Genetic Susceptibility to Chronic Kidney Disease – Some More Pieces for the Heritability Puzzle

Abstract

Chronic kidney disease (CKD) is a major global health problem with an increasing prevalence partly driven by aging population structure. Both genomic and environmental factors contribute to this complex heterogeneous disease. CKD heritability is estimated to be high (30–75%). Genome-wide association studies (GWAS) and GWAS meta-analyses have identified several genetic loci associated with CKD, including variants in UMOD, SHROOM3, solute carriers, and E3 ubiquitin ligases. However, these genetic markers do not account for all the susceptibility to CKD, and the causal pathways remain incompletely understood; other factors must be contributing to the missing heritability. Less investigated biological factors such as telomere length; mitochondrial proteins, encoded by nuclear genes or specific mitochondrial DNA (mtDNA) encoded genes; structural variants, such as copy number variants (CNVs), insertions, deletions, inversions and translocations are poorly covered and may explain some of the missing heritability. The sex chromosomes, often excluded from GWAS studies, may also help explain gender imbalances in CKD. In this review, we outline recent findings on molecular biomarkers for CKD (telomeres, CNVs, mtDNA variants, sex chromosomes) that typically have received less attention than gene polymorphisms. Shorter telomere length has been associated with renal dysfunction and CKD progression, however, most publications report small numbers of subjects with conflicting findings. CNVs have been linked to congenital anomalies of the kidney and urinary tract, posterior urethral valves, nephronophthisis and immunoglobulin A nephropathy. Information on mtDNA biomarkers for CKD comes primarily from case reports, therefore the data are scarce and diverse. The most consistent finding is the A3243G mutation in the MT-TL1 gene, mainly associated with focal segmental glomerulosclerosis. Only one GWAS has found associations between X-chromosome and renal function (rs12845465 and rs5987107). No loci in the Y-chromosome have reached genome-wide significance. In conclusion, despite the efforts to find the genetic basis of CKD, it remains challenging to explain all of the heritability with currently available methods and datasets. Although additional biomarkers have been investigated in less common suspects such as telomeres, CNVs, mtDNA and sex chromosomes, hidden heritability in CKD remains elusive, and more comprehensive approaches, particularly through the integration of multiple –“omics” data, are needed.

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fgene-10-00453 May 30, 2019 Time: 19:58 # 1
REVIEW
published: 31 May 2019
doi: 10.3389/fgene.2019.00453
Edited by:
Martin H. De Borst,
University Medical Center Groningen,
Netherlands
Reviewed by:
Alexander Teumer,
University of Greifswald, Germany
Nelson L. S. Tang,
The Chinese University of Hong Kong,
China
*Correspondence:
Marisa Cañadas-Garre
m.canadasgarre@qub.ac.uk
†These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Genetic Disorders,
a section of the journal
Frontiers in Genetics
Received: 24 January 2019
Accepted: 30 April 2019
Published: 31 May 2019
Citation:
Cañadas-Garre M, Anderson K,
Cappa R, Skelly R, Smyth LJ,
McKnight AJ and Maxwell AP (2019)
Genetic Susceptibility to Chronic
Kidney Disease – Some More Pieces
for the Heritability Puzzle.
Front. Genet. 10:453.
doi: 10.3389/fgene.2019.00453
Genetic Susceptibility to Chronic
Kidney Disease – Some More Pieces
for the Heritability Puzzle
Marisa Cañadas-Garre1*, Kerry Anderson1†, Ruaidhri Cappa1†, Ryan Skelly1†,
Laura Jane Smyth1†, Amy Jayne McKnight1†and Alexander Peter Maxwell1,2
1Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University of Belfast, Belfast,
United Kingdom, 2Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom
Chronic kidney disease (CKD) is a major global health problem with an increasing
prevalence partly driven by aging population structure. Both genomic and environmental
factors contribute to this complex heterogeneous disease. CKD heritability is estimated
to be high (30–75%). Genome-wide association studies (GWAS) and GWAS meta-
analyses have identified several genetic loci associated with CKD, including variants in
UMOD,SHROOM3, solute carriers, and E3 ubiquitin ligases. However, these genetic
markers do not account for all the susceptibility to CKD, and the causal pathways
remain incompletely understood; other factors must be contributing to the missing
heritability. Less investigated biological factors such as telomere length; mitochondrial
proteins, encoded by nuclear genes or specific mitochondrial DNA (mtDNA) encoded
genes; structural variants, such as copy number variants (CNVs), insertions, deletions,
inversions and translocations are poorly covered and may explain some of the missing
heritability. The sex chromosomes, often excluded from GWAS studies, may also
help explain gender imbalances in CKD. In this review, we outline recent findings on
molecular biomarkers for CKD (telomeres, CNVs, mtDNA variants, sex chromosomes)
that typically have received less attention than gene polymorphisms. Shorter telomere
length has been associated with renal dysfunction and CKD progression, however,
most publications report small numbers of subjects with conflicting findings. CNVs have
been linked to congenital anomalies of the kidney and urinary tract, posterior urethral
valves, nephronophthisis and immunoglobulin A nephropathy. Information on mtDNA
biomarkers for CKD comes primarily from case reports, therefore the data are scarce
and diverse. The most consistent finding is the A3243G mutation in the MT-TL1 gene,
mainly associated with focal segmental glomerulosclerosis. Only one GWAS has found
associations between X-chromosome and renal function (rs12845465 and rs5987107).
No loci in the Y-chromosome have reached genome-wide significance. In conclusion,
despite the efforts to find the genetic basis of CKD, it remains challenging to explain
all of the heritability with currently available methods and datasets. Although additional
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Cañadas-Garre et al. Genetic Predisposition to Kidney Disease
biomarkers have been investigated in less common suspects such as telomeres, CNVs,
mtDNA and sex chromosomes, hidden heritability in CKD remains elusive, and more
comprehensive approaches, particularly through the integration of multiple –“omics”
data, are needed.
Keywords: telomeres, copy number variants, single nucleotide polymorphisms, whole exome sequencing,
mitochondria, chronic kidney disease
INTRODUCTION
Chronic kidney disease is a major global health problem with
an increasing prevalence (Levey et al., 2007;Bash et al., 2009;
Centers for Disease Control and Prevention, 2015). By 2040,
it is estimated that CKD will have become the fifth leading
cause of death (Foreman et al., 2018). This increasing CKD
burden is driven in part by aging population structure (CKD
is ∼8x more common in adults >70 years old compared to
persons <40 years of age) (Bash et al., 2009;Centers for Disease
Control and Prevention, 2015). Diabetes and hypertension are
common risk factors for kidney damage (Kazancio˘
glu, 2013) and
are therefore major contributors to the increased CKD prevalence
(Bash et al., 2009).
There is a marked gender imbalance in CKD with a higher
incidence (11.0 vs. 9.6 per 1,000 person-years) and higher
prevalence (16.0% vs. 12.4%) in women (Bash et al., 2009;Centers
for Disease Control and Prevention, 2015). Nevertheless, women
have a lower risk of CKD progression and men are more likely to
develop ESRD (Ricardo et al., 2018).
Chronic kidney disease is a complex heterogeneous disease,
with contributions from both genomic and environmental
factors. CKD heritability has been estimated to be high (30–
75%) (Satko and Freedman, 2005;O’Seaghdha and Fox, 2011;
Regele et al., 2015). CKD can be identified by well-established
clinical biomarkers such as SCr levels, eGFR, albuminuria, or
UACR (Cañadas-Garre et al., 2018a,b). Unfortunately, these
clinical biomarkers are limited in their utility to predict individual
risk of CKD or likelihood for later progression to ESRD.
Major efforts have been made to understand the heritability in
CKD but the causal pathways remain incompletely understood.
Four major approaches have been proposed to uncover the
missing heritability; exploration of rare variants, increased
samples sizes, study of molecular factors not involving variants
in the DNA sequence and consideration of whether family
studies overestimated heritability risk (Bourrat et al., 2017).
In CKD, meta-analyses of GWAS have provided a useful and
Abbreviations: ATP, adenosine triphosphate; CAKUT, congenital anomalies of
the kidney and urinary tract; CKD, chronic kidney disease; CKiD, chronic kidney
disease in children cohort study; CNVs, copy number variants; CRISIS, chronic
renal insufficiency standards implementation; DKD, diabetic kidney disease;
eGFR, estimated glomerular filtration rate; ESRD, end-stage renal disease; FSGS,
focal segmental glomerulosclerosis; GWAS, genome-wide association studies;
IgAN, immunoglobulin A nephropathy; MMKD, mild to moderate kidney
disease; mtDNA, mitochondrial DNA; NGS, next generation sequencing; NPH,
nephronophthisis; OXPHOS, oxidative phosphorylation; PGRS, polygenic risk
scores; PREVEND, Prevention of Renal and Vascular Endstage Disease study; PUV,
posterior urethral valves; ROS, reactive oxygen species; SCr, serum creatinine;
SNVs, single nucleotide variants; T2DM, type 2 diabetes mellitus; UACR, urinary
albumin/creatinine ratio; WES, whole-exome sequencing.
relatively inexpensive strategy to increase the statistical power
by combining data summaries from different individual GWAS,
helping to attenuate the issue of small sample size and identifying
many genetic loci associated with CKD and/or kidney function
traits (Köttgen et al., 2009, 2010;Chambers et al., 2010;Böger
et al., 2011;Parsa et al., 2013;Pattaro et al., 2016;Gorski
et al., 2017). Rare variants in UMOD,SHROOM3, solute carriers,
and E3 ubiquitin ligases have also been associated with CKD,
eGFR or SCr (Köttgen et al., 2012;Sveinbjornsson et al.,
2014;Prokop et al., 2018). However, these genetic markers
do not account for all the susceptibility to CKD, therefore
other factors must be contributing to the missing heritability.
Part of the missing heritability may correspond to genetic
interactions (epistasis), rather than to missing variants (Zuk
et al., 2012). Telomere length is a biological factor that has
been associated with CKD prevalence and/or CKD progression
in a small number of studies (Ameh et al., 2017). Structural
variants, such as CNVs, insertions, deletions, inversions and
translocations are, in general, poorly covered in commercial
arrays and may explain part of the missing heritability (Manolio
et al., 2009). Mitochondrial proteins, encoded by nuclear
genes, and specific mtDNA encoded genes have also been
associated with CKD (Skelly et al., 2019). The sex chromosomes,
often excluded from GWAS studies, may help explain gender
imbalances in CKD.
In this review, we outline some recent findings on molecular
biomarkers for CKD (telomeres, CNVs, mtDNA variants, X and
Y chromosomes) that typically have received less attention than
single nucleotide polymorphisms (SNPs) present on, or imputed
from, GWAS arrays. These less commonly studied biomarkers
may be part of the “missing heritability” for CKD.
Telomeres and CKD
Telomeres are specialized nucleoprotein complexes that help
protect the ends of linear chromosomes (Sfeir, 2012). There are
inter-individual and intra-individual differences in the length
of telomeres. Shorter telomere length has been associated
with multi-system diseases, early life stressors, increasing
chronological age and all-cause mortality (Dlouha et al., 2014;De
Meyer et al., 2018;Desai et al., 2018;Mangaonkar and Patnaik,
2018;Wang et al., 2018;Willis et al., 2018) (Figure 1). The
majority of studies have analyzed relative telomere length in
peripheral blood leukocytes, but telomere length differs between
tissues within a single individual, with greater heterogeneity in
telomere length evident in older people (Butler et al., 1998;
Dlouha et al., 2014). Telomere length has a reported heritability
of 28–82%, however, not all genetic factors (Broer et al., 2013;
Codd et al., 2013) or environmental influences on telomere
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FIGURE 1 | Telomeres and kidney disease. Copyright disclosure: mouse: https://commons.wikimedia.org/wiki/File:Vector_diagram_of_laboratory_mouse_%28black
_and_white%29.svg; https://creativecommons.org/licenses/by-sa/4.0/deed.en; attribution, “By Gwilz [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/
4.0)], from Wikimedia Commons.” Kidney: https://commons.wikimedia.org/wiki/File:Kidney_Cross_Section.png; By Artwork by Holly Fischer [CC BY 3.0
(https://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons.
length are known (Cubiles et al., 2018;Dugdale and Richardson,
2018;Gao et al., 2018;Lu et al., 2018). Meta-analysis of
telomere length may help confirm discovery associations across
multiple collections, however, this is challenging with different
wet-lab techniques (such as time at sample collection, storage
and processing of biological material, absolute compared to
relative telomere length evaluation, platform employed) and
in silico analyses (such as normalization, controls, covariates,
association, and correction tools) having significant effects on
the reported measurements. There is also limited traditional
epidemiological evidence exploring the mechanistic basis or
causality of reported associations.
Nonetheless, there is evidence that telomere length is
associated with disease states, particularly age-related diseases,
beyond the most commonly studied cancers (Rizvi et al., 2014;
Jafri et al., 2016). Conflicting reports have been published for
the association of telomere length with renal disease, however,
most publications, albeit in relatively small sample sizes with
modest significance values, report that shorter telomere length
is associated with renal dysfunction. Shorter telomeres have
been reported as associated with progression of CKD (defined
as a doubling of baseline SCr and/or ESRD), in the MMKD
(n= 59 patients had confirmed CKD progression) and CRISIS
(n= 105 patients had confirmed CKD progression) studies, with
the effect size strengthened by smoking and the presence of
diabetes (Raschenberger et al., 2015). Telomere shortening has
been associated with IgAN in 177 patients, but not in 30 patients
with DKD or 30 patients with FSGS compared to 83 controls
(Lu et al., 2014). A study examining DNA from peripheral blood
and urine in 15 patients with IgAN showed shorter telomere
length correlated with declining renal function (Szeto et al.,
2005). Multiple studies have been performed for DKD, with
the majority linking shorter telomere length to the development
and progression of kidney disease in people with both type 1
(Astrup et al., 2010, 273 patients; Fyhrquist et al., 2010, 176
patients, 21 progressed) and type 2 diabetes (Tentolouris et al.,
2007, 168 patients; Verzola et al., 2008, 17 patients; Testa et al.,
2011, 501 patients; Gurung et al., 2018, 691 patients). Shorter
telomere length is associated with diabetic complications (Testa
et al., 2011) and all-cause mortality (Astrup et al., 2010). The
Heart and Soul Study is a longitudinal cohort of individuals
with stable coronary heart disease; shorter telomere length at
baseline and more rapid telomere shortening over 5 years were
associated with reduced kidney function, but these changes were
not significant when accounting for age (Bansal et al., 2012). It
is noteworthy that the largest study published considered less
than 1,000 individuals (Testa et al., 2011), which provides limited
power to draw robust conclusions in this era of mega-consortia
studying the genetics of CKD.
Premature telomere shortening is associated with duration of
dialysis treatment in terms of months to years (Boxall et al., 2006).
A cross-sectional study of 175 hemodialysis patients reported
shorter telomere length in men with CKD, despite women
having an older average age in this cohort; association of shorter
telomeres was also observed with increasing age and male sex
(Carrero et al., 2008). Shorter telomeres were associated with
CKD in 203 Japanese hemodialysis patients compared to 203 age
and sex-matched controls without CKD, with shorter telomeres
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also associated with new onset cardiovascular events (Hirashio
et al., 2014). A less reactive immune system is associated with
healthy aging in the general population and ESRD enhances
premature immunological aging with shorter telomeres observed
in 137 patients with ESRD compared to 144 individuals without
kidney disease (Betjes et al., 2011).
Histologically normal and abnormal human kidney tissue
samples from 24 individuals highlighted age-related shorter
telomere length with telomeres typically shorter in the cortex
than in the medulla (Melk et al., 2000). Premature senescence is
an important feature of renal fibrosis that accelerates when cells
are exposed to stressful environments such as more ROS and
higher glucose (Verzola et al., 2008;Carracedo et al., 2013;Cao
et al., 2018). Increasing age and sex related telomere shortening is
observed in kidneys, with shorter telomeres observed in male rats
(Cherif et al., 2003). Multiple animal models of kidney disease
show telomere shortening associated with renal dysfunction,
however, a careful experimental design is required for accurate
telomere measurement (Hastings et al., 2004). Exploring renal
ischemia/reperfusion injury in wild-type and telomerase deficient
mice also suggests that shorter telomeres impair recovery from
acute kidney injury (Westhoff et al., 2010;Song et al., 2011;Cheng
et al., 2015). Severe renal failure induces telomere shortening
(Wong et al., 2009) with rapid telomere loss observed during
kidney transplantation in a rat model of chronic rejection
(Joosten et al., 2003). Tucker and colleagues demonstrated that
high-intensity interval training was beneficial protecting against
telomere erosion in a rat model of CKD (Tucker et al., 2015).
Large-scale studies using carefully collected biological samples
with harmonized phenotypes and analysis protocols will help
determine the true association of telomere length for CKD.
Potential therapies exist to minimize premature telomere
shortening (Townsley et al., 2016;Rodrigues et al., 2017), but
further work is needed to define the mechanistic links between
telomere length and kidney function.
Copy Number Variation and Larger
Chromosomal Re-arrangements
Association With CKD
Copy number variants are genetic structural variants which
involve DNA regions being deleted or duplicated. This can occur
throughout the genome affecting stretches of DNA ranging from
kilo- to mega-base pairs in length and can result in abnormal gene
amplification (Thapar and Cooper, 2013;Sampson, 2016). CNVs
can be both inherited and arise de novo, and are increasingly
being recognized as a significant source of genetic variation
relating to both population diversity and disease, including renal
diseases (Sampson, 2016), neuropsychiatric diseases (Lew et al.,
2018), and cancer (Liang et al., 2016).
There is often uncertainty about the genetic basis of CKD
in pediatric patients, but recent studies have indicated that
chromosomal microarrays have the potential to partly address
this. Verbitsky et al. (2015) assessed 419 children enrolled in
the CKD in children (CKiD) study alongside 21,575 children
and adults who had undergone microarray genotyping for non-
CKD studies. CNV disorders were identified in 31 children
with CKD and 10 known pathogenic genomic disorders were
detected including HNF1B deletion at 17q12. A further 12
pathogenic genomic imbalances were identified using this
technique, distributed evenly among patients diagnosed with
congenital and non-congenital forms of CKD. Overall, large
gene-altering CNVs were more common in the CKiD population
compared with the controls (38 vs. 23%), but the specific genetic
alterations identified in several of the individuals would require
personalized recommendations in future healthcare.
Copy number variants have been linked to CAKUT (Sanna-
Cherchi et al., 2012;Caruana et al., 2014;Bekheirnia et al.,
2017;Siomou et al., 2017). In a study by Caruana et al. (2014),
DNA from 178 Australian children who presented with any
abnormality associated with CAKUT was screened using SNP
arrays. In total, CNVs were identified in 18 children, of which
11 children presented with genomic disorders of unknown
significance. Of these 11 participants, four were reported as
having duplications of 1q23.1, 4p16.1, 7q33, and 8q13.2q13.3
regions, containing genes NEPH1, SLC2A9, AKR1B1, and EYA1,
respectively. Each of these genes have previously been associated
with renal abnormalities.
In an investigation undertaken by Siomou et al. (2017),
seven children with CAKUT were assessed from three unrelated
families using array comparative genomics hybridization. Of
these participants, one reportedly had ureterovesical junction
obstruction and a 1.4 Mb deletion at 17q12, containing two genes,
HNF1B, which has been previously associated with CAKUT, and
ACACA (Thomas et al., 2011;Caruana et al., 2014).
A recent study published by Bekheirnia et al. (2017),
suggested whole exome sequencing (WES) as a viable method
to detect CNVs in individuals with CAKUT. These investigators
performed WES in 112 individuals from 62 families, to identify
SNVs and CNVs in 35 genes previously related to CAKUT. They
identified a de novo triplication in one family at 22q11, and
overall, 6.5% of the individuals assessed in this investigation were
shown to have pathogenic CNVs.
Posterior urethral valves are one of the most common causes
of CKD in children. Faure et al. (2016) assessed the phenotypic
effects of and relationship between renal outcomes and CNVs
in 45 boys with PUV. In total, 13 CNVs were identified in 12
boys, two of which, at positions 3p25.1p25.2 and 17p12, were
pathogenic in nature. Additionally, those CNVs identified which
were >100 kb in size, were significantly associated with earlier
onset of renal failure in children with PUV.
Nephronophthisis (NPH) is a Mendelian genetic disease,
which often leads to ESRD by around 13 years of age. Snoek
et al. (2018) sought to investigate the prevalence of NPH in
adult-onset ESRD, through assessment of the CNVs in the
NPHP1 gene (>90 kb) because a homozygous full gene deletion
is a prominent cause of NPH. These investigators assessed
5,606 adult renal transplant recipients, 26 of whom showed
evidence of the homozygous NPHP1 deletion, compared to none
of the 3,311 controls. Despite this, only 12% of the patients
with the homozygous NPHP1 gene deletion were clinically
diagnosed with NPH.
Copy number variants have also been investigated in
association with IgAN, which is the most common cause of
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primary glomerulonephritis (Ai et al., 2016). The multi-allelic
CNV in the defensin alpha 1 and alpha 3 gene locus (DEFA1A3)
was assessed in two independent IgAN cohorts of Chinese
Han individuals (Ai et al., 2016). This locus can present as
tandem repeats of a 19kb DNA stretch, containing one copy of
either DEFA1 or DEFA3, and several bi-allelic polymorphisms.
The protein products of DEFA1A3, human neutrophil peptides
1–3, are abundant neutrophil granule proteins and function
in the regulation of both the complement system and pro-
inflammatory cytokine production. Each of these have been
previously linked with IgAN.
Evaluation of the presence of CNVs yields potentially
useful clinical information, especially for pediatric
individuals with CKD.
Copy number variants in the human genome are likely to
contribute to healthy development, but have additionally been
linked to several human diseases (Sampson, 2016;Liang et al.,
2016;Lew et al., 2018). The molecular mechanisms that trigger
the formation of CNVs are not fully understood, but recurrent
CNVs with common breakpoints reportedly arise through
unequal meiotic or non-allelic homologous recombination (Arlt
et al., 2012). Recent evidence has suggested that de novo and non-
recurrent CNVs may develop following either replicative errors,
chromosome shattering or chromothripsis (Kloosterman et al.,
2011;Arlt et al., 2012;Nazaryan-Petersen et al., 2018).
Replication stress occurring during DNA replication has been
linked to the collapse of the DNA replication fork and creation
of a single-ended double strand break (Arlt et al., 2012). It has
been considered that this could result in a high frequency of
de novo CNVs. Both the fork collapse and strand break could
result in the activation of damage checkpoint and repair pathways
to correctly reactivate replication, thus preventing the creation
of structural variants. However, CNVs are understood to be
created if this reactivation occurs in an incorrect location using
a template switch, or when an incorrect repair occurs, which
joins two distant DNA breaks and causes a large deletion (Arlt
et al., 2012). Any present mutations which inhibit the ability of
the cell to accurately respond to a collapsed fork, are thought to
ultimately increase the formation of CNVs (Arlt et al., 2012).
Single Nucleotide Polymorphisms and
Chronic Kidney Disease
In the last decade, GWAS have become essential for investigating
the genetic contribution to CKD, with over 50 germline genetic
loci identified as biomarkers of kidney disease risk or associated
with SCr, cystatin-C and/or microalbuminuria (Cañadas-Garre
et al., 2018a). The UMOD gene, coding for uromodulin, the
most abundant urinary protein (Devuyst et al., 2017), is the
gene with most of the consistently replicated genetic associations
(Cañadas-Garre et al., 2018a). Several common UMOD
variants (rs12917707, rs4293393, rs11864909, rs13329952) are
associated with both CKD and eGFR (Köttgen et al., 2009,
2010;Gudbjartsson et al., 2010;Pattaro et al., 2012, 2016). More
recently, the higher frequency in ESRD of another common
UMOD variant (rs13333226), has been confirmed in 638 Chinese
patients with ESRD and 366 controls (Chen et al., 2016). Several
common variants in the myosin heavy chain type II isoform A
(MYH9) gene have been associated with non-diabetic ESRD in
African Americans (Kao et al., 2008;Kopp et al., 2008;Chambers
et al., 2010). Common variants in APOL1 are also associated
with non-diabetic ESRD (Genovese et al., 2010;Tzur et al.,
2010;Foster et al., 2013). Common variants in ELMO1 gene
have been associated with DKD and its progression to ESRD in
several populations, although in this case with less consistency
(Shimazaki et al., 2005;Leak et al., 2009;Pezzolesi et al., 2009a,b;
Narres et al., 2016). A more recent meta-analysis of GWAS,
including data from 133,413 individuals and subsequently
replicated in 42,166 individuals, identified 24 new loci associated
with eGFR (BCAS1,AP5B1,A1CF,PTPRO,UNCX,NFKB1,
TP53INP2,KCNQ1,CACNA1S,WNT7A,TSPAN9,IGFBP5,
KBTBD2,RNF32,SYPL2,SDCCAG8,ETV5,DPEP1,LRP2,
SIPA1L3,INHBC,ZNF204,SKIL, and NFATC1) (Pattaro et al.,
2016). The trans-ethnic meta-analysis showed that 12 loci had
fully consistent effect direction on eGFR across European,
Asian and African individuals (SDCCAG8,LRP2,IGFBP5,SKIL,
UNCX,KBTBD2,A1CF,KCNQ1,AP5B1,PTPRO,TP53INP2,
and BCAS1). Regarding other measures of kidney function, a
variant rs1801239 in the CUBN gene was proposed as a predictor
of UACR and microalbuminuria in a meta-analysis of 63,153
individuals of European ancestry (Böger et al., 2011), and another
variant in the same gene, rs10795433, has been associated with
UACR in 5,825 individuals of European ancestry with diabetes
compared to 46,061 without diabetes (Teumer et al., 2016).
A recent discovery GWAS of UACR in 382,500 unrelated
European participants of the UK Biobank, a population-based
cohort, reported 33 common variants, 20 of them sharing a
consistent direction of effect with the study by Teumer et al.
(2016), including CUBN,HOTTIP,LOC101927609,NR3C2,
ARL15,SHROOM3,MAPKBP1,ICA1L,SNX17,LRMDA,SBF2,
SPATA5L1,FUT1/IZUMO1 genes and additional variants in
chromosomes 1, 2, 7, 14, and 15: rs10157710, rs12032996,
rs1276720, rs17158386, rs2023844, rs2472297, rs4410790,
rs6535594, rs702634, rs7654754, rs8035855, rs10207567,
rs1047891, rs4665972, rs13394343, rs67339103, rs17368443,
rs4288924, rs1145074, and rs838142 (Haas et al., 2018).
This GWAS also identified 11 common novel associations
in CUBN, PRKCI, EFNA3-EFNA1,MIR548AR-LOC646736,
COL4A4, SPHKAP-PID1,INC01262-FRG1,RIB1-LINC00861,
and BAHCC1 genes. UACR had previously been associated
with another common variant in SHROOM3 (rs17319721) in a
meta-analysis of 31,580 and 27,746 Caucasian patients, although
it did not reach GWA significance (pdiscovery = 1.9 ×10−6)
(Böger et al., 2011).
Although GWAS have successfully identified SNPs
associations for the different traits associated with CKD,
most of them are common DNA variants of small effect size.
The proportion of phenotypic variance of eGFR explained by
the 24 novel loci and the 29 previously identified by Pattaro
et al. was 3.22%, therefore of limited help in CKD prediction
(Pattaro et al., 2016).
An alternative to the concept of SNPs as single biomarkers
is the use of PGRS, which provide individual estimates of
the risk of presenting a determinate trait calculated from the
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combination of specific risks associated to SNPs. However, PGRS
may provide only a partial solution in complex diseases. A recent
analysis of 32 highly relevant traits related to five disease areas
in 13,436 subjects of the Lifelines Cohort reported only 10.7%
of the common-SNP heritability of these traits was explained
by the different weighted PGRS, compiled from genome-wide
significantly associated index SNPs based on previous GWAS
(Nolte et al., 2017). The percentage of variance explained by the
PGRS for SCr, composed of three SNPs of high imputation quality
(R2>0.5) was 0.2% for both weighted and unweighted PGRS
(Nolte et al., 2017). Addition of one low-quality SNP increased
the variance up to 0.21% (weighted PGRS). For the eGFR PGRS,
composed of 33 SNPs (high-quality), the percentage of variance
was 1.6% for unweighted PGRS and 1.8% for the weighted
PGRS. Addition of 19 low-quality SNPs increased the variance
up to 2.01% (weighted PGRS). There were no high-quality SNPs
associated with UACR, so it was not possible to construct this
PGRS. The inclusion of one low-quality SNP explained 0.12% of
the variance with both weighted and unweighted PGRS (Nolte
et al., 2017). The PGRS for urate, composed of 20 SNPs (high-
quality), explained from 2.0 to 4.2%, depending if either an
unweighted or weighted PGRS was considered. Addition of eight
low-quality SNPs increased the variance up to 4.52% (weighted
PGRS) (Nolte et al., 2017).
Next generation sequencing has an increasing role for both
research and diagnosis of kidney disease. Recently, a NGS panel
for a spectrum of genetic nephropathies, covering 301 genes,
was designed and validated in a CLIA-approved laboratory
(Larsen et al., 2016). The assay showed excellent performance
characteristics and was able to provide a specific molecular
pathogenesis-based diagnosis in 46% of biopsies studied. An
NGS panel covering all coding and regulatory regions of UMOD
identified 119 genetic variants in 23 ESRD patients (compared
to 22 controls without renal disease). Ninety of those variants
were SNPs, 60 of them with minor allele frequency greater
than 5%. Linkage disequilibrium allowed 20 SNPs to capture
100% of the alleles with a mean R2of 0.97, providing a set
of independent SNPs suitable for association analysis in larger
cohorts (Bailie et al., 2017).
Whole-exome sequencing provided a diagnosis in 22 out of
92 adults with CKD of unknown cause, familial nephropathy or
hypertension (22/92; 24%) (Lata et al., 2018). The confirmation
of the clinical diagnosis by WES allowed the appropriate
genetic counseling and screening for the family members of
some affected patients and helped in clarifying or entirely
reclassified the disease in other cases (Lata et al., 2018). WES
also identified PARN haploinsufficiency as a new genetic cause
of CKD in this study (Lata et al., 2018). The PARN gene
encodes a poly(A)-specific ribonuclease which mediates the post-
transcriptional maturation of the telomerase RNA component
(TERC) and causes telomere disorders (Moon et al., 2015). Exome
sequencing has recently identified 11 loci (p<1×10−4)
in eight genes (PLEKHN1,NADK,RAD51AP2,RREB1,PEX6,
GRM8,PRX,APOL1) associated with T2DM-ESRD in 2476
cases and 2057 non-nephropathy control individuals of African
American origin (Guan et al., 2018). However, exome data from
7974 self-identified healthy adults has recently demonstrated
an implausibly high rate of candidate pathogenic variants for
kidney and genitourinary diseases (1.4%), much higher than
the prevalence of genetic renal/genitourinary disorders, even
after stringent filtering criteria (removal of indels and minor
allele frequency cutoffs of <0.01% and <0.1% for dominant
and recessive disorders, respectively) (Rasouly et al., 2018). This
overestimation of potential pathogenic variants may increase the
burden of uncertain diagnoses and medical referrals rather than
alleviate it, therefore minimizing the utility of exome sequencing
in clinical practice (Rasouly et al., 2018).
Mitochondria and Their Association With
Chronic Kidney Disease
Mitochondria are organelles which generate ATP through
OXPHOS and thus represent the primary energy source for
normal function of the cell and body (Cooper, 2000;Lodish
et al., 2012;Chaban et al., 2014). The majority of mitochondrial
proteins are encoded by nuclear genes (Timmis et al., 2004;
Dolezal et al., 2006). However, mitochondria also have their
own circular genome 16,569 base pairs long that contains 37
genes which encode 13 proteins of the electron transport chain
essential for OXPHOS (Meiklejohn et al., 2013) along with two
rRNAs and 22 tRNAs (Taanman, 1999;Cooper, 2000;Gray et al.,
2008). Mitochondrial dysfunction in kidney tissue may severely
impact renal health and has previously been implicated in CKD
development (Rahman and Hall, 2013;Wallace, 2013;Zhan et al.,
2013;Che et al., 2014;Douglas et al., 2014;Swan et al., 2015;
Galvan et al., 2017).
If mitochondrial metabolism is adversely affected by genetic
variants it can result in kidney disease, sometimes as part of
a wider clinical disorder (Rahman and Hall, 2013). Somatic
mtDNA mutations may be associated with aging, resulting in
decline of mitochondrial function in older individuals (Wallace,
2013). Increased levels of mtDNA mutations have previously
been associated with several disorders including various forms of
kidney disease (Figure 2) (Wallace, 2013).
Mitochondrial dysfunction can occur via a number of
pathways, for example persistent hyperglycemia (associated with
diabetes) results in increased tubular oxygen consumption, and
in turn leads to hypoxia of the kidney tissue (Hansell et al., 2013).
Mitochondrial dysfunction can be associated with increased
electron leakage from the respiratory chain during OXPHOS,
which results in ROS being generated which can cause kidney
injury (Granata et al., 2015) including direct damage to DNA
(Marnett, 2000). Genetic variation in mtDNA (Figure 2) or
nuclear genes (Figure 3) which influence mitochondrial function
may impair respiratory chain complex activities leading to an
increase in production of ROS resulting in a negative feedback
loop, increasing mitochondrial dysfunction, OXPHOS defects
and ROS generation along with a reduction in ATP production
which leads to increased oxidative stress which may lead to
uncontrolled autophagy, mitophagy, and further ROS production
(Fernandez-Marcos and Auwerx, 2011;Kim et al., 2012;Zaza
et al., 2013). Mitochondrial dysfunction, ROS generation and the
resulting dysregulation of autophagic mechanisms may also lead
to an upregulation of the intrinsic pathway of apoptosis which in
turn leads to inflammation and fibrosis in the renal tubules and
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FIGURE 2 | Increased mutation rate in mtDNA have previously been associated with several diseases including various forms of kidney disease. These mutations
include point mutations, deletions and single nucleotide polymorphisms. Some of these mutations may result in several pathological phenotypes and these have
been highlighted by a solid line between such genes.
glomeruli (Tanaka et al., 2005;Song et al., 2010;Ye et al., 2010;
Coughlan et al., 2016).
Despite the mitochondrial genome being widely ignored
in relation to CKD, a number of studies have identified
mitochondrial genomic loci associated with specific forms of
renal disease (Table 1). SNPs within MT-HV2,MT-CO1, and
MT-CO2c have been associated with IgAN (Douglas et al., 2014);
the A3243G point mutation in the leucineUUR tRNA gene (MT-
TL1) was identified in patients with FSGS (Jansen et al., 1997;
Kurogouchi et al., 1998;Nakamura et al., 1999;Doleris et al.,
2000;Hotta et al., 2001;Hirano et al., 2002;Guéry et al., 2003),
other forms of renal disease (Guéry et al., 2003) and in a male
with a history of MELAS syndrome including kidney cancer, who
rapidly developed renal failure after removal of the cancerous
kidney (Piccoli et al., 2012). In general, mtDNA biomarkers
have not been considered as potential biomarkers in association
studies, therefore most findings concerning the mitochondrial
genome in relation to CKD come from case reports. The MT-TW
tRNA (m.5538 G >A) mutation was identified as causing FSGS
in a male (Lim et al., 2017). The (m.547 A >T) and tRNAPhe
(m.616 T >C) mutations were found in patients suffering from
inherited tubulointerstitial kidney disease, who did not display
typical symptoms of mitochondrial disease (Connor et al., 2017).
A novel mutation in mtDNA (09155 A >G) was described
in a Caucasian female with a history of renal disease, and
symptoms of Maternally inherited deafness and diabetes (MIDD)
(Adema et al., 2016). Mutations in nuclear genes associated with
mitochondrial function have also been associated with renal
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FIGURE 3 | Genetic variation or altered expression of nuclear genes which
encode mitochondrial proteins may impair respiratory chain complex activities
leading to an increase in production of reactive oxygen species (ROS). This
initiates a negative feedback loop, further reducing mitochondrial function,
and ATP production along with an increase in OXPHOS defects and ROS
generation leading to increased oxidative stress which may lead to
uncontrolled autophagy, mitophagy and further ROS production.
Mitochondrial dysfunction, ROS generation and the resulting dysregulation of
autophagic mechanisms may also activate intrinsic apoptotic mechanisms
resulting in inflammation and fibrosis in the renal tubules, glomerulus and
podocytes eventually leading to kidney disease. Red arrows indicate
underexpression and green arrows overexpression.
disease. The P99L mutation in the BCS1L gene was found in
a female infant suffering from Neonatal Toni–Debré–Fanconi
Syndrome, including renal tubulopathy (Ezgu et al., 2013). R45C
and R56X mutations in the BCS1L gene were described in
two siblings suffering from congenital lactic acidosis, including
renal tubulopathy (De Meirleir et al., 2003), and nine different
mutations in FBXL4 were identified in nine individuals suffering
from mitochondrial encephalomyopathy including renal tubular
acidosis (Gai et al., 2013).
Despite the limited published literature, the known
significance of the mitochondrial genome with relation to
renal function and the multiple case reports relating to
individuals suffering from renal dysfunction associated with
mutations in mitochondrial or mitochondrial-associated
genes, suggest that there exists considerable potential for
genetic mutations, resulting in mitochondrial dysfunction, to
contribute toward CKD.
X and Y Chromosomes
In CKD research, despite the efforts of extensive GWAS
and other genomic analyses in this area, a “blind spot” still
exists in the form of X- and Y-chromosome analysis. Fifty-
three of the 3,643 publications found in the online GWAS
catalog (hosted by the National Human Genome Research
Institute-European Bioinformatics Institute) examined CKD
and/or kidney-associated traits (MacArthur et al., 2017). Over
450 genome-wide associations (p<5×10−8) with renal
disease and/or related traits were found at 140 loci across the
genome (Table 2).
As depicted in Table 2, the number of associations per
chromosome is the lowest for chromosome Y (no associations)
and the fourth lowest number of associations for chromosome
X (four associations). This is not surprising for chromosome Y.
Historically thought of as a “genetic wasteland” (Skaletsky et al.,
2003), association analyses usually exclude the Y-chromosome.
Indeed, in the 53 studies examining renal disease/traits, only
one included the Y-chromosome in the association analysis
(Nanayakkara et al., 2014). Given that the Y-chromosome is the
smallest and contains the fewest number of genes per number
of base pairs (Zerbino et al., 2018), the lack of significant
associations in this study is not unexpected.
However, for a chromosome of its size and gene content,
the small number of associations found between X-chromosome
SNPs and renal disease/traits raises questions as to why there
are so few reported. Indeed, the only chromosomes with fewer
reported associations are chromosomes 14 and 21, both of which
are smaller and contain fewer genes than chromosome X. The
lack of reported associations with sex chromosome SNPs could
be due to a true lack of association or under-representation of sex
chromosomes in GWAS.
Of the 53 GWAS in renal disease/traits, 10 are unclear
as to whether X- and Y-chromosome SNPs were included
in association analysis. Over half (62%) of the studies did
not report sex chromosome association results, with many
actively excluding the X- and Y-chromosomes from the
association (Chambers et al., 2010;McDonough et al., 2011)
or meta-analysis stages (Köttgen et al., 2009). Of the 10
studies (18%) that explicitly state that the X-chromosome
analysis was included, only one study found associations
between X-chromosome SNPs and renal traits (SCr and
eGFR) that reached genome-wide significance (Kanai
et al., 2018). Two SNPS, rs12845465, and rs5987107, were
both associated with SCr and eGFR (p<5×10−8). In
only one study does Y-chromosome analysis appear to be
included, where no SNPs reached genome-wide significance
(Nanayakkara et al., 2014).
Therefore, with less than 20% of studies reporting
X-chromosome results and Y-chromosome exclusion
almost ubiquitous, it is not surprising that very few sex
chromosome SNPs have shown association in studies of renal
disease/traits. A possible explanation for sex chromosome
exclusion is that traditional imputation methods call for
the use of autosomes only (Marchini et al., 2007). Even
now that methods of X-chromosome imputation have been
introduced (Marchini and Howie, 2010;König et al., 2014),
greater expertise is required and the X-chromosome is
imputed separately from the autosomes, and these issues
may lead some researchers to simply exclude it. The lack of
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TABLE 1 | Studies in mitochondrial genome and in nuclear genes associated with mitochondrial function (FSGS, focal segmental glomerulosclerosis; MELAS, mitochondrial encephalomyopathy, lactic acidosis, and
stroke-like episodes; MIDD, maternally inherited diabetes and deafness).
Gene Mutation Disease Methodology Function References
? 09155 A >G MIDD, renal disease Whole exome sequencing ? Adema et al., 2016
MT-TL1 3243 A >G MELAS syndrome, renal cancer, renal
failure
Restriction fragment length
polymorphism (ApaI)
Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Piccoli et al., 2012
“Kidney disease” Allele-specific PCR Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Guéry et al., 2003
FSGS Restriction fragment length
polymorphism (ApaI)
Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Kurogouchi et al., 1998
Kidney disease, diabetes, hearing loss Restriction fragment length
polymorphism (ApaI)
Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Jansen et al., 1997
Kidney disease, diabetes, hearing loss,
cataract
Restriction fragment length
polymorphism (ApaI)
Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Nakamura et al., 1999
FSGS, diabetes, hearing loss (3/4
cases), Cerebellar syndrome (1/4
cases)
Allele-specific amplification Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Doleris et al., 2000
FSGS, diabetes (2/4 cases), hearing
loss (1/4 cases)
Restriction fragment length
polymorphism (ApaI)
Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Hotta et al., 2001
Renal disease, hearing loss ? Encodes tRNALeu(UUR), forms
amino acids containing Leucine
Hirano et al., 2002
Control
region
m.547 A >T Inherited tubulointerstitial kidney
disease
Sanger sequencing ? Connor et al., 2017
MT-TF m.616 T >C Inherited tubulointerstitial kidney
disease
Sanger sequencing Encodes tRNA Phenylalanine,
forms amino acids containing
phenylalanine
Connor et al., 2017
MT-TW m.5538 G >A FSGS Whole exome sequencing Encodes tRNA tryptophan,
forms amino acids containing
tryptophan
Lim et al., 2017
BCS1L P99L Neonatal Toni–Debré–Fanconi
Syndrome, renal tubulopathy
? Assembly of mitochondrial
respiratory chain complex III
Ezgu et al., 2013
BCS1L R45C, R56X Congenital lactic acidosis, renal
tubulopathy
Direct sequencing Assembly of mitochondrial
respiratory chain complex III
De Meirleir et al., 2003
FBXL4 (c.1703G >C [p.Gly568Ala], c.1444C >T
[p.Arg482Trp], c.1694A >G (p.Asp565Gly),
c.1652T >A (p.Ile551Asn), c.1067del
(p.Gly356Alafs∗15), c.1790A >C (p.Gln597Pro),
(c.[614T >C; 106A >T], p.[Ile205Thr; Arg36∗]),
(c.1229C >T [p.Ser410Phe])
Mitochondrial encephalomyopathy,
renal tubular acidosis
Whole exome sequencing Phosphorylation-dependent
ubiquitination in mitochondria
Gai et al., 2013
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reported analysis of X-chromosome SNPs in renal disease then
leads to its exclusion from meta-analysis, as X-chromosome
results are not common between all included studies. Poor
genotyping of X-chromosome SNPs may also account for
a reduced number of significant associations. Evidence has
suggested that removal of X-chromosome SNPs during quality
control is significantly more likely, due to a higher rate of
chromosomal anomalies or missing call rate than autosomal
SNPs (Wise et al., 2013). However, despite the successful
imputation of the X-chromosome, chromosome Y lags behind.
Despite recent efforts (Zhang et al., 2013), haplogroup-based
Y-chromosome imputation is still not widely used, with
authors opting to instead use only directly genotyped SNPs
(Charchar et al., 2012).
The lack of sex chromosome inclusion in CKD GWAS
may be one reason that the relationship between sex and
CKD incidence/progression is so unclear. By regularly excluding
these chromosomes from renal GWAS, we may miss SNPs
that infer either increased CKD risk or protection to one
gender in particular.
Traditionally, a greater risk of CKD incidence and progression
to ESRD was associated with males. While current evidence
still supports an increased rate of progression in men to ESRD
(Yang et al., 2014), the risk inferred by gender on incidence
of CKD is unclear. A study which used several definitions
of incidence found that when using eGFR-based definitions
of CKD (<60 ml/min/1.732), incident CKD was significantly
higher in women than men (p= 0.02), but when using a
minimum increase in SCr to detect CKD, men had a significantly
higher incidence (p= 0.001) (Bash et al., 2009). Gender
adjustment occurs in eGFR calculation, which may explain this
difference. A study conducted to develop a CKD risk score
also found that female sex was associated with prevalent CKD
(p= 0.02) (Bang et al., 2007), as did a Turkish population
study (p<0.001) (Süleymanlar et al., 2011). Additionally, a
comprehensive review revealed that 38 studies found CKD
was more prevalent in women, while 13 found it was more
prevalent in men (Hill et al., 2016). Therefore, while women
seem to make up a larger proportion of the individuals
affected by CKD, affected men seem to progress at a much
faster rate, highlighting the difference in the way that CKD
affects men and women.
Clinical evidence and recent literature support a
link between the sex chromosomes and impaired renal
function. Arising as a result of a mutation in COL4A5
on chromosome X, Alport syndrome is caused by
impaired production or function of collagen in various
basement membranes throughout the body, including
in the glomerulus (Kashtan, 2017). The condition is
characterized by hearing loss, ocular abnormalities and
TABLE 2 | Comparison of associations reaching genome-wide significance (5 ×10−8) per chromosome in renal disease or related traits (bp, base pairs; Chr,
chromosome; GWAS, genome-wide association studies).
Chr Size Chr (bp) Coding Genes per Chr Number of Associations with Renal Traits in GWAS Catalog
Total Renal-Associated Loci
1 248956422 2050 21 15
2 242193529 1301 47 12
3 198295559 1079 18 8
4 190214555 753 25 6
5 181538259 884 19 7
6 170805979 1045 60 11
7 159345973 992 26 6
8 145138636 1021 16 5
9 138394717 778 8 3
10 133797422 731 14 6
11 135086622 1316 33 10
12 133275309 1036 26 10
13 114364328 321 7 3
14 107043718 821 1 1
15 101991189 616 33 8
16 90338345 862 29 5
17 83257441 1188 24 4
18 80373285 269 13 5
19 58617616 1474 9 4
20 64444167 543 9 5
21 46709983 232 2 1
22 50818468 492 8 3
X 156040895 846 4 2
Y 57227415 63 0 0
Total: 452 Total: 140
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progression to ESRD, where up to 30% of women reach ESRD
by age 60 (Savige et al., 2016) and the majority of affected
males will require transplant or dialysis by their late twenties
(Temme et al., 2012).
DISCUSSION AND CONCLUSION
Extensive efforts have been made to harness existing GWAS
data and improve the sample size statistical power via GWAS
meta-analyses to uncover true associations between genetic
variants and CKD. Nevertheless, it remains challenging to
explain all of the heritability of CKD with currently available
methods and datasets.
The definition of CKD phenotype (based on SCr, eGFR and/or
urinary albumin measurements) varies between published studies
which impacts on the strength of genetic associations observed.
CKD is phenotypically heterogeneous and CKD risk may be
amplified by co-morbidities such as obesity. Many genetic studies
have a cross-sectional case-control design with the determination
of CKD based on a single measurements of kidney function.
This limits the ability to explore dynamic gene-environment
interactions over time, e.g., the impact of diet, gut microbiome,
smoking, physical activity, stress, medication use or long-term
glycemic control on genetic risk of developing CKD (Simon et al.,
2016;Sandoval-Motta et al., 2017).
Prospective follow up of longitudinal cohorts at risk of
developing CKD, such as the UK Biobank population may help
to unravel some of the complex interplay of genetic background
and environmental stressors contributing to kidney damage (Kim
et al., 2017). Stratification by co-morbidity, e.g., elevated BMI in
T2DM patients, may help identify additional risk variants with a
stronger genetic predisposition to CKD (Perry et al., 2012).
The molecular biomarkers for CKD that have received
less attention (telomeres, CNVs, mtDNA variants, X and Y
chromosomes) are pieces of the missing heritability puzzle.
Shorter telomere length is associated with renal dysfunction and
CKD progression, even though reported results are conflicting.
CNVs have been linked to CAKUT (1q23.1, 22q11, 4p16.1,
7q33, 8q13.2q13.3, and 17q12 regions), PUV (3p25.1p25.2 and
17p12), nephronophthisis (NPHP1 gene) and IgAN (DEFA1A3
locus). Information on mtDNA biomarkers is mostly from case
reports, but the A3243G mutation in the MT-TL1 gene has
been associated with FSGS. One GWAS has found associations
between X-chromosome SNPs and renal function (rs12845465
and rs5987107). No SNPs in the Y-chromosome have reached
genome-wide significance.
Unraveling the missing heritability of CKD will need
coherent integration of the different sources contributing to total
heritability, and not just inclusion of missing gene variants. Using
multiple –“omics” data by combining elements of the phenome,
genome, epigenome, transcriptome, metabolome, proteome, and
microbiome and translating these data into a useful individual
CKD risk assessment remains a major challenge. These research
goals efforts will likely help to increase our understanding of
the mechanisms of kidney function and disease, and improve
disease prediction.
AUTHOR CONTRIBUTIONS
MC-G, KA, RC, RS, LJS, AJM, and APM contributed to the
conception or design of the work, acquisition, analysis and
interpretation of data for the work, drafting the work and
revising it critically for important intellectual content, provided
the approval for publication of the content, and agreed to be
accountable for all aspects of the work in ensuring that questions
related to the accuracy or integrity of any part of the work are
appropriately investigated and resolved.
FUNDING
This work has been partly funded by the Medical Research
Council (Award Reference MC_PC_15025) and the Public
Health Agency R&D Division (Award Reference STL/4760/13).
MC-G and KA are funded by a Science Foundation Ireland-
Department for the Economy (SFI-DfE) Investigator Program
Partnership Award (15/IA/3152). LJS is the recipient of a
postdoctoral research fellowship from the Northern Ireland
Kidney Research Fund. RS and RC are funded by individual
Ph.D. studentships from the Department for the Economy,
Northern Ireland.
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