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Published in final edited form as: J Genet Genomics. 2015 Feb 17;42(3):107–117. doi: 10.1016/j.jgg.2015.02.003

Genome-Wide Linkage and Positional Association Analyses Identify Associations of Novel AFF3 and NTM Genes with Triglycerides: The GenSalt Study

Changwei Li a, Lydia AL Bazzano a,b, Dabeeru C Rao c, James E Hixson d, Jiang He a,b, Dongfeng Gu e, Charles C Gu c, Lawrence C Shimmin d, Cashell E Jaquish f, Karen Schwander c, De-Pei Liu g, Jianfeng Huang e, Fanghong Lu h, Jie Cao e, Shen Chong i, Xiangfeng Lu e, Tanika N Kelly a,*
PMCID: PMC4761343  NIHMSID: NIHMS755675  PMID: 25819087

Abstract

We conducted a genome-wide linkage scan and positional association study to identify genes and variants influencing blood lipid levels among participants of the Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study. The GenSalt study was conducted among 1906 participants from 633 Han Chinese families. Lipids were measured from overnight fasting blood samples using standard methods. Multipoint quantitative trait genome-wide linkage scans were performed on the high-density lipoprotein, low-density lipoprotein, and log-transformed triglyceride phenotypes. Using dense panels of single nucleotide polymorphisms (SNPs), single-marker and gene-based association analyses were conducted to follow-up on promising linkage signals. Additive associations between each SNP and lipid phenotypes were tested using mixed linear regression models. Gene-based analyses were performed by combining P-values from single-marker analyses within each gene using the truncated product method (TPM). Significant associations were assessed for replication among 777 Asian participants of the Multi-ethnic Study of Atherosclerosis (MESA). Bonferroni correction was used to adjust for multiple testing. In the GenSalt study, suggestive linkage signals were identified at 2p11.2–2q12.1 [maximum multipoint LOD score (MML) = 2.18 at 2q11.2] and 11q24.3–11q25 (MML = 2.29 at 11q25) for the log-transformed triglyceride phenotype. Follow-up analyses of these two regions revealed gene-based associations of charged multivesicular body protein 3 (CHMP3), ring finger protein 103 (RNF103), AF4/FMR2 family, member 3 (AFF3), and neurotrimin (NTM ) with triglycerides (P = 4 × 10−4, 1.00 × 10−5, 2.00 × 10−5, and 1.00 × 10−7, respectively). Both the AFF3 and NTM triglyceride associations were replicated among MESA study participants (P = 1.00 × 10−7 and 8.00 × 10−5, respectively). Furthermore, NTM explained the linkage signal on chromosome 11. In conclusion, we identified novel genes associated with lipid phenotypes in linkage regions on chromosomes 2 and 11.

Keywords: Lipids, Linkage analysis, Positional association analysis, Gene-based analysis

INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide (He et al., 2005; Lozano et al., 2013; Murray et al., 2013). Suboptimal lipid levels contribute to the atherosclerotic process, with clinical trials and observational studies demonstrating a strong relation between blood lipid concentrations and CVD (Hokanson and Austin, 1996; LaRosa et al., 1999; Di Angelantonio et al., 2009; Huxley et al., 2011). The heritabilities of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride concentrations have long been established (Friedlander et al., 1997; Malhotra and Wolford, 2005; Luo et al., 2010; Zhang et al., 2010). More recently, genome-wide association studies (GWASs) have made important strides in identifying single nucleotide polymorphisms (SNPs) that contribute to the inter-individual variability in these complex phenotypes (Saxena et al., 2007; Kathiresan et al., 2008; Kooner et al., 2008; Wallace et al., 2008; Willer et al., 2008; Aulchenko et al., 2009; Kathiresan et al., 2009; Teslovich et al., 2010; Waterworth et al., 2010; Kim et al., 2011; Tan et al., 2012). Despite such progress, up to 75% of the variance in lipid levels due to genetic factors remains unexplained (Teslovich et al., 2010). Further research is needed to identify novel variants, genes, and biological pathways with important influences on lipid phenotypes.

We conducted genome-wide linkage analyses to identify chromosomal regions harboring quantitative trait loci (QTLs) for LDL-C, HDL-C, and triglyceride phenotypes among Han Chinese participants of the Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study. This unique analysis takes advantage of the large, multi-generational family-based design of the GenSalt study, which included very few participants taking lipid-lowering medications. To localize promising linkage signals, genome-wide scans were followed-up with positional association analyses leveraging dense panels of SNP markers. The association study included not only single-marker testing but also gene-based analyses, which may increase power to detect the modest effects of common SNPs by examining their joint genic contributions (Ma et al., 2013).

RESULTS

Characteristics of participants

The baseline characteristics of the 1865 GenSalt participants, including 661 probands, 68 spouses, 936 siblings, and 200 offspring from 633 families, are presented in Table 1. The average LDL-C level ranged from 78.4 mg/dL among offspring to 101.6 mg/dL among spouses, HDL-C ranged from 48.0 mg/dL among offspring to 51.8 mg/dL among siblings, and triglycerides ranged from 108.6 mg/dL among offspring to 138.7 mg/dL among probands.

Table 1.

Characteristics of 1865 GenSalt participants from 633 Han Chinese families

Proband (n = 661) Spouse (n = 68) Sibling (n = 936) Offspring (n = 200)
Male (%) 60.5 32.4 50.9 43.5
Age (y, mean ± SD) 41.0 ± 8.3 49.0 ± 6.7 39.5 ± 7.7 23.5 ± 6.5
BMI (kg/m2, mean ± SD) 24.2 ± 3.2 23.4 ± 3.6 23.0 ± 2.8 21.6 ± 3.3
Blood lipids (mg/dL, mean ± SD)
 LDL-C 99.2 ± 28.0 101.6 ± 27.9 94.9 ± 26.2 78.4 ± 22.7
 HDL-C 50.8 ± 12.2 50.3 ± 11.7 51.8 ± 10.8 48.1 ± 9.3
 Triglycerides* 138.7 ± 91.8 124.7 ± 65.7 114.8 ± 69.0 108.6 ± 59.8
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BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SD, standard deviation.

*

Geometric mean ± SD.

Linkage analysis

The heritabilities of LDL-C, HDL-C and triglycerides were 51.9%, 42.7% and 30.8%, respectively, in the GenSalt sample (all P < 0.001). Genome-wide linkage scan results for LDL-C, HDL-C, and triglycerides are shown in Fig. 1. We observed suggestive linkage (LOD > 2) of chromosomal regions 2p11.2–2q12.1 and 11q24.3–11q25 to the triglycerides phenotype. For triglycerides, maximum multipoint LOD scores of 2.177 and 2.288 were achieved at 2q11.2 and 11q25, respectively (Table 2).

Fig. 1. Genome-wide linkage scan results for lipid phenotypes.

Fig. 1

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Genome-wide linkage scan results for HDL-C (blue), LDL-C (red), and log-TG (green). HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; log-TG, log-transformed triglycerides.

Table 2.

Chromosome regions harboring LOD scores >2 for the blood lipid phenotypes

Chromosomal location Physical distance (kb) Map distance (cM) Maximum multipoint LOD score
HDL-C LDL-C* Log-TG
2p11.2–2q12.1 84,800–103,700 106.00–115.00 0.132 0.927 2.177
11q24.3–11q25 128,826–134,170 141.00–158.00 0 0 2.288
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HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Log-TG, log-transformed triglycerides.

*

Kurtosis adjusted LOD score.

Positional association analysis

Fig. 2 shows the associations between 1929 tag-SNPs within the two suggestive linkage regions and LDL-C, HDL-C and triglyceride concentrations. No variants were significantly associated with the lipid phenotypes after adjustment for multiple testing.

Fig. 2. −Log10 P-values for the association between 1929 tag-SNPs in suggestive linkage regions and lipid phenotypes.

Fig. 2

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−Log10 P-values for the association between 1929 SNPs in suggestive linkage regions (LOD > 2) and HDL-C (blue), LDL-C (red), and log-TG (green). HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; log-TG, log-transformed triglycerides.

Gene-based analysis

A total of eight genes [charged multivesicular body protein 3 (CHMP3), ring finger protein 103 (RNF103), AF4/FMR2 family, member 3 (AFF3), mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), type II interleukin 1 receptor (IL1R2), type I interleukin 1 receptor (IL1R1), trans-membrane protein 182 (TMEM182), and neurotrimin (NTM )] from the identified linkage regions were significantly associated with lipid phenotypes in the GenSalt study (Table 3). Four of the identified genes were associated specifically with triglyceride levels, which corresponded to the identified linkage signals. These genes included CHMP3, RNF103 and AFF3 at 2p11.2–2q12.3 and NTM at 11q24.3–11q25 (P = 4.00 × 10−4, 1.00 × 10−5, 2.00 × 10−5, and 1.00 × 10−7, respectively). AFF3 and NTM were successfully replicated among the Asian participants of the Multi-ethnic Study of Atherosclerosis (MESA) (Table 3). P-values for all gene-based association tests are shown in the Table S1.

Table 3.

Significant gene-based findings (P-value) under linkage regions of log-transformed triglycerides

Gene Chr. Start End GenSalt
 MESA
Number
of SNPs		 LDL-C HDL-C Log-TG Top
SNP P* 		Number
of SNPs		 LDL-C HDL-C Log-TG Top SNP P*
CHMP3 2 86730554 86948245 15 0.3851 0.5142 0.0004 0.0178 13 0.4394 0.4963 0.4778
RNF103 2 86830516 86850989 7 0.3597 0.5712 1.00E-5 0.0178 6 0.5538 0.5391 0.5649
AFF3 2 100162323 100759201 59 0.8594 0.2188 2.00E-5 0.0059 58 <1.00E-7 0.1659 <1.00E-7 0.0008†/0.0014€
MAP4K4 2 102313312 102511149 9 0.3259 2.00E-7 0.5073 0.0023 9 0.4639 0.5065 3.00E-5 0.0040
IL1R2 2 102608306 102645006 8 0.1400 1.00E-5 0.0996 0.0030 8 0.4639 0.3207 0.2333
IL1R1 2 102681004 102796334 34 0.7557 1.00E-7 0.7905 0.0003 34 0.1106 0.8421 0.4863
TMEM182 2 103353367 103460352 6 0.0002 0.4977 0.3901 0.0181 6 0.4670 0.2300 0.1708
NTM 11 131240373 132206716 123 0.9623 0.6806 1.00E-7 0.0003 119 0.7680 0.2313 8.00E-5 0.0009
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Chr., chromosome; SNP, single nucleotide polymorphism; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride.

*

P-value of the most significant SNP in each significant gene-lipid association.

†

P-value of the most significant SNP for the AFF3 and LDL-C association.

€

P-value of the most significant SNP for the AFF3 and log-TG association. Bolded are significant findings after adjustment for multiple testing.

Sensitivity analysis

Linkage analyses before and after phenotype adjustment for the identified AFF3 and NTM genes are shown in Figs. 3 and 4, respectively. The NTM gene appeared to explain the linkage signal for log-transformed triglycerides on chromosome 11, with the maximum LOD score on chromosome 11 dropping from 2.29 to 1.01 after phenotype adjustment for the identified NTM gene signal (Fig. 4). The linkage signal on chromosome 2 only slightly changed after controlling for the AFF3 gene signal (Fig. 3).

Fig. 3. Sensitivity analysis for linkage signal on chromosome 2.

Fig. 3

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Linkage scan results on chromosome 2 for log-transformed triglyceride before and after controlling for the AFF3 gene signal among 1906 GenSalt participants.

Fig. 4. Sensitivity analysis for linkage signal on chromosome 11.

Fig. 4

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Linkage scan results on chromosome 11 for log-transformed triglyceride before and after controlling for the NTM gene signal among 1906 GenSalt participants.

DISCUSSION

The current analysis identified QTLs at 2p11.2–2q12.1 and 11q24.3–11q25 which may influence lipid phenotypes. We observed maximum multipoint LOD scores of 2.177 and 2.288 at 2q11.2 and 11q25, respectively, for triglycerides. Follow-up analyses of the 2p11.2–2q12.1 linkage signal revealed significant associations of seven protein-coding genes (RNF103, AFF3, MAP4K4, ILIR2, ILIR1, CHMP3, and TMEM182) with the lipid phenotypes. Under 11q24.3–11q25, follow-up gene-based analyses identified one protein-coding gene, NTM, significantly associated with triglycerides. The associations of both the AFF3 and NTM genes with triglycerides were successfully replicated among Asian MESA participants. Of particular interest, the NTM gene appeared to explain the observed linkage signal for triglycerides on chromosome 11. Given the lack of single-marker associations, these findings highlight the relevance of joint SNP analyses for detecting potentially important genes and biological pathways influencing serum lipid concentrations.

Chromosomal region 2p11.2–2q12.1 showed the first evidence of suggestive linkage to triglyceride levels in the current study. Follow-up gene-based analyses of protein-coding genes under this region revealed seven genes which were significantly associated with lipid phenotypes. Three of these genes, CHMP3, RNF103, and AFF3 were associated with triglycerides, corresponding to the observed linkage signal. Particularly noteworthy was the strong association of the AFF3 gene with triglycerides in GenSalt participants, which was robustly replicated among Asian MESA participants. AFF3 represents a biologically plausible candidate gene, having been associated previously with conditions which commonly include serum lipid alterations (Moschovi et al., 2004; Liu and Rosner, 2006; Guy et al., 2009; Steiner and Urowitz, 2009), such as acute lymphoblastic leukemia (von Bergh et al., 2002), end stage renal disease (Sandholm et al., 2012), type I diabetes (Barrett et al., 2009; Wallace et al., 2012), and rheumatoid arthritis (Barton et al., 2009; Plant et al., 2010). Since the AFF3 gene could not explain the observed linkage signal, it is likely that other genes and variants in this regions influence triglyceride levels. While CHMP3 and RNF103 showed an association that was consistent with the linkage signal among GenSalt participants, these findings could not be replicated in the smaller replication study. Future work in larger sample sizes will be needed to determine the relevance of these genes in serum lipid concentrations. Among the remaining four genes (MAP4K4, ILR2, ILIR1, and TMEM182), none were associated with triglycerides (the linked phenotype) nor were they replicated among MESA participants. While MAP4K4 significantly associated with HDL-C in the GenSalt study, it is interesting to note an association of this gene with triglycerides in MESA. Previous studies showed that MAP4K4 is involved in the suppression of lipid synthesis (Puri et al., 2008; Danai et al., 2013). Furthermore, Danai and colleagues (2013) showed that silencing the MAP4K4 gene in adipocytes enhanced the expression of lipogenic enzymes, and increased the level of triglycerides and fatty acids. Future studies focusing on this gene may be warranted.

Region 11q24.3–11q25 also showed suggestive evidence of linkage to triglyceride levels in the current study. This finding is similar to that of Bosse and colleagues who reported linkage of the 11q24 region to triglycerides among participants of the Quebec Family Study (Bosse et al., 2004). Under the 11q24.3–11q25 signal, the NTM gene was strongly associated with the triglyceride levels among GenSalt study participants. Interestingly, this finding appeared to explain the linkage signal for triglycerides in this region. Furthermore, NTM was successfully replicated among Asian MESA participants. Our findings are bolstered by previous functional and epidemiological studies demonstrating that the NTM gene may be involved in cholesterol homeostasis (Ramirez et al., 2011). Ramirez and colleagues (2011) reported that cellular cholesterol content in macrophages and livers of mice fed with high-fat diet can down-regulate miR-758, a microRNA involved in the down-regulation of NTM expression. In addition, Lukkonen and colleagues (2012) identified an association of NTM with intracranial and thoracic aortic aneurysm, an important dyslipidemia related disease. In aggregate, these findings provide strong evidence for a role of NTM in the regulation of serum lipid concentrations.

Our study has several important strengths. The large sample size and homogeneity of the GenSalt study population with respect to lifestyle and environmental factors should provide increased power to detect both linkage and association signals. In addition, study attributes, including the recruitment of only Han Chinese individuals, should make the association analysis robust to population stratification. Furthermore, stringent quality control procedures were employed during phenotype measurement, genotyping, and data cleaning. Certain limitations should also be addressed. The linkage signal on chromosome 2 could not be explained by the identified AFF3 gene, suggesting that other genomic factors in this region may influence triglycerides. While coverage of common genetic variation should be excellent in the current study (Nishida et al., 2008), further examination of any untagged common variants, structural variation, and low-frequency or rare variants in this region may be warranted to explain the linkage signal. Furthermore, due to the limited number of SNPs, gene-based analyses for 56 genes could not be conducted. Further research will be needed to explore any associations of these genes with lipid levels.

The current study described two chromosomal regions, including 2p11.2–2q12.1 and 11q24.3–11q25, which may harbor important susceptibility loci for blood lipid levels. Follow-up gene-based analysis of GenSalt participants identified eight protein-coding genes associated with lipid phenotypes in these regions. Two of these genes, AFF3 at 2p11.2 and NTM at 11q25, demonstrated robust replication of the observed triglyceride associations among Asian participants of the MESA study. These findings highlight the utility of gene-based analyses in helping to elucidate the biological pathways underlying serum lipid concentration. Furthermore, this research contributes additional information to our growing understanding of the genomic mechanisms underlying lipoprotein metabolism.

MATERIALS AND METHODS

Study population

The GenSalt study is a unique dietary feeding study designed to examine gene-dietary sodium and potassium interactions on blood pressure (BP). The GenSalt dietary intervention included 1906 Han Chinese participants from 633 families recruited from six field centers located in rural areas of northern China. A detailed description of the study design and participants has been presented elsewhere (Group, 2007). In brief, probands and their families were identified through a community-based BP screening conducted among persons aged 18–60 years in the study villages. Probands with a mean systolic BP of 130–160 mmHg and/or a mean diastolic BP between 85–100 mmHg and no use of antihypertensive medications were recruited for the study, along with their siblings, spouses, and offspring. Individuals who had stage-2 hypertension, secondary hypertension, clinical cardiovascular disease, chronic kidney disease, or diabetes, who used antihypertensive medications, or who were pregnant, heavy alcohol drinkers, or currently on a low-sodium diet were excluded from the study. After additional exclusion of 16 participants taking lipid-lowering medications and 25 participants missing genotype data, phenotype data or information on important covariables, a total of 1,865 GenSalt participants were included in the current analysis (97.8%).

Phenotype measurement

During the 3-day GenSalt baseline examination period, a standard questionnaire was administered by trained staff to collect information on family structure, demographic characteristics, personal and family medical history, and lifestyle risk factors. Body weight and height were measured twice with participants in light indoor clothing and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Overnight fasting blood samples were drawn by venipuncture on day one of the baseline examination. Blood specimens were processed at the field center and immediately frozen until they were shipped to the central clinical laboratory in Beijing. Total cholesterol, HDL-C and triglycerides were analyzed enzymatically using commercially available reagents (Allain et al., 1974). LDL-C was calculated using the Friedewald equation for participants with triglyceride levels <400 mg/dL: LDL-C = total cholesterol – HDL-C – triglyceride/5 (Friedewald et al., 1972).

Microsatellite marker and SNP genotyping

Lymphocytic DNA was extracted from whole blood samples and used for genotyping microsatellite markers spaced at approximately 9 cM intervals (407 markers, Marshfield Screening Set 12). Microsatellite genotyping used fluorescently labeled PCR primers for marker amplification followed by capillary electrophoresis on automated DNA sequencers (ABI 3730xl DNA Analyzer; Applied Biosystems, USA). Quality control samples included blind duplicates, no DNA controls, and Centre d’Etudes du Polymorphisme Humain (CEPH) DNA standards (mother, father, offspring with known genotypes). GeneMapper software (Applied Biosystems) was used to assign genotypes. ASPEX and GRR were used to check for potential misreported relationships in the GenSalt family pedigrees (Hinds and Risch, 1996; Abecasis et al., 2001). MapMaker/Sibs and PedCheck were used to check for Mendelian inconsistencies within families for each marker (O’Connell and Weeks, 1998; Pratt et al., 2000). A total of 359 microsatellite markers passed quality control and were included in the analysis.

SNPs located in promising linkage regions were genotyped using chip based hybridization assays (Affymetrix 6.0, Santa Clara, CA, USA). SNPs were excluded if they had a call rate less than 95%, were not in Hardy Weinberg Equilibrium, or had a minor allele frequency (MAF) less than 1%. Among 3999 SNPs in the linkage regions, 3284 SNPs met the quality control criteria. From these SNPs, 1929 were tagged (r2 < 0.9) using Haploview software, and were selected for inclusion in the single-marker analysis. Among the 116 protein-coding genes located in the promising linkage regions (Flicek et al., 2014), 60 genes with genotype data for at least three SNPs were included in the gene-based analysis (Table S1).

Statistical analysis

Triglyceride values were logarithmically transformed in order to normalize their distribution for all analyses. The means, geometric means, or percent of important covariables and lipid phenotypes were calculated for GenSalt probands, as well as their siblings, spouses and offspring.

Prior to linkage analysis, multipoint identity by descent estimates were calculated using Merlin software. In addition, HDL-C, LDL-C, and log-transformed triglyceride phenotypes were adjusted for the effects of age, BMI, gender and field center. In brief, each phenotype was regressed on the covariates in a stepwise manner, and only significant terms (P < 0.05) were retained. The residual variance was also examined (i.e., heteroscedasticity) by regressing the squared residual from the first regression on the same covariates (stepwise) and retaining significant terms. The final adjusted indicator was computed as the residual from the first regression, divided by the square root of the predicted score from the second regression. Heritability and multipoint genome-wide linkage scans of the adjusted blood lipid phenotypes were performed with SOLAR software (Almasy and Blangero, 1998). For linkage analyses, we used a multipoint linkage scan interval of 1 cM. Due to high residual kurtosis of the LDL-C phenotype (kurtosis = 2.60), we use a LOD score adjusted method implemented in SOLAR to ensure reliable results for this phenotype.

Additive single-marker associations between each SNP located in promising linkage regions (LOD > 2) and the lipid phenotypes (HDL-C, LDL-C, and log-transformed triglycerides) were examined using a mixed linear regression model to account for familial correlations. The same covariates used in the linkage analysis, including age, BMI, gender, and field center, were adjusted in multivariable analysis. We used a Bonferroni correction to adjust for the multiple testing (α-threshold = 0.05/1929 = 2.59 × 10−5). Significant SNPs were examined for replication among 777 Asian participants of MESA with GWAS and phenotype data available in the database of Genotypes and Phenotypes (dbGaP accession phs000209v11.p3.c2 and phs00420v4.p3.c2) (Mailman et al., 2007). The association analyses were conducted using SAS software (Version 9.3; SAS Institute, Inc., Cary, North Carolina, USA).

Gene-based associations with each lipid phenotype were tested by combining P-values from single SNP association analyses within each gene using TPM (Sheng and Yang, 2013; Yang et al., 2012). This method accommodates the correlations between SNPs through simulation, and has high power to detect gene-based associations compared to other meta-analysis techniques (Yang et al., 2012; Ma et al., 2013; Sheng and Yang, 2013). TPM has been evaluated extensively through simulation and applied to several gene-based studies of cardiometabolic phenotypes (Yang et al., 2012, 2013; Li et al., 2014; Zhu et al., 2014). For the current analysis, the truncation point was set as τ = 0.10, and the P-values for genes were estimated by 10,000 simulations. The simulations were increased up to 10,000,000 if 10,000 simulations failed to generate P-values. Genes significantly associated with lipid phenotypes in GenSalt were further tested for replication among the Asian participants of the MESA study. Bonferroni correction was applied to account for testing of 60 genes in the GenSalt study (α-threshold = 0.05/60 = 8.33 × 10−4) and eight genes in MESA replication study (α-threshold = 0.05/8 = 6.25 × 10−3). Gene-based analyses were performed using R software (Version 2.15.2, http://www.r-project.org).

Sensitivity analyses were conducted to determine whether those genes and variants identified in GenSalt and successfully replicated in MESA could explain the observed linkage signals. For this analysis, linkage scans were reconducted after phenotype adjustment for significant genes variants (along with adjustment for all previously described covariables) (Almasy and Blangero, 1998). Dampening of the LOD score after phenotype adjustment for identified association signals would provide evidence that such signals were responsible for the observed linkage result.

Ethnics statement

Institutional review boards at the Tulane University Health Sciences Center, Washington University School of Medicine, University of Texas School of Public Health, Fuwai Hospital and Chinese National Human Genome Center at Beijing, and Chinese Academy of Medical Sciences approved the GenSalt study. Written informed consents for the baseline observation and for the intervention program were obtained from each participant. The institutional review board at Tulane University approved of the use of publicly available genotype and phenotype data from MESA participants.

Supplementary Material

Supplemental Material

ACKNOWLEDGMENTS

The GenSalt is supported by a cooperative agreement project grant (Nos. U01HL072507, R01HL087263, and R01HL090682) from the National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health, Bethesda, MD. Dr. Bazzano was supported by a career development award (No. K08HL091108) from NHLBI. MESA and the MESA SHARe project were conducted and supported by NHLBI in collaboration with MESA investigators. Support for MESA is provided by contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169 and CTSA UL1-RR-024156. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. Genotyping was performed at Affymetrix (Santa Clara, California, USA) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, USA) using the Affymetric Genome-Wide Human SNP Array 6.0.

Footnotes

SUPPLEMENTARY DATA

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jgg.2015.02.003.

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