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To compare etonogestrel pharmacokinetic and pharmacodynamic outcomes by both self-reported race/ethnicity and genetically determined ancestry among contraceptive implant users. We conducted a secondary analysis of our parent pharmacogenomic study of 350 implant users. We genotyped these reproductive-aged (18-45 years) women for 88 ancestry-informative single nucleotide polymorphisms. We then assigned each participant a proportion value for African (AFR), European (EUR), and Indigenous American (AMR) ancestry based on reference population data. We correlated genetic ancestry with self-reported race/ethnicity and utilized genetic ancestry proportion values as variables for previously performed association analyses with serum etonogestrel concentrations and progestin-related side effects (e.g., bothersome bleeding and subjective weight gain). We successfully estimated genetically determined ancestry for 332 participants. EUR, AFR, and AMR ancestry were each highly correlated with self-reported White/non-Hispanic race (r = 0.64, p = 4.14 × 10-40 ), Black/African American race (r = 0.88, p = 1.36 × 10-107 ), and Hispanic/Latina ethnicity (r = 0.68, p = 4.03 × 10-47 ), respectively. Neither genetically determined ancestry nor self-reported race/ethnicity were significantly associated with serum etonogestrel concentrations. AFR ancestry and self-reported Black race had similar associations with reporting monthly periods (odds ratio [OR] 2.18, p = 0.09 vs. OR 2.22, p = 0.02) and having received treatment for bothersome bleeding (OR 5.19, p = 0.005 vs. OR 4.73, p = 2.0 × 10-4 ). In multivariable logistic regression for subjective weight gain, AMR ancestry dropped out of the model in preference for self-reported Hispanic/Latina ethnicity. We found no new associations between genetically determined ancestry and contraceptive implant pharmacodynamics/pharmacokinetics. Self-reported race/ethnicity were strong surrogates for genetically determined ancestry among this population of contraceptive implant users. Our data suggest that self-reported race/ethnicity, capturing societal and cultural aspects, remain important to the investigation of progestin-related side effects.
Pubmed ID: 33650294
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A software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets. It uses the same statistical model as STRUCTURE but calculates estimates much more rapidly using a fast numerical optimization algorithm. It uses a block relaxation approach to alternately update allele frequency and ancestry fraction parameters. Each block update is handled by solving a large number of independent convex optimization problems, which are tackled using a fast sequential quadratic programming algorithm. Convergence of the algorithm is accelerated using a novel quasi-Newton acceleration method.
View all literature mentionsInternational collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
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