基于全局最小残差法快速分析混合STR图谱

doi:10.16288/j.yczz.23-101

遗传 ›› 2023, Vol. 45 ›› Issue (10): 933-944.doi: 10.16288/j.yczz.23-101

• 技术与方法 • 上一篇

基于全局最小残差法快速分析混合STR图谱

李鑫¹(), 范虹¹(), 赵兴春², 范晓诺³, 姚若侠¹()

1.陕西师范大学计算机科学学院，西安 710119
2.公安部鉴定中心，北京 100038
3.波士顿大学生物系，波士顿 02215，美国

收稿日期:2023-06-15 修回日期:2023-08-14 出版日期:2023-10-20 发布日期:2023-08-30
通讯作者: 范虹,姚若侠 E-mail:15514872144@163.com;rxyao@snnu.edu.cn;fanhong@snnu.edu.cn
作者简介:李鑫，硕士研究生，专业方向：生物信息。E-mail: 15514872144@163.com
基金资助:
国家自然科学基金项目(12271324);陕西省自然科学基金重点项目(2022ZJ-39);法医遗传学公安部重点实验室开放课题(2021FGKFKT07)

Rapid analyzing mixed STR profiles based on the global minimum residual method

Xin Li¹(), Hong Fan¹(), Xingchun Zhao², Xiaonuo Fan³, Ruoxia Yao¹()

1. School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
2. The institute of Forensic Science, Ministry of Public Security, Beijing 100038, China
3. Department of Biology, Boston University, Boston 02215, USA

Received:2023-06-15 Revised:2023-08-14 Published:2023-10-20 Online:2023-08-30
Contact: Hong Fan,Ruoxia Yao E-mail:15514872144@163.com;rxyao@snnu.edu.cn;fanhong@snnu.edu.cn
Supported by:
National Natural Science Foundation of China(12271324);Key Program of Shaanxi Natural Science Foundation(2022ZJ-39);Open Projects of the Key Laboratory of Forensic Genetics of the Ministry of Public Security(2021FGKFKT07)

摘要/Abstract

摘要：

在法医DNA分析领域，混合短串联重复序列(short tandem repeats，STR)图谱的分析一直是研究难点。当前，国内主要依靠法医进行人工分析，不仅效率低下，分析结果还存在着主观性偏好，难以满足日益增长的STR图谱分析的需求。本文提出一种新的混合STR图谱分析方法——全局最小残差法，不仅可以计算出分析结果，还可以预测出每个组分的混合比例。该方法首先给混合比例赋予了新的定义，然后对等位基因模型进行优化，进而综合考虑STR图谱中的所有基因座，将每个基因座的残差值进行累加求和，选择累加和最小的混合比例作为推断结果，并使用灰狼优化算法快速寻找混合比例的最优值。对于二组分STR图谱，全局最小残差法能够兼顾分析的准确性和分析速度，有利于实现大量的图谱分析。本文提出的算法在实际应用中取得了不错的效果，具有较高的应用价值，可为混合STR图谱分析领域的研究提供新的解决方案。

关键词: 法医遗传学, 混合STR图谱, 等位基因模型, 灰狼优化算法

Abstract:

The analysis of mixed short tandem repeat (STR) profiles has been long considered as a difficult challenge in the forensic DNA analysis. In the context of China, the current approach to analyze mixed STR profiles depends mostly on forensic manual method. However, besides the inefficiency, this technique is also susceptible to subjective biases in interpreting analysis results, which can hardly meet up with the growing demand for STR profiles analysis. In response, this study introduces an innovative method known as the global minimum residual method, which not only predicts the proportion of each contributor within a mixture, but also delivers accurate analysis results. The global minimum residual method first gives new definitions to the mixture proportion, then optimizes the allele model. After that, it comprehensively considers all loci present in the STR profile, accumulates and sums the residual values of each locus and selects the mixture proportion with the minimum accumulative sum as the inference result. Furthermore, the grey wolf optimizer is also employed to expedite the search for the optimal value. Notably, for two-person STR profiles, the high accuracy and remarkable efficiency of the global minimum residual method can bring convenience to realize extensive STR profile analysis. The optimization scheme established in this research has exhibited exceptional outcomes in practical applications, boasting significant utility and offering an innovative avenue in the realm of mixed STR profile analysis.

Key words: forensic genetics, mixed STR profile, allele model, grey wolf optimizer

李鑫, 范虹, 赵兴春, 范晓诺, 姚若侠. 基于全局最小残差法快速分析混合STR图谱[J]. 遗传, 2023, 45(10): 933-944.

Xin Li, Hong Fan, Xingchun Zhao, Xiaonuo Fan, Ruoxia Yao. Rapid analyzing mixed STR profiles based on the global minimum residual method[J]. Hereditas(Beijing), 2023, 45(10): 933-944.

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参考文献 20

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等位基因	可能的基因型组合
a	(aa, aa)
a, b	(aa, ab)	(aa, bb)	(ab, aa)	(ab, ab)	(ab, bb)	(bb, ab)	(bb, aa)
a, b, c	(aa, bc)	(ab, ac)	(ab, bc)	(ab, cc)	(ac, bc)	(ac, bb)
a, b, c	(bc, aa)	(ac, ab)	(bc, ab)	(cc, ab)	(bc, ac)	(bb, ac)
a, b, c, d	(ab, cd)	(ac, bd)	(ad, bc)	(bc, ad)	(bd, ac)	(cd, ab)

等位基因	基因型组合
a, b	(ab, aa)	(aa, ab)	(ab, ab)	(bb, aa)
a, b, c	(bc, aa)	(aa, bc)	(ac, ab)	(ab, ac)	(cc, ab)	(bb, ac)
a, b, c, d	(cd, ab)

基因座类型	基因型组合 (个体1, 个体2)	等位基因				Mx取值范围
基因座类型	基因型组合 (个体1, 个体2)	a	b	c	d	Mx取值范围
四带型	(cd, ab)	(1-Mx)/2	(1-Mx)/2	Mx/2	Mx/2	(0, 50%]
三带型	(bc, aa)	1-Mx	Mx/2	Mx/2		(0, 50%]
	(aa, bc)	Mx	(1-Mx)/2	(1-Mx)/2		(33.3%, 50%]
	(ac, ab)	0.5	(1-Mx)/2	Mx/2		(0, 50%]
	(ab, ac)	0.5	Mx/2	(1-Mx)/2		50%
	(cc, ab)	(1-Mx)/2	(1-Mx)/2	Mx		(0, 33.3%]
	(bb, ac)	(1-Mx)/2	Mx	(1-Mx)/2		33.3%
二带型	(ab, aa)	1-Mx/2	Mx/2			(0, 50%]
	(aa, ab)	(1+Mx)/2	(1-Mx)/2			(0, 50%]
	(ab, ab)	0.5	0.5			(0, 50%]
	(bb, aa)	1-Mx	Mx			(0, 50%]

基因座	等位基因	归一化结果	基因座	等位基因	归一化结果
vWA	15	0.335198	FGA	21	0.529154
	19	0.305188		22	0.320254
	18	0.201679		23	0.150592
	16	0.157935	D16S539	32.2	0.365978
D8S1179	12	0.399721		28	0.342649
	16	0.275419		30	0.291373
	13	0.168436	D7S820	10	0.543115
	14	0.156425		8	0.326411
D18S51	17	0.385067		11	0.130474
	18	0.335507	TH01	5	0.375796
	12	0.15357		6	0.372213
	13	0.125856		8	0.25199
D16S539	11	0.355794	TPOX	8	0.467634
	13	0.354102		10	0.366071
	12	0.145193		11	0.166295
	14	0.144911	CSF1PO	11	0.388085
D3S1358	15	0.464937		12	0.343541
	18	0.362319		10	0.268374
	16	0.172744	D5S818	12	0.846892
			D5S818	13	0.153108
			D13S317	11	0.658337
			D13S317	12	0.341663

基因型组合 (个体1, 个体2)	等位基因
基因型组合 (个体1, 个体2)	a	b	c
(bc, aa)	1-Mx=0.8	Mx/2=0.1	Mx/2=0.1
(aa, bc)	Mx=0.2	(1-Mx)/2=0.4	(1-Mx)/2=0.4
(ac, ab)	0.5	(1-Mx)/2=0.4	Mx/2=0.1
(ab, ac)	0.5	Mx/2=0.1	(1-Mx)/20.4
(cc, ab)	(1-Mx)/2=0.4	(1-Mx)/2=0.4	Mx=0.2
(bb, ac)	(1-Mx)/2=0.4	Mx=0.2	(1-Mx)/2=0.4

基于全局最小残差法快速分析混合STR图谱

Rapid analyzing mixed STR profiles based on the global minimum residual method

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参考文献 20

相关文章 6

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Metrics

基因型组合 (个体1, 个体2)	$residual_{D3S1358}^{combination}$
(bc, aa)		0.186370
(aa, bc)		0.123257
(ac, ab)		0.007941
(ab, ac)		0.121686
(cc, ab)		0.006380
(bb, ac)		0.082209

基因座	residual_locus*	分析结果 (个体1, 个体2)
D3S1358	0.006380	(16/16，15/18)
FGA	0.009769	(21/23，21/22)
D16S539	0.012796	(30/30，32.2/28)
D7S820	0.008203	(10/11，10/8)
TH01	0.004061	(8/8，5/6)
TPOX	0.006594	(8/11 8/10)
CSF1PO	0.008005	(10/10，11/12)

基因座locus	分析结果 (个体1, 个体2)	residual_locus
vWA	(16/18，19/15)	0.026884
D8S1179	(14/13，16/12)	0.023388
D18S51	(13/12，18/17)	0.007921
D16S539	(14/12，13/11)	0.008120
D3S1358	(16/16，15/18)	0.006380
FGA	(21/23，21/22)	0.009769
D16S539	(30/30，32.2/28)	0.012796
D7S820	(10/11，10/8)	0.008203
TH01	(8/8，5/6)	0.004061
TPOX	(8/11 8/10)	0.006594
CSF1PO	(10/10，11/12)	0.008005
D5S818	(13/13，12/12)	0.004398
D13S317	(11/11，11/12)	0.006806
residual_sum*		0.133323

基因座	最小残差法		全局最小残差法		mixsep
基因座	个体1	个体2	个体1	个体2	个体1		个体2
vWA	16/18	15/19	16/18	15/19	16/18	15/19
D8S1179	13/14	12/16	13/14	12/16	13/14	12/16
D18S51	12/13	17/18	12/13	17/18	12/13	17/18
D16S539	12/14	11/13	12/14	11/13	12/14	11/13
D3S1358	15/16	15/18	15/16	15/18	15/16	15/18
FGA	21/23	21/22	21/23	21/22	21/23	21/22
D16S539	32.2/28	32.2/30	30/30	28/32.2	30/30	28/32.2
D16S539	30/30	28/32.2
D7S820	10/11	8/10	10/11	8/10	10/11	8/10
TH01	8/8	5/6	8/8	5/6	8/8	5/6
TPOX	8/11	8/10	8/11	8/10	8/11	8/10
CSF1PO	10/10	11/12	10/10	11/12	10/10	11/12
D5S818	12/13	12/12	12/13	12/12	12/13	12/12
D13S317	11/11	11/12	11/11	11/12	11/11	11/12
	12/12	11/11

	全局最小残差法	GWO-全局最小残差法
Mx'	30%	29.48%
residual_sum最小值	0.030011	0.0298612
准确率	13/13	13/13
分析耗时	0.37s	0.13s

基因型组合	期望峰形	residual
7/9 9/9		0.004855
9/9 7/9		0.020229
7/9 7/9		0.151966
7/7 9/9		0.031656

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