±0.013	0.228±0.012	0.217±0.013	0.232±0.011	0.211±0.013
dCarvone(M)	0.256±0.011	0.241±0.011	0.238±0.011	0.274±0.011	0.268±0.011	0.256±0.011	0.241±0.011	0.242±0.011	0.252±0.010
ethylAcetate(F)	0.435±0.007	0.433±0.006	0.427±0.007	0.403±0.008	0.433±0.007	0.401±0.007	0.426±0.008	0.400±0.007	0.434±0.007
ethylAcetate(M)	0.336±0.014	0.338±0.014	0.329±0.014	0.311±0.015	0.327±0.014	0.314±0.014	0.328±0.014	0.279±0.015	0.334±0.014
ethylButyrate(F)	0.383±0.007	0.383±0.008	0.373±0.008	0.347±0.008	0.382±0.007	0.356±0.008	0.381±0.007	0.336±0.008	0.382±0.007
ethylButyrate(M)	0.463±0.007	0.468±0.007	0.456±0.007	0.432±0.008	0.460±0.007	0.422±0.008	0.460±0.007	0.391±0.008	0.460±0.007
etoh_e1(F)	0.185±0.007	0.177±0.008	0.168±0.008	0.158±0.008	0.178±0.008	0.174±0.009	0.167±0.008	0.179±0.009	0.172±0.008
etoh_e1(M)	0.068±0.009	0.060±0.010	0.063±0.009	0.033±0.010	0.062±0.009	0.059±0.011	0.051±0.010	0.041±0.009	0.059±0.009
etoh_e2(F)	0.197±0.010	0.189±0.009	0.183±0.010	0.160±0.010	0.189±0.010	0.179±0.009	0.177±0.010	0.171±0.011	0.175±0.012
etoh_e2(M)	0.058±0.010	0.047±0.010	0.047±0.010	0.059±0.009	0.048±0.009	0.070±0.009	0.052±0.009	0.063±0.008	0.053±0.008
eugenol(F)	0.235±0.012	0.244±0.012	0.233±0.013	0.201±0.014	0.217±0.012	0.180±0.013	0.228±0.013	0.191±0.015	0.228±0.012
eugenol(M)	0.176±0.009	0.205±0.009	0.200±0.008	0.151±0.010	0.153±0.010	0.144±0.009	0.165±0.010	0.107±0.012	0.169±0.009
helional(F)	0.136±0.014	0.146±0.015	0.135±0.015	0.100±0.015	0.111±0.014	0.085±0.015	0.125±0.014	0.106±0.013	0.112±0.013
helional(M)	0.101±0.012	0.062±0.013	0.092±0.013	0.056±0.014	0.088±0.013	0.031±0.014	0.092±0.012	0.061±0.014	0.091±0.012
hexanal(F)	0.310±0.008	0.303±0.007	0.301±0.008	0.310±0.008	0.303±0.008	0.298±0.008	0.306±0.008	0.274±0.008	0.306±0.008
hexanal(M)	0.209±0.007	0.214±0.007	0.198±0.007	0.224±0.009	0.209±0.007	0.215±0.009	0.200±0.007	0.200±0.009	0.202±0.008
lCarvone(F)	0.279±0.010	0.276±0.010	0.268±0.010	0.274±0.010	0.265±0.010	0.293±0.010	0.274±0.010	0.276±0.011	0.277±0.010
lCarvone(M)	0.341±0.007	0.284±0.007	0.350±0.007	0.312±0.007	0.349±0.007	0.312±0.006	0.350±0.007	0.296±0.008	0.349±0.007
methylSalicylate(F)	0.365±0.009	0.377±0.009	0.369±0.009	0.364±0.008	0.351±0.009	0.353±0.008	0.350±0.009	0.336±0.008	0.358±0.009
methylSalicylate(M)	0.337±0.007	0.336±0.008	0.320±0.007	0.308±0.008	0.335±0.007	0.293±0.009	0.336±0.007	0.276±0.008	0.336±0.007
startle_msb_sensitivity(F)	0.056±0.010	0.064±0.011	0.057±0.010	0.093±0.011	0.089±0.011	0.088±0.010	0.082±0.010	0.097±0.011	0.092±0.011
startle_msb_sensitivity(M)	0.006±0.014	0.015±0.013	0.006±0.014	0.082±0.014	0.070±0.013	0.082±0.014	0.071±0.013	0.078±0.015	0.066±0.014
survival_paraquat(F)	0.285±0.007	0.264±0.008	0.274±0.007	0.252±0.008	0.284±0.006	0.265±0.008	0.276±0.007	0.237±0.008	0.286±0.006
survival_paraquat(M)	0.101±0.009	0.108±0.009	0.087±0.009	0.115±0.011	0.106±0.010	0.138±0.010	0.129±0.010	0.091±0.012	0.084±0.010

表3

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模型	表达式	遗传效应
GBLUP	${{y}^{}}=\mu +Z{{u}_{0}}+e$*	$u\tilde{\ }N(0,G\sigma _{{{u}_{0}}}^{2})$
GmBLUP	${{y}^{}}=\mu +Zu+e$*	$u\tilde{\ }N(0,{{G}_{m}}\sigma _{u}^{2})$
GHBLUP\|T		$u\tilde{\ }N(0,{{G}_{H}}\sigma _{u}^{2})$
CHM\|T		$u\tilde{\ }N(0,S\sigma _{u}^{2})$
CHE\|T		$u\tilde{\ }N(0,E\sigma _{u}^{2})$
GmBLUP*	${{y}^{}}=\mu +Zu+Z{u}'+e$*	$u\tilde{\ }N(0,{{G}_{m}}\sigma _{u}^{2})$，${u}'\tilde{\ }N(0,{G}'\sigma _{{{u}'}}^{2})$
GHBLUP\|T*		$u\tilde{\ }N(0,{{G}_{H}}\sigma _{u}^{2})$，${u}'\tilde{\ }N(0,{G}'\sigma _{{{u}'}}^{2})$
CHM\|T*		$u\tilde{\ }N(0,S\sigma _{u}^{2})$，${u}'\tilde{\ }N(0,{G}'\sigma _{{{u}'}}^{2})$
CHE\|T*		$u\tilde{\ }N(0,E\sigma _{u}^{2})$，${u}'\tilde{\ }N(0,{G}'\sigma _{{{u}'}}^{2})$

项目	最小值	最大值	中位数	平均数	标准差
外显子数量	1	140	32	104	6.82
CDS数量	1	116	3	4.55	4.57
转录本数量	1	75	2	2.23	2.31
转录本SNP数量	1	995	18	30.45	47.61
转录本等位基因数量 (质控前)	2	195	37	49.74	42.83
转录本等位基因数量 (质控后)	2	30	14	13.30	6.22
转录本基因型数量 (质控前)	2	183	37	49.63	42.45
转录本基因型数量 (质控后)	2	32	14	13.46	6.29

整合mRNA转录本与基因组信息的基因组选择方法研究

Integrating mRNA transcripts and genomic information into genomic prediction

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