Diving into the abyss of undiscovered kidney function–related factors
Authors: Sophie Limou, Afshin Parsa
Journal: Kidney International (2016)

Meta-analyses and reintroduction of biological knowledge are 2 classic strategies to increase genomewide association study statistical power and overcome the burden of multiple testing. These strategies have empowered the nephrology community to discover new signals associated with kidney function and nephropathies. Here we discuss the current genomewide association study limitations and strategies to dive further into the abyss of yet-to-be discovered kidney function–related factors. Meta-analyses and reintroduction of biological knowledge are 2 classic strategies to increase genomewide association study statistical power and overcome the burden of multiple testing. These strategies have empowered the nephrology community to discover new signals associated with kidney function and nephropathies. Here we discuss the current genomewide association study limitations and strategies to dive further into the abyss of yet-to-be discovered kidney function–related factors. Although the basic DNA code was revealed back in 1953, the human genome sequence was not fully realized until nearly half a century later. By the early 2000s, rapid technological advances in DNA genotyping microarrays made it feasible for the first time to scan the whole genome in an unbiased manner for common genetic variants in a large number of individuals. Similar to a breach in a dam, this newfound ability led to a flood of an ever-increasing number of genomewide-association study (GWAS) publications. Indeed, 16,617 genotype-phenotype associations have been reported in the GWAS Catalog (https://www.ebi.ac.uk/gwas/home) as of May 2016. Not surprisingly, the volume of information has greatly exceeded our ability to properly capture, digest, and interpret all of the new information. As a case in point, >50 validated genetic variants have been associated with estimated glomerular filtration rate (eGFR) alone, as well as many other reported associations with individual renal diseases (282 reported kidney-related associations). These discoveries have provided a significant number of novel insights and are already modifying our understanding of renal physiology and pathophysiology. However, only UMOD, APOL1, and a minute proportion of these are thoroughly being studied. Nonetheless, despite our high discovery rate, we can only account for a small fraction of the population-level genetic variance. This, to some, has been a disappointment, as the popular notion that the genome may unlock all the secrets of individual variability in health and disease has not been realized. Arguably, at the present moment we are merely skimming the surface of the genomics waters. The rapid evolution in DNA array coverage density for common gene variants has been akin to moving closer to the water surface, resulting in improved resolution. To start looking just beneath the surface, 1 strategy has been to increase the number of individuals per study, commonly via meta-analyses, resulting in increased power of signal detection. This has led to an unprecedented number of large-scale collaborative consortia, such as CKDGen and with >133,000 participants.1Jing J. Pattaro C. Hoppmann A. et al.Combination of mouse models and genomewide association studies highlights novel genes associated with human kidney function.Kidney Int. 2016; 90: 764-773Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar To further overcome the burden of GWAS-related multiple testing, another strategy has been to reintroduce biological a priori knowledge into an initially agnostic GWAS approach, thereby narrowing the analysis to a smaller number of biologically plausible genetic variants and hence relaxing the statistical threshold for discovery. In this issue of the journal, Jing et al.1Jing J. Pattaro C. Hoppmann A. et al.Combination of mouse models and genomewide association studies highlights novel genes associated with human kidney function.Kidney Int. 2016; 90: 764-773Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar (2016) leveraged mouse model data to run a targeted GWAS on human kidney function. From the Mouse Genome Informatics resource, which links gene knockouts and mutations to mouse phenotype, the authors established a list of genes leading to abnormal kidney function (defined by abnormal GFR, kidney physiology, kidney morphology, or urinary albumin-to-creatinine ratio) in mice. They then tested the common variants (frequency ≥1%) within the human orthologous genes (n = 759 unique genes) for association with eGFR and urinary albumin-to-creatinine ratio in 133,814 and 54,451 European individuals from the CKDGen consortium, respectively. By integrating mouse biological knowledge, Jing et al. restricted their focus from the whole genome to only genetic loci involved in mouse renal phenotypes, therefore minimizing the number of statistical tests and increasing the power for detecting signals with clear biological relevance. Accordingly, the authors derived a Bonferroni statistical significance threshold adjusting for the number of independent (as characterized by linkage disequilibrium) tested SNPs, and identified 18 and 1 gene(s) significantly associated with eGFR and urinary albumin-to-creatinine ratio, respectively. The previously reported CUBN gene was the only significant association with urinary albumin-to-creatinine ratio. Of the 18 genes associated with eGFR, 15 genes were previously reported, 2 CYP26A1 (P = 5 × 10−9) and BMP4 (P = 9 × 10−7) were previously suggested but not validated, and LAMA5 (P = 10−6) was identified as a novel gene. These 3 new eGFR-associated genes were targeted for replication in the East Asian AGEN consortium (N = 42,296) and both CYP26A1 and BMP4 were successfully replicated. Finally, by running conditional analyses for the significant and suggestive (P < 5 × 10−5) signals mapping in previously reported kidney function–associated loci, CYP24A1 emerged as a novel gene strongly associated with eGFR (P = 10−8). If the combination of experimental mouse data and human population–based data is ingenious, it also presents some limitations. The overlap between human and mouse models is not complete, as illustrated by the fact that 64% (34 of 53) of recently eGFR-associated loci2Pattaro C. Teumer A. Gorski M. et al.Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.Nat Commun. 2016; 7: 10023Crossref PubMed Scopus (305) Google Scholar have not been shown to cause renal phenotypes in mice. Additionally, the selected Mouse Genome Informatics genes were associated with abnormal mouse kidney function, when the CKDGen consortium gathers primarily healthy individuals with cross-sectional eGFR measures, which could miss genes primarily involved in kidney injury and disease-related processes. Overall, Jing et al. have presented an innovative approach that revealed 3 new genes associated with renal function and additional potential candidates for kidney disease pathogenesis. The biological relevance of these candidates, as well as the confirmation of previously reported genes, validates this integrative method. This likely would have been even more successful if the Mouse Genome Informatics resource would have been complete (only 45% of the mouse genes are currently reported), if all Mouse Genome Informatics mouse models were comprehensively phenotyped for kidney-related metrics and defects, and/or if a large European replication cohort would have been readily available. Although the replication within a population of different ancestral background is valid, it enhances false-negative results due to the differences in genetic architecture (frequency and linkage disequilibrium patterns) across populations. Indeed, only 7 of 24 novel validated signals associated with eGFR in a recent CKDGen study were nominally significant in the East Asian AGEN consortium.2Pattaro C. Teumer A. Gorski M. et al.Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.Nat Commun. 2016; 7: 10023Crossref PubMed Scopus (305) Google Scholar Therefore, LAMA5 and the 13 novel genes of 16 genes suggestively associated with eGFR in this report (not tested for replication) represent promising targets for further studies in both general and disease-specific populations. Initiatives such as the one presented in this commentary can successfully uncover novel findings in the shallow genomic waters, but do not enable a dive in the deeper waters of rare variants (traditionally defined by a frequency <1%), structural variations such as copy number variants, and common variants with low penetrance. Moreover, GWAS alone cannot create a comprehensive picture of the genomic waters “ecosystem.” The traditional definition of an ecosystem pertains to a community of living organisms in conjunction with the nonliving components of their environment, interacting as a system. Although the genome does not constitute a true ecosystem, the analogy may be useful in better outlining the complexity of the various interacting molecules regulating gene function and protein translation. Certainly, the common notion of a linear nucleotide sequence coding for amino acids determining most biological functions is overly simplistic to the point of being inaccurate. Indeed, nearly all biological functions are based on multiple gene-gene and gene-environment interactions, resulting in both fixed and temporal differential gene expressions. Even single gene regulation is proving more complex than imagined by most. Notably, various epigenetic mechanisms, such as DNA methylation and histone modifications, can have profound implications on gene expression and cellular phenotype, as demonstrated by monozygotic twins who can experience discordant clinical outcomes despite an identical gene sequence and relatively similar environment.3Roberts N.J. Vogelstein J.T. Parmigiani G. et al.The predictive capacity of personal genome sequencing.Sci Transl Med. 2012; 4: 133ra58Crossref PubMed Scopus (171) Google Scholar This complex structure of interactions between molecules is arguably akin to interactions between different organisms in an ecosystem (Figure 1). They affect each other, are in part differentially regulated, and are yet interdependent. Of course, as with any ecosystem, the environment and its multiple dynamic interactions with different molecular entities also define and shape the system. As illustrated in the current article, the application of new analytical and integrative (system biology) strategies and the development of new technologies in the genomics field have led and will likely continue to lead to further novel discoveries. Notably, the focus on structural variants has revealed unsuspected genomic imbalance in children with chronic kidney disease (CKD)4Verbitsky M. Sanna-Cherchi S. Fasel D.A. et al.Genomic imbalances in pediatric patients with chronic kidney disease.J Clin Invest. 2015; 125: 2171-2178Crossref PubMed Scopus (58) Google Scholar and the targeted next-generation sequencing of genes expressed in podocytes highlighted an enrichment for rare variants in new factors associated with sporadic focal segmental glomerulosclerosis.5Yu H. Artomov M. Brahler S. et al.A role for genetic susceptibility in sporadic focal segmental glomerulosclerosis.J Clin Invest. 2016; 126: 1603Crossref PubMed Scopus (4) Google Scholar Despite the challenges of collecting kidney tissue samples, some studies have successfully examined related variables needed to delineate genetic contributions to renal outcomes, such as the DNA methylation epigenetic profiles of CKD samples6Ko Y.A. Mohtat D. Suzuki M. et al.Cytosine methylation changes in enhancer regions of core pro-fibrotic genes characterize kidney fibrosis development.Genome Biol. 2013; 14: R108Crossref PubMed Scopus (167) Google Scholar and the kidney-specific transcriptome and proteome.7Habuka M. Fagerberg L. Hallstrom B.M. et al.The kidney transcriptome and proteome defined by transcriptomics and antibody-based profiling.PLoS One. 2014; 9: e116125Crossref PubMed Scopus (31) Google Scholar However, the full picture of kidney function and diseases will not appear as long as we do not consider the multiplicity of the ecosystem actors and their complex interactions (Figure 1). The example of IgA nephropathy demonstrates the complexity of a system embracing pleiotropic factors and complex interactions with the microbiota environment.8Kiryluk K. Li Y. Scolari F. et al.Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens.Nat Genet. 2014; 46: 1187-1196Crossref PubMed Scopus (367) Google Scholar Further advances in integrative and interactives analytic methodologies to allow more complex systems biology–based approaches are absolutely mandatory if we are to comprehensively delineate genetic contributions to both physiological and pathophysiological renal processes. Again, this will require large collaborative efforts, a challenge that the nephrology community has previously conquered. Even though the quest to comprehensively realize the role of individual-level gene variations on health and disease will require more patience than we would like, the ultimate goal to translate research findings into diagnostic, preventive, and/or therapeutic tools continues to be progressively delineated. It is important to bear in mind that the strength of a gene variant association conveys little of the functional relevance of the gene and/or its potential for pharmacologic intervention, as emphasized by the weak association between 3-hydroxy-3-methylglutaryl coenzyme A reductase gene variants and low-density lipoprotein, in contrast to the established efficacy of targeting it with statins and diminishing cardiovascular disease. Similarly, the fraction of explained variance associated with a factor can vary tremendously in response to the stress exerted on the system. For example, APOL1 high-risk genotypes are very strongly associated with focal segmental glomerulosclerosis and HIV-associated nephropathy, more modestly with CKD, and minimally with eGFR in the general population,9Grams M.E. Rebholz C.M. Chen Y. et al.Race, APOL1 risk, and eGFR decline in the general population [e-pub ahead of print].J Am Soc Nephrol. 2016; (http://dx.doi.org/10.1681/ASN.2015070763. Accessed May 2016.)Google Scholar highlighting the often highly phenotype- specific nature of gene associations and, importantly, the value of homogeneous, extreme, and disease phenotypes that are enriched in risk factors. In conclusion, we are still finding pieces of the puzzle faster than we can assemble them, and although many are still missing, some of these are proving most valuable. It is clear by now that strategies to better integrate the pieces together should reveal much of the missing picture and further identify needed actionable therapeutic targets for kidney disease. All the authors declared no competing interests. Combination of mouse models and genomewide association studies highlights novel genes associated with human kidney functionKidney InternationalVol. 90Issue 4PreviewGenomewide association studies have identified numerous chronic kidney disease–associated genetic variants, but often do not pinpoint causal genes. This limitation was addressed by combining Mouse Genome Informatics with human genomewide association studies of kidney function. Genes for which mouse models showed abnormal renal physiology, morphology, glomerular filtration rate (GFR), or urinary albumin-to-creatinine ratio were identified from Mouse Genome Informatics. The corresponding human orthologs were then evaluated for GFR-associated single-nucleotide polymorphisms in 133,814 individuals and urinary albumin-to-creatinine ratio-associated SNPs in 54,451 individuals in genome-wide association studies meta-analysis of the CKDGen Consortium. Full-Text PDF Open Archive