Genome-wide association study in Japanese females identifies fifteen novel skin-related trait associations

Abstract

Skin trait variation impacts quality-of-life, especially for females from the viewpoint of beauty. To investigate genetic variation related to these traits, we conducted a GWAS of various skin phenotypes in 11,311 Japanese women and identified associations for age-spots, freckles, double eyelids, straight/curly hair, eyebrow thickness, hairiness, and sweating. In silico annotation with RoadMap Epigenomics epigenetic state maps and colocalization analysis of GWAS and GTEx Project eQTL signals provided information about tissue specificity, candidate causal variants, and functional target genes. Novel signals for skin-spot traits neighboured AKAP1/MSI2 (rs17833789; P = 2.2 × 10−9), BNC2 (rs10810635; P = 2.1 × 10−22), HSPA12A (rs12259842; P = 7.1 × 10−11), PPARGC1B (rs251468; P = 1.3 × 10−21), and RAB11FIP2 (rs10444039; P = 5.6 × 10−21). HSPA12A SNPs were the only protein-coding gene eQTLs identified across skin-spot loci. Double edged eyelid analysis identified that a signal around EMX2 (rs12570134; P = 8.2 × 10−15) was also associated with expression of EMX2 and the antisense-RNA gene EMX2OS in brain putamen basal ganglia tissue. A known hair morphology signal in EDAR was associated with both eyebrow thickness (rs3827760; P = 1.7 × 10−9) and straight/curly hair (rs260643; P = 1.6 × 10−103). Excessive hairiness signals’ top SNPs were also eQTLs for TBX15 (rs984225; P = 1.6 × 10−8), BCL2 (rs7226979; P = 7.3 × 10−11), and GCC2 and LIMS1 (rs6542772; P = 2.2 × 10−9). For excessive sweating, top variants in two signals in chr2:28.82-29.05 Mb (rs56089836; P = 1.7 × 10−11) were eQTLs for either PPP1CB or PLB1, while a top chr16:48.26–48.45 Mb locus SNP was a known ABCC11 missense variant (rs6500380; P = 6.8 × 10−10). In total, we identified twelve loci containing sixteen association signals, of which fifteen were novel. These findings will help dermatologic researchers better understand the genetic underpinnings of skin-related phenotypic variation in human populations.

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

“…Out of these, we extracted the subset of 25 papers that were both applied papers (rather than methodological) and for which full text could be accessed (S1 Table). The studies covered a variety of trait pairs, generally integrating a disease GWAS with molecular quantitative trait loci (QTL) data, [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] but also comparing pairs of disease GWAS, [40] eQTL and pQTL [41,42] or eQTL and other molecular traits. [43,44] Only four studies considered the potential for multiple causal variants in a region, either discussing the implications on their results, or using conditioning in at least one trait, and 22 out of 25 studies used the software default priors across this diverse range of trait pairs.…”

How well do we understand the basis of classic selective sweeps in humans?

“…For example, rs1426654 in SLC24A5 accounted for 33% of the variance in skin colour in a South Asian cohort, and rs16891982 in SLC45A2 for 3.6% . rs3827760 in EDAR has been associated with a range of hair , chin and ear shape phenotypes. In other cases, associations can be more difficult to interpret: for example ACKR1 rs2814778 is associated with lower numbers of neutrophils , or LCT rs4988235 with increased BMI .…”

Whole-genome sequencing of European autochthonous and commercial pig breeds allows the detection of signatures of selection for adaptation of genetic resources to different breeding and production systems

“…Other interesting H P signals, which potentially affect traits that could define breed-specific features or production characteristics, included the MC1R and EDAR genes (affecting hair-related traits [68][69][70][71]), ITFG1 (associated with average daily gain in cattle [72]), NR4A2 (involved in female reproduction [73]), MC4R (affecting fat deposition, growth performances and feed intake [74]), and NR6A1 (affecting the number of vertebrae [75]).…”