Susceptibility variants for male-pattern baldness on chromosome 20p11

Hillmer, Axel M.; Brockschmidt, Felix F.; Hanneken, S.; Eigelshoven, Sibylle; Steffens, Michael; Flaquer, Antònia; Herms, Stefan; Becker, Tim; Kortüm, Anne-Katrin; Nyholt, Dale R.; Zhao, Zhen; Montgomery, Grant W.; Martin, Nicholas G.; Mühleisen, Thomas W.; Alblas, Margrieta; Moebus, Susanne; Jöckel, Karl‐Heinz; Bröcker-Preuß, Martina; Erbel, Raimund; Reinartz, Roman; Betz, Regina C.; Cichon, Sven; Propping, Peter; Baur, Max P.; Wienker, Thomas F.; Kruse, Roland; Nöthen, Markus M.

doi:10.1038/ng.228

Cited by 124 publications

(125 citation statements)

References 15 publications

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“…Except 6p25.1, all loci have been previously associated with MPB. [14][15][16] The associated SNP at 6p25.1 is a single genome-wide significant finding not supported by additional SNPs in the region and thus warrants further validation.…”

Section: Meta-analysis Of Gwasmentioning

confidence: 90%

“…In the present meta-analysis of the three cohorts, the only locus that did not show significant association was 3q25.1, but the allelic effect was in the same direction as Li et al Overall, the observed allele effect sizes were similar between RS and ERF, and similar to previous estimates in other populations. [14][15][16] Noticeable exceptions were: (1) the chromosome 20p11 SNPs showed much smaller effects in RS and ERF (eg, rs6047844 OR = 1.09 in RS and 1.18 in ERF, Table 1) than previous estimations (OR = 1.60 in Li et al and 1.82 in BONN); and (2) the SNPs at the EDA2R/AR locus showed much larger effect (rs1511061 OR = 9.07) in BONN than in RS and ERF (OR 2.68-2.75). These differences are likely explained by the extreme design in BONN, as BONN included only early-onset MPB cases (o40 years of age) and a set of super controls (⩾ 60 years, no signs of MPB) while no MPB pre-selection was made in RS and ERF, in which the majority of the subjects were elderly people.…”

Section: Candidate Snp Analysismentioning

confidence: 99%

“…11,12 In addition, two genetic loci on chromosome 20p11 (PAX1/FOXA2) and 7p21.1 (HDAC9) were identified to be involved in MPB. [13][14][15] A meta-analysis of seven GWASs for early-onset MPB involving~13 000 individuals of European origin conducted by the International MAAN Consortium 16 replicated these loci and highlighted five additional loci showing genome-wide significant association with MPB; these included 1p36.22, 2q37.3, 7q11.22, 17q21.31, and 18q12.3. Individuals in the highest-risk quartile of a genotype score had an approximately sixfold increased risk of earlyonset MPB.…”

Section: Introductionmentioning

confidence: 98%

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Prediction of male-pattern baldness from genotypes

et al. 2015

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The global demand for products that effectively prevent the development of male-pattern baldness (MPB) has drastically increased. However, there is currently no established genetic model for the estimation of MPB risk. We conducted a prediction analysis using single-nucleotide polymorphisms (SNPs) identified from previous GWASs of MPB in a total of 2725 German and Dutch males. A logistic regression model considering the genotypes of 25 SNPs from 12 genomic loci demonstrates that early-onset MPB risk is predictable at an accuracy level of 0.74 when 14 SNPs were included in the model, and measured using the area under the receiver-operating characteristic curves (AUC). Considering age as an additional predictor, the model can predict normal MPB status in middle-aged and elderly individuals at a slightly lower accuracy (AUC 0.69-0.71) when 6-11 SNPs were used. A variance partitioning analysis suggests that 55.8% of early-onset MPB genetic liability can be explained by common autosomal SNPs and 23.3% by X-chromosome SNPs. For normal MPB status in elderly individuals, the proportion of explainable variance is lower (42.4% for autosomal and 9.8% for X-chromosome SNPs). The gap between GWAS findings and the variance partitioning results could be explained by a large body of common DNA variants with small effects that will likely be identified in GWAS of increased sample sizes. Although the accuracy obtained here has not reached a clinically desired level, our model was highly informative for up to 19% of Europeans, thus may assist decision making on early MPB intervention actions and in forensic investigations.

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Section: Meta-analysis Of Gwasmentioning

confidence: 90%

Section: Candidate Snp Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Prediction of male-pattern baldness from genotypes

et al. 2015

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“…GWAS of traits in humans have shown that most of the associations are to quantitative trait loci (QTL) or risk alleles for complex traits (RACT) that are small to very small in size (Burton et al 2007;Weedon et al 2008). However, GWAS have also identified Mendelian factors causing discrete phenotypes in several species (Klein et al 2005;Karlsson et al 2007;Charlier et al 2008), as well as either discovering, or rediscovering, QTL or RACT of moderate to large effect in humans (Todd et al 1987;Ellis et al 2001;Burton et al 2007;Gieger et al 2008;Hillmer et al 2008;Pollin et al 2008;Benyamin et al 2009;Daly et al 2009). Some of these studies found genetic effects of moderate to large size even though the sample sizes used in those studies were quite small (i.e.…”

Section: Introductionmentioning

confidence: 99%

A genome-wide association study of tick burden and milk composition in cattle

Turner¹,

Harrison

Bunch

et al. 2010

Anim. Prod. Sci.

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Abstract.To study the genetic basis of tick burden and milk production and their interrelationship, we collected a sample of 1961 cattle with multiple tick counts from northern Australia of which 973 had dairy production data in the Australian Dairy Herd Information Service database. We calculated heritabilities, genetic and phenotypic correlations for these traits and showed a negative relationship between tick counts and milk and milk component yield. Tests of polymorphisms of four genes associated with milk yield, ABCG2, DGAT1, GHR and PRLR, showed no statistically significant effect on tick burden but highly significant associations to milk component yield in these data and we confirmed separate effects for GHR and PRLR on bovine chromosome 20. To begin to identify some of the molecular genetic bases for these traits, we genotyped a sample of 189 of these cattle for 7397 single nucleotide polymorphisms in a genome-wide association study. Although the allele effects for adjusted milk fat and protein yield were highly correlated (r = 0.66), the correlations of allele effects of these milk component yields and tick burden were small (|r| 0.10). These results agree in general with the phenotypic correlations between tick counts and milk component yield and suggest that selection on markers for tick burden or milk component yield may have no undesirable effect on the other trait.

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“…There are at least ten published studies presenting methods to explore the predictive values of combinations of SNP alleles of pigmentation phenotypes (skin, eye and hair colour) and presence/absence of freckles [33][34][35][36][37][38][39][40][41][42], two of which [34,40] have already been validated [43][44][45]. Additional research is under development regarding these and other phenotypes, such as estimation of age [46][47][48][49], height [50,51], hair shape [52][53][54], facial features [55,56], baldness [54, 57,58], and adult stuttering [59,60]. Besides physical phenotypes, additional information of individuals can be obtained with some reliability by analysing DNA, such as biogeographical ancestry [61,62] and surname of the sample [6,19,[63][64][65][66][67].…”

Section: Pims For Prediction Of Normal Human Traitsmentioning

confidence: 99%

Predicting Physical Features and Diseases by DNA Analysis: Current Advances and Future Challenges

Cerqueira¹,

Ramallo²,

Hünemeier³

et al. 2016

J Forensic Res

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The 'omics' era and its concomitant technological advances have brought great insight into genetics. One of the most promising fields within human genetics is the prediction of physical traits from analysis of genetic material. Besides the predictive potential of DNA, the traceability of pathogenic agents in the human body through molecular analysis is also a field to be further exploited. In this review, we aim to discuss specific aspects of phenotypic prediction by analysing DNA, with special emphasis on normal variation, and the application of a technology known as 'Forensic DNA Phenotyping' (FDP). We also suggest the term 'Phenotype Informative Markers' (PIMs) to designate any molecular markers responsible for normal or pathological human phenotypic variation. In addition, we raise some recommendations related to forensic genetics, the molecular diagnosis of human diseases, and the traceability of pathogens in the human body, giving special emphasis to the need for validation of these tests with strict protocols. Some relevant concerns about privacy, ethics, and legality of such predictions have also been discussed. Finally, we look at perspectives on the use of epigenetic tools, and quote some examples of what has been done in this specific field.

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Susceptibility variants for male-pattern baldness on chromosome 20p11

Cited by 124 publications

References 15 publications

Prediction of male-pattern baldness from genotypes

Prediction of male-pattern baldness from genotypes

A genome-wide association study of tick burden and milk composition in cattle

Predicting Physical Features and Diseases by DNA Analysis: Current Advances and Future Challenges

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