Genome-wide Association and Replication Studies Identified TRHR as an Important Gene for Lean Body Mass

Article (PDF Available)inThe American Journal of Human Genetics 84(3):418-23 · April 2009with59 Reads
DOI: 10.1016/j.ajhg.2009.02.004 · Source: PubMed
Low lean body mass (LBM) is related to a series of health problems, such as osteoporotic fracture and sarcopenia. Here we report a genome-wide association (GWA) study on LBM variation, by using Affymetrix 500K single-nucleotide polymorphism (SNP) arrays. In the GWA scan, we tested 379,319 eligible SNPs in 1,000 unrelated US whites and found that two SNPs, rs16892496 (p = 7.55 x 10(-8)) and rs7832552 (p = 7.58 x 10(-8)), within the thyrotropin-releasing hormone receptor (TRHR) gene were significantly associated with LBM. Subjects carrying unfavorable genotypes at rs16892496 and rs7832552 had, on average, 2.70 and 2.55 kg lower LBM, respectively, compared to those with alternative genotypes. We replicated the significant associations in three independent samples: (1) 1488 unrelated US whites, (2) 2955 Chinese unrelated subjects, and (3) 593 nuclear families comprising 1972 US whites. Meta-analyses of the GWA scan and the replication studies yielded p values of 5.53 x 10(-9) for rs16892496 and 3.88 x 10(-10) for rs7832552. In addition, we found significant interactions between rs16892496 and polymorphisms of several other genes involved in the hypothalamic-pituitary-thyroid and the growth hormone-insulin-like growth factor-I axes. Results of this study, together with the functional relevance of TRHR in muscle metabolism, support the TRHR gene as an important gene for LBM variation.


Genome-wide Association and Replication Studies
Identified TRHR as an Important Gene
for Lean Body Mass
Xiao-Gang Liu,
Li-Jun Tan,
Shu-Feng Lei,
Yong-Jun Liu,
Hui Shen,
Liang Wang,
Han Yan,
Yan-Fang Guo,
Dong-Hai Xiong,
Xiang-Ding Chen,
Feng Pan,
Tie-Lin Yang,
Yin-Ping Zhang,
Yan Guo,
Nelson L. Tang,
Xue-Zhen Zhu,
Hong-Yi Deng,
Shawn Levy,
Robert R. Recker,
Christopher J. Papasian,
and Hong-Wen Deng
Low lean body mass (LBM) is related to a series of health problems, such as osteoporotic fracture and sarcopenia. Here we report a genome-
wide association (GWA) study on LBM variation, by using Affymetrix 500K single-nucleotide polymorphism (SNP) arrays. In the GWA
scan, we tested 379,319 eligible SNPs in 1,000 unrelated US whites and found that two SNPs, rs16892496 (p ¼ 7.55 3 10
and rs7832552 (p ¼ 7.58 3 10
), within the thyrotropin-releasing hormone receptor (TRHR) gene were significantly associated with
LBM. Subjects carrying unfavorable genotypes at rs16892496 and rs7832552 had, on average, 2.70 and 2.55 kg lower LBM, respectively,
compared to those with alternative genotypes. We replicated the significant associations in three independent samples: (1) 1488 unrelated
US whites, (2) 2955 Chinese unrelated subjects, and (3) 593 nuclear families comprising 1972 US whites. Meta-analyses of the GWA scan
and the replication studies yielded p values of 5.53 3 10
for rs16892496 and 3.88 3 10
for rs7832552. In addition, we found
significant interactions between rs16892496 and polymorphisms of several other genes involved in the hypothalamic-pituitary-thyroid
and the growth hormone-insulin-like growth factor-I axes. Results of this study, together with the functional relevance of TRHR in muscle
metabolism, support the TRHR gene as an important gene for LBM variation.
Loss and function impairment of skeletal muscle in the
elderly is related to a series of diseases or health problems,
such as sarcopenia,
mobility limitation, osteoporosis
(MIM 166710), higher risk of fracture, impaired protein
balance, dyslipidemia (MIM 151660), obesity (MIM
601665), insulin resistance, overall frailty, and increased
Lean body mass (LBM) measured by dual
energy X-ray absorptionmetry (DXA) is a good index for
quantity and quality of skeletal muscle.
LBM is under
strong genetic determination with heritability ranging
from 52% to 84%.
However, specific genes underlying
variation in LBM are largely unknown.
Here we report a genome-wide association (GWA) study
for LBM by using Affymetrix 500K SNP arrays in a sample
of 1000 unrelated US whites. The interested associations
were further replicated in three independent samples,
including an unrelated US white sample comprising 1488
subjects, 593 nuclear families comprising 1972 US whites,
and an unrelated Chinese sample comprising 2955
All our study subjects of US whites were identified from
an established cohort containing ~6000 subjects recruited
from the Midwestern US. This cohort was initiated to
search quantitative trait loci and/or genes underlying
body compositions (bone mass for osteoporosis, fat body
mass for obesity, and lean body mass for sarcopenia
studies). The inclusion and exclusion criteria have been
described in detail elsewhere.
The Chinese sample
contains 2955 healthy Chinese Han adults living in
Changsha City, China. The same inclusion and exclusion
criteria used for the white subjects were applied in the
recruitment of the Chinese sample. All studies were
approved by local Institutional Review Boards. Signed
informed-consent documents were obtained from all study
LBM and fat body mass were measured with Hologic
DXA 4500 machines (Hologic Inc., Bedford, MA) for all
the study samples. Anthropometric measures and a struc-
tured questionnaire covering lifestyle, diet, family infor-
mation, medical history, etc. were obtained for all the
study subjects. Body mass index (BMI) was calculated as
the ratio of weight to square of height in unit of kg/m
The basic characteristics of all the four study samples are
provided in Table 1.
For the GWA scan, genotyping was performed for
1000 US whites with the GeneChip Human Mapping
500K Array Set (Affymetrix, Santa Clara, CA) by the
Vanderbilt Microarray Shared Resource (Vanderbilt Univer-
sity Medical Center, Nashville, TN) with the standard
protocol recommended by the manufacturer. Genotyping
calls were determined with the DM algorithm
a 0.33 p value setting as well as with the BRLMM
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and
Technology, Xi’an Jiaotong University, Xi’an Shaanxi 710049, P R China;
School of Medicine, University of Missouri-Kansas City, Kansas City,
MO 64108, USA;
Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081,
P R China;
Department of Chemical Pathology and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, P R China;
Vanderbilt Microarray Shared Resource, Vanderbilt University, Nashville, TN 37232, USA;
Osteoporosis Research Center, Creighton University, Omaha,
NE 68131, USA
DOI 10.1016/j.ajhg.2009.02.004. ª2009 by The American Society of Human Genetics. All rights reserved.
418 The American Journal of Human Genetics 84, 418–423, March 13, 2009
DM calls were used for quality control of the
genotyping experiment, and unsatisfactory arrays were
subject to regenotyping. Eventually, 997 subjects who
had at least one array (Nsp or Sty) reaching 93% call rate
were retained. Because of missing data of LBM or applied
covariates among the 997 subjects, the effective sample
size for the GWA scan is 973. The average call rate for the
973 analyzed subjects reached >95%. BRLMM calls were
used for the association analyses. The final average
BRLMM call rate in the GWA cohort reached 99.27%.
Out of the initial full set of 500,568 SNPs, we discarded
32,961 SNPs with SNP-wised call rate <95%, 36,965 SNPs
with allele frequencies deviating from Hardy-Weinberg
equilibrium (HWE) (p < 0.001), and 51,323 SNPs with
minor allele frequency (MAF) <1%. Therefore, the final
SNP set for the GWA scan contained 379,319 SNPs,
yielding a genomic marker spacing of ~7.9 kb on average.
For replication studies in the US white samples, genotyp-
ing was performed by KBioscience (Herts, UK) with the
technology of competitive allele-specific PCR (KASPar).
The replication rate (duplicate concordance rate) was
99.7% for genotyping and the average call rate was
97.8%. For the replication study in the Chinese sample,
SNP genotyping was performed with a primer-extension
method with MALDI-TOF mass spectrometry on a
MassARRAY system as suggested by the manufacturer
(Sequenom, Inc., San Diego, CA). The replication rate is
99.2% and the average call rate is 97.2%.
To control potentialpopulation stratification that may lead
to spurious association results, we used the software Structure
to investigate the population structure of the GWA
cohort. With 200 randomly selected unlinked SNPs, all the
973 subjects were tightly clustered together under all the
three assumed number of population strata (i.e., k ¼ 2, 3, or
4), suggesting no significant population substructure.
For both the initial GWA scan and subsequent replica-
tion studies, stepwise regression was performed to screen
the effects of age, sex, age
, age-by-sex interaction, and
fat body mass on LBM variation. Age, sex, and fat body
mass were significant effectors (p < 0.05) and raw LBM
values were adjusted for these factors. The adjusted pheno-
type values, if departing from normal distribution, were
further subjected to BoxCox transformation to ensure
the normality with the software Minitab (Minitab Inc.,
State College, PA). F-tests were conducted to achieve the
genotype-wise association tests for the unrelated samples,
whereas the FBAT method was used for association tests
in the family-based sample. All the association analyses
were performed with the software HelixTree 5.3.1, in
which FBAT was implemented.
To further ensure robust-
ness of the association tests, we also used EIGENSTRAT
perform GWA analyses and cross-check the results against
those obtained with HelixTree. EIGENSTRAT can detect
and correct for potential population admixture and differ-
ences in laboratory treatment among samples.
We adopted the conservative Bonferroni method to
account for the multiple-testing problem in the GWA study.
Because 379,319 eligible SNPs were eventually tested, the
significance level for the GWA scan was set as 1.32 3 10
(i.e., 0.05/379319). The quantile-quantile (Q-Q) plotting
method was used to determine the significance level for
‘suggestive association.’ The Q-Q plot was constructed by
ranking the observed p values from smallest to largest and
plotting them against the same ordered p values under
the null hypothesis of no association (expected p values).
We calculated the expected p values for 1000 times and
superimposed them in the Q-Q plot to construct a 95%
concentration band after the method proposed by Stir-
Deviations from the band in the Q-Q plot suggest
that the corresponding associations are highly likely to be
true ones;
therefore, we took the p value at the point of
deviation (p < 1.26 3 10
, Figure S1 available online) as
the threshold for suggestive associations.
Point estimates and their standard errors for per-allele
effect sizes of the favorable alleles of interested SNPs on
LBM were calculated through linear regression analyses
with SAS 9.1 (SAS Institute Inc., Cary, NC). The effect
estimate and standard error of the most significant SNP for
each separate association analysis in the initial GWA scan
and subsequent replication studies were combined in the
meta-analysisviatheinverse variance method implemented
in the software Comprehensive Meta Analysis.
In the GWA scan, we identified two genome-level signif-
icant SNPs (p values of 7.55 3 10
and 7.58 3 10
rs16892496 and rs7832552, respectively), and other 146
Table 1. Basic Characteristics of the Study Samples
Sample and Trait Males Females
Initial GWA study n ¼ 492 n ¼ 481
Age (years) 50.52 (18.89) 50.13 (17.13)
Height (m) 1.78 (0.070) 1.64 (0.065)
Lean body mass (kg) 63.67 (8.22) 43.49 (6.69)
Fat body mass (kg) 23.46 (8.88) 26.92 (10.33)
BMI (kg/m
) 28.92 (4.30) 27.29 (5.98)
Replication study in US
white population
n ¼ 659 n ¼ 829
Age (years) 63.18 (10.27) 61.71 (10.72)
Height (m) 1.77 (0.06) 1.62 (0.07)
Lean body mass (kg) 65.09 (9.21) 45.16 (6.84)
Fat body mass (kg) 25.06 (8.04) 28.78 (9.79)
BMI (kg/m
) 29.19 (4.50) 27.83 (5.65)
Replication study in
Chinese population
n ¼ 1437 n ¼ 1518
Age (years) 30.53 (12.54) 35.44 (15.74)
Height (m) 1.69 (0.06) 1.58 (0.05)
Lean body mass (kg) 51.80 (5.76) 37.39 (4.21)
Fat body mass (kg) 11.33 (5.22) 15.56 (5.07)
BMI (kg/m
) 22.24 (2.90) 21.38 (3.15)
Replication study in US
white families
n ¼ 570 n ¼ 1402
Age (years) 52.89 (16.22) 47.45 (15.24)
Height (m) 1.78 (0.08) 1.64 (0.06)
Lean body mass (kg) 65.88 (9.37) 45.72 (7.13)
Fat body mass (kg) 23.24 (8.92) 26.01 (10.42)
BMI (kg/m
) 28.83 (6.30) 26.84 (5.91)
Note: Data presented are unadjusted means (SD).
The American Journal of Human Genetics 84, 418–423, March 13, 2009 419
suggestive SNPs associated with LBM (Figure 1, Table S1).
The two SNPs showing genome-level significant associa-
tions with LBM are located in the only intron of the thyro-
tropin-releasing hormone receptor (TRHR) gene (MIM
188545) (Figure 2). These two SNPs also achieved the stron-
gest associations with LBM in EIGENSTRAT analyses with
p values of 1.42 3 10
and 1.79 3 10
for rs16892496
and rs7832552, respectively. Interestingly, 15 other SNPs
of the TRHR gene also showed suggestive associations
with LBM (Table 2, Figure 2). The two most significant
SNPs, rs16892496 and rs7832552, are in strong linkage
disequilibrium (LD) (r
¼ 0.98). Compared to subjects
carrying TT or GT genotypes at rs16892496, those carrying
the GG genotype had 2.70 kg higher LBM, on average.
Similarly, subjects carrying the TT genotype at rs7832552
had 2.55 kg higher LBM compared to subjects with CC
or CT genotypes.
The replication studies largely confirmed the significant
findings in the GWA scan (Table 3). The association of
rs16892496 with LBM was replicated in the independent
unrelated US white sample and unrelated Chinese sample
(p ¼ 0.018 and 0.013, respectively; Table 3). The SNP
rs7832552 was consistently and significantly associated
with LBM in all the three replication samples. Meta-
analyses of the initial GWA scan and replication studies
achieved the combined p values of 5.53 3 10
Figure 1. Results of the GWA Scan for Lean Body Mass
The y axis represents –log
P. Both height and colors of the bars correspond to p values of the SNPs, which are arranged along the x axis
according to their physical position on chromosomes.
Figure 2. Association Signals and LD
Structure of the TRHR Gene SNPs
(A) Association results for SNPs of the
TRHR gene in the GWA scan. The y axis
shows –log
P of the association results
and the x axis shows the physical position
(Mb) on chromosome 8.
(B) Position of the TRHR gene and the
tested SNPs. The TRHR gene was repre-
sented with two red bars for exons and
a red line for intron. The red arrow denotes
the transcriptional direction of the TRHR
gene. SNPs were illustrated by vertical
lines. Lines above the horizontal axis
represent SNPs showing significant and
suggestive associations with LBM.
(C) The LD structure of the tested SNPs. LD
pattern was analyzed in our GWA sample
and plotted with the Haploview program.
420 The American Journal of Human Genetics 84, 418–423, March 13, 2009
rs16892496 and 3.88 3 10
for rs7832552, respectively.
A forest plot for the association between rs16892496 and
LBM is given in Figure 3.
TRHR encodes the thyrotropin-releasing hormone
receptor, which belongs to the G protein-coupled receptor 1
family. Thyrotropic-releasing hormone (TRH) is a tripeptide
(Glu-His-Pro) hormone secreted by the hypothalamus.
TRH exerts its effect by binding to TRHR on the surface
of pituitary thyrotrophs. The primary consequence of
TRH:TRHR binding is activation of the inositol phospho-
lipid-calcium-protein kinase C transduction pathway,
which, in turn, stimulates secretion of thyroid-stimulating
hormone (TSH) and prolactin (PRL). The TSH response to
TRHR is the first step in the hormonal cascade of hypotha-
lamic-pituitary-thyroid axis (HPTA) that eventually leads
to the release of thyroxin, which is important in the devel-
opment of vertebrate skeletal muscle.
Mutations in the
TRHR gene may decrease affinity of TRHR for TRH and result
in central hypothyroidism,
which causes impaired
expression of myosin heavy chain (MHC) isoforms
diminished muscle cross-sectional areas.
thyroxin is necessary for full anabolic action of the growth
hormone-insulin-like growth factor-I (GH-IGF1) axis,
which plays an important role in muscle protein balance
and adaptative changes to load.
Motivated by known physiological relevance of TRHR to
the HPTA and GH-IGF1 pathways, we further performed
interaction analyses for SNP rs16892496 in the TRHR
gene, which showed the most significant association with
LBM in the GWA scan, with 912 SNPs in other 33 genes
Table 2. Association Results for SNPs of the TRHR Gene in the GWA Scan
SNP Position Role Alleles
p Value Effect Size
Favorable Allele
rs4236794 109955588 upstream C/T 0.37 0.3 0.043 (0.031) T
rs4617129 109956715 upstream G/A 0.33 0.14 0.034 (0.032) A
rs4639483 109965846 upstream C/T 0.38 0.12 0.055 (0.033) T
rs6469211 109976702 upstream C/T 0.34 0.11 0.038 (0.032) T
rs12543698 109996108 upstream A/T 0.2 1.86 3 10
0.028 (0.032) A
rs10098562 110022238 upstream T/C 0.04 0.6 0.036 (0.032) T
rs10086780 110047705 upstream G/A 0.29 0.28 0.021 (0.030) G
rs6469224 110124863 upstream A/G 0.46 4.69 3 10
0.086 (0.032) G
rs4276659 110133336 upstream C/A 0.44 1.94 3 10
0.081 (0.032) A
rs4631432 110143381 upstream A/G 0.4 1.73 3 10
0.096 (0.032) G
rs4349964 110144137 upstream A/G 0.41 1.38 3 10
0.094 (0.032) G
rs4469428 110144284 upstream A/G 0.41 1.23 3 10
0.082 (0.032) G
rs4735085 110145588 upstream C/A 0.32 0.6 0.033 (0.030) A
rs4506180 110154994 upstream A/C 0.5 7.87 3 10
0.046 (0.030) A
rs4466373 110155042 upstream T/C 0.21 7.41 3 10
0.061 (0.032) T
rs4297015 110155444 upstream T/C 0.18 9.69 3 10
0.028 (0.032) C
rs7012225 110156031 upstream G/A 0.18 0.07 0.042 (0.030) G
rs6469232 110157417 upstream T/C 0.11 0.3 0.031 (0.032) C
rs3134106 110170951 intron A/C 0.17 0.06 0.044 (0.032) A
rs3134115 110175985 intron A/G 0.4 8.69 3 10
0.079 (0.032) G
rs12544197 110177001 intron A/G 0.48 1.17 3 10
0.037 (0.032) A
rs7829028 110178706 intron T/C 0.0971 0.36 0.026 (0.032) C
rs16892496 110179027 intron G/T 0.32 7.55 3 10
0.107 (0.032) G
rs7832552 110184852 intron T/C 0.32 7.58 3 10
0.102 (0.030) T
rs3925087 110191854 intron C/T 0.36 4.34 3 10
0.096 (0.032) C
rs4546626 110194938 intron G/T 0.36 1.37 3 10
0.077 (0.032) G
rs4735098 110225067 downstream G/A 0.38 2.57 3 10
0.082 (0.030) G
rs4314624 110235192 downstream C/G 0.35 2.42 3 10
0.078 (0.032) C
rs4607576 110235212 downstream T/C 0.35 2.65 3 10
0.078 (0.032) T
rs4628236 110241321 downstream T/C 0.35 3.09 3 10
0.073 (0.032) T
rs7845815 110247020 downstream A/G 0.35 2.04 3 10
0.075 (0.032) A
rs4734197 110247681 downstream G/A 0.34 2.39 3 10
0.079 (0.032) G
rs10111874 110254194 downstream G/A 0.35 2.35 3 10
0.075 (0.032) G
rs10112296 110254469 downstream T/C 0.35 6.20 3 10
0.072 (0.032) T
rs11785243 110259359 downstream A/G 0.36 3.12 3 10
0.075 (0.032) A
rs10087444 110288911 downstream A/G 0.44 0.06 0.035 (0.030) G
rs6469245 110289983 downstream C/G 0.41 9.42 3 10
0.076 (0.030) C
rs4735116 110290780 downstream G/T 0.41 8.51 3 10
0.075 (0.030) G
rs1380098 110317808 downstream A/G 0.41 6.28 3 10
0.069 (0.032) A
Note: The two SNPs significantly associated with lean body mass after Bonferroni correction in the GWA scan are shown in italics and bold.
The former allele represents the minor allele of each locus in our GWA sample.
Minor allele frequency calculated in our GWA sample.
Per-allele effect size of the favorable allele is expressed by beta coefficients derived from linear regression analyses.
Subjects with more favorable alleles generally have higher values of lean body mass than subjects having alternative genotypes in the population.
The American Journal of Human Genetics 84, 418–423, March 13, 2009 421
putatively involved in the HPTA and GH-IGF1 pathways
(Table S2). The most significant interaction with
rs16892496 was detected for SNP rs12474719 (p ¼ 6.04 3
) of the insulin-like growth factor binding protein 5
(IGFBP5) gene. A joint modeling of rs16892496 and
rs12474719 increased the LBM variation explained solely
by rs16892496 from 2.29% to 2.91%.
To our knowledge, this is the first GWA study for LBM
variation. We identified a significant association between
the TRHR gene and LBM variation. The association find-
ings were further supported by three independent replica-
tion studies in both US white subjects and Chinese Han
population. In addition, we found that LBM variation is
influenced by interactions between TRHR and several
other genes involved in the HPTA and GH-IGF1 pathways.
The identified association between TRHR SNPs and LBM
are unlikely to be artifacts. First, conservative Bonferroni
correction was used to claim significant associations in
the initial GWA scan. In addition, besides the two SNPs
achieving genome-level association signal with LBM, 15
other SNPs of the TRHR gene got suggestive association
signals in the GWA scan. Thus, individual genotyping
errors are unlikely to be responsible for these associations.
Moreover, we strictly controlled potential population strat-
ification by using Structure and EIGENSTRAT. Genomic
control analyses
showed a small inflation factor of
1.036. Most importantly, the significant associations
were replicated in three independent samples.
LBM accounts for ~60% or more of body weight and
thus may significantly contribute to variation in BMI,
an index commonly used for obesity.
previous linkage studies found that a locus at 8q23, which
spans the TRHR gene, was linked to BMI.
We specu-
late that the observed linkage may be partially attributable
to the association between TRHR and LBM. As a support
of the speculated effect of the GH-IGF1 pathway on
LBM, the insulin-like growth factor 1 receptor (IGF1R)
gene and the GHRH gene were also linked to fat-free
mass in previous studies.
In summary, our GWA scan and multiple replication
studies, in conjunction with the known functional
involvement of TRHR in muscle metabolism, suggest that
polymorphisms in TRHR gene, and possibly other genes
in the HPTA and GH-IGF1 pathways, significantly
contribute to LBM variation. The mechanisms underlying
the observed associations merit further investigation.
Supplemental Data
Supplemental Data include one figure and three tables and can be
found with this article online at
Investigators of this work were partially supported by grants from
NIH (R01 AR050496-01, R21 AG027110, R01 AG026564, R21
AA015973, and P50 AR055081).The study also benefited from
grants from National Science Foundation of China, Huo Ying
Dong Education Foundation, Hunan Province, Xi’an Jiaotong
University, and the Ministry of Education of China. The authors
declare that they have no conflict of interest.
Table 3. Association Results between Lean Body Mass and the Two Significant TRHR SNPs in the GWA Scan and Three Replication
Studies and Meta-analyses
Replication Studies
Discovery Scan
(n ¼ 973)
Sample 1
(n ¼ 1488)
Sample 2
(n ¼ 2955)
Sample 3
(n ¼ 1972)
(n ¼ 7415)
rs16892496 7.55 3 10
(0.107 5 0.032)
0.018 (0.112 5 0.038) 0.013 (0.074 5 0.024) 0.083 (0.087 5 0.029) 5.53 3 10
(0.090 5 0.015)
rs7832552 7.58 3 10
(0.102 5 0.030)
0.0056 (0.105 5 0.036) 0.012 (0.076 5 0.024) 0.015 (0.081 5 0.028) 3.88 3 10
(0.061 5 0.014)
Values in the table are p values followed by the point estimates of effect sizes expressed by beta coefficients 5 standard errors.
The replication studies were performed in three samples: Sample 1, the independent unrelated US white sample; Sample 2, the unrelated Chinese sample;
Sample 3, the family-based US white sample.
The effect sizes for sample 3 were calculated in founders.
Accumulative p values and beta coefficients were generated under the random-effect model.
Figure 3. Forest Plot for the Association between rs16892496
and LBM
Black boxes denote point estimates for effect sizes (beta coeffi-
cients) in corresponding association studies, and the sizes of the
box are proportional to the inverse variance weight of the estimate
in the meta-analysis. Horizontal lines represent 95% confidence
intervals (CIs). The diamond represents the accumulative beta
coefficient computed under the random-effect model, with the
95% CI given by its width. The scale of the plot was indicated by
the bottom values. Samples 1–3 represent the three samples
used for the replication studies, with #1 for the unrelated US white
sample, #2 for the unrelated Chinese sample, and #3 for the family-
based US white sample.
422 The American Journal of Human Genetics 84, 418–423, March 13, 2009
Received: November 17, 2008
Revised: February 6, 2009
Accepted: February 13, 2009
Published online: March 5, 2009
Web Resources
The URLs for data presented herein are as follows:
Comprehensive Meta Analysis,
Haploview program,
KBioscience genotyping technology,
NetAffx Analysis Center,
Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.
1. Holloszy, J.O. (1995). Workshop on sarcopenia: muscle
atrophy in old age. J. Gerontol. A Biol. Sci. Med. Sci. 50 Special
issue, 1–161.
2. Sipila, S., Heikkinen, E., Cheng, S., Suominen, H., Saari, P.,
Kovanen, V., Alen, M., and Rantanen, T. (2006). Endogenous
hormones, muscle strength, and risk of fall-related fractures
in older women. J. Gerontol. A Biol. Sci. Med. Sci. 61, 92–96.
3. Karakelides, H., and Sreekumaran Nair, K. (2005). Sarcopenia of
aging and its metabolic impact. Curr. Top.Dev. Biol. 68, 123–148.
4. Hansen, R.D., Raja, C., Aslani, A., Smith, R.C., and Allen, B.J.
(1999). Determination of skeletal muscle and fat-free mass
by nuclear and dual-energy x-ray absorptiometry methods in
men and women aged 51-84 y (1-3). Am. J. Clin. Nutr. 70,
5. Hsu,F.C.,Lenchik,L.,Nicklas,B.J.,Lohman,K.,Register,T.C.,
and Carr , J.J. (2005). Heritability of body composition measured
by DXA in the diabetes heart study. Obes. Res. 13, 312–319.
6. Arden, N.K., and Spector, T.D. (1997). Genetic influences on
muscle strength, lean body mass, and bone mineral density:
a twin study. J. Bone Miner. Res. 12, 2076–2081.
7. Nguyen, T.V., Howard, G.M., Kelly, P.J., and Eisman, J.A.
(1998). Bone mass, lean mass, and fat mass: same genes or
same environments? Am. J. Epidemiol. 147, 3–16.
8. Deng, H.W., Deng, H., Liu, Y.J., Liu, Y.Z., Xu, F.H., Shen, H.,
Conway, T., Li, J.L., Huang, Q.Y., Davies, K.M., et al. (2002).
A genomewide linkage scan for quantitative-trait loci for
obesity phenotypes. Am. J. Hum. Genet. 70, 1138–1151.
9. Di, X., Matsuzaki, H., Webster, T.A., Hubbell, E., Liu, G., Dong,
S., Bartell, D., Huang, J., Chiles, R., Yang, G., et al. (2005).
Dynamic model based algorithms for screening and genotyp-
ing over 100 K SNPs on oligonucleotide microarrays. Bioinfor-
matics 21, 1958–1963.
10. Rabbee, N., and Speed, T.P. (2006). A genotype calling
algorithm for affymetrix SNP arrays. Bioinformatics 22, 7–12.
11. Pritchard, J.K., Stephens, M., and Donnelly, P. (2000). Infer-
ence of population structure using multilocus genotype
data. Genetics 155, 945–959.
12. Lange, C., DeMeo, D.L., and Laird, N.M. (2002). Power and
design considerations for a general class of family-based associa-
tion tests: quantitativetraits. Am. J.Hum. Genet. 71, 1330–1341.
13. Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E.,
Shadick, N.A., and Reich, D. (2006). Principal components
analysis corrects for stratification in genome-wide association
studies. Nat. Genet. 38, 904–909.
14. Stirling, W.D. (1982). Enhancements to aid interpretation of
probability plots. Statistician 31, 211–220.
15. Burton, P.R., Clayton, D.G., Cardon, L.R., Craddock, N.,
Deloukas, P., Duncanson, A., Kwiatkowski, D.P., McCarthy,
M.I., Ouwehand, W.H., Samani, N.J., et al. (2007). Genome-
wide association study of 14,000 cases of seven common
diseases and 3,000 shared controls. Nature 447, 661–678.
16. Larsson, L., Li, X., Teresi, A., and Salviati, G. (1994). Effects of
thyroid hormone on fast- and slow-twitch skeletal muscles in
young and old rats. J. Physiol. 481, 149–161.
17. Collu, R., Tang, J., Castagne, J., Lagace, G., Masson, N., Huot, C.,
Deal, C., Delvin, E., Faccenda, E., Eidne, K.A., et al. (1997).
A novel mechanism for isolated central hypothyroidism: inac-
tivating mutations in the thyrotropin-releasing hormone
receptor gene. J. Clin. Endocrinol. Metab. 82, 1561–1565.
18. Vadaszova, A., Hudecova, S., Krizanova, O., and Soukup, T.
(2006). Levels of myosin heavy chain mRNA transcripts and
content of protein isoforms in the slow soleus muscle of
7 month-old rats with altered thyroid status. Physiol. Res.
55, 221–225.
19. Norenberg, K.M., Herb, R.A., Dodd, S.L., and Powers, S.K.
(1996). The effects of hypothyroidism on single fibers of the
rat soleus muscle. Can. J. Physiol. Pharmacol. 74, 362–367.
20. Van den Berghe, G., Baxter, R.C., Weekers, F., Wouters, P.,
Bowers, C.Y., Iranmanesh, A., Veldhuis, J.D., and Bouillon,
R. (2002). The combined administration of GH-releasing
peptide-2 (GHRP-2), TRH and GnRH to men with prolonged
critical illness evokes superior endocrine and metabolic effects
compared to treatment with GHRP-2 alone. Clin. Endocrinol.
(Oxf.) 56, 655–669.
21. Lang, C.H., and Frost, R.A. (2002). Role of growth hormone,
insulin-like growth factor-I, and insulin-like growth factor
binding proteins in the catabolic response to injury and infec-
tion. Curr. Opin. Clin. Nutr. Metab. Care 5, 271–279.
22. Gibney, J., Healy, M.L., and Sonksen, P.H. (2007). The growth
hormone/insulin-like growth factor-I axis in exercise and
sport. Endocr. Rev. 28
, 603–624.
Devlin, B., and Roeder,
K. (1999). Genomic control for associ-
ation studies. Biometrics 55, 997–1004.
24. Liu, X.G., Zhao, L.J., Liu, Y.J., Xiong, D.H., Recker, R.R., and
Deng, H.W. (2008). The MTHFR gene polymorphism is associ-
ated with lean body mass but not fat body mass. Hum. Genet.
123, 189–196.
25. Chagnon, Y.C., Rice, T., Perusse, L., Borecki, I.B., Ho-Kim, M.A.,
Lacaille, M., Pare, C., Bouchard, L., Gagnon, J., Leon, A.S., et al.
(2001). Genomic scan for genes affecting body composition
before and after training in Caucasians from HERITAGE.
J. Appl. Physiol. 90, 1777–1787.
26. Platte, P., Papanicolaou, G.J., Johnston, J., Klein, C.M.,
Doheny, K.F., Pugh, E.W., Roy-Gagnon, M.H., Stunkard, A.J.,
Francomano, C.A., and Wilson, A.F. (2003). A study of linkage
and association of body mass index in the Old Order Amish.
Am. J. Med. Genet. C. Semin. Med. Genet. 121, 71–80.
27. Chagnon, Y.C., Borecki, I.B., Perusse, L., Roy, S., Lacaille, M.,
Chagnon, M., Ho-Kim, M.A., Rice, T., Province, M.A., Rao,
D.C., et al. (2000). Genome-wide search for genes related to
the fat-free body mass in the Quebec family study. Metabolism
49, 203–207.
The American Journal of Human Genetics 84, 418–423, March 13, 2009 423
    • "A genome wide association study indicates a significant association between polymorphisms (rs16892496 and rs7832552) of the TRH-R1 gene and lean body mass in humans. Subjects carrying the theoretically highest expressing (TT) genotype in the rs7832552 single nucleotide polymorphism [according to [119]; see below] had higher lean body mass compared to the other subjects [120]. An additional study in older women is consistent with an association of the rs16892496 polymorphism with fat-free mass, and suggests additionally an association with muscle strength [121]. "
    [Show abstract] [Hide abstract] ABSTRACT: The activity of the hypothalamus-pituitary-thyroid axis (HPT) is coordinated by hypophysiotropic thyrotropin releasing hormone (TRH) neurons present in the paraventricular nucleus of the hypothalamus. Hypophysiotropic TRH neurons act as energy sensors. TRH controls the synthesis and release of thyrotropin, which activates the synthesis and secretion of thyroid hormones; in target tissues, transporters and deiodinases control their local availability. Thyroid hormones regulate many functions, including energy homeostasis. This review discusses recent evidence that covers several aspects of TRH role in HPT axis regulation. Knowledge about the mechanisms of TRH signaling has steadily increased. New transcription factors engaged in TRH gene expression have been identified, and advances made on how they interact with signaling pathways and define the dynamics of TRH neurons response to acute and/or long-term influences. Albeit yet incomplete, the relationship of TRH neurons activity with positive energy balance has emerged. The importance of tanycytes as a central relay for the feedback control of the axis, as well as for HPT responses to alterations in energy balance, and other stimuli has been reinforced. Finally, some studies have started to shed light on the interference of prenatal and postnatal stress and nutrition on HPT axis programing, which have confirmed the axis susceptibility to early insults.
    Article · Aug 2016
    • "Genome-wide association studies of BMD, osteoporosis, and osteoporotic fracture have also been reported (Urano et al., 2010Urano et al., , 2012 Richards et al., 2012). Recently, GWA studies have also identified common genetic variants associated with lean body mass (Liu et al., 2009; Hai et al., 2012; Guo et al., 2013). To the best of our knowledge, this is the first large-scale association study for lean body mass variation in the Japanese population. "
    [Show abstract] [Hide abstract] ABSTRACT: Genetic factors are important for the development of sarcopenia, a geriatric disorder characterized by low lean body mass. The aim of this study was to search for novel genes that regulate lean body mass in humans. We performed a large-scale search for 250K single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD) using SNP arrays in 1081 Japanese postmenopausal women. We focused on an SNP (rs12409277) located in the 5′-flanking region of the PRDM16 (PRD1-BF-1-RIZ1 homologous domain containing protein 16) gene that showed a significant P value in our screening. We demonstrated that PRDM16 gene polymorphisms were significantly associated with total body BMD in 1081 postmenopausal Japanese women. The rs12409277 SNP affected the transcriptional activity of PRDM16. The subjects with one or two minor allele(s) had a higher lean body mass than the subjects with two major alleles. Genetic analyses uncovered the importance of the PRDM16 gene in the regulation of lean body mass.
    Full-text · Article · May 2014
    • "Molecular genetic studies have identified a series of candidate genes for low BMI including a thyrotropin-releasing hormone (TRH) receptor polymorphism [7], the Ser23 allele of the serotonin 2C receptor [8], a genetic variant on chromosome 16p11.2 [9] and a copy number variant identified as gremlin1 [10]. "
    [Show abstract] [Hide abstract] ABSTRACT: The low body mass index (BMI) phenotype of less than 18.5 has been linked to medical and psychological morbidity as well as increased mortality risk. Although genetic factors have been shown to influence BMI across the entire BMI, the contribution of genetic factors to the low BMI phenotype is unclear. We hypothesized genetic factors would contribute to risk of a low BMI phenotype. To test this hypothesis, we conducted a genealogy data analysis using height and weight measurements from driver's license data from the Utah Population Data Base. The Genealogical Index of Familiality (GIF) test and relative risk in relatives were used to examine evidence for excess relatedness among individuals with the low BMI phenotype. The overall GIF test for excess relatedness in the low BMI phenotype showed a significant excess over expected (GIF 4.47 for all cases versus 4.10 for controls, overall empirical p-value<0.001). The significant excess relatedness was still observed when close relationships were ignored, supporting a specific genetic contribution rather than only a family environmental effect. This study supports a specific genetic contribution in the risk for the low BMI phenotype. Better understanding of the genetic contribution to low BMI holds promise for weight regulation and potentially for novel strategies in the treatment of leanness and obesity.
    Full-text · Article · Dec 2013
Show more

Recommended publications

Discover more