Where Does Your Balding Gene Come From In Dna Makeup

Eur J Hum Genet. 2016 Jun; 24(6): 895–902.

Prediction of male-design alopecia from genotypes

Fan Liu,^ane, ² Merel A Hamer,³ Stefanie Heilmann,^four, ⁵ Christine Herold,^{half dozen} Susanne Moebus,⁷ Albert Hofman,^viii André Chiliad Uitterlinden,^9, ⁸ Markus Thousand Nöthen,^4, ⁵ Cornelia Chiliad van Duijn,⁸ Tamar EC Nijsten,³ and Manfred Kayser^i, ^*

Fan Liu

¹Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Holland

ⁱⁱFundamental Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese University of Sciences, Beijing, China

Merel A Hamer

³Department of Dermatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

Stefanie Heilmann

^fourDepartment of Genomics, Life and Brain Middle, University of Bonn, Bonn, Frg

^fiveInstitute of Human Genetics, University of Bonn, Bonn, Germany

Christine Herold

⁶German language Center for Neurodegenerative Disease (DZNE), Bonn, Germany

Susanne Moebus

⁷Institute of Medical Computer science, Biometry, and Epidemiology, Academy Hospital of Essen, University Duisburg-Essen, Essen, Frg

Albert Hofman

⁸Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

André G Uitterlinden

⁸Department of Epidemiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands

^ixDepartment of Internal Medicine, Erasmus MC Academy Medical Heart Rotterdam, Rotterdam, Kingdom of the netherlands

Markus One thousand Nöthen

⁴Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany

^vInstitute of Man Genetics, Academy of Bonn, Bonn, Germany

Cornelia M van Duijn

⁸Department of Epidemiology, Erasmus MC Academy Medical Center Rotterdam, Rotterdam, The netherlands

Tamar EC Nijsten

^threeDepartment of Dermatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

Manfred Kayser

¹Department of Genetic Identification, Erasmus MC University Medical Eye Rotterdam, Rotterdam, The Netherlands

Received 2015 Apr 17; Revised 2015 Aug 27; Accepted 2015 Sep 1.

Supplementary Materials: Supplementary Table S1.

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Supplementary Tabular array S2.

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Supplementary Table S3.

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Supplementary Table S4.

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Abstract

The global demand for products that effectively prevent the development of male-blueprint alopecia (MPB) has drastically increased. All the same, there is currently no established genetic model for the estimation of MPB risk. We conducted a prediction assay using single-nucleotide polymorphisms (SNPs) identified from previous GWASs of MPB in a total of 2725 German and Dutch males. A logistic regression model because the genotypes of 25 SNPs from 12 genomic loci demonstrates that early-onset MPB take a chance is predictable at an accurateness level of 0.74 when 14 SNPs were included in the model, and measured using the expanse nether the receiver-operating characteristic curves (AUC). Considering age as an additional predictor, the model tin can predict normal MPB status in centre-aged and elderly individuals at a slightly lower accurateness (AUC 0.69–0.71) when 6–xi SNPs were used. A variance partitioning analysis suggests that 55.eight% of early on-onset MPB genetic liability can be explained by common autosomal SNPs and 23.iii% by Ten-chromosome SNPs. For normal MPB status in elderly individuals, the proportion of explainable variance is lower (42.iv% for autosomal and nine.8% for 10-chromosome SNPs). The gap between GWAS findings and the variance partitioning results could be explained by a large trunk of common DNA variants with small furnishings that will probable exist identified in GWAS of increased sample sizes. Although the accurateness obtained here has non reached a clinically desired level, our model was highly informative for upward to xix% of Europeans, thus may assist decision making on early on MPB intervention deportment and in forensic investigations.

Introduction

Male-pattern baldness (MPB) or androgenic alopecia is the well-nigh common type of pilus loss in men, with a prevalence of effectually twenty% at age 20–30, and the incidence growing at 10% per decade.^{i, 2} MPB is a chronic problem commonly seen by dermatologists,³ with severe psychosocial consequences⁴ and limited effective therapeutic options.^v The effectiveness of most treatments (eg, minoxidil or finasteride) relies on how early they are applied. Therefore, the power to predict the early-onset or normal MPB condition using Deoxyribonucleic acid variants may have important implications for handling strategies. Furthermore, owing to its widespread prevalence and the fact that most criminals are men, MPB in principle could help identify unknown perpetrators via the concept of forensic Dna phenotyping,^{6, vii} especially in light of the electric current progress in predicting chronological age from DNA data.^{8, 9} Still, then far there is no established genetic model for predicting MPB from genetic information providing motivation for the present study.

MPB is a highly heritable visible trait, with estimated heritability of about 80% in young² and elderly males.^ten A locus on chromosome Xq12 harboring the androgen receptor gene (AR) and its neighboring ectodysplasin A2 receptor cistron (EDA2R) is known as the major locus for MPB.^{11, 12} In addition, two genetic loci on chromosome 20p11 (PAX1/FOXA2) and 7p21.one (HDAC9) were identified to be involved in MPB.^{13, 14, 15} A meta-analysis of seven GWASs for early-onset MPB involving ~xiii 000 individuals of European origin conducted by the International MAAN Consortium¹⁶ replicated these loci and highlighted five boosted loci showing genome-wide meaning association with MPB; these included 1p36.22, 2q37.3, 7q11.22, 17q21.31, and 18q12.iii. Individuals in the highest-risk quartile of a genotype score had an approximately sixfold increased chance of early-onset MPB. In addition, the most recent study by Heilmann et al ¹⁷ comprising 2759 cases and 2661 controls successfully identified four additional autosomal loci showing genome-broad pregnant clan with MPB; these included 2q35, 3q25.one, 5q33.iii, and 12p12.ane (identifying a total of 12 genetic loci so far). These findings on one hand highlight a relatively strong X-linked major cistron locus event (odds ratio per chance allele ~two.five at the AR locus) and on the other manus suggest a highly polygenetic autosomal component (odds ratios at private loci <1.5). Here we estimate to what degree MPB is predictable using the currently known Dna markers discovered from GWAS and then far. This study includes a total of 2725 German and Dutch males, with included early-onset MPB patients and controls as well as heart-anile and elderly cases and controls.

Materials and Methods

Ideals statement

All studies were canonical by the institutional ethics review committees at the relevant organizations, and written informed consent was provided by all participating individuals.

Rotterdam Report

The Rotterdam Study (RS) is a population-based prospective report of Dutch elderly subjects (>45 years of historic period) consisting of an initial cohort and 2 extensions.^xviii MPB condition was assessed by trained physicians co-ordinate to the Norwood–Hamilton grading scale^{1, 19} with grades 1–12. Cases were defined as form IV–VIII and otherwise controls. The current study included 1161 male person RS subjects. RS samples take not been used for MPB GWAS earlier and are not part of MAAN; hence, RS sample are completely independent from previous MPB SNP discoveries. Anonymized individual-level phenotype and genotype information used for the prediction analysis from the Rotterdam Written report participants are available in Supplementary Table S1.

Erasmus Rucphen Family Study

Erasmus Rucphen Family (ERF) is a family-based study that includes inhabitants of a genetically isolated community in the south–west of the Netherlands, studied as role of the Genetic Research in Isolated Population programme.^twenty Study population includes ~3000 individuals who are living descendants of 22 couples who had at least six children baptized in the community church. All data were collected between 2002 and 2005. The population shows minimal immigration and high inbreeding. Cases were defined as Norwood–Hamilton grading scale^{ane, 19} 4–8 at any age or form Ii–III between 50 and sixty years of age or grade I earlier 50 years of age, and controls otherwise. The current study included 567 male ERF subjects. ERF samples have not been used for MPB GWAS before and are not role of MAAN; hence, ERF samples are completely independent from previous MPB SNP discoveries. The ERF Report data are archived in European Genome–Phenome Database (EGA) with the accretion code EGAS00001001134.

BONN Written report

The pilus status of each participant was assessed by a dermatologist according to the Hamilton/Norwood (HN) nomenclature.^{one, 3} Affected men were anile <30 years with HN grades IV–VII, or <twoscore years with HN grades V–VII, and were thus representative of the most severely affected ten% for the respective age classes (N=581). The control sample comprises 270 men anile ⩾threescore years with no signs of AGA (20% least afflicted individuals in the population) and 146 male controls (HN⩽5) that were recruited every bit office of the Heinz Nixdorf Recollect accomplice (gamble factors, evaluation of coronary calcium and lifestyle) at the University of Essen. All the cases and controls were of German language descent.^thirteen Note that the 581 cases and the 146 controls from the BONN Study used here were part of the initial MAAN study,¹⁶ and all BONN subjects were also part of the previous study of Heilmann et al,¹⁷ based on which the SNPs used here for MPB prediction were initially discovered. At that place is thus a potential take a chance in overestimating the prediction accuracy in BONN simply at most by only a pocket-sized degree as BONN was but a small component of the MAAN study¹⁶ (ie, xiv.9% of cases and ane.6% of controls in MAAN and 21.one% of cases and 15.half dozen% of controls in the Heilmann study). The RS and ERF subjects are completely independent from previous studies. The BONN Study information are archived in European Genome–Phenome Database (EGA) with the accession code EGAS00001001354.

Genotyping and quality control

The genotyping platforms, quality controls, and imputation methods used in participant studies have been described in particular previously. In brief, extensive quality command thresholds were applied to include mutual SNPs (minor allele frequency >1%) with a high call charge per unit (95%) for genotyped SNPs; SNPs demonstrating difference from Hardy–Weinberg equilibrium (P<ten^−vi) were excluded. SNP genotypes from all cohorts were imputed using the m-Genome Projection as the reference panel with loftier-quality metrics (variance ratio 0.three for MACH and proper info statistic 0.4 for IMPUTE).^{21, 22} The X-chromosome in ERF was imputed using HapMap-CEU samples as the reference. After all quality controls, this report includes 2 444 603 autosomal SNPs and 239 421 X-chromosome SNPs in RS; 2 266 959 autosomal SNPs and 89 191 10-chromosome SNPs in BONN; and half-dozen 099 730 autosomal SNPs and 16 708 X-chromosome SNPs in ERF. All SNPs are described using dbSNP ID according to man reference assembly GRCh37.p13.

Statistical analyses

We initially selected twenty SNPs from 12 genomic loci for prediction analysis from the Table two and the Supplementary Table S2 of Li et al ^sixteen as well as Table 1 of Heilmann et al. ¹⁷ A candidate SNP analysis was conducted using logistic regression in all three cohorts for the 20 SNPs assuming additive allele issue adapted for age at examination when appropriate. As the AR locus has a relatively large upshot, we additionally selected four SNPs from the AR locus showing some residual consequence based on a conditional logistic regression analysis of all SNPs within 66.5–67.ix Mbp of the AR locus in a stepwise way, that is, until the newly included SNP is not significant anymore at P<0.05 level in a multivariate analysis of all SNPs accumulated in previous steps. A prediction analysis including all 24 selected SNPs was conducted separately in all three cohorts; age at examination was included as a predictor in RS and ERF only not in BONN consisting of early-onset cases and screened elderly controls. Then the final selection and ranking of the SNP predictors was based on stepwise analysis of the Akaike data criterion²³ using R part 'footstep'. The prediction models were trained separately in the iii cohorts based on multivariate logistic regression. Because our sample size is not large and an explicit validation set was non available, we used the leave-one-out cross-validation method to forestall overfitting, that is, the prediction models were trained in all subjects except one, who was used for validation by applying the trained model on this subject. We repeated this procedure iteratively over the whole cohort by leaving each subject out, and obtained the predicted probability of baldness status for all subjects. The predicted probabilities are compared with observed baldness condition, where the AUCs²⁴ were derived as an overall measurement of prediction accuracy. An AUC value ranges from 0.five representing random prediction to i.0 representing perfect prediction. Binary prediction of alopecia condition for each field of study was defined if the predicted probability is >0.v otherwise non-bald. The predicted and observed baldness status was compared using a confusion table, where sensitivity and specificity values are derived, both ranging from 0 to 1. Sensitivity and specificity values are a pair of inseparable accurateness parameters for a binary classifier; a perfect classifier would exist described as 100% sensitive and 100% specific. All candidate SNP analysis and prediction assay were conducted in R version 3.2.0 (http://www.r-project.org/).

Table 2

Multivariate analysis and leave-one-out cross-validated prediction of male-blueprint alopecia based on 25 SNPs from 12 genomic loci in BONN, RS, and ERF

Rank	Predictor	CHR	EA	Multivariate analysis		Accumulative
				Beta	P	AUC	SENS	SPEC
BONN
1	rs1511061	Xq12	C	−1.303	2.52E-10	—	0.971	0.230
2	rs804520	20p11.22	A	−0.575	i.49E-07	0.543	0.868	0.410
three	rs2073963	7p21.1	1000	0.379	5.90E-04	0.651	0.894	0.385
four	rs6945541	7q11.22	C	0.355	six.76E-04	0.696	0.872	0.425
5	rs7349332	2q35	T	0.480	1.69E-03	0.705	0.897	0.423
6	rs10502861	18q12.3	T	−0.369	2.22E-03	0.719	0.865	0.471
seven	rs9287638	2q37.iii	A	0.341	iii.54E-03	0.726	0.848	0.456
8	rs929626	5q33.three	Thousand	−0.661	7.66E-03	0.731	0.830	0.491
9	rs12565727	1p36.22	K	−0.332	1.15E-02	0.731	0.848	0.466
ten	rs17762954	17q21.31	T	−0.324	i.19E-02	0.733	0.836	0.491
eleven	rs113043121	Xq12	A	−0.247	2.87E-02	0.739	0.829	0.509
12	rs471205	Xq12	T	0.277	ii.89E-02	0.741	0.845	0.501
13	rs9668810	12p12.i	T	0.229	6.33E-02	0.741	0.836	0.516
xiv	rs1081073	5q33.3	T	−0.410	9.70E-02	0.741	0.838	0.501

RS
1	Age (year)			0.094	four.14E-24	0.671	0.728	0.514
two	rs2497938	Xq12	C	−0.695	2.30E-04	0.695	0.725	0.554
3	rs12565727	1p36.22	Grand	−0.329	ii.31E-03	0.700	0.718	0.574
4	rs141476270	Xq12	T	0.581	2.36E-03	0.704	0.718	0.574
5	rs1081073	5q33.three	A	−0.228	ane.05E-02	0.704	0.714	0.573
half dozen	rs182829063	Xq12	Grand	0.496	2.41E-02	0.706	0.712	0.569
seven	rs113043121	Xq12	A	0.266	3.34E-02	0.708	0.722	0.567
eight	rs6945541	7q11.22	C	0.186	4.42E-02	0.708	0.718	0.559
nine	rs12373124	17q21.31	C	−0.194	6.62E-02	0.709	0.722	0.584
10	rs1511061	Xq12	C	−0.280	i.07E-01	0.710	0.725	0.573
eleven	rs9287638	2q37.3	A	0.158	1.08E-01	0.710	0.727	0.578
12	rs2073963	7p21.1	G	0.147	1.12E-01	0.711	0.727	0.582

ERF
1	Historic period (year)			0.041	1.27E-07	0.615	0.479	0.691
2	rs1511061	Xq12	G	−0.513	1.01E-04	0.654	0.545	0.649
3	rs2073963	7p21.1	Thousand	0.384	iv.13E-03	0.673	0.574	0.677
four	rs12565727	1p36.22	G	−0.461	five.80E-03	0.677	0.591	0.681
5	rs804520	20p11.22	A	0.470	1.22E-02	0.678	0.574	0.699
6	rs6047844	20p11.22	C	−0.348	3.99E-02	0.679	0.574	0.695
7	rs929626	5q33.iii	M	−0.250	6.24E-02	0.685	0.599	0.691

Estimates of the proportion of variance explained were calculated using the Genome-broad Complex Trait Analysis (GCTA) tool v1.24 (http://gump.qimr.edu.au/gcta/).²⁵ Genetic relationships were estimated using all autosomal SNPs (—make-grm, —maf 0.03). The tiptop ten eigenvectors from PCA analysis (—pca) were and so used every bit covariates in a restricted maximum likelihood analysis (—reml) to estimate the proportion of the variance explained by SNPs (V _G/V _P or narrow-sense heritability h ²), repeated for all autosomes as a whole, each autosome separately, and the 10 chromosome.

A GWAS for MPB condition was separately conducted in all cohorts using logistic regression considering historic period at examination equally a covariate (except in BONN, which used early-onset cases) using PLINK1.9 beta.²⁶ Family relationship was adapted using first four main components from EIGENSTRAT²⁷ assay. A meta-analysis of GWAS results was conducted using inverse variance fixed-outcome analysis. P-values equal to or smaller than 5 × ten^−eight were considered every bit genome-wide significant.

Results

The characteristics of study subjects (all male) are summarized in Supplementary Table S2 (BONN: 581 early on-onset cases and 416 controls; RS: 619 cases and 542 controls; and ERF: 252 cases and 315 controls). The BONN information take been described in a previous GWAS,¹³ whereas RS and ERF data accept not been used earlier for the genetic investigation of male-pattern baldness. Note that the BONN Report included only early-onset MPB cases (<40 years of age) and screened elderly controls while the RS (mainly elderly individuals: mean historic period 67.79 years, min 51.54, max 96.73) and ERF (mainly middle-aged: mean age 49.05 years, min xviii.07, max 79.03) are population-based studies without sample selection based on MPB status. The deviation in MPB prevalence observed betwixt RS (53.3%) and ERF (44.four%) is adequately consequent with a ~10% increase per decade of MPB¹ (Supplementary Table S2).

Candidate SNP analysis

We focused on xx candidate SNPs from 11 autosomal loci and the AR/EDA2R locus on X chromosome previously reported by Li et al ¹⁶ and Heilmann et al ¹⁷ (Tabular array one). Part of the BONN subjects was used in Li et al and Heilmann et al, and so as expected, the allelic effects in BONN for all tested SNPs was consistent with previous reports and near of the tested SNPs showed significant association with MPB (P<0.05). In RS and ERF, the chance alleles previously reported in other populations also showed higher frequencies in cases than in controls for all SNPs tested, that is, OR>ane.0, and seven SNPs at v genetic loci were nominally significantly associated with MPB (chr1p36: rs12565727 P=2.71 × 10⁻⁴; chr7q11: rs6945541 P=0.039; chr17q21: rs17762954 P=0.014 and rs12373124 P=5.18 × 10^−three; chr18q12: rs10502861 P=0.036; chrXq12: rs1511061 P=ii.sixteen × x⁻¹¹; and rs2497938 P=1.38 × x⁻¹²; Table 1). In ERF, the allelic outcome for all tested SNPs was consistent with previous studies and five SNPs at four genetic loci showed significant association with MPB (chr1p36: rs12565727 P=i.37 × 10⁻³; chr7q11: rs2073963 P=9.63 × x⁻⁴; chr7q11: rs6945541 P=0.037; chrXq12: rs1511061 P=4.98 × 10⁻⁵; and rs2497938 P=half dozen.38 × x⁻⁵). The genetic associations at 2q37 and 20p11 were not statistically significant associated in both RS and ERF, probable due to the smaller sample sizes compared with the previous GWASs. In the present meta-analysis of the three cohorts, the just locus that did not show pregnant clan was 3q25.i, but the allelic effect was in the same direction as Li et al. Overall, the observed allele issue sizes were similar between RS and ERF, and similar to previous estimates in other populations.^{14, 15, xvi} Noticeable exceptions were: (1) the chromosome 20p11 SNPs showed much smaller effects in RS and ERF (eg, rs6047844 OR=1.09 in RS and 1.eighteen in ERF, Table 1) than previous estimations (OR=1.60 in Li et al and i.82 in BONN); and (2) the SNPs at the EDA2R/AR locus showed much larger consequence (rs1511061 OR=9.07) in BONN than in RS and ERF (OR 2.68–2.75). These differences are likely explained past the extreme design in BONN, as BONN included merely early-onset MPB cases (<twoscore years of historic period) and a set of super controls (⩾60 years, no signs of MPB) while no MPB pre-selection was fabricated in RS and ERF, in which the majority of the subjects were elderly people.

Table i

SNPs from previous Li et al ^xvi and Heilmann et al ¹⁷ associated with male-blueprint baldness in BONN, RS, and ERF

Chr	SNP	HGVS	Previous studies				BONN		RS		ERF		Meta
			EA/NEA	OR	P	Ref	OR	P	OR	P	OR	P	OR	P
1p36.22	rs12565727	yard.11033082A>G	A/G	ane.33	9.07E-11	Li et al ¹⁶	1.40	three.55E-03	1.35	2.71E-04	i.64	1.37E-03	1.42	3.26E-07
2q35	rs10193725	k.218861775T>C	C/T	1.25	1.46E-x	Heilmann et al ¹⁷	1.thirty	1.99E-02	1.01	7.74E-01	—	—	one.13	1.05E-01
	rs7349332	k.219756383C>T	T/C	1.34	3.55E-15	Heilmann et al ¹⁷	1.46	iv.18E-03	1.02	seven.08E-01	—	—	1.twenty	three.73E-02
2q37.3	rs9287638	g.239694631C>A	A/C	1.31	1.01E-12	Li et al ^sixteen	1.31	7.05E-03	one.13	2.53E-01	—	—	one.21	four.59E-03
	rs9751918	g.239735812A>G	Thousand/A	1.27	ane.71E-08	Li et al ¹⁶	one.16	one.81E-01	1.19	4.60E-01	ane.13	three.90E-01	1.16	2.51E-02
3q25.1	rs7648585	g.151639765A>Thousand	Chiliad/A	i.eighteen	1.20E-09	Heilmann et al ¹⁷	1.08	4.33E-01	1.01	six.54E-01	1.11	4.35E-01	1.01	eight.53E-01
	rs4679955	thousand.151653368A>T	T/A	ane.xix	1.79E-ten	Heilmann et al ¹⁷	one.10	2.88E-01	1.01	7.42E-01	1.05	6.82E-01	i.03	5.83E-01
5q33.3	rs929626	m.158310631A>Yard	A/M	one.19	2.12E-11	Heilmann et al ¹⁷	1.39	4.39E-04	1.16	1.73E-01	i.26	vi.66E-02	one.26	4.57E-05
	rs1081073	g.158381512T>A	T/A	ane.17	viii.52E-09	Heilmann et al ¹⁷	1.25	two.00E-02	1.19	8.32E-02	1.23	9.98E-02	one.20	viii.66E-03
7p21.ane	rs2073963	yard.18877874T>M	G/T	1.29	one.08E-12	Li et al ¹⁶	1.43	1.87E-04	one.fifteen	seven.03E-01	1.50	ix.63E-04	1.32	1.31E-06
7q11.22	rs6945541	g.68611960C>T	C/T	1.27	one.71E-09	Li et al ¹⁶	i.52	4.38E-06	1.15	3.97E-02	1.30	three.75E-02	1.31	1.34E-06
12p12.1	rs9668810	yard.26426420T>C	T/C	one.21	1.09E-10	Heilmann et al ¹⁷	i.25	three.16E-02	i.02	4.23E-01	i.28	8.73E-02	1.xv	two.86E-02
	rs7975017	g.26428793T>C	T/C	1.21	iv.03E-10	Heilmann et al ¹⁷	ane.28	3.16E-02	ane.02	5.35E-01	i.25	one.49E-01	one.09	iii.25E-01
17q21.31	rs17762954	m.43899786C>T	C/T	one.32	iv.87E-08	Li et al ¹⁶	1.17	1.58E-01	1.eighteen	1.40E-02	—	—	one.18	3.74E-02
	rs12373124	one thousand.43924219T>C	T/C	i.33	5.07E-x	Li et al ¹⁶	—	—	1.22	5.18E-03	—	—	—	—
18q12.three	rs10502861	g.42800148C>T	C/T	1.28	2.62E-09	Li et al ^xvi	i.42	7.96E-04	i.12	iii.68E-02	1.ten	5.20E-01	1.17	one.12E-02
20p11.22	rs6047844	g.22037575T>C	T/C	1.60	one.71E-39	Li et al ^sixteen	—	—	1.09	ii.77E-01	one.eighteen	1.77E-01	1.12	1.25E-01
	rs804520	yard.22119141A>Grand	1000/A	1.56	6.82E-35	Li et al ^xvi	1.83	2.78E-10	1.11	vii.59E-02	ane.12	4.17E-01	1.29	one.85E-05
Xq12	rs1511061	m.66316124C>T	T/C	2.38	vi.82E-90	Li et al ¹⁶	nine.07	4.52E-18	two.68	2.16E-xi	2.85	4.98E-05	3.57	2.56E-22
	rs2497938	g.66563018T>C	T/C	2.20	2.40E-91	Li et al ^sixteen	vii.12	one.64E-17	2.73	1.38E-12	two.35	half dozen.38E-05	3.52	6.27E-25

Prediction assay

A prediction analysis included all 20 SNPs listed in Table ane as predictors. We also added 4 extra Ten chromosome SNPs within a 66.five–67.9-Mbp region of the AR locus (rs113043121, rs471205, rs141476270, and rs182829063), which showed significant residual issue after conditioning on the top-associated Ten SNPs based on a stepwise provisional analysis (Tabular array 2). For predicting MPB in RS and ERF we as well added age as boosted predictor (which in BONN was left out because of an extreme case–control design, that is, early-onset MBP patients and elderly screened controls). The overall prediction accuracy was derived using receiver-operating characteristic curves generated separately in the three studies (Effigy 1a). In BONN, the AUC was estimated at 0.74 (sensitivity=0.84, specificity=0.50; Table 2) using fourteen DNA markers and adding additional markers did not improve the prediction accuracy. In RS, the maximal AUC value was estimated at 0.71 (sensitivity=0.73, specificity=0.58) when age and xi Deoxyribonucleic acid markers were included (Table two). In ERF, AUC was estimated at 0.69 (sensitivity=0.60, specificity=0.69) when age and 6 Dna markers were included (Tabular array 2). Notation that in ERF, some SNPs on chr2q35, chr17q21, and the 10 chromosome were not available, which could explain a lower accuracy in ERF than in RS. The extra SNPs were not available in ERF due to differences in the imputation procedures on the X chromosome (see Materials and Method department). The slightly lower prediction accuracies in RS/ERF versus BONN are likely explained by the fact that farthermost early-onset MPB cases were selected in BONN, while RS and ERF are population-based cohorts without early-onset MPB option. Equally may be expected, chronological historic period alone was the strongest predictor of MPB in both population-based sample sets (RS AUC=0.67, ERF AUC=0.62). Excluding the extra chrX-SNPs reduced the prediction accuracy in both RS and BONN, but only marginally (BONN AUC=0.73, RS AUC=0.71). Although the AUC values are lower than a clinically desired level (0.85) for diagnosis, our prediction model provided very accurate prediction results for a good proportion of individuals, that is, individuals with predicted probabilities of alopecia <0.2 or >0.8 (BONN 8.2% <0.two and 10.9% >0.8, Effigy 1b; RS 5.2% <0.2 and eight.3% >0.8, Figure 1c; ERF 7.two% <0.2 and 0.9% >0.8, Figure 1d; too see Supplementary Table S3). In practise, our model may provide a highly informative test for these individuals (19% in BONN, 14% in RS, and eight% in ERF) but less informative for the rest.

An external file that holds a picture, illustration, etc. Object name is ejhg2015220f1.jpg

Prediction results of male-pattern baldness in the three written report populations. (a) Receiver-operating characteristic curves for predicting male person-blueprint baldness in three European population samples (BONN, RS, and ERF). In BONN Written report the prediction model for early-onset MPB included 14 SNPs as predictors (Table ii); the model for predicting MPB in elderly RS people included eleven SNPs and age every bit predictors (Table 2); and the prediction model in ERF included 6 SNPs and age as predictors (Table two). (b) Histogram of predicted probability overplayed with per centum of baldness in each probability bin (BONN Study). (c) Histogram of predicted probability overplayed with percentage of baldness in each probability bin (Rotterdam Written report). (d) Histogram of predicted probability overplayed with pct of baldness in each probability bin (ERF Report).

GCTA analysis

A GCTA analysis was performed to judge the proportion of MPB variance explained by all mutual SNPs available in BONN and RS (Table 3). ERF was excluded from this analysis as it is a family-based report and information technology has a limited number of bachelor X-chromosome SNPs. In BONN, the variance in liability to early-onset MPB was partitioned 55.viii% to all autosomal mutual variants and 23.3% to 10-chromosome variants. In RS, the variance was partitioned 42.4% to all autosomes and 9.viii% to the X chromosome. Not surprisingly, early-onset MPB in BONN demonstrated an overall higher heritable component explainable past mutual DNA variants than normal MPB status in elderly individuals in RS. Nosotros further partitioned the proportion of variance explained by variants to each chromosome (Table three,Supplementary Figure S1). In BONN, chromosomes one (h ²=13.9%), six (12.5%), 17 (9.half-dozen%), 20 (24.3%), and X (27.two%) all explained a significant portion of early-onset MPB variance (Supplementary Figure S1A); the variance partitioned to other chromosomes was inconclusive due to large standard errors as the 95% confidence intervals included 0. In RS, only chromosome X showed a significant effect (h ²=9.8%, 95% interval: 0.2–nineteen.4%, Supplementary Figure S1B) in explaining MPB status in elderly individuals.

Table 3

Proportion of variance in male-blueprint baldness liability explained past mutual SNPs

Chr	Rotterdam Report		BONN
	VG/VP	SE	VG/VP	SE
All autosomes	0.424	0.234	0.558	0.216
X	0.098	0.049	0.233	0.011
1	0.104	0.075	0.139	0.067
two	0.000	0.069	0.000	0.061
iii	0.051	0.067	0.000	0.060
iv	0.064	0.065	0.000	0.053
5	0.095	0.068	0.006	0.050
6	0.000	0.060	0.125	0.063
7	0.000	0.061	0.102	0.057
eight	0.072	0.062	0.006	0.053
9	0.056	0.060	0.000	0.050
x	0.056	0.060	0.000	0.049
xi	0.000	0.083	0.000	0.051
12	0.029	0.089	0.000	0.050
13	0.000	0.077	0.018	0.043
xiv	0.075	0.077	0.039	0.046
15	0.078	0.077	0.047	0.038
16	0.039	0.078	0.000	0.039
17	0.063	0.077	0.096	0.044
18	0.027	0.070	0.000	0.042
xix	0.025	0.059	0.031	0.034
20	0.000	0.072	0.243	0.051
21	0.013	0.057	0.092	0.058
22	0.106	0.062	0.017	0.028

Meta-analysis of GWAS

GWASs for MPB were conducted in BONN, RS, and ERF. The QQ and Manhattan plot in ERF looks very dissimilar from RS and BONN (Supplementary Figure S2), that is, in ERF no genome-wide significant associations was detected including the EDA2R/AR locus (rs1511061 P=7.ii × 10^−five). This underlies the importance of big sample sizes in GWAS even for detecting major gene effects. A meta-analysis of the GWAS results in the three cohorts identified 946 SNPs in four genetic loci that showed genome-broad pregnant association with MPB (P<5 × 10^−eight, Supplementary Table S4, Supplementary Effigy S2). These include 739 SNPs on chromosome Xq12 between the EDA2R and AR genes, 205 SNPs on chromosome 20p11 (min P=1.76 × 10⁻¹⁵ observed for rs6075850), one SNP on chromosome 7p21.i (rs756853 P=three.3 × 10⁻⁸), and ane SNP on chromosome 6p25.1 (rs4959410 P=3.35 × 10^−viii). Except 6p25.1, all loci have been previously associated with MPB.^{14, xv, 16} The associated SNP at 6p25.1 is a unmarried genome-wide significant finding not supported by boosted SNPs in the region and thus warrants further validation.

Discussion

Candidate DNA variants in 12 previously discovered loci showed robust association with early-onset, also equally normal, MPB in German and Dutch males. Although ane locus (3q25.one) was non statistically significantly associated, overall genetic issue sizes from all loci were like to previous estimates. A major cistron issue on Xq12 was confirmed with an allelic OR of upwards to ix.07 for early-onset MPB and 2.7–2.viii for normal MPB in middle-aged and elderly men. The combined predictive ability of all candidate DNA variants was too higher for predicting early-onset MPB cases (AUC 0.74) than predicting MPB in middle-anile and elderly men from population-based studies (AUC 0.68–0.71), for whom age served equally the strongest predictor (historic period-alone AUC 0.62–0.67). Although the overall predictive accuracy has non reached a practically desired level (>0.85), our gamble model may evidence useful in assisting conclusion making on early preventive deportment for MPB and in forensic investigations for virtually 8–xix% European individuals. Contrasting the observation that the 12 known DNA loci together explained rather express variation of MPB liability, a variance partitioning analysis demonstrated that a large proportion of the phenotypic variance can exist explained by all genotyped common SNPs available in the microarray data sets. This gap is probable due to many common variants with minor effect sizes, which volition likely be identified in hereafter larger studies. Finally, our meta-assay of the GWAS results identified one new locus at 6p25.1, which demonstrated significant association with MPB.

Information technology has long been known that Dna variants in or near the AR gene increase the hazard of patterned hair loss in both men and women, but the exact identity of the causal DNA variant(s) remains unclear. A common synonymous coding variant rs6152 G>A (StuI restriction site) in the exon 1 of the AR gene has been associated with MPB in previous studies.^{eleven, 12} For example, Ellis et al ¹¹ institute that in an Australian accomplice the G allele was present in 98.1% of young bald men, 92.3% of older bald men, and merely 76.6% of non-bald men. The variant rs6152 is available in RS and was also highly significantly associated with MPB (P=v.6 × ten^−viii) only much less then than the acme-associated X-chromosomal SNP rs1511061 (P=2.6 × 10^−eleven in RS), and information technology became nonsignificant (P>0.05) when conditioning on the genotypes of rs1511061. The SNP rs1511061 is an intergenic noncoding SNP located 236 kbp upstream of the AR coding region. These results advise noncoding sequences upstream of AR containing functional variants. These could have regulatory effects equally we recently established for noncoding variants in some other homo appearance trait – pigmentation.^{28, 29} The T allele (or A allele on the reverse strand of the genome) of rs1511061 is the major allele in our sample with a pronounced frequency in MPB cases (run into Tabular array i). Bold MPB is a monogenic phenotype caused past a single variant (ie, rs1511061), it would suggest alopecia is the default phenotype in advanced ages, that is, the majority of males (wild-blazon allele) will eventually develop baldness while the minor allele provides a protective effect. The T allele of rs1511061 is also the major allele in HapMap-CEU (due north=226, f=0.885), fixed in HapMap-HCB (due north=ninety, f=1.0) and JPT (n=88, f=1.0), merely reversely fixed in HapMap-YRI (n=120, f=0.0). This contradicts rs1511061 (and its high LD SNPs such as rs2497938) being causal because such a design of allele frequency distribution tin hardly be correlated to the prevalence pattern at a global level equally Asians and African–Americans have lower MPB prevalence and less severe MPB than Europeans.^{30, 31} Functional analyses of the noncoding sequence in this region, such equally those we previously carried out for pigmentation gene regions,^{28, 29} are required to reveal the exact identity of the causal MPB genetic variant(s).

It has been suggested that at that place might be different genetic influences on balding in immature men (ie, early on-onset MPB) and on not-balding in elderly men, based on the ascertainment of an increased frequency of non-balding in the fathers of non-bald elderly men.³² All the same, twin studies take shown that the heritability of MPB estimated in young men^two was similar to that estimated in elderly men¹⁰ (both around 80%). In our report, the genetic component of early-onset MPB (ie, BONN) showed somewhat different signature than that of normal MPB in elderly people (ie, RS), that is, the variance partitioning assay suggests that a higher percentage of variance in early-onset MPB can be addressed past all mutual SNPs compared with that in elderly men. This is probable due to the extreme case–control design used in BONN Study, where early on-onset cases and screened elderly controls were contrasted to enhance the genetic dissimilarity. Furthermore, it is uncertain whether this discrepancy is due to phenotyping errors, as classifying MPB status in elderly people is much more fault prone than defining early-onset cases. The variance partitioning analysis might exist more sensitive to measurement errors than in twin studies, whereas for the latter even the consequence of some differential misclassifications tin exist canceled out between monozygotic and dizygotic twins, that is, if the nomenclature for 1 twin is differentially biased due to an unobserved factor, the same bias probable occurs to the other twin at a similar degree. Nevertheless, it is at least clear that the allelic effects at Xq12 and 20p11 are pronounced in early-onset MPB cases. For case, in our data the chance allele at Xq12 was presented in 96.5% of the early-onset cases, in 88% of the middle-aged and elderly cases, and in 75% of all controls.

The genetic architecture of human circuitous traits differs essentially even between the ones with like heritability. For example, eye color and adult torso height both have heritability estimates of about 80%. The genetic architecture of eye color, all the same, is relatively elementary with a very strong major gene effect provided by a noncoding SNP at the HERC2 cistron regulating transcription of the neighboring OCA2 pigmentation cistron,²⁹ and several minor-effect SNPs, which together predict the phenotype at very high accurateness (AUC>0.nine for blue/brown, explaining over 50% phenotypic variance).^{33, 34} On the other mitt, adult trunk elevation has long served as a model trait of extreme genetic complexity, that is, without whatever major factor effect, 180 SNPs together could predict height at AUC of 0.75 and explain ~12% of the phenotypic variance,³⁵ and with contempo progress³⁶ the AUC for predicting superlative is expected to be larger. In calorie-free of the current and previous studies, MPB appears something in the middle. It does have a major factor effect, that is, on the Ten chromosome (although much weaker than that of eye color) and likely involves many common variants with small effects (probably more than eye color). Improving the prediction accuracy for MPB volition rely on the identification of more than trait-associated DNA variants in GWAS of increased sizes, which will be a continuous and accumulative effort but certainly achievable in the future, as scientists already take achieved in studying homo height (accumulative sample size over 250 000, several thousand SNPs together explicate about xxx% of the phenotypic variance).³⁶ Therefore, we may await that the prediction accuracy of MPB will somewhen surpass that of torso height given information technology has a known major cistron effect, which is absent for height.

Our meta-analysis identified ane new locus on chromosome 6p25.1 showing significant association with MPB. The associated SNP rs4959410 is surrounded by a pseudogene (BTF3P7, basic transcription factor 3 pseudogene vii) and several uncharacterized genes. The closest known genes are LY86 (lymphocyte antigen 86) and RREB1 (ras responsive element-bounden protein 1). No existing evidence supports the involvement of whatsoever of these genes in hair loss. Therefore, this finding yet needs to be confirmed in independent samples.

In conclusion, past taking all bachelor SNPs previously found to exist associated with MPB at a genome-wide significant level and testing their predictive value in 2725 German and Dutch males with early on-onset MPB patients too equally from middle-anile to elderly men, we achieved prevalence-adjusted prediction accuracies expressed as AUC values of around 0.7 (where 0.5 ways random prediction and 1.0 ways completely accurate prediction). Although the prediction accuracy has non reached a level useful in do, our preliminary genetic model may already assist decision making on early MPB preventive actions and in forensic investigations. Furthermore, our results imply that with more genome-broad significantly associated SNPs identified in the hereafter and included in the prediction model together with the DNA markers presented here, male person-pattern baldness will likely go predictable from Dna with high plenty accuracy to allow routine practical applications such as in medicine and forensics.

Acknowledgments

We give thanks Dr David Gunn for his valuable discussions and useful comments on the manuscript. We thank Pascal Arp; Mila Jhamai; Marijn Verkerk; Lizbeth Herrera; Marjolein Peters, MSc; Carolina Medina-Gomez, MSc; and Fernando Rivadeneira, Physician PhD, for their help in creating the GWAS database, and Karol Estrada, PhD; Yurii Aulchenko, PhD; and Carolina Medina-Gomez, MSc, for the creation and analysis of imputed data. We thank Sophie Flohil, Emmilia Dowlatshahi, Robert van der Leest, Leonie Jacobs, Joris Verkouteren, Ella van der Voort, and Shmaila Talib for collecting the phenotype data in the RS. This work was supported in part by the Erasmus MC Academy Medical Center Rotterdam and funds from kingdom of the netherlands Genomics Initiative/Netherlands Organization of Scientific Research (NWO) within the framework of the Netherlands Consortium of Salubrious Ageing (NCHA).

The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS 2, RS III) was executed by the Man Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The netherlands. The GWAS data sets are supported by the Netherlands Organisation of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO), and holland Consortium for Healthy Aging (NCHA), project no. 050-060-810. FL is supported by Chinese Thousand Talent Program for Distinguished Young Scholars and MAH is supported by Unilever. The Rotterdam Report is funded past Erasmus Medical Center and Erasmus University, Rotterdam; Netherlands System for the Health Research and Evolution (ZonMw); the Inquiry Institute for Diseases in the Elderly (RIDE); the Ministry of Teaching, Culture and Science; the Ministry building for Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists.

ERF Study equally a part of EUROSPAN (European Special Populations Research Network) was supported past the European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and as well received funding from the European Community'due south Seventh Framework Plan (FP7/2007–2013)/grant agreement Health-F4-2007-201413 by the European Commission under the program 'Quality of Life and Management of the Living Resources' of 5th Framework Programme (no. QLG2-CT-2002-01254) besides equally the FP7 project EUROHEADPAIN (no. 602633). High-throughput assay of the ERF data was supported by articulation grant from Netherlands Organization for Scientific Research and the Russian Foundation for Bones Research (NWO-RFBR 047.017.043).

The BONN Study is supported by Heinz Nixdorf Foundation, the High german Ministry building of Education and Science and the High german Research Council (D Glass; Project SI 236/8-1, SI236/ix-one, ER 155/six-1); High german Research Council (D Drinking glass; FOR 423); and the Life and Encephalon GmbH (Bonn, Frg; project grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Notes

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies this paper on European Journal of Man Genetics website (http://world wide web.nature.com/ejhg)

Supplementary Material

Supplementary Tabular array S1

Supplementary Table S2

Supplementary Table S3

Supplementary Tabular array S4

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