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

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.

Japanese GWAS identifies variants for bust-size, dysmenorrhea, and menstrual fever that are eQTLs for relevant protein-coding or long non-coding RNAs.

Traits related to primary and secondary sexual characteristics greatly impact females during puberty and day-to-day adult life. Therefore, we performed a GWAS analysis of 11,348 Japanese female volunteers and 22 gynecology-related phenotypic variables, and identified significant associations for bust-size, menstrual pain (dysmenorrhea) severity, and menstrual fever. Bust-size analysis identified significant association signals in CCDC170-ESR1 (rs6557160; P = 1.

Genome-wide association study of self-reported food reactions in Japanese identifies shrimp and peach specific loci in the HLA-DR/DQ gene region.

Food allergy is an increasingly important health problem in the world. Several genome-wide association studies (GWAS) focused on European ancestry samples have identified food allergy-specific loci in the HLA class II region. We conducted GWAS of self-reported reactivity with common foods using the data from 11011 Japanese women and identified shrimp and peach allergy-specific loci in the HLA-DR/DQ gene region tagged by rs74995702 (P = 6.

Estimation of the risk of a qualitative phenotype: dependence on population risk.

Individual disease risk estimated based on the data from single or multiple genetic loci is generally calculated using the genotypes of a subject, frequencies of alleles of interest, odds ratios and the average population risk. However, it is often difficult to estimate accurately the average population risk, and therefore it is often expressed as an interval. To better estimate the risk of a subject with given genotypes, we built R scripts using the R environment and constructed graphs to examine the change in the estimated risk as well as the relative risk according to the change of the average population risk.