Overview of Genome-Wide Association Studies
CKD, and by extension kidney function, has a strong genetic basis, with heritability of eGFR and albuminuria calculated to be around 16%–50% (1,2). Since the early 2000s, genome-wide association studies (GWASs) have been a powerful platform for identifying associations between common disease development and common DNA variants in the human population. These studies have identified hundreds of genetic regions, or loci, where nucleotide sequences differ between those with disease and those without disease (3–4567891011). Because these studies survey the entire human genome, many of their reported genetic variants lie within loci not previously studied in the context of the disease of interest. For example, the first GWAS for CKD in 2009 and a larger GWAS for CKD in 2016 identified dozens of novel loci for CKD (4,12). Most (close to 90%) of the genetic variants associated with development of complex disease are located in noncoding regions of the genome (13,14), and they are distinct from genetic variants associated with Mendelian, or monogenic, diseases. Each DNA variant associated with complex CKD or kidney-relevant traits exerts a small effect on disease pathogenesis. As such, small effects from numerous DNA variants in aggregate drive the incidence of complex CKD and are best detected in a large sample size in population studies such as the latest GWAS for CKD, which involved over 1 million individuals and detected more than 300 loci for kidney function (10,15).
Although most GWAS data are not yet ready for direct development of novel therapeutics, they must highlight critical disease-causing genes for experimental studies and eventual downstream therapeutic targeting. This concept forms the basis of functional genomics, which leverages unbiased genetic data to assign function to DNA variants that are causal in disease mechanisms (Figure 1). Ideally, the GWAS functional genomics workflow would result in the development of novel therapeutic strategies for complex diseases such as CKD. However, despite the many genetic regions reported to associate with disease traits, only a handful of GWAS loci and variants have been shown to be causally linked to disease development, emphasizing the importance of interrogating these loci for function. In this review, we introduce concepts relevant to understanding GWAS, bioinformatics approaches for prioritizing GWAS variants for functional validation, and experimental approaches for validating causality.
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Figure 1.: Genome-wide association analysis. Human genome-wide association studies (GWASs) compare single nucleotide polymorphism frequency in healthy control and disease patients (A). Genetic variation (B) in many loci show association with kidney disease development, as seen in a GWAS Manhattan plot (C). Within a GWAS locus, many variants show significant association with disease development, as seen in a locus zoom plot (D). The causal variant, the target gene, the target cell type, and the disease mechanism understanding are critical to translate GWAS discoveries into clinically actionable functions.
Basic Study Design
For a glossary of the terms used in this overview, see Table 1. The purpose of a GWAS is to determine which genomic loci are associated with a particular complex disease or trait. Typically, the variants tested are single nucleotide polymorphisms (SNPs), millions of which can be assayed at the same time using SNP arrays, and more recently, whole-genome sequencing (16). DNA variants can also include insertions and deletions, although these are less common. If the queried phenotype is dichotomous (present or absent), a GWAS will evaluate whether an SNP’s allele frequency differs between a group of cases and a group of controls (Figure 1). If the disease phenotype is a continuous quantitative trait such as eGFR, a GWAS will evaluate whether there are statistically significant differences in the trait among the groups of people with the three different genotypes at the SNP. These genotypes include being homozygous for the major (or more common) allele, homozygous for the minor allele, or heterozygous for both (17). Robust statistical significance for a GWAS is typically P<5×10−8 because of a stringent Bonferroni correction for multiple testing accounting for the roughly 1 million independent SNPs tested simultaneously. The corrected P value is obtained by dividing the designated α (0.05) by the number of tests performed (1 million, because of testing 1 million independent SNPs): 0.05/106=5×10−8.
Table 1. -
Glossary of terms
| Term |
Definition |
| Allele |
A variant form of a gene’s locus, often refers to alternative nucleotide at an SNP location |
| Bonferroni correction |
A type of statistical correction for multiple comparisons |
| Causal |
Contributing to causing a disease or trait |
| Chromatin |
Substance within a chromosome consisting of DNA and protein |
| Enhancer |
Short genomic DNA sequence where transcriptional modifiers can bind |
| Linkage disequilibrium (LD) |
Nonrandom association of one variant with another |
| Locus |
Genomic region flanked by recombination hotspots |
| Plasmid |
Circular DNA. In research, it is introduced into cells for transgenic expression of the cargo sequence |
| Single nucleotide polymorphism (SNP) |
A sequence variant consisting of a difference in a single nucleotide |
| Transcription factor |
A protein that controls rate of transcription by binding to DNA sequences |
Linkage Disequilibrium and Minor Allele Frequency
During meiosis, relatively large regions of the human genome are inherited together. Genes and DNA variants located within these regions are therefore “linked”. Because of genetic linkage, two SNPs (that are usually close to each other) can have a high degree of linkage disequilibrium (LD), which is the nonrandom association of alleles (Figure 1). For an SNP to be in high LD with another SNP, the minor allele of one SNP is often inherited with the minor allele of the other SNP (17). Close correlation among DNA variants, owing to genetic linkage and LD, has been essential for the success of GWAS efforts, which leverage the limited number of independent LDs to calculate the association between an SNP and a disease or trait. Although the term GWAS includes the words “genome-wide,” not all SNPs in the human genome are captured by commercial genotyping microarray platforms typically used in a GWAS. As multiple SNPs are inherited together, it is sufficient to genotype one or a few SNPs from each independent LD, whereas the rest of the variants can be estimated on the basis of their LD. The tag-SNPs on these microarray platforms are chosen for genotyping purposes because they have a large amount of LD with neighboring SNPs (18).
Another important issue to consider is the minor allele frequency (MAF). As a GWAS looks for a statistical difference of genetic variation between cases and control, statistical power to detect significant associations between variant and trait is partially a function of MAF. A lower MAF for an allele (e.g., a rarer variant) reduces the number of individuals available for association analysis and thus makes detection of an association less likely unless that variant exerts a large phenotypic effect. Of note, the MAF of an SNP is calculated by population and thus may vary among different ethnicities with contrasting evolutionary histories (9,17). These ancestry-specific MAFs correlate with differences in disease prevalence among populations. For example, IgA nephropathy is more commonly seen in individuals of East Asian and European descent, and risk variants associated with IgA nephropathy are higher in frequency in these populations (19). Another example of how ancestry-related differences in MAFs can drive population-based differences in disease prevalence is apo L1 (APOL1) nephropathy. The G1 and G2 disease risk alleles for nondiabetic CKD are common among individuals of African ancestry but rare in other ethnicities; they are also inherited in an ancestry-specific haplotype (20,21). These risk alleles have been validated to drive a portion of the excess CKD risk seen in the black population (20,22–2324). It is therefore very important to adjust the genetic data for population ancestry before conducting genotype–disease association because ancestry-driven allele frequency changes can bias the results (25), highlighting the importance of cohort demographics when designing, conducting, and interpreting a GWAS.
From Association to Function
A GWAS reports the associations between variants and disease trait. Because genetic associations do not prove causality, “post-GWAS” work must be performed to interrogate these associations for function and identify the variants and genes that are causal, or directly and biologically relevant, for the disease of interest (Figure 1). In a GWAS, an association between a tag-SNP and an inherited trait or disease may be indirect if the causal variant is in high LD with the tag-SNP. Within a locus, additional fine mapping, the methods for which are beyond the scope of this review (26), may identify select candidate causal SNPs for functional validation. As mentioned above, only a handful of GWAS-identified loci have been causally linked to disease. In these regions, the causal variant is usually located within a regulatory region specific to cell type. These regions typically include enhancers, which are short sequences of DNA that interact with transcription factors to increase the likelihood that a gene is transcribed into mRNA. A causal nucleotide change in an enhancer region alters transcription factor binding strength, resulting in a small but measurable change in mRNA transcript and protein levels of a causal gene that contributes to disease development. Correct identification of causal variants and causal genes then allows for opportunities to interrogate the mechanisms by which those genes may alter the phenotype of interest.
Post-GWAS Validation of Causal Variants and Causal Genes
A major GWAS challenge stems from having many DNA variants inherited together with some degree of LD. Because hundreds or even thousands of SNPs could be present at a specific genetic locus, identifying causal variants and causal genes is extremely difficult and has only been reported to less than a handful of loci. In addition careful bioinformatics analyses to narrow down candidate variants, cellular and in vivo studies are needed to demonstrate that these variants and their target genes are directly causal, or relevant to disease pathogenesis. Here, we highlight some commonly used approaches to prioritize, identify, and functionally validate causal variants and causal genes in disease development (Figure 1).
Causal Variants
As mentioned above, many DNA variants are inherited together with varying degrees of LD. One approach to narrowing down the genomic “neighborhood” of the causal variant(s) is to leverage epigenetic and epigenomic data, which help mark functionally important areas in the genome. Functionally active areas in the genome are usually defined by open chromatin where DNA is not tightly bound to histones and where transcription factors can bind and RNA can be transcribed (Figure 2). Of note, patterns of open chromatin tend to be specific to cell type (18,25), so an area of open chromatin in a proximal tubular cell may not be open in a hepatocyte or even a podocyte. One method to map open chromatin within a cell type is to perform a DNase I hypersensitivity assay, in which DNA fragments are digested out for high-throughput sequencing (27). However, this method is technically difficult, and more investigators are using assay for transposase-accessible chromatin using sequencing, which involves tagging regions of open chromatin with hyperactive Tn5 transposases and sequencing these purified DNA fragments (28). In addition to using open chromatin maps, cell type–specific epigenetic data from chromatin immunoprecipitation followed by sequencing can be leveraged to determine whether candidate SNPs lie within promoter, enhancer, or repressor regions (29,30). For example, histone tail modification marks such as monomethylated H3K4 and acetylated H3K27 indicate active enhancer regions (31). These epigenome maps for various (nonkidney) cell types and tissues are publicly available through the Encyclopedia of Coding Elements and International Human Epigenome Consortium projects (32,33). Comparing GWAS loci with the open chromatin and histone maps can narrow the window of candidate SNPs to prioritize, although this approach cannot fully validate causality of these variants.
Figure 2.: Epigenomic annotation of genomic locus to prioritize candidate causal variants. Large amounts of the noncoding genome have regulatory function (A). These regions include the promoter, enhancer, or insulator that regulate the transcribed region. Regulatory regions can be identified by epigenome mapping as specific histone tail modifications are enriched on specific regulatory elements. Candidate causal variants (purple stars) often fall within these regions (B). DNA folds such that the enhancer region (yellow) interacts with the promoter region (red) to increase transcription of a gene. These spatial regulatory relationships can be captured through molecular biology techniques (C). Although show association with kidney disease development (top graph of single nucleotide polymorphisms [SNPs] above dashed line indicating statistical significance), only a portion of this region demonstrates regulatory activity in kidney tubule cells. The bottom plots are visualizations of enhancer-specific histone tail modification from chromatin immunoprecipitation sequencing of human kidney and macrophages, revealing cell and tissue specificity of transcriptional regulation.
To drill down further on whether candidate variants are causal in regulating gene expression, experimental reporter assays can be performed. A reporter assay consists of inserting a DNA sequence containing the candidate SNP either upstream or downstream of a promoter reporter gene cassette in a DNA plasmid. The plasmid is introduced into the cell type of interest, which expresses the relevant proteins regulating transcription, such as transcription factors, which often are specific to cell type. If the SNP alters transcriptional efficiency by affecting the binding of transcription factors, its presence will change the expression level of the reporter protein, such as luciferase or green fluorescent protein (17). This type of reporter assay can interrogate a portion of one locus at a time. Increasing throughput and efficiency, the massively parallel reporter assay (MPRA) can be used to assay hundreds or thousands of DNA regions simultaneously (34). The MPRA leverages the use of unique barcodes for each DNA region represented by an oligonucleotide, and these regions with their barcodes are cloned into reporter plasmids. Cells that have alterations in reporter expression are then sorted and sequenced for the barcodes to identify SNPs in regions that regulate transcription. The major limitation of using either a single reporter assay or the MPRA approach is that the transcriptional interrogation occurs not in the native genomic or epigenomic context of the candidate SNPs. The assayed regions are shorter than the full regulatory sequences at the native genomic loci, and in being cloned into plasmids they may also change the native chromatin structure that plays a key role in gene transcription.
To circumvent the challenges and shortcomings of reporter assays, candidate SNPs and loci can be interrogated in their native genomic context through genome editing. Although several methods for genome editing exist, CRISPR-Cas9 has taken center stage as a feasible and accessible method to knock in candidate SNPs at their natural chromosomal position. A detailed primer on CRISPR-Cas9 can be reviewed elsewhere (35,36). Briefly, the CRISPR-Cas9 system leverages a guide RNA that directs a nuclease enzyme to make a cut in the genomic DNA. Because the guide RNA is derived from a carefully designed and engineered oligonucleotide, the nuclease action is very precise in location. The break in genomic DNA is then repaired either (1) through nonhomologous end joining or (2) homology-directed repair. Nonhomologous end joining is an imperfect process by which the cut ends of the genomic DNA are religated without a template sequence to guide repair. Without such a template, additional nucleotides may be added, or existing nucleotides excluded, in the repair process, thereby potentially causing a frame shift that knocks out expression of that gene. Unlike nonhomologous end joining, homology-directed repair involves an exogenous homologous template sequence that can bind to the cut blunt ends. To introduce a variant or SNP into the genome, that homologous template contains the desired sequence, which then gets incorporated into the genomic DNA during the repair process. When a desired SNP is introduced into a kidney cell, for example, differences in expression of nearby genes can be measured compared with an isogenic control, and other cellular phenotypes can also be evaluated.
Causal Genes
To investigate potential novel pathways involved in disease pathogenesis, the genes regulated by causal variants need to be identified (Figure 3). One approach to identify causal genes is through expression quantitative trait locus (eQTL) mapping, which assesses whether a candidate SNP modulates expression of nearby genes (cis regulation) or distant genes on the same or different chromosome (trans regulation). An eQTL is a gene–SNP pair for which a gene’s expression associates with the allelic configuration of the SNP. As such, eQTL analysis requires both the genotype and gene expression data for cells of tissues of interest.
Figure 3.: Expression of quantitative trait locus (eQTL) mapping to identify causal genes GWAS reports association between genetic variants and disease development. A variant identified in GWAS will then undergo eQTL mapping to identify which gene(s) that variant regulates in terms of transcriptional abundance. eQTL mapping has demonstrated that variant effects on gene expression can be specific to compartments of the nephron. In this figure, the risk allele, B, located in the yellow oval region increases expression of the eGene measured in tubules, as shown in the box plot of gene expression level (y axis) and genotype (x axis). However, the B allele does not alter expression of that same gene in the glomerulus. This is consistent with tubule-specific eQTL.
One of the largest data sets to incorporate both genotype and transcriptomic data are from the Genotype-Tissue Expression (GTEx) project, which characterized gene expression levels across hundreds of postmortem donors and over 40 tissues in the human body (37). GTEx data have facilitated eQTL analyses for a variety of diseases and traits, including schizophrenia, coronary artery disease, and CKD (38–39404142). These analyses have highlighted potential candidate causal variants and causal genes, although the limited number of kidney samples has limited the power of eQTL analyses for CKD. In addition, RNA sequencing (RNA-seq) data for GTEx samples reflect whole tissue that includes different cell types if different organs can have different expression patterns and eQTLs, can different cell types within an organ also have different eQTLs from each other?
With at least 26 different cell types in the kidney (43) and at least 16 detected by single-cell RNA-sequencing (44–4546), interrogating how eQTLs differ among these cell types will help prioritize key candidate causal variants and target genes for functional validation and mechanistic insights. Although eQTL data from single-cell RNA-sequencing of kidneys are not yet available, an eQTL atlas for the glomerular and tubular compartments of the human kidney has revealed the existence of compartment-specific eQTLs not seen in whole kidney data (42,47). Because CKD spans many different diseases—some with glomerular pathophysiology and others with significant tubular and interstitial pathology—eQTLs specific to each compartment reflect the expected differences in disease pathogenesis. In addition, CKD GWAS loci tend to associate with compartment-specific eQTLs and are further away from promoter regions, suggesting that compartment-specific eQTLs may indeed act in enhancer regions (42). The concept of cell-specific or compartment-specific eQTL is not limited to the kidney: studies of other organ systems, such as skin and adipose, also highlight the importance of cell-specific programs of transcriptional regulation (48,49). Because GWAS SNPs and even subthreshold GWAS SNPs (that do not meet genome-wide significance) tend to map to regulatory regions and have been shown to modulate transcriptional activity (50,51), knowledge of cell specificity of these regulatory features can provide clues for causal cell types and pathophysiology. Taken together with high-resolution epigenomic data that are also cell type–specific, evaluation of the causal cell type for a disease-associated variant will assist in validating candidate variants and target genes.
Although expression of quantitative trait loci (QTLs) is currently most widely used to annotate GWAS loci, multiple additional molecular QTLs can be used to annotate genetic regions, such as histone QTLs, accessible chromatin QTLs, methylation QTLs, protein QTLs, or metabolite QTLs (52–5354). The concept around additional molecular QTLs is the same: they are based on the generation of an external database where genetic variations and other molecular trait associations are established. Integration of the GWAS and multiple molecular traits are critical for gene prioritization. Variants that are associated both with disease states and influence molecular traits are usually prioritized for further analysis.
In addition to QTLs, another approach to identifying causal genes, especially for variants in noncoding regions that could be hundreds of kilobases away from their target genes, is to test physical associations between candidate SNPs and promoter regions. Chromosomes can bend and fold in different conformations that allow for distal regulatory elements, such as enhancers housing GWAS SNPs, to make direct contact with promoters, thereby modulating transcription of putative target genes. These looping interactions can be interrogated through experimental methods known as chromosome conformation capture accompanied by high-throughput sequencing, which can range from large-scale genome-wide assays to examining where one enhancer region binds (55,56). Indeed, chromosome capture has been instrumental in our understanding of GWAS SNP–gene interactions for obesity and kidney (57,58).
Once candidate target genes of causal variants are identified, further mechanistic studies to evaluate functional significance of these genes can be performed. Experimental approaches for studying kidney GWAS traits include genetic deletion of candidate genes in zebrafish (4,59) and mice (42) to evaluate whether altering expression of those genes induces or protects against kidney injury. Once a phenotype is established from perturbation of candidate gene expression, mechanistic studies to test how these genes contribute to CKD and other kidney-relevant traits can provide new insights into disease pathogenesis and reveal potential novel therapeutic strategies.
Examples of Functional Follow-Up of Kidney Disease–Associated Variants
Hundreds of GWAS loci have been reported to associate with CKD and other kidney traits. However, determining whether these loci have functional significance in kidney physiology and disease requires probing beyond bioinformatics analyses and performing experimental science. To illustrate how the link between genotype and kidney phenotype has been explored, below we review the functional studies examining noncoding variants from two loci, SHROOM3 and DAB2, that have been associated with kidney traits in multiple GWAS reports (3,4,12,60).
SHROOM3
GWASs of largely European ancestry individuals have linked the SNP rs17319721 in an intronic region of SHROOM3 with CKD and eGFR (4,60), although the role of SHROOM3 expression had not previously been established for CKD. Menon et al. demonstrated through integration of human patient data, reporter assays, and in vivo experiments that the risk allele at the SHROOM3 locus increases SHROOM3 expression through increased binding of a transcription factor TCF7L2 in an Wnt/β-catenin–dependent fashion. They showed that the risk allele increases SHROOM3 expression in kidney allograft samples, and that the SNP overlies a region with a consensus binding sequence for TCF7L2 that then activates SHROOM3 transcription (61). In addition, the risk allele and higher SHROOM3 expression correlated with higher chronic allograft nephropathy fibrosis scores (61). In a complementary experiment, mice undergoing unilateral ureteral obstruction to induce fibrosis had attenuation of profibrotic signaling on knockdown of Shroom3 both in a whole-body knockdown model and a tubule-specific overexpression model (61).
These results differ somewhat from other functional studies. Prokop et al. demonstrated that introducing wild-type Shroom3 into the fawn-hooded hypertensive rat, which develops spontaneous proteinuric disease, improved glomerular sclerosis. This directionality conflicts with the study by Menon et al. In addition, the same group later published that the rs17319721 risk allele does increase expression of a shorter isoform of SHROOM3, but that TCF7L2 acts as a repressor that gets released in the presence of the risk allele (62). These differences in proposed mechanisms are likely because of different model systems, divergent methods for interrogating the risk variant (reporter assays versus genome editing), and different cultured cells such as tubule cells and podocytes. Indeed, establishing a robust and high-fidelity model system is a key component of functional validation of GWAS loci. Differences among model systems, as well as between humans and mice, have led to conflicting study results for other kidney loci, such as UMOD (63,64) and APOL1 (65,66). Although the SHROOM3 studies affirm that the risk allele modulates SHROOM3 expression, they also highlight the importance of methodology in mechanistic interrogation of candidate SNPs and target genes.
DAB2
Further illustrating the importance of cell types in studying variants is the DAB2-C9 locus. GWAS for kidney traits identified an association between rs11959928 and CKD (3,4). However, on whole-kidney eQTL analysis by Ko et al. (59) the SNP did not associate with expression levels of nearby genes DAB2 or C9. Upon closer examination with compartment-specific eQTL analysis, the risk allele for rs11959928 did associate with tubular expression of DAB2 but not C9, and there were no significant eQTLs for this SNP in the glomerular compartment, highlighting the importance of cell specificity in eQTL studies of complex tissues (Figure 4) (42). In addition, although DAB2 is expressed in multiple tissues, the specific genetic variant only influenced the level of DAB2 in kidney tubules and not in other tissues.
Figure 4.: Identification of DAB2 as a kidney disease gene. Prior GWAS for kidney disease traits identified an association between the DAB2-C9 locus and eGFR. The tag SNP is located between the two genes, leading to the questions of which gene is regulated by a causal variant or variants, which cell type is relevant to the association, and what the effect of the regulated gene is on kidney disease. Compartment-specific eQTL mapping of the GWAS variant revealed that DAB2 expression is modulated by the variant in the tubule portion of the nephron. C9 expression levels were not influenced by the variant. To confirm DAB2’s role in tubule health, tubule-specific knockout mice for Dab2 and C9 were generated and stressed with folic acid, which induces fibrotic injury. The Dab2 tubule-specific knockout mice developed less fibrosis than the wild-type control mice, suggesting that mice with genetic lowering of DAB2 protein is protected from development of kidney disease. No effect was seen in the C9 knockout mice.
To drill down further on whether DAB2 or C9 is the causal gene, Qiu et al. examined the response to profibrotic injury in tubule-specific knockdown of these genes in mice. No differences were seen in the C9 knockdown mice, but the Dab2 knockdown mice demonstrated partial protection against profibrotic injury states (Figure 4). Primary cell culture experiments showed that less Dab2 expression led to less TGFβ-induced Smad2 and Smad3 phosphorylation and less TGFβ-induced fibronectin and collagen1 production (42). Without the level of resolution compartment-specific or single-cell analyses offer, the causal gene would not have been identified.
Challenges and Moving Forward
In this era of high-throughput sequencing and massive amounts of data collected on the human genome, multiple GWAS efforts have probed the genetic basis of CKD, revealing many novel loci that associate with kidney function and other kidney-related traits. However, only a few of these CKD-relevant loci have yielded published functional studies, some with conflicting results. With the causal variants likely exerting only small effects on kidney function, recapitulating disease phenotype experimentally by interrogating SNPs from one locus may be difficult. The GWAS field is moving toward development of polygenic risk scores for risk prediction of disease (67–6869), aligning with the concept that an aggregate of variants exerting small effects contributes to disease development. Furthermore, CKD, eGFR, and albuminuria are broad traits encompassing heterogeneous disease causes and overlapping presentations. Because the strength of association between genotype and phenotype relies on the quality of phenotype characterization, phenotypes based on serum creatinine, which can vary in accuracy among centers (70), may limit functional validation.
Even with further optimization of genetic epidemiology studies of CKD, assigning function to variants will require integrative and cell-specific approaches, as evident by the DAB2 example presented above. Merging genetic data with epigenetics and transcriptomics helps identify causal variants and causal genes involved in CKD pathogenesis in a cell type–specific manner. To ensure success, these data from human kidney biobanks will be instrumental, and a coordinated effort among CKD investigators will be needed for biospecimen collection and appropriate processing for the technologies relevant to epigenetics and single-cell transcriptomics. With these collaborative efforts in place, further experimental studies with cellular and in vivo models will be needed to validate causal candidates, and are essential to elucidating novel kidney disease mechanisms for eventual therapeutic targeting.
Disclosures
Dr. J. Lin and Dr. K. Susztak report work in the laboratory is supported by Boehringer Ingelheim, Gilead, GSK, Merck, Bayer and Regeneron. Dr. K. Susztak has consulted for Janssen, Chemocentryx, Maze Pharma, and Jnana.
Funding
Dr. J. Lin is supported by National Institutes of Health grant K08 HL135348, and the laboratory of Dr. K. Susztak is supported by National Institutes of Health grants R01 DK076077, DK087635, and DK105821.
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