This is an interesting question. Short answer: I would have to do more research to decide whether I think there is, and whether it's intentional or not. Long answer: In my experience, "samples of convenience" is often the approach when a particular type of analysis is still becoming established (particularly because IRBs rightly have rules about avoiding coercion and ensuring informed consent), and due to historical malfeasance by researchers within a lot of communities, the group of people that is often most willing to participate in and trust academic research is white or European individuals. Funding within European countries of course will more often reflect their own populations, which may have limited representation, and there is a lot of funding for really interesting types of 'omics in Europe right now (I didn't even touch on epigenomics!). Within the United States I think particularly clinical trials are either required to be inclusive or justify why they aren't, which is a good step, but as far as I know there are no mechanisms within funding agencies that ask about data in grant applications. But I also think as sequencing-based methods that are sufficiently powered with lower sample sizes (single-cell RNA seq as opposed to GWAS, for example) become more common, there is a danger that researchers will think--as they did in the early GWAS era of <5,000 sample sizes--if their population is only going to be 100 people anyway, striving for homogeneity will limit the risk of stratification issues and allow for the detection of rare variants within the restricted population. In the US and Europe that type of approach will almost always result in a majority- or exclusively European-ancestry population, despite work showing there isn't a need for this type of approach and the results can be harmful (particularly re: PRS). My hope is that with increased awareness about the limitations of exclusive study design, more researchers will begin to learn about how the methods they employ can (or in some cases cannot, as is currently the case for LD score regression) be successfully and better implemented with more ancestrally representative sampling.

I love this question! First, I will distinguish sex & gender. Sex is a biological variable referring very broadly to the number/distribution of sex chromosomes, XX and XY being the most common but far from the only combinations observed in humans. Gender is a social construct that may or may not correspond to an individual's biological sex, and for which there are innumerous possible identities. As far as I am aware there has never been a GWAS of any complex trait that incorporated gender as a covariate in the model, especially because most studies both historically and contemporarily don't even ask about gender identity and therefore have no information about the gender of their participants. On the other hand, biological sex is almost always adjusted for in GWAS because many complex traits are associated with sex although, as you mention, sex chromosomes are often excluded. Not only are sex chromosomes excluded, but individuals with non-XX or XY sex chromosome combinations or recorded sex (often based on outward physical appearance) not matching corresponding genotype data are also excluded, despite up to 2% of the global population falling outside that category (in a sample the size of the UK BioBank of 500,000 participants, this could lead to the exclusion of up to 10,000 individuals who are presumably also at risk for the complex disease being studied, which far exceeds the total sample sizes of early GWAS). Sex differences in associated SNPs have been performed for a fair number of phenotypes by stratifying the study population into males and females and then identifying variants within each group, although these analyses still often exclude the sex chromosomes. I would be thrilled to see increased representation of both gender and sex diversity in genomics research.

With regard to exclusion of sex chromosomes, this is an excellent point. The Y chromosome has very few genetic variants per megabase compared to all other chromosomes, although of course some might be present and have phenotypic effects in biological males. On the other hand, the X chromosome is larger than 14 of the autosomal chromosomes and has approximately 800 genes. The typical stated reason for excluding the X chromosome is that analyzing it is more complex than simply sex-stratifying a sample and analyzing in men and women separately, then meta-analyzing the summary statistics. While X inactivation (usually) occurs on one X chromosome per cell and is expected to be random, it's (1) not always random in ways that researchers can't really predict, (2) there are numerous genes that escape X inactivation and therefore variants in open chromatin around those regions would need to be analyzed separately, but those are not particularly well characterized either, and (3) X inactivation may be tissue-specific, although evidence is strong in mice than humans for this phenomenon occurring. I personally did not include the X chromosome in a GWAS I did for my dissertation because the methods I was employing were not really appropriate for this type of complex analysis, but I 100% agree that there are probably hundreds of real genetic associations and maybe thousands of trans-eQTLs for dozens of phenotypes on the X chromosome just waiting to be explored. Over the past several years some new methods development has occurred in this area, and I think once a consensus is reached in the field about the premier way to evaluate the sex chromosomes but particularly the X, a large number of analyses will be published confirming your suspicion that these associations exist and need to be more closely examined.

Thank you very much! I am grateful to be able to work with other people who are passionate about improved representation in genomics research.