Exploring the possibility of predicting human head hair greying from DNA using whole-exome and targeted NGS data

BMC Genomics

Table 2 Validation of SNPs associated with hair greying by Adhikari et al. (2016) in a replication cohort of 849 individuals from Poland

SNP_ID	Chr position GRCh38	Gene	fMA	BLR		MLR3		MLR6
SNP_ID	Chr position GRCh38	Gene	fMA	beta	P-value^a	beta	P-value^a	beta	P-value^*
rs12203592	chr6:396321	*IRF4*	T 0.08	0.700	0.003	0.669	0.002	0.780	1.121 × 10⁻⁴
rs2361506	chr2:233830694	*MROH2A*	T 0.37	0.360	0.008	0.220	0.077	0.156	0.183
rs2085601	chr4:88974793	FAM13A	C 0.31	−0.068	0.630	−0.099	0.460	−0.056	0.658
rs7009516	chr8:24351334	ADAM28	G 0.46	0.036	0.785	−0.099	0.420	−0.107	0.361
rs1912702	chr11:79462038	MIR708; TENM4	T 0.37	0.029	0.823	−0.022	0.854	−0.012	0.917
rs11621135	chr14:71192892	PCNX; LOC145474; SNORD56B	A 0.44	0.028	0.829	0.065	0.588	0.031	0.786
rs281229	chr15:47426258	SEMA6D	T 0.00	-^b	-^b	-^b	-^b	-^**	-^**
rs1005241	chr22:47291868	LOC101927722; TBC1D22A	C 0.45	−0.153	0.252	−0.097	0.441	− 0.075	0.530

Significant results (P-value < 0.05) are marked with bold
BLR, binomial logistic regression; MLR3, multinomial ordinal logistic regression for 3 hair greying categories; MLR6, multinomial ordinal logistic regression for 6 hair greying categories; MA, minor allele; fMA, frequency of minor allele
^aResults adjusted for age and sex
^bMonomorphic SNP

ISSN: 1471-2164