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

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

• Technique and Method • Previous Articles Next Articles

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 Online:2024-07-20 Published: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]

Abstract

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

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.

Figures/Tables 6

Fig. 1

Fig. 2

Fig. 3

Table 1

Table 2

Table 3

Predictive ability of the studied models for traits in the Drosophila data"

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

Table 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

Integrating mRNA transcripts and genomic information into genomic prediction

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