Novel Genetic Loci Identified for the Pathophysiology of Childhood Obesity in the Hispanic Population

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DOI: 10.1371/journal.pone.0051954 · Source: PubMed
Abstract
Genetic variants responsible for susceptibility to obesity and its comorbidities among Hispanic children have not been identified. The VIVA LA FAMILIA Study was designed to genetically map childhood obesity and associated biological processes in the Hispanic population. A genome-wide association study (GWAS) entailed genotyping 1.1 million single nucleotide polymorphisms (SNPs) using the Illumina Infinium technology in 815 children. Measured genotype analysis was performed between genetic markers and obesity-related traits i.e., anthropometry, body composition, growth, metabolites, hormones, inflammation, diet, energy expenditure, substrate utilization and physical activity. Identified genome-wide significant loci: 1) corroborated genes implicated in other studies (MTNR1B, ZNF259/APOA5, XPA/FOXE1 (TTF-2), DARC, CCR3, ABO); 2) localized novel genes in plausible biological pathways (PCSK2, ARHGAP11A, CHRNA3); and 3) revealed novel genes with unknown function in obesity pathogenesis (MATK, COL4A1). Salient findings include a nonsynonymous SNP (rs1056513) in INADL (p = 1.2E-07) for weight; an intronic variant in MTNR1B associated with fasting glucose (p = 3.7E-08); variants in the APOA5-ZNF259 region associated with triglycerides (p = 2.5-4.8E-08); an intronic variant in PCSK2 associated with total antioxidants (p = 7.6E-08); a block of 23 SNPs in XPA/FOXE1 (TTF-2) associated with serum TSH (p = 5.5E-08 to 1.0E-09); a nonsynonymous SNP (p = 1.3E-21), an intronic SNP (p = 3.6E-13) in DARC identified for MCP-1; an intronic variant in ARHGAP11A associated with sleep duration (p = 5.0E-08); and, after adjusting for body weight, variants in MATK for total energy expenditure (p = 2.7E-08) and in CHRNA3 for sleeping energy expenditure (p = 6.0E-08). Unprecedented phenotyping and high-density SNP genotyping enabled localization of novel genetic loci associated with the pathophysiology of childhood obesity.

Figures

Novel Genetic Loci Identified for the Pathophysiol ogy of
Childhood Obesity in the Hispanic Population
Anthony G. Comuzzie
1
, Shelley A. Cole
1
, Sandra L. Laston
1
, V. Saroja Voruganti
1
, Karin Haack
1
,
Richard A. Gibbs
2
, Nancy F. Butte
3
*
1 Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, United States of America, 2 Human Genome Sequencing Center, Department of
Molecular & Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America, 3 USDA/ARS Children’s Nutrition Research Center, Department of
Pediatrics, Baylor College of Medicine, Houston, Texas, United States of America
Abstract
Genetic variants responsible for susceptibility to obesity and its comorbidities among Hispanic children have not been
identified. The VIVA LA FAMILIA Study was designed to genetically map childhood obesity and associated biological
processes in the Hispanic population. A genome-wide association study (GWAS) entailed genotyping 1.1 million single
nucleotide polymorphisms (SNPs) using the Illumina Infinium technology in 815 children. Measured genotype analysis was
performed between genetic markers and obesity-related traits i.e., anthropometry, body composition, growth, metabolites,
hormones, inflammation, diet, energy expenditure, substrate utilization and physical activity. Identified genome-wide
significant loci: 1) corroborated genes implicated in other studies (MTNR1B, ZNF259/APOA5, XPA/FOXE1 (TTF-2), DARC, CCR3,
ABO); 2) localized novel genes in plausible biological pathways (PCSK2, ARHGAP11A, CHRNA3); and 3) revealed novel genes
with unknown function in obesity pathogenesis (MATK, COL4A1). Salient findings include a nonsynonymous SNP
(rs1056513) in INADL (p = 1.2E-07) for weight; an intronic variant in MTNR1B associated with fasting glucose (p = 3.7E-08);
variants in the APOA5-ZNF259 region associated with triglycerides (p = 2.5-4.8E-08); an intronic variant in PCSK2 associated
with total antioxidants (p = 7.6E-08); a block of 23 SNPs in XPA/FOXE1 (TTF-2) associated with serum TSH (p = 5.5E-08 to 1.0E-
09); a nonsynonymous SNP (p = 1.3E-21), an intronic SNP (p = 3.6E-13) in DARC identified for MCP-1; an intronic variant in
ARHGAP11A associated with sleep duration (p = 5.0E-08); and, after adjusting for body weight, variants in MATK for total
energy expenditure (p = 2.7E-08) and in CHRNA3 for sleeping energy expenditure (p = 6.0E-08). Unprecedented phenotyping
and high-density SNP genotyping enabled localization of novel genetic loci associated with the pathophysiology of
childhood obesity.
Citation: Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, et al. (2012) Novel Genetic Loci Identified for the Pathophysiology of Childhood Obesity in the
Hispanic Population. PLoS ONE 7(12): e51954. doi:10.1371/journal.pone.0051954
Editor: Dana C. Crawford, Vanderbilt University, United States of America
Received August 15, 2012; Accepted November 7, 2012; Published December 14, 2012
Copyright: ß 2012 Comuzzie et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the National Institutes of Health (NIH) (DK080457), and the USDA/ARS (Cooperative Agreement 6250-51000-
053). Work performed at the Texas Biomedical Research Institute in San Antonio, Texas was conducted in facilities constructed with support from the Research
Facilities Improvement Program of the National Center for Research Resources, NIH (C06 RR013556, C06 RR017515). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: nbutte@bcm.edu
Introduction
Obesity is a complex disease influenced by genetic and
environmental factors and their interactions. The current surge
in childhood obesity in the U.S. is attributable to an interaction
between a genetic predisposition toward efficient energy storage
and a permissive environment of readily available food and
sedentary behaviors [1]. Genetic architecture of common poly-
genic childhood obesity remains largely unknown. In genetic
studies, the phenotypic description of the obese child usually has
been limited to body mass index (BMI). BMI represents
a composite trait of fat free mass (FFM) and fat mass (FM) and
thus loci influencing BMI may differ from more direct measures of
adiposity. In addition, markers of biological processes underlying
the development of obesity such as dietary intake, energy
expenditure and nutrient partitioning may be more effectual in
identifying causal genetic variants [2]. In epidemiology studies,
childhood obesity has been shown to be genetically correlated with
glucose intolerance, hypertension, dyslipidemia, insulin resistance,
chronic inflammation, and risk for fatty liver disease [3,4].
Identification of genes underlying these distinct patterns of
association also may unravel important biological pathways
involved in the pathophysiology of childhood obesity.
Genome-wide association studies (GWAS) have the potential to
localize genetic loci contributing to obesity down to a few 100 kb
[5]. In fact, a recent meta-analysis of the adult GIANT
Consortium established 32 susceptibility BMI loci [6], several of
which were confirmed in French and German children with
extreme obesity [7] and European adults with early-onset obesity
[8]. Two novel loci near OLFM4 and within HOXB5 were recently
reported based on a meta-analysis of 14 pediatric studies of BMI
[9]. These pediatric GWAS were confined to BMI and cohorts of
European ancestry.
Here, we present findings from a GWAS designed to identify
genetic variants influencing childhood obesity and its comorbid-
ities in the Hispanic population. We have published evidence of
heritability, pleiotropy amongst traits, and chromosomal regions
implicated in obesity among Hispanic children in our VIVA LA
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FAMILIA Study [4,10–15] In-depth phenotyping was performed
to characterize the children, including anthropometry, body
composition, growth, metabolites, hormones, inflammation, diet,
energy expenditure and substrate utilization and physical activity.
Our high-density SNP genotyping and phenotypes representing
not only adiposity, but also biological processes associated with the
development and consequences of childhood obesity enabled
localization of novel genetic loci associated with the pathophys-
iology of childhood obesity.
Materials and Methods
The VIVA LA FAMILIA Study was designed to identify genetic
variants influencing pediatric obesity and its comorbidities. Family
recruitment and phenotyping were conducted in 2000–2005 in
Houston, TX. All enrolled children and parents gave written
informed consent or assent. The protocol was approved by the
Institutional Review Boards for Human Subject Research for
Baylor College of Medicine and Affiliated Hospitals and for Texas
Biomedical Research Institute.
The VIVA LA FAMILIA study design and methodology have
been described in detail elsewhere
4
. GWAS was performed on 815
children from 263 Hispanic families. The number of families by
sibships was: 8 (one child), 40 (two children), 155 (three children),
48 (four children), 6 (five children), 3 (six children), 2 (seven
children) and 1 (eight children). Each family was ascertained on an
obese proband, defined as a BMI .95
th
percentile, between the
ages 4–19 y. Once identified, the obese proband and all siblings, 4
to 19 y of age, and their parents were invited to the Children’s
Nutrition Research Center for a tour and full explanation of the
study prior to consenting. The cross-sectional, longitudinal study
design consisted of baseline measurements, with a one-year follow-
up to track children’s growth and body compositional changes. In-
depth baseline phenotyping included vital signs, anthropometry
and body composition, diet and physical fitness, 24-h calorimetry,
eating behavior, physical activity, fasting blood sampling for DNA
and other biochemistries.
Briefly, blood pressure, heart rate and temperature were taken
using an automated monitor. Anthropometric measurements were
performed using standardized techniques according to Lohman
[16]. Body composition was determined by dual-energy x-ray
absorptiometry. Repeated measures after one year were used to
compute growth velocities and changes in FM, FFM and energy
storage [17]. Methods used to measure fasting blood and 24-h
urinary biochemistries are described elsewhere [14,18,19]. A
multiple-pass 24-h dietary recall was recorded on two occasions
using Nutrition Data System (NDS) [20]. Eating behavior was
assessed with a dinner meal and eating in the absence of hunger
[21]. Room respiration calorimetry was used to make 24-h
measurements of energy expenditure and substrate oxidation [13].
Maximum oxygen consumption (VO
2
max) and heart rate
maximum (HRmax) were measured on a treadmill [22]. Actiwatch
accelerometers were used to measure frequency, duration and
intensity of physical activity [23].
Genotyping
The Illumina HumanOmni1-Quad v1.0 BeadChips were used
to genotype 1.1 million single nucleotide polymorphisms (SNPs) in
815 children enrolled in the VIVA LA FAMILIA Study. Genotype
calls were obtained after scanning on the Illumina BeadStation
500GX and analysis using the GenomeStudio software. Our
genotyping error rate (based on duplicates) was 2 per 100,000
genotypes. Illumina has included sample-dependent and -in-
dependent controls on their BeadChips to test for accuracy of the
procedure. The average call rate for all SNPs per individual
sample was 97%.
SNP genotypes were checked for Mendelian consistency using
the program SimWalk2 [24]. The estimates of the allele
frequencies and their standard errors were obtained using SOLAR
[25]. Specific SNPs were removed from analysis if they had call
rates ,95% (n = 6,596), were uncommon alleles in less than 5
participants (26,537), deviated from Hardy-Weinberg equilibrium
(n = 0), or were monoallelic (n = 56,448). The number of SNPs
that passed quality control and were included in the GWA analysis
was 899,892.
Genome-wide Association Analysis
Measured genotype analysis (MGA) was performed using the
SOLAR program [25]. All phenotypes were transformed by
inverse normalization to meet assumptions of normality. We
obtained residuals using linear regression models adjusted for age,
sex, their interaction and higher order terms. Because energy
expenditure is strongly influenced by body weight across the age
range of 4 to 19 years of age, total energy expenditure and sleeping
energy expenditure were additionally adjusted for body weight.
Also, observed energy intakes consumed in a snack and dinner
were adjusted for total energy expenditure or estimated energy
requirement, again to compensate for the wide range of ages in
our cohort.
Each SNP genotype was converted in SOLAR to a covariate
measure equal to 0, 1, or 2 copies of the minor allele (or, for
missing genotypes, the weighted covariate based on imputation).
These covariates were included in the variance-components mixed
models for measured genotype analyses [26] versus null models
that incorporated the random effect of kinship and fixed effects
such as age, sex, their interaction and higher order terms. For the
initial GWA screen, we tested each SNP covariate independently
as a 1 degree of freedom likelihood ratio test. The p-value
threshold for genome-wide significance (alpha = 0.05) was set at
1.01610
27
. The p-value threshold for genome-wide significance
was computed for our family-based cohort that takes into account
pedigree structure. The effective number of SNPs given linkage
disequilibrium (LD) was calculated by the method of Moskvina
and Schmidt [27] as implemented in SOLAR. LD was computed
in SOLAR using all available information (all genotyped SNPs on
all individuals). The average ratio of SNP effective number/actual
number obtained from analysis of 1,989 non-overlapping bins of
SNPs was used to calculate the genome-wide effective number of
tests and thus the significance threshold for genome-wide
association. We performed quantitative transmission disequilibri-
um test (implemented in SOLAR) to test for population
stratification.
Results
GWAS was performed on 815 children from 263 Hispanic
families. Mean 6 SD (range) was 25.267.5 (13.4 to 61.9) for the
children’s BMI, 85.6620.8 (4 to100) for BMI percentile and
1.5261.01 (23.0 to 4.5) for BMI z-score. Measured genotype
analysis examined 129 obesity-related traits including BMI and
adiposity as well as biological processes associated with the
pathophysiology of childhood obesity; a description of the
phenotypes is provided in Table S1. In our GWAS, population
stratification was not significant and therefore did not confound
our associations. A complete listing of all suggestive (p,1.0E-06)
and genome-wide significant (p,1.0E-07) genetic variants and
their associated traits are presented by chromosomal position in
Table S2.
Obesity GWAS in Hispanic Children
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Anthropometry and Body Composition
BMI status, body composition and the growth process were
assessed from repeated measurements of body weight, height,
FFM and FM at baseline and after one-year (Table 1 ). A
nonsynonymous SNP (rs1056513; G1178S (NP_005790.2)) in
INADL on chromosome 1 attained near genome-wide significance
(p = 1.2E-07) for weight, and BMI, FFM, FM, trunk FM, and hip
circumference (p = 8.3E-06 to 1.6E-07). SNP rs1056513 is
common (MAF = 0.50) and accounted for 3% of the variance in
body weight and body composition in this cohort. Weight z-score
change was significantly associated with an intronic variant in
COL4A1 on chromosome 13 (p = 4.7E-08) (Supporting Informa-
tion Figure S1). Linear growth (height change) was associated
with a variant in the 59UTR region of TSEN34 on chromosome 19
(p = 4.5E-08).
Endometabolic Traits
Genome-wide significant variants were identified for several
endometabolic traits (Table 2). An intronic variant in MTNR1B
on chromosome 11 was strongly associated with fasting glucose
(p = 3.7E-08). Intronic and 39UTR variants in the APOA5-ZNF259
region on chromosome 11 were associated with triglycerides
(p = 2.5-4.8E-08). An intronic variant in PCSK2 on chromosome 20
was associated with total antioxidants (p = 7.6E-08). Variants in the
flanking 39UTR regions of RNASE1 on chromosome 14 and
ASS1P11 on chromosome 7 were associated with 24-h urinary
nitrogen excretion and creatinine excretion, respectively (p = 8.2-
8.4E-08). An intronic SNP in GCH1 on chromosome 14 was
associated with 24-h urinary dopamine: creatinine ratio (p = 6.3E-
08). A nonsynonymous SNP (rs3733402; S143T (NP_000883.2))
in KLKB1 on chromosome 4 was identified for serum free IGF-1.
An intronic SNP in MPRIP on chromosome 17 was associated
with serum IGFBP-3 (p = 7.2E-08). A block of twenty-three SNPs
in the flanking 59UTR region of XPA/FOXE1 (TTF-2) on
chromosome 9 were highly associated with serum TSH
(p = 5.5E-08 to 1.0E-09) and found to be in strong linkage
disequilibrium with one another (R = 0.9121.00).
Inflammation Markers
Genome-wide significant variants were identified for inflamma-
tion markers (Table 3). Highly significant associations for
a nonsynonymous SNP (rs12075; G42D (NP_002027.2)
(p = 1.3E-21) and an intronic SNP (p = 3.6E-13) in DARC on
chromosome 1 were identified for MCP-1. Coding variants in
GREB1 on chromosome 2 (p = 6.5E-08) and DFNB31 on
chromosome 9 (p = 2.0E-08) were also identified for MCP-1. A
variants in the 39UTR for CCR3 was highly associated with
MCP1, as well as an intronic SNP in RASGEF1A (p = 4.6-9.6E-08).
A variant in the intronic region of ABO was strongly associated
with IL-6 (p = 2.0E-08).
Diet, Energy Expend iture, Substrate Utilization and
Physical Activity
Genome-wide significant variants were associated with energy
intake, energy expenditure and substrate utilization, and physical
activity (Table 4). An intronic variant in TMEM229B on
chromosome 14 was associated with ad libitum energy intake at
dinner (p = 5.1E-08). An intronic variant in ARHGAP11A on
chromosome 15 was associated with sleep duration (p = 5.0E-08).
A SNP in the intronic region of C21orf34 was detected for
respiratory quotient (RQ) during sleep (p = 5.3E-08). A variant was
identified for accelerometer-measured light activity in the 59UTR
region of RPL7P3 on chromosome 9 (p = 7.5E-08) and sedentary-
Table 1. Measured genotype analysis for anthropometric and body composition traits.
SNP Chr
Coordinate
(GB 36.2)
Location
(GB 36.2)
SNP (GB
36.2) Trait
MGA
p-value
Effect
Size
Minor
Allele
Minor Allele
Frequency Gene Symbol Gene Name
rs1056513 1 62152886 coding-nonsyn [A/G] Weight (kg) 1.18E-07 0.031 A 0.495 INADL InaD-like (Drosophila)
rs1056513 1 62152886 coding-nonsyn [A/G] BMI (kg/m
2
) 8.34E-06 0.021 A 0.495 INADL InaD-like (Drosophila)
rs1056513 1 62152886 coding-nonsyn [A/G] Fat mass (kg) 1.59E-07 0.035 A 0.495 INADL InaD-like (Drosophila)
rs1056513 1 62152886 coding-nonsyn [A/G] Trunk fat mass (kg) 2.36E-07 0.035 A 0.495 INADL InaD-like (Drosophila)
rs1056513 1 62152886 coding-nonsyn [A/G] Fat free mass (kg) 2.80E-07 0.034 A 0.495 INADL InaD-like (Drosophila)
rs1056513 1 62152886 coding-nonsyn [A/G] Hip circumference (cm) 2.47E-06 0.022 A 0.495 INADL InaD-like (Drosophila)
rs494558 13 109727163 intron [T/C] Weight z-score change
(SD/y)
4.66E-08* 0.045 G 0.078 COL4A1 collagen, type IV, alpha 1
rs40357 19 59385339 5UTR [T/C] Height change (cm/y) 4.49E-08* 0.045 G 0.391 TSEN34 tRNA splicing endonuclease 34
homolog (S. cerevisiae)
Abbreviations: SNP, single nucleotide polymorphism; chr, chromosome; MGA, measured genotype analysis; nonsyn, nonsynonomous; BMI, body mass index; UTR, untranslated region.
*Significant according to the widely used significance threshold p-value of ,5610
28
.
doi:10.1371/journal.pone.0051954.t001
Obesity GWAS in Hispanic Children
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Table 2. Measured genotype analysis for endometabolic traits.
SNP Chr
Coordinate
(GB 36.2)
Location
(GB 36.2)
SNP (GB
36.2) Trait
MGA p-
value
Effect
Size
Minor
Allele
Minor Allele
Frequency Gene Symbol Gene Name
rs3733402 4 187395028 coding-nonsym [A/G] IGF-1 free (ng/mL) 9.01E-**08 0.037 G 0.337 KLKB1 kallikrein B, plasma (Fletcher factor) 1
rs11974269 7 21114203 flanking_3UTR [T/G] Urinary creatinine
(mmol/d)
8.38E-08** 0.051 C 0.128 ASS1P11 argininosuccinate synthetase 1 pseudogene 11
rs7030241 9 99590196 flanking_5UTR [T/A] TSH (mIU/mL) 1.03E-09 0.038 T 0.253 XPA/FOXE1 xeroderma pigmentosum, complementation group
A/forkhead box E1 (thyroid transcription factor 2)
rs10830963 11 92348358 intron [G/C] Glucose (mg/dL) 3.72E-08* 0.048 G 0.205 MTNR1B melatonin receptor 1B
rs3741298 11 116162771 intron [A/G] Triglycerides
(mg/dL)
2.47E-08* 0.043 G 0.480 ZNF259 zinc finger protein 259
rs2266788 11 116165896 3UTR [A/G] Triglycerides
(mg/dL)
4.82E-08 0.042 G 0.163 APOA5 apolipoprotein A-V
rs10131141 14 20331573 flanking_3UTR [A/G] Urinary nitrogen (g/
d)
8.19E-08** 0.040 G 0.280 RNASE1 ribonuclease, RNase A family, 1 (pancreatic)
rs3783637 14 54417868 intron [T/C] Urinary free
dopamine:
creatinine
6.29E-08** 0.051 A 0.121 GCH1 GTP cyclohydrolase 1
rs61744862 17 17008907 intron [A/G] IGFBP-3 (ng/mL) 7.24E-08** 0.037 A 0.041 MPRIP myosin phosphatase Rho interacting protein
rs6044834 20 17384473 intron [A/C] Total antioxidants
(mM)
7.60E-08 0.036 C 0.069 PCSK2 proprotein convertase subtilisin/kexin type 2
Abbreviations: SNP, single nucleotide polymorphism; chr, chromosome; MGA, measured genotype analysis; nonsyn, nonsynonomous.
IGF-1, insulin-like growth factor-1; TSH, thyroid stimulating hormone; UTR, untranslated region; IGFBP3, binding protein 3.
*Significant according to the widely used significance threshold p value of ,5610
28
.
**Significant according to the our population-specific significance threshold p value of ,1.01610
27
.
doi:10.1371/journal.pone.0051954.t002
Obesity GWAS in Hispanic Children
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light activity in 39UTR of CTCFL on chromosome 20 (p = 3.6E-
08). In addition, adjusting for body weight, highly significant
variants were identified for total energy expenditure (p = 2.7E-08)
for MATK on chromosome 19 and sleeping energy expenditure
(p = 6.0E-08) for CHRNA3 on chromosome 15.
Discussion
Extensive phenotyping and high-density SNP genotyping
enabled localization of novel genetic loci associated with the
pathophysiology of obesity in Hispanic children. Our unprece-
dented phenotypes represent not only adiposity, but also biological
processes underlying the development of childhood obesity. The
number of genome-wide significant genetic variants detected
substantiates our analytical strategy and statistical power to
identify variants.
Genome-wide significant and suggestive genetic variants were
associated with anthropometric indices, body composition and
growth rate. The common nonsynonymous SNP rs1056513 in
INADL accounted for 3% of the variance in body weight and body
composition, and is highly conserved across mammalian species.
INADL encodes a PDZ domain-containing protein thought to play
a role in tight junctions and adipocyte differentiation [28]. Weight
z-score change observed over one-year was associated with an
intronic variant in COL4A1, which encodes a basement membrane
collagen. Height change was associated with a 59UTR variant
inTSEN34, which is involved in tRNA splicing, a fundamental
process required for cell growth and division [29].
Fasting serum glucose was associated with an intronic variant
(MAF = 0.20; effect size 4.8%) in MTNR1B which encodes one of
the melatonin receptors expressed in the retina and brain.
Corroborating our findings, this variant (rs10830963) has been
strongly associated with fasting glucose levels in adults [30,31] and
children [32–35]. Melatonin, the ligand to MTNR1B, has an
inhibitory effect on insulin secretion resulting in elevated fasting
glucose.
Fasting serum triglycerides were associated with variants in the
intron and 39UTRs within the APOA5-ZNF259 region. APOA5 is
an important determinant of circulating triglyceride levels [36].
SNPs detected in our GWAS have been associated with
triglycerides in other populations [37]. In the Kosrae population,
triglyceride levels were associated with seven SNPs near APOC3/
A5 [38]. Variants in the APOA5-ZNF259 region (including
rs3741298) were associated with HDL-C and ApoA-1 response
to therapy with statins and fenofibric acid in patients with
dyslipidemia [39].
Association with total antioxidants was shown for a variant in
PCSK2, a proprotein convertase that is involved in proteolytic
processing of neuropeptide and hormone precursors. PCSK2 is
highly expressed in the islets of Langerhans, where it plays a role in
the conversion of proinsulin to insulin.
A LD block of 23 SNPs in the XPA/FOXE1 (TTF-2) region was
highly associated with fasting serum TSH (MAF = 0.25-0.29, effect
size = 2.9-3.8%). The association is likely attributed to FOXE1
rather than XPA which is involved in DNA excision repair [40]
and the skin disease xeroderma pigmentosum [41]. FOXE1 or
thyroid transcription factor 2 (TTF-2) belongs to the ‘forkhead’ gene
family and is involved in promoting the migration process or in
repressing differentiation of the thyroid follicular cells until
migration has occurred [42] and has been associated with thyroid
cancer [43–46]. In the Kosrae population, plasma TSH levels
were strongly associated with 10 SNPs in a region encompassing
TTF-2 on chromosome 9 [38]. In a cohort of Caucasian adults,
genetic variation in FOXE1(TTF-2) showed significant effects on
Table 3. Measured genotype analysis for inflammation markers.
SNP Chr
Coordinate (GB
36.2)
Location
(GB 36.2)
SNP (GB
36.2) Trait
MGA
p-value
Effect
Size
Minor
Allele
Minor Allele
Frequency Gene Symbol Gene Name
rs863002 1 157441544 intron [A/G] MCP-1 (pg/mL) 3.59E-13* 0.062 A 0.244 DARC Duffy blood group, chemokine receptor
rs12075 1 157441978 coding-nonsyn [A/G] MCP-1 (pg/mL) 1.31E-21* 0.103 A 0.436 DARC Duffy blood group, chemokine receptor
rs73175262 2 11675882 coding [A/G] MCP-1 (pg/mL) 6.46E-08** 0.049 A 0.053 GREB1 growth regulation by estrogen in breast
cancer 1
rs7645716 3 46311785 flanking_3UTR [A/G] MCP-1 (pg/mL) 9.56E-08** 0.041 A 0.381 CCR3 chemokine (C-C motif) receptor 3
rs79509430 9 116280727 coding [T/C] MCP-1 (pg/mL) 2.03E-08* 0.056 A 0.068 DFNB31 deafness, autosomal recessive 31
rs657152 9 135129086 intron [A/C] IL-6 (pg/mL) 2.03E-08* 0.041 A 0.254 ABO ABO blood group (transferase A, alpha 1-3-
N-acetylgalactosaminyltransferase;
transferase B, alpha 1-3-
galactosyltransferase)
rs28461806 10 43075760 intron [A/G] MCP-1 (pg/mL) 4.58E-08* 0.053 G 0.049 RASGEF1A RasGEF domain family, member 1A
Abbreviations: SNP, single nucleotide polymorphism; chr, chromosome; MGA, measured genotype analysis; nonsyn, nonsynonomous;
MCP-1, monocyte chemotactic protein-1; UTR, untranslated region; IL-6, interluekin-6.
*Significant according to the widely used significance threshold p value of ,5610
28
.
**Significant according to the our population-specific significance threshold p value of ,1.01610
27
.
doi:10.1371/journal.pone.0051954.t003
Obesity GWAS in Hispanic Children
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Table 4. Measured genotype analysis for diet, energy expenditure, substrate utilization and physical activity.
SNP Chr
Coordinate
(GB 36.2)
Location
(GB 36.2)
SNP (GB
36.2) Trait
MGA
p-value
Effect
Size
Minor
Allele
Minor Allele
Frequency Gene Symbol Gene Name
rs16933006 9 15325914 flanking_5UTR [A/C] Light activity (min/d) 7.49E-08** 0.035 C 0.110 RPL7P3 ribosomal protein L7 pseudogene 33
rs17104363 14 67009236 intron [T/C] Dinner intake, adj
EER (kcal)
5.13E-08** 0.044 G 0.050 TMEM229B transmembrane protein 229B
rs8037818 15 30714768 intron [A/G] Sleep duration (min/d) 4.95E-08* 0.042 G 0.181 ARHGAP11A Rho GTPase activating protein 11A
rs8040868 15 76698236 coding [A/G] Sleep energy
expenditure adj
weight (kcal/d)
5.95E-08** 0.048 G 0.239 CHRNA3 cholinergic receptor, nicotinic, alpha 3
(neuronal)
rs12104221 19 3748100 intron [A/G] Total energy
expenditure adj
weight (kcal/d)
2.65E-08* 0.054 A 0.381 MATK megakaryocyte-associated tyrosine
kinase
rs6025590 20 55503911 flanking_3UTR [T/C] Sedentary&light
activity (min/d)
3.61E-08* 0.039 A 0.308 CTCFL CCCTC-binding factor (zinc finger
protein)-like
rs2823615 21 16405004 intron [T/A] Sleep RQ 5.28E-08** 0.044 A 0.178 C21orf34 long intergenic non-protein coding
RNA 478 (LINC00478)
Abbreviations: SNP, single nucleotide polymorphism; chr, chromosome; MGA, measured genotype analysis;
EER, estimated energy expenditure; UTR, untranslated region; RQ, respiratory quotient.
*Significant according to the widely used significance threshold p value of ,5610
-8
.
**Significant according to the our population-specific significance threshold p value of ,1.01610
27
.
doi:10.1371/journal.pone.0051954.t004
Obesity GWAS in Hispanic Children
PLOS ONE | www.plosone.org 6 December 2012 | Volume 7 | Issue 12 | e51954
free T4 levels and borderline effects on serum TSH [47]. Three of
the SNPs (rs925488, rs1877431, rs1588635) detected in the VIVA
cohort was also reported by Lowe et.al (38); there was no overlap
with Medici et al (43). Also, three of the SNPs (rs2805809,
rs2668804, rs2808693) reported by Lowe et al. (38) was in high
linkage disequilibrium (1.00) with rs2805771, rs2808699,
rs7875482 detected in the VIVA cohort. This is a region of high
LD, located in the 59UTR-region of the gene which has been
shown to influence transcriptional regulation of FOXE1(TTF-2).
The functional variant is likely to be situated at this locus, but its
exact localization remains to be elucidated in future studies,
involving in-depth resequencing.
A unifying role of inflammation in chronic diseases including
cardiovascular disease, diabetes, hypertension and obesity is
emerging. In the VIVA GWAS, variants in six genes were
associated with proinflammatory markers. Our findings support
a major role of Duffy antigen receptor for chemokines (DARC)in
the regulation of the circulating levels of the cysteine-cysteine (CC)
chemokine, MCP-1 (effect size = ,10%). In circulation, MCP-1 is
bound to erythrocyte DARC that acts as a chemokine receptor/
reservoir of proinflammatory cytokines [48]. Our strongest
association for MCP-1 was with a nonsynonymous, highly
conservedSNP in DARC (rs12075; MAF = 0.44) which replicated
results from the Framingham Heart Study GWAS in Caucasian
adults [49]. MCP-1 was also associated with SNPs in GREB1,
DFNB31, RASGEF1A, and CCR3. Huber et al. found increased
expression of CCR3 in subcutaneous and visceral adipose tissue in
obese patients compared to lean controls [50]. Studies by Schnabel
et al [49] and Naitza et al. [51] found suggestive associations of
serum MCP-1 with CCR2 which is also a MCP-1 receptor and is in
the same region of chromosome 3 as CCR3, and referred to as the
CCR2/CCR3 cytokine receptor gene cluster.
Fasting serum IL-6 levels were associated with variants in the
ABO gene that determines blood group. ABO blood group has
been found to be associated with a number of biomarkers such as
von Willebrand factor levels, Factor VIII levels, thrombomodulin,
TNF-a and ICAM-1 [52,53], and in our case IL-6. The
mechanism by which the A and B alleles affect these biomarkers
is uncertain. In Caucasians with and without type 1 diabetes,
a variant rs579459 near the ABO blood group gene accounted for
19% of the variance in E-selectin levels [52]; in our GWAS, this
same variant was associated with IL-6 levels (p = 1.7E-07; Table
S2).
Total energy expenditure, adjusted for body weight, was
significantly associated with rs12104221 in MATK which encodes
a protein-tyrosine kinase involved in signal transduction pathways
[54]. Sleeping energy expenditure, adjusted for body weight, was
associated with rs8040868 in CHRNA3 (cholinergic receptor,
neuronal nicotinic, alpha polypeptide 3), a member of a superfam-
ily of ligand-gated ion channels that mediate fast signal trans-
mission at synapses. After binding to acetylcholine, the receptor
responds by opening ion-conducting channels across the plasma
membrane, suggesting a plausible role of the coding variant
(rs8040868) in energy metabolism [55]. Acetylcholine receptors
activate proopiomelanocortin neurons that in turn activate
melanocortin-4 receptors that are involved in the regulation of
energy intake and expenditure [56], [57].
Sleep duration was associated with an intronic SNP in
ARHGAP11A (rho GTPase activating protein 11A) that encodes
a 1,023-amino acid protein that has a rhoGAP domain and
tyrosine phosphorylation site. Evidence is emerging that obesity
affects sleep, and that sleep patterns and disorders may have an
effect on weight [58]. Although the mechanism is unclear, sleep
disturbances are characteristic of Prader Willi Syndrome, caused
by a deletion in 15q11-q13 that encompasses ARHGAP11A [59].
Sedentary-light physical activity was associated with a variant in
CTCFL, an 11-zinc-finger factor involved in gene regulation.
CTCFL forms methylation-sensitive insulators that regulate X-
chromosome inactivation which may play a role in epigenetic
regulation [60].
Variants in the 11 genes known to cause extreme early-onset
obesity also may contribute to milder forms of obesity [61,62].
None of the genotyped variants in genes for monogenic obesity
reached genome-wide significance in our GWAS, although several
variants in CRHR1, CRHR2, MCHR1, MC3R, MC4R and POMC
were nominally associated. Similarly, variants in or nearby the
susceptibility genes for obesity did not attain genome-wide
significance but several - CHST8, KCTD15, MTCH2, SFRS10,
SH2B1 and TMEM18 - were nominally associated with obesity-
related traits, consistent with GWAS in children of European-
American [63] or European ancestry [9]. The lack of genome-
wide significant findings for monogenic causes of obesity or
susceptibility genes may be a function of our sample size and
statistical power, or the presence of rare variants in the Hispanic
population not represented in the Illumina platform.
The VIVA LA FAMILIA Study is unique in its consideration of
a pediatric Hispanic population. We believe that our extensive
phenotyping and genotyping enabled localization of novel genetic
loci associated with obesity in Hispanic children, despite our
relatively small sample size. Our phenotypes represent not only
adiposity, but also biological processes underlying the development
and consequences of childhood obesity. We applied a standard
and stringent approach to our measured genotype analysis, but do
agree replication is desirable. We believe we have identified genes
that warrant further investigation.
In conclusion, unprecedented in-depth phenotyping and high-
density SNP genotyping enabled the localization of novel genetic
loci associated with the pathophysiology of obesity in Hispanic
children. Identified genome-wide significant loci: 1) corroborated
genes implicated in other studies (MTNR1B, ZNF259/APOA5,
XPA/FOXE1 (TTF-2), DARC, CCR3, ABO); 2) localized novel
genes in plausible biological pathways (PCSK2, ARHGAP11A,
CHRNA3); and 3) revealed novel genes with unknown function in
obesity pathogenesis (MATK, COL4A1). As with other GWAS, the
variants identified are likely not the actual causal variants but
rather markers for genomic regions or loci in which the causal
variants lie. Characterization of the underlying functional genetic
variants contributing to this serious public health problem in
Hispanic children will involve additional study.
Supporting Information
Figure S1 GWAS Manhattan plots are displayed for
three phenotypes: total energy expenditure, adjusted for
body weight, measured by 24-h room calorimetry;
fasting serum thyroid stimulating hormone; and 1-y
change in weight z-score. The genomic coordinates are shown
along the X-axis, and the negative logarithm of the association p-
value for each SNP on the Y-axis.
(TIFF)
Table S1 Description of the phenotypes used in the
genome-wide association study.
(DOCX)
Table S2 Suggestive and genome-wide significant ge-
netic variants identified by measured genotype analysis.
(DOCX)
Obesity GWAS in Hispanic Children
PLOS ONE | www.plosone.org 7 December 2012 | Volume 7 | Issue 12 | e51954
Acknowledgments
We thank all the families who participated in the VIVA LA FAMILIA
Study. The authors wish to acknowledge the contributions of Grace-Ellen
Meixner, B.S. and Maria del Pilar Villegas, B.S., M.S. for technical
assistance while working on this project at the Texas Biomedical Research
Institute.
This work is a publication of the U.S. Department of Agriculture
(USDA)/Agricultural Research Service (ARS) Children’s Nutrition Re-
search Center, Department of Pediatrics, Baylor College of Medicine and
Texas Childre n’s Hospital, Houston, Texas. The contents of this
publication do not necessarily reflect the views or policies of the USDA,
nor does mention of trade names, commercial products, or organizations
imply endorsement by the U.S. Government.
Author Contributions
Conceiv ed and designed the experiments: AGC SAC RA G NFB.
Performed the experiments: SAC KH NFB. Analyzed the data: SLL
VSV KH. Contributed reagents/materials/analysis tools: SAC KH. Wrote
the paper: AGC SAC SLL VSV KH RAG NFB.
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    • "The pathway has been shown to regulate pancreas development [33] and adipocyte development [34]. Interestingly, a prior study has found that INADL was associated with children's weight [35] . It is difficult to interpret the unique interaction for case group because both of the SNPs are located in non-coding regions. "
    [Show abstract] [Hide abstract] ABSTRACT: Background The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. Results For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. Conclusions Developing the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.
    Full-text · Article · Dec 2016
    • "A recent study suggested that rare deletions in MACROD2 may have a limited role in the Kabuki syndrome associated with ID and perhaps also in ADHD and SCZ, as this gene is in a hotspot for deletions and found more frequently deleted in endometriosis [Bradley et al., 2010]. Also, genome-wide significant signals in this gene were found in diseases apparently unrelated to autism, like childhood obesity, typical sporadic amyotrophic lateral sclerosis, coronary artery disease and hypertension and in traits such as male fertility and body mass index [Comuzzie et al., 2012; Cotsapas et al., 2009; Kosova, Scott, Niederberger, Prins, & Ober, 2012; Slavin, Feng, Schnell, Zhu, & Elston, 2011; Xie et al., 2014]. However, pleiotropy may be an alternative explanation for this gene being involved in these distinct traits. "
    [Show abstract] [Hide abstract] ABSTRACT: Common variants contribute significantly to the genetics of autism spectrum disorder (ASD), although the identification of individual risk polymorphisms remains still elusive due to their small effect sizes and limited sample sizes available for association studies. During the last decade several genome-wide association studies (GWAS) have enabled the detection of a few plausible risk variants. The three main studies are family-based and pointed at SEMA5A (rs10513025), MACROD2 (rs4141463) and MSNP1 (rs4307059). In our study we attempted to replicate these GWAS hits using a case-control association study in five European populations of ASD patients and gender-matched controls, all Caucasians. Results showed no association of individual variants with ASD in any of the population groups considered or in the combined European sample. We performed a meta-analysis study across five European populations for rs10513025 (1,904 ASD cases and 2,674 controls), seven European populations for rs4141463 (2,855 ASD cases and 36,177 controls) and five European populations for rs4307059 (2,347 ASD cases and 2,764 controls). The results showed an odds ratio (OR) of 1.05 (95% CI = 0.84-1.32) for rs10513025, 1.0002 (95% CI = 0.93-1.08) for rs4141463 and 1.01 (95% CI = 0.92-1.1) for rs4307059, with no significant P-values (rs10513025, P = 0.73; rs4141463, P = 0.95; rs4307059, P = 0.9). No association was found when we considered either only high functioning autism (HFA), genders separately or only multiplex families. Ongoing GWAS projects with larger ASD cohorts will contribute to clarify the role of common variation in the disorder and will likely identify risk variants of modest effect not detected previously. Autism Res 2016. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
    Full-text · Article · Jul 2016
    • "Genome wide association studies (GWAS) catalogue hosted by the National Human Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EMBL-EBI) [94] was searched to identify any association of candidate fatty acid receptors with obesity or similar conditions. Analysis of the GWAS catalogue identified the association of two DRK channels with obesity, specifically variants rs6063399 in KCNB1 (p = 8 × 10 −6 ) and rs7311660 in KCNC2 (p = 4 × 10 −6 ) [95]. Further analysis of the GWAS literature failed to identify further associations of candidate fatty acid receptors with obesity. "
    [Show abstract] [Hide abstract] ABSTRACT: Energy homeostasis plays a significant role in food consumption and body weight regulation with fat intake being an area of particular interest due to its palatability and high energy density. Increasing evidence from humans and animal studies indicate the existence of a taste modality responsive to fat via its breakdown product fatty acids. These studies implicate multiple candidate receptors and ion channels for fatty acid taste detection, indicating a complex peripheral physiology that is currently not well understood. Additionally, a limited number of studies suggest a reduced ability to detect fatty acids is associated with obesity and a diet high in fat reduces an individual's ability to detect fatty acids. To support this, genetic variants within candidate fatty acid receptors are also associated with obesity reduced ability to detect fatty acids. Understanding oral peripheral fatty acid transduction mechanisms and the association with fat consumption may provide the basis of novel approaches to control development of obesity.
    Full-text · Article · Jul 2016
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