Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits

Abstract

In genome-wide association studies (GWAS), sample size is the most important factor affecting statistical power that is under control of the investigator, posing a major challenge in understanding the genetics underlying difficult-to-measure traits. Combining data sets available from different populations for joint or meta-analysis is a promising alternative to increasing sample sizes available for GWAS. Simulation studies indicate statistical advantages from combining raw data or GWAS summaries in enhancing quantitative trait loci (QTL) detection power. However, the complexity of genetics underlying most quantitative traits, which itself is not fully understood, is difficult to fully capture in simulated data sets. In this study, population-specific and combined-population GWAS as well as a meta-analysis of the population-specific GWAS summaries were carried out with the objective of assessing the advantages and challenges of different data-combining strategies in enhancing detection power of GWAS using milk fatty acid (FA) traits as examples. Gas chromatography (GC) quantified milk FA samples and high-density (HD) genotypes were available from 1,566 Dutch, 614 Danish, and 700 Chinese Holstein Friesian cows. Using the joint GWAS, 28 additional genomic regions were detected, with significant associations to at least 1 FA, compared with the populationspecific analyses. Some of these additional regions were also detected using the implemented meta-analysis. Furthermore, using the frequently reported variants of the diacylglycerol acyltransferase 1 (DGAT1) and stearoyl-CoA desaturase (SCD1) genes, we show that significant associations were established with more FA traits in the joint GWAS than the remaining scenarios. However, there were few regions detected in the population-specific analyses that were not detected using the joint GWAS or the meta-analyses. Our results show that combining multi-population data set can be a powerful tool to enhance detection power in GWAS for seldom-recorded traits. Detection of a higher number of regions using the meta-analysis, compared with any of the population-specific analyses also emphasizes the utility of these methods in the absence of raw multipopulation data sets to undertake joint GWAS.

The New Genetic Evidence on Same-Gender Sexuality: Implications for Sexual Fluidity and Multiple Forms of Sexual Diversity

“…Methodological critiques also received well-justified attention: Self-selection may have influenced the results, given that less than 6% of UK Biobank members and less than 2% of 23andMe members agreed to participate in the study (Saey, 2019), potentially because of its focus on sexual orientation, which might introduce bias into the study. The sample was criticized for excluding individuals of non-European ancestry, although this exclusion was necessitated by the study's methodology: Because gene frequencies and genotypic effects can vary across different populations, combining different populations in a single GWAS study risks distorting the effects of interest (see Gebreyesus et al, 2019). In cases of population heterogeneity, it is preferable to conduct separate GWAS studies within different populations.…”

Genome-wide association study for methane emission traits in Danish Holstein cattle

“…Based on these results (Gebreyesus et al, 2019), we can infer that we have much more power of QTL detection for MeC (n = 1962) and MeP (n = 1,844) than for the other traits, especially for MeYc (n = 379) and MeYp (n = 353). Therefore, ideally, we would need around 3,000 animals per trait to have an optimal power of detection for QTL explaining at least 5% of the genetic variance.…”

“…In Figure 1, MeY traits showed very strong association signals for chromosome 1 (P = 8.22 × 10 −12 ; MeYp) and 24 (P = 1.59 × 10 −14 ; MeYc); however, these results should be taken carefully, as the number of animals/genotypes for these traits is much lower than for the other traits (Table 1). According to Gebreyesus et al (2019) who performed a power detection test on a several Holstein populations, as a function of sample size and proportion of explained variance by a QTL, a population of 2,880 animals could have a detection power of 0.97, whereas a population of 1,566 animals could have a detection power of 0.57 and a population of 614 animals only 0.05 to detect QTL explaining 5% of the genetic variance.…”

“…More CH 4 data are necessary to achieve higher power in the GWAS study to detect QTL associated with CH 4 traits and be able to calculate the percentage of genetic variance associated with it. According to Gebreyesus et al (2019), QTL explaining 5% of genetic variance can be detected with a power of 0.97 with records from 3,000 cows. Ideally, those animals should also have milk production, weight, and feed intake data, not only to calculate some of the composite CH 4 traits (as MeY, MeI, and RMET) but also to estimate the genetic correlations between those traits.…”

Genome-wide association study for genotype by lactation stage interaction of milk production traits in dairy cattle

“…Lopdell et al (2017) reported several QTL for lactose content on BTA19 and proposed several candidate genes, including GHDC (43.6 Mbp), KCNH4 (43.6 Mbp), STAT5A (43.7 Mbp), and STAT5B (43.7 Mbp); STAT5A and STAT5B play a key role in controlling milk protein gene expression, including lactalbumin, which is essential for lactose synthesis (Osorio et al, 2016). In addition, a chromosomal region on BTA19 from approximately 33 Mbp to 62 Mbp showed significant associations with multiple fatty acids, in particular, de novo-synthesized fatty acids C8:0, C10:0, C12:0, and C14:0 (e.g., Bouwman et al, 2012;Gebreyesus et al, 2019). Candidate genes on BTA19 involved in fatty acid biosynthesis include SREBF1 (35.7 Mbp), ACLY (43.4 Mbp), STAT5A, STAT5B, GH (49.7 Mbp), and FASN (52.2 Mbp).…”