Evaluating GWAS-Identified SNPs for Age at Natural Menopause among Chinese Women

<div><p>Background</p><p>Age at natural menopause (ANM) is a complex trait with high heritability and is associated with several major hormonal-related diseases. Recently, several genome-wide association studies (GWAS), conducted exclusively among women of European ancestry, have discovered dozens of genetic loci influencing ANM. No study has been conducted to evaluate whether these findings can be generalized to Chinese women.</p> <p>Methodology/Principal Findings</p><p>We evaluated the index single nucleotide polymorphisms (SNPs) in 19 GWAS-identified genetic susceptibility loci for ANM among 3,533 Chinese women who had natural menopause. We also investigated 3 additional SNPs which were in LD with the index SNP in European-ancestry but not in Asian-ancestry populations. Two genetic risk scores (GRS) were calculated to summarize SNPs across multiple loci one for all SNPs tested (GRS<i>all</i>), and one for SNPs which showed association in our study (GRS<i>sel</i>). All 22 SNPs showed the same association direction as previously reported. Eight SNPs were nominally statistically significant with <i>P</i>≤0.05: rs4246511 (<i>RHBDL2</i>), rs12461110 (<i>NLRP11</i>), rs2307449 (<i>POLG</i>), rs12611091 (<i>BRSK1</i>), rs1172822 (<i>BRSK1</i>), rs365132 (<i>UIMC1</i>), rs2720044 (<i>ASH2L</i>), and rs7246479 (<i>TMEM150B</i>). Especially, SNPs rs4246511, rs365132, rs1172822, and rs7246479 remained significant even after Bonferroni correction. Significant associations were observed for GRS. Women in the highest quartile began menopause 0.7 years (<i>P</i> = 3.24×10⁻⁹) and 0.9 years (<i>P</i> = 4.61×10⁻¹¹) later than those in the lowest quartile for GRS<i>sel</i> and GRS<i>all</i>, respectively.</p> <p>Conclusions</p><p>Among the 22 investigated SNPs, eight showed associations with ANM (P<0.05) in our Chinese population. Results from this study extend some recent GWAS findings to the Asian-ancestry population and may guide future efforts to identify genetic determination of menopause.</p> </div

Association analysis of GRS and ANM.

<p>Association analysis of GRS and ANM.</p

Characteristics of study participants.

a<p><i>P</i> for ANOVA.</p>b<p> <i>adjusting for Education.</i></p

Association results of 16 SNPs and age of natural menopause in Chinese women.

a<p>In LD with GWAS-identified SNP rs10183486 in CEU (r² = 0.863) but not in CHB (r² = 0.005).</p>b<p>In LD with GWAS-identified SNP rs2517388 in CEU (r² = 0.843) but not in CHB (r² = 0.169).</p>c<p>In LD with GWAS-identified SNP rs12294104 in CEU (r² = 0.877) but not in CHB (r² = 0.070).</p>d<p>Chr: chromosome.</p>e<p>EAF: effect allele frequency.</p>f<p>Partial regression coefficient was calculated for years per allele change in ANM (years).</p>g<p>GRS: genetic risk score, Significant SNPs were selected in the calculation of the GRS<i>sel</i>.</p

Summary of results for the three SNPs showing a statistically or marginally significant association in all four stages with breast cancer risk, the Asia Breast Cancer Consortium.

a<p>Effect/reference alleles based on forward strand.</p>b<p>From NCBI genome build 36.</p>c<p>Effect allele frequency in controls.</p>d<p>Adjusted for age and study sites.</p

Overview of the study design.

<p>Overview of the study design.</p

A regional plot of the −log₁₀P-values for SNPs at 6q25.1.

<p>The LD is estimated using data from HapMap Asian population. Also shown are the SNP Build 36 coordinates in kilobases (Kb), recombination rates in centimorgans (cM) per megabase (Mb) and genes in the region (below) based on the March 2006 UCSC genome browser assembly.</p

Selected characteristics of studies participating in the Asia Breast Cancer Consortium.

a<p>See the methods section for the full names of participating studies.</p>b<p>Mean value for cases/controls.</p>c<p>Percentage for cases/controls.</p>d<p>Significant at α = 0.01 level.</p

ORs per risk allele and 95% CIs for breast cancer associated with three SNPs by study site and ethnicity.

<p>A: rs9485372, B: rs9383951; and C: rs7107217.</p

Principal Component Analysis (PCA) based on the first two eigenvectors obtained by PCA.

<p>A: all individuals from Stage I and HapMap; B: breast cancer cases and controls from Stage I.</p