Little progress has been made in the fight against health research inequality: Swift action is needed, urge the creators of an online tool to tackle the problem in genetics research
One year on from the launch of an online monitor that reveals and tracks the persistent lack of diversity in health research, its creators are urging scientists and funders to take action.
The GWAS Diversity Monitor from the Leverhulme Centre for Demographic Science at the University of Oxford and Nuffield College was developed to help solve the overwhelming bias of European ancestry groups in genomics (the study of a person’s genetic makeup). This lack of diversity exacerbates one of the world’s most pressing problems: health inequality. (Just what is a GWAS and what does ancestry and diversity mean? See below).
Interactive, free, and updated regularly, the GWAS (Genome Wide Association Study) Diversity Monitor allows users to track progress in the diversity of ancestry across participants used for genomic research. The Monitor continues to show that the overwhelming majority of participants are recruited from just three countries: the United Kingdom, the United States of America and Iceland.
Director of the Leverhulme Centre for Demographic Science (LCDS), Professor Melinda Mills, and co-creator of the Monitor which is linked to in the current issue of Nature focussing on the 20th Anniversary of publishing the human genome, says: ‘Your genome contains many important clues about you - from your ancestry to the way your body responds to diseases and medications, and behaviour. We have long been calling for more diversity in genetic health research. But sadly, a year after the launch of our dedicated Monitor, little has changed.
Currently, around 88 per cent of genetic discoveries use samples taken from people of European ancestry, leaving African, Asian, Hispanic or Latin American, Afro-Caribbean and other populations largely ignored. Humans are 99.9% identical to each other (about one SNP per 1000 bases), and it is the 0.1% by which we differ that makes us all genetically unique (see below for more explanation). The differences can determine our reactions to treatments. Medicine needs to be inclusive, or it risks doing harm.
‘We are asking scientists and funders to use our Monitor to easily see where investment, and diversity more generally, is urgently needed. This will benefit everyone living with disease now and for generations to come.’
This week, to further facilitate solutions to the problem, the LCDS launches its dedicated GWAS Diversity Monitor Twitter account @GWASDiversity. The account will regularly post updates about new developments on the issue and build awareness of the need for action.
Catherine Potenski, Chief Editor of Nature Genetics — the leading journal in the field which published the article in March 2020 launching the GWAS Diversity Monitor, says: ‘The GWAS Diversity Monitor has quantitatively highlighted the extreme disparity in the makeup of genetic cohorts used in GWAS, further emphasizing the need for the genetics community to commit to helping reduce health inequalities across populations through conducting responsible and inclusive genetic research. This open tool provides up-to-date information about the current GWAS landscape, and thus can be used to help shape and prioritize research activity that aims to achieve the ultimate goal of equitable global representation in GWAS analyses.’
Studying the human genome is recognised as key to understanding not just disease, but also behavioural and physical growth characteristics. The rapidly emerging field of genomics is transforming our understanding of human health and enabling advances that benefit all of humankind. The technique is increasingly important in the era of personalised medicine.
The GWAS Diversity Monitor is driven by data taken from the NHGRI-EBI GWAS Catalog. Helen Parkinson, Head of Molecular Archives at the European Bioinformatics Institute (EMBL-EBI) curates the Catalog, adds: ‘We are delighted that the GWAS Catalog data powers the GWAS Diversity Monitor as we are committed to acquiring and representing data from diverse populations in the GWAS Catalog.’
Out of a commitment to the Open Science revolution, users of the GWAS Diversity Monitor can download its data, code, and figures without charge or having to sign up. Its creators request the Monitor is always correctly cited. Citation available here.
Many scientists around the world have seen the value of using the app in research, including Dr Ahmad Abou Tayoun, Associate Professor of Genetics at Mohammed Bin Rashid University of Medicine and Health Sciences, and Director of Al Jalila Children’s Genomics Center, Dubai. Dr Ahmad Abou Tayoun says: ‘Genetics-based health research, heavily skewed in favour of populations of European origin, must be put right so that nations across the world benefit from what medicine has to offer. The GWAS Diversity Monitor is a tool which continuously highlights this bias, as its map and data reveal very clearly to scientists and funders where research is needed. The Monitor displays the latest information and is intuitive to use. For example, we have used this tool to determine the representation of populations from the Middle East in GWAS studies, and observed a severe gap, almost lack of genetics data, from this population. This lack of representation raises serious concerns for patients in the Middle East, but also it is a missed opportunity for the human genetics field.
‘Understanding the Middle Eastern Arab genome will enrich our understanding of genetic diseases. Failing to do this work risks the national interests of any country by depriving residents of the long-term advances in genomics. Equality in health research is to everyone’s benefit. We all share most of the human genome sequence, and we can only understand it better if we collectively share our diverse genetic variation.’
The GWAS Diversity Monitor, which includes a Q&A, Additional Information, and a suggested citation, can be found here. An introduction to the Monitor by Melinda Mills can be viewed here, along with a demonstration of how to use the Monitor here.
Additional information
What is a Genome-Wide Association Study (GWAS)?
A Genome-Wide Association Study or GWAS (pronounced Gee-WAS) is a search across the entire human genome, examining each genetic locus (or region) one by one to see if there is a statistical relationship (an 'association') between 'traits'. A trait is often interchangeably called a phenotype or outcome. The genetic loci that are identified contain SNPs (pronounced SNIPs: single-nucleotide polymorphisms), or in other words, the genetic variants that distinguish us from each other. Humans are 99.9% identical to each other (about one SNP per 1000 bases), and it is the 0.1% by which we differ that makes us all genetically unique. Only in the case of 'Mendelian diseases' like Huntington's disease is there only one gene that is at play. The majority of traits which we are interested in are complex and are the result of multiple genetic loci combining (referred to as 'polygenic traits'), where often hundreds and thousands of genetic variants have a small influence on a phenotype.
Is ancestry as it is characterised in genetics and GWAS the same as race?
No. Absolutely not.
It is essential to understand how we characterise diversity as a first step following current standards, but to also recognise limitations. As we write about in various places, including the Supplementary Material of our original article, we rely upon the ancestry categories in our monitor based on agreed standards developed by Morales et al (2018).
As many, we also acknowledge that these categories are not ideal. Although considerable efforts are taken by GWAS researchers, there remain challenges in the examination of ancestral categories in GWAS research. First, ancestry and race are often incorrectly conflated and remain sensitive topics. As we have covered in-depth elsewhere, the terms ancestry and race are not interchangeable in human genetics. Race is not a biological category, but rather has a socially, culturally and politically constructed meaning. Genetic variation as measured by ancestry is traced to geographical locations and migration and does not map into perpetually changing racial or ethnic categories. For an excellent summary, see Box 1 of Peterson et al. (2019).
Populations are the product of repeated mixtures over tens of thousands of years. The concentration of genetic alleles and higher or lower numbers of alleles between groups is related to where they have descended from. Most evidence places the origins of Homo sapiens in sub-Saharan Africa where the patterns of DNA sequence variation are the greatest. As humans migrated, they took progressively smaller amounts of genetic variation in the gene pool with them. Each new and younger population thus has less time to accumulate new mutations. Sequencing of Khoi-San bushmen showed that even two people from adjacent villages were as different from one another as any two European or non-African ancestry individuals (see Schuster et al (2010) and Lachance et al (2012)). GWAS which utilize data from diverse populations will provide more accurately targeted therapeutic treatments to more of the world’s population, extend insights in the allelic architecture of traits and uncover rare variants with significant effect sizes which replicate across ancestries. A recent example of this, for instance, was a GWAS on Greenlandic Inuit which found selected alleles with large effect sizes for height: a finding which replicates in Europeans but was hitherto undetected due to infrequent genetic occurrence across commonly utilized samples. A second issue is that due to patterns of migration, evolution and population shifts, characterizing populations into ancestry groups remains challenging and complex. The general standard is to rely on reference populations or markers of ancestry that allow populations to be easily distinguished, with methods developed to adjust for population stratification and clustering of samples (see for e.g., Paschou et al. 2010). A third and increasingly pressing issue is that as migration and admixture increase, new admixed categories have already and will undoubtedly continue to emerge in the future. How to classify and analyse increasingly admixed populations at a more precise and granular level presents a new frontier of research.
