SNP密度对亲缘关系推断效能的影响及IBS算法的机器学习优化

doi:10.16288/j.yczz.25-287

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Hereditas(Beijing) ›› 2026, Vol. 48 ›› Issue (6): 570-588.doi: 10.16288/j.yczz.25-287

• Research Article • Previous Articles Next Articles

SNP density impact on kinship inference and IBS-machine learning optimization

Lv Dai¹^,²(), Zichen Tang³, Zhen Jia¹^,², Li Jiang², Chuantong Zhao¹^,², Zhiyuan Zhao¹^,², Wenting Zhao²(), Caixia Li¹^,²()

¹ School of Criminal Investigation, People’s Public Security University of China, Beijing 100038, China
² Beijing Engineering Research Center of Crime Scene Evidence Examination, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing 100038, China
³ School of Life Sciences, Jiangsu Normal University, Xuzhou 221116, China

Received:2025-12-08 Revised:2026-02-05 Online:2026-06-20 Published:2026-02-12
Contact: Wenting Zhao, Caixia Li E-mail:1130039162@qq.com;wtzhao@sibs.ac.cn;licaixia@tsinghua.org.cn
Supported by:
Fundamental Research Funds for Institute of Forensic Science(2024JB043);Fundamental Research Funds for Institute of Forensic Science(2024JB026);National Natural Science Foundation of China(82171870);Beijing Nova Program of Science and Technology(20220484149)

Abstract

Abstract:

In recent years, multiple panels containing varying numbers of single nucleotide polymorphisms (SNPs) have been reported in forensic genetics for kinship inference. However, systematic exploration of the impact of SNP number on inference performance and the application of machine learning algorithms remains lacking. Therefore, we evaluated the impact of SNP number on kinship inference performance and the optimization effects of machine learning methods on the identity-by-state (IBS) algorithm. We constructed multiple SNP panels with SNP numbers ranging from 15,476 to 20,838, and evaluated the performance of the likelihood ratio (LR) method and the IBS algorithm for kinship inference under different SNP numbers based on simulated pedigrees. After selecting the optimal SNP panel, we validated it using real pedigrees and further combined the IBS algorithm with machine learning methods to enhance inference performance. Our results showed that for the LR method, the sensitivity in inferring sixth and seventh degree kinships exhibited a significant positive correlation with SNP number. For the IBS algorithm, although the sensitivity in inferring fourth to seventh degree kinships showed a significant positive correlation with SNP number, the actual improvement was limited (only 0.5%~2.2% increase). Based on these results, we determined the optimal panel containing 20,838 SNPs (21K panel). The 21K panel based on the LR method could accurately infer kinships within sixth degree (with a sensitivity of 93.65% for sixth degree kinship inference), and the 21K panel based on the IBS algorithm could accurately infer kinships within third degree (with a sensitivity of 86.79% for third degree kinship inference). After combining the IBS algorithm with machine learning, the sensitivity for fourth degree kinship inference improved from 69.10% to 87.66%, the sensitivities for fifth and sixth degree kinships improved from 38.03% and 21.41% to 48.75% and 37.80%, respectively.

Key words: kinship inference, likelihood ratio method, IBS method, machine learning

Lv Dai, Zichen Tang, Zhen Jia, Li Jiang, Chuantong Zhao, Zhiyuan Zhao, Wenting Zhao, Caixia Li. SNP density impact on kinship inference and IBS-machine learning optimization[J]. Hereditas(Beijing), 2026, 48(6): 570-588.

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亲缘关系	亲缘关系系数	推断标准	零IBD共享统计量	推断标准
MZ	$\frac{1}{2}$	>$\frac{1}{{2}^{3/2}}$	0	<0.1
PO	$\frac{1}{4}$	($\frac{1}{{2}^{5/2}}$,$\frac{1}{{2}^{5/2}}$)	0	<0.1
FS	$\frac{1}{4}$	($\frac{1}{{2}^{5/2}}$, $\frac{1}{{2}^{5/2}}$)	$\frac{1}{4}$	(0.1, 0.365)
2nd	$\frac{1}{8}$	($\frac{\text{1}}{{\text{2}}^{\text{7}/\text{2}}}$, $\frac{\text{1}}{{\text{2}}^{\text{7}/\text{2}}}$)	$\frac{1}{2}$	(0.365, 1$-\frac{\text{1}}{{\text{2}}^{\text{3}/\text{2}}}$)
3rd	$\frac{1}{16}$	($\frac{\text{1}}{{\text{2}}^{\text{9}/\text{2}}}$, $\frac{\text{1}}{{\text{2}}^{\text{9}/\text{2}}}$)	$\frac{3}{4}$	(1$-\frac{\text{1}}{{\text{2}}^{\text{3}/\text{2}}}$, 1$-\frac{\text{1}}{{\text{2}}^{\text{3}/\text{2}}}$)
4th	$\frac{1}{32}$	($\frac{\text{1}}{{\text{2}}^{\text{11}/\text{2}}}$, $\frac{\text{1}}{{\text{2}}^{\text{11}/\text{2}}}$)	$\frac{7}{8}$	(1$-\frac{\text{1}}{{\text{2}}^{\text{5}/\text{2}}}$, 1$-\frac{\text{1}}{{\text{2}}^{\text{5}/\text{2}}}$)
5th	$\frac{1}{64}$	($\frac{\text{1}}{{\text{2}}^{\text{13}/\text{2}}}$, $\frac{\text{1}}{{\text{2}}^{\text{13}/\text{2}}}$)	$\frac{15}{16}$	(1$-\frac{\text{1}}{{\text{2}}^{\text{7}/\text{2}}}$, 1$-\frac{\text{1}}{{\text{2}}^{\text{7}/\text{2}}}$)
6th	$\frac{1}{128}$	($\frac{\text{1}}{{\text{2}}^{\text{15}/\text{2}}}$, $\frac{\text{1}}{{\text{2}}^{\text{15}/\text{2}}}$)	$\frac{31}{32}$	(1$-\frac{\text{1}}{{\text{2}}^{\text{9}/\text{2}}}$, 1$-\frac{\text{1}}{{\text{2}}^{\text{9}/\text{2}}}$)
7th	$\frac{1}{256}$	($\frac{\text{1}}{{\text{2}}^{\text{17}/\text{2}}}$, $\frac{\text{1}}{{\text{2}}^{\text{17}/\text{2}}}$)	$\frac{63}{64}$	(1$-\frac{\text{1}}{{\text{2}}^{\text{11}/\text{2}}}$, 1$-\frac{\text{1}}{{\text{2}}^{\text{11}/\text{2}}}$)
UN	0	≤0	1	1

SNP panel	亲缘关系	最小值	最大值	中位数	四分位差
15,476 SNPs	FS	1,072.95	3,024.46	1,947.54	345.67
	2nd	198.14	868.27	483.15	105.27
	3rd	41.03	432.76	214.26	77.44
	4th	2.14	221.04	80.55	38.53
	5th	-2.73	137.78	33.42	25.07
	6th	-1.89	91.12	13.44	15.10
	7th	-1.01	67.31	4.65	9.13
16,335 SNPs	FS	1,092.61	3,055.49	2,029.01	372.75
	2nd	202.07	796.77	505.16	112.90
	3rd	53.98	497.24	225.83	82.32
	4th	1.54	225.36	85.95	40.29
	5th	-2.53	152.54	36.09	25.72
	6th	-1.71	92.45	14.37	16.23
	7th	-0.96	52.27	5.10	9.34
17,190 SNPs	FS	1,124.99	3,391.81	2,123.87	382.52
	2nd	212.60	832.31	528.93	114.69
	3rd	43.67	498.70	235.10	84.76
	4th	2.72	257.00	90.38	42.63
	5th	-3.13	132.66	38.51	27.04
	6th	-1.65	104.99	15.60	17.00
	7th	-0.97	60.74	5.58	10.12
18,113 SNPs	FS	1,190.91	3,388.22	2,203.48	393.76
	2nd	208.09	862.78	551.54	120.46
	3rd	20.75	512.22	246.60	88.67
	4th	5.86	244.06	95.36	44.32
	5th	-2.74	149.03	41.03	29.33
	6th	-1.52	126.21	16.50	17.35
	7th	-0.86	54.70	6.02	10.36
19,022 SNPs	FS	1,251.28	3,499.48	2,302.01	407.16
	2nd	244.14	914.15	577.05	125.82
	3rd	55.03	537.41	259.37	91.19
	4th	9.56	258.09	101.22	46.68
	5th	-0.97	160.18	43.36	30.33
	6th	-1.57	109.03	17.75	18.81
	7th	-0.77	68.84	6.60	11.27
19,934 SNPs	FS	1,257.21	3,610.17	2,371.70	421.40
	2nd	235.84	962.51	600.05	127.67
	3rd	78.03	587.94	269.73	95.92
	4th	11.21	322.68	106.23	48.72
	5th	-2.28	172.12	45.85	31.39
	6th	-1.46	106.58	18.58	19.38
	7th	-0.66	72.88	6.90	11.54
20,838 SNPs	FS	1,259.09	3,726.38	2,466.37	445.04
	2nd	248.86	967.85	622.00	133.54
	3rd	63.64	620.12	279.62	97.89
	4th	1.51	297.68	111.32	51.26
	5th	-2.49	177.54	47.33	32.46
	6th	-1.26	116.53	19.80	20.38
	7th	-0.62	86.17	7.24	12.06

亲缘关系	t	灵敏度(%)	假阴性(%)	特异度(%)	假阳性(%)
FS/unrelated	4	100.00	0.00	100.00	0.00
2nd/unrelated	4	100.00	0.00	100.00	0.00
3rd/unrelated	4	100.00	0.00	99.90	0.10
4th/unrelated	4	100.00	0.00	99.80	0.20
5th/unrelated	4	99.56	0.44	99.50	0.50
6th/unrelated	4	93.65	6.35	99.30	0.70
	3	95.49	4.51	99.20	0.80
	2	96.72	3.28	98.30	1.70
	1	97.85	2.15	95.40	4.60
7th/unrelated	4	68.70	31.30	99.30	0.70
	3	74.24	25.76	98.90	1.10
	2	79.32	20.68	98.10	1.90
	1	86.21	13.79	93.60	6.40

真实亲缘关系	推断亲缘关系									灵敏度(%)	假阴性(%)	特异度(%)	假阳性(%)
真实亲缘关系	PO	FS	2nd	3rd	4th	5th	6th	7th	UN	灵敏度(%)	假阴性(%)	特异度(%)	假阳性(%)
PO	76	3	165							31.00	0.00
FS		131								100.00	0.00
2nd		7	316	9	1					94.89	0.00
3rd			3	300	136					68.34	0.00
4th				46	549	7				91.20	0.00
5th				3	413	469	28	1	1	51.54	0.11
6th					16	566	312	58	24	31.97	2.46
7th					5	126	407	226	121	35.70	13.67
UN						1	22	139	838			83.80	16.20

真实亲缘关系	推断亲缘关系										灵敏度(%)	假阴性(%)	特异度(%)	假阳性(%)
真实亲缘关系	PO	FS	2nd	3rd	4th	5th	6th	7th	>7th	UN	灵敏度(%)	假阴性(%)	特异度(%)	假阳性(%)
PO	244										100.00	0.00
FS		131									100.00	0.00
2nd			323	10							97.00	0.00
3rd			6	381	51	1					86.79	0.00
4th				36	416	123	13	7	2	5	69.10	0.83
5th					116	348	232	76	59	84	38.03	9.18
6th					13	148	209	142	159	305	21.41	31.25
7th					1	44	95	113	136	496	12.77	56.05
UN						17	64	97	133	689			68.90	31.10

SNP density impact on kinship inference and IBS-machine learning optimization

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IBS-ML	评估参数	PO (%)	FS (%)	2nd (%)	3rd (%)	4th (%)	5th (%)	6th (%)	7th (%)	UN (%)
IBS-XGBoost	灵敏度	100.0±0.0	100.0±0.0	97.0±2.7	83.2±7.3	81.2±5.2	47.6±11.7	35.7±13.0	14.0±9.0
	假阴性	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	5.5±2.7	33.9±11.2	44.9±9.9
	特异度									77.3±10.5
	假阳性									22.7±10.5
IBS-LightGBM	灵敏度	100.0±0.0	99.8±0.6	97.5±2.4	85.2±5.0	76.3±7.9	46.8±8.6	25.3±8.7	15.1±6.8
	假阴性	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	7.5±5.0	32.0±8.1	47.7±2.6
	特异度									74.6±16.2
	假阳性									25.4±16.2
IBS-RF	灵敏度	100.0±0.0	100.0±0.0	97.5±2.4	83.3±4.6	80.5±9.9	48.8±10.9	39.3±10.8	9.6±6.7
	假阴性	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	5.4±5.2	30.8±8.0	46.5±6.6
	特异度									76.3±11.7
	假阳性									23.7±11.7
IBS-DT	灵敏度	100.0±0.0	100.0±0.0	93.1±1.2	82.1±4.8	73.2±9.8	55.2±10.4	45.0±5.9	1.8±1.8
	假阴性	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	0.3±0.3	7.1±5.4	28.5±4.3	52.2±2.6
	特异度									72.3±13.5
	假阳性									27.7±13.5
IBS-KNN	灵敏度	100.0±0.0	100.0±0.0	97.2±2.6	85.2±7.3	74.3±8.1	44.1±5.5	25.0±6.3	24.5±3.4
	假阴性	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	0.3±0.3	9.0±2.3	20.7±6.8	33.3±13.0
	特异度									46.5±14.8
	假阳性									53.5±14.8
IBS-SVM	灵敏度	100.0±0.0	99.4±1.5	97.3±2.5	87.6±4.8	61.7±7.8	49.4±11.0	44.3±10.4	40.1±22.4
	假阴性	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	0.0±0.0	2.2±2.0	8.7±7.2	15.0±12.9
	特异度									26.5±22.3
	假阳性									73.5±22.3

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