NEW YORK – A research team based in Switzerland, Estonia, and Austria has found that almost three-quarters of genetic variants with known ties to natural menopause timing have effects that vary depending on an individual's age.
"These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data," co-senior and corresponding author Matthew Robinson, a researcher at the Institute of Science and Technology Austria, and his colleagues wrote in the American Journal of Human Genetics on Friday.
The team noted that menopause — marked by an end to the menstrual cycle after ovarian function subsides — occurs when women are around 51 years old, on average, though some 4 percent of women go through this process before turning 45. While past research points to an uptick in osteoporosis and heart disease risk in women who experience early menopause, breast cancer risk tends to increase in individuals with late menopause, pointing to a potential interplay between menopause and other biological processes.
To explore the age-related aspects of menopause further, the researchers started with genotyping and age-at-menopause data for more than 173,400 UK Biobank participants of self-reported or genetically determined European ancestry, including almost 125,700 individuals who had gone through menopause.
In particular, the team turned to a two-stage mixed linear-association analytical model to search for variants linked to "age at natural menopause" (ANM) and to estimate the effect sizes of these relationships over time.
Potential associations that turned up in the team's analysis were subsequently validated with the help of SNP genotyping profiles for 22,740 Estonian Biobank participants with menopause and 47,342 women without menopause from the Estonian Biobank, landing on a set of 245 variants with genome-wide associations to ANM.
"By modeling the quantitative genetic basis of ANM in a way that enables detection of the age at which genetic risk factors have the greatest influence, we report evidence for widespread age-specific genetic effects underlying population-level variation in ovarian aging in both the UK and Estonian Biobank data," the authors wrote.
With the help of an analytical approach that combined "Cox age-specific mixed proportional hazards" modeling with a significance testing, the researchers tracked down 19 genetic contributors to menopause timing that were not described in the past, while highlighting apparent age-related effects for some 74 percent of the variants linked to ANM overall.
The ANM-associated variants were also overrepresented in genes with higher- or lower-than-usual expression in tissues from the female reproductive system, based on tissue expression data generated from the Genotype-Tissue Expression consortium and other publicly available or published datasets.
"Taken together, we find that the majority of ANM genetic associations display some form of age specificity in their effects," the authors reported. "In turn, that translates into the associations being differentially enriched in different biological pathways across ages, which then leads to different genetic associations of ANM and other health indicators and outcomes depending upon the timing of ANM, with different potential statistical causal relationships."
In particular, the team's analyses suggested that ovarian depletion and ovarian reserve features found during early menopause appeared to be influenced by DNA damage processes, while later-than-usual ANM tended to coincide with altered genetic risk for conditions such as heart failure, high cholesterol, breast cancer, or leiomyoma.
"Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships," the authors explained, noting that the strategy used to explore menopause contributors in the current study "applies to any form of time-to-event phenotype."