Page 15 - 2021_09-Haematologica-web

Editorials
make clinically relevant predictions for any given per- son. The gambit for detecting additional disease-rele- vant genetic factors has been to the assemble ever larger subject cohorts, reaching hundreds of thousands of par- ticipants for some conditions. Still the majority of the disease-relevant genetic background remains untouch- able,1 with thousands of weak-effect alleles still hidden, trapped in what is termed the ‘missing heritability’. General frustration with the GWAS approach is preva- lent among researchers.
Corre et al.2, on page 2499 of this issue, report a study of the type that offers a way out of this trap. The authors present a quantitative-trait association study, comparing circulating levels of the hormone erythropoi- etin with the genotype of a genome-wide SNP set. In contrast to the original case-control setup, such quanti- tative-trait GWAS offer crucial advantages. They allow ‘drilling down’ into the pathways underlying biological characters and disease pathogenesis, thereby reducing complexity and increasing the signal-to-noise ratio of genetic analysis. Quantitative-trait GWAS can utilise various large subject cohorts assembled for other stud- ies, such as groups of patients or population samples, if the parameter of interest or related biological traits have been recorded. Loci and variants discovered in quantita- tive-trait studies can subsequently be evaluated with more complex traits, such as disease risk.
Several large GWAS with red blood cell traits have been conducted and the genes identified have con- tributed to our understanding of anemia. This has been complemented with GWAS investigation of circulating erythropoietin levels, the main hormonal regulator of the system. Unsurprisingly, the set of genes detected overlap between the two approaches, e.g., HBS1L-MYB, which is a quantitative-trait locus (QTL) for various red- blood cell traits (HbF%, MCV, MCH, RBC), has also shown strong association with erythropoietin levels in a 2018 Dutch population study with 6,777 participants (Grote Beverborg et al.3). The present study of Corre et al., while smaller, has provided confirmation of HBS1L- MYB as an erythropoietin locus and the joint analysis of both cohorts has yielded a significance level of P<10-22. Heritability of erythropoietin levels was found to be higher than in another previous study (by Wang et al.4) and the set of genes detected is also somewhat different.
In quantitative-trait studies, heritability estimates and the spectrum of loci detected is fluid, and specific out- comes depend on peculiarities of subject recruitment, trait assay method, and measurement routines. However, with multiple cohorts available to study a given parameter and its related traits, a network of quantitative-trait studies can be built that, together with
knowledge gained from laboratory-experimental stud- ies, paints a picture of functional and genetic architec- ture of the investigated tissue system and any disease risk connected with it.
The most intriguing outcome of the present paper is the detection of a putative new QTL for erythropoietin levels on chromosome 15, with evidence for trait associ- ation (P=1.05x10-7) just short of the acknowledged level of genome-wide statistical significance. Corre et al. have started to harness data from GWAS performed with blood cell parameters in an attempt to confirm the valid- ity of this preliminary result: in the UK Biobank study variants at this locus were found associated with ery- throid traits, e.g., with hemoglobin concentration and reticulocyte count at P<10-5, but it is not clear why Corre and colleagues have not presented a ‘look up’ of their new locus in the erythropoietin GWAS dataset of Grote Beverborg et al.3 Confirmation of initial, ‘suggestive’, findings in a set of related studies must be integral part to any QTL GWAS, thus harnessing the full power of this approach. Obtaining data for that from colleagues in the field is usually straightforward.
It will be fascinating to see how this story develops following publication in Haematologica. The possibility of uncovering a new mechanism regulating oxygen transport capacity through erythropoietin is tantalising. In general, present efforts to build large population cohorts of extensively phenotyped individuals with complementary genotype data (genome array or sequence) will generate increasingly powerful datasets allowing to decipher our genetic blueprint and help to fulfil the promise of genetics for the improvement of human health.
Dislcosures
No conflicts of interest to disclose.
Funding
SM is presently supported by an MRC project grant to investigate the genetic determination of fetal-hemoglobin levels in sickle cell disease.
References
1. Goldstein DB. Common genetic variation and human traits. N Engl J Med. 2009;360(17):1696-1698.
2. Corre T, Ponte B, Pivin E, et al. Heritability and association with distinct genetic loci of erythropoietin levels in the general popula- tion. Haematologica. 2021;106(8):2499-2501.
3. Grote Beverborg N, Verweij N, Klip IT, et al. Erythropoietin in the general population: reference ranges and clinical, biochemical and genetic correlates. PLoS One. 2015;10(4):e0125215.
4.Wang Y, Nudel R, Benros ME, et al. Genome-wide association study identifies 16 genomic regions associated with circulating cytokines at birth. PLoS Genet. 2020;16(11):e1009163.
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