This article has Open Peer Review reports available.
Common genetic variants of the ion channel transient receptor potential membrane melastatin 6 and 7 (TRPM6 and TRPM7), magnesium intake, and risk of type 2 diabetes in women
© Song et al; licensee BioMed Central Ltd. 2009
Received: 18 June 2008
Accepted: 17 January 2009
Published: 17 January 2009
Ion channel transient receptor potential membrane melastatin 6 and 7 (TRPM6 and TRPM7) play a central role in magnesium homeostasis, which is critical for maintaining glucose and insulin metabolism. However, it is unclear whether common genetic variation in TRPM6 and TRPM7 contributes to risk of type 2 diabetes.
We conducted a nested case-control study in the Women's Health Study. During a median of 10 years of follow-up, 359 incident diabetes cases were diagnosed and matched by age and ethnicity with 359 controls. We analyzed 20 haplotype-tagging single nucleotide polymorphisms (SNPs) in TRPM6 and 5 common SNPs in TRPM7 for their association with diabetes risk.
Overall, there was no robust and significant association between any single SNP and diabetes risk. Neither was there any evidence of association between common TRPM6 and TRPM7 haplotypes and diabetes risk. Our haplotype analyses suggested a significant risk of type 2 diabetes among carriers of both the rare alleles from two non-synomous SNPs in TRPM6 (Val1393Ile in exon 26 [rs3750425] and Lys1584Glu in exon 27 [rs2274924]) when their magnesium intake was lower than 250 mg per day. Compared with non-carriers, women who were carriers of the haplotype 1393Ile-1584Glu had an increased risk of type 2 diabetes (OR, 4.92, 95% CI, 1.05–23.0) only when they had low magnesium intake (<250 mg/day).
Our results provide suggestive evidence that two common non-synonymous TRPM6 coding region variants, Ile1393Val and Lys1584Glu polymorphisms, might confer susceptibility to type 2 diabetes in women with low magnesium intake. Further replication in large-scale studies is warranted.
Magnesium is an essential mineral derived primarily from dietary sources, including whole-grains, green leafy vegetables, legumes, and nuts . Adequate magnesium intake is critical in maintaining magnesium balance and normal magnesium-dependent cellular reactions in the human body. Magnesium deficiency has been correlated with a cluster of metabolic abnormalities and associated chronic diseases such as oxidative stress, systemic inflammation, endothelial dysfunction, insulin resistance, hypertension, type 2 diabetes, and coronary heart disease [2–6]. Although the precise mechanisms underlying magnesium metabolism are unclear, magnesium homeostasis in the human body seems to be tightly regulated via the balance between intestinal absorption and renal excretion. Recent studies suggest that transient receptor potential membrane melastatin 6 and 7 (TRPM6 and TRPM7), two members of the "transient receptor potential" (TRP) family of cation channels, may play a central role in the regulation of magnesium homeostasis [7–10].
The TRPM6 gene, located on chromosome 9q21.13, comprises 39 exons spanning 163 kb of genomic sequence and encodes 2022 amino acids . TRPM6 is thought to be an ion channel subunit primarily expressed in intestinal epithelia and kidney tubules that may play an important role in intestinal and renal magnesium handling [12, 13]. Several loss-of-function mutations in TRPM6 have been identified among patients with autosomal-recessive familial hypomagnesemia with secondary hypocalcemia [12–14]. TRPM7, located on chromosome 15q21, spans 127 kb of DNA sequence and encodes a protein of 1865 amino acids. TRPM7 is ubiquitously expressed in various tissues or cell lines [8, 9] and may be part of a magnesium sensing and/or uptake mechanism underlying cellular magnesium homeostasis [8, 9]. Although TRPM6 and TRPM7 share 52% homology , their biological functions are not redundant, and they can form functional heterometic channels with different permeability . Thus, these two genes are candidate genes for evaluating the genetic basis of hypomagnesemia-related metabolic traits such as the common form of type 2 diabetes. To date, no study has assessed common genetic variation in these two genes or their contribution to susceptibility to chronic metabolic disorders highly correlated with hypomagenesemia. Furthermore, a magnesium-deficient diet was shown to upregulate TRPM6 mRNA expression in mice . Low serum magnesium levels caused by TRPM6 mutations among HSH patients can be ameliorated by oral supplementation of high doses of magnesium . Thus, it is important to further examine in human population data whether magnesium intake modifies any common genetic effects of TRPM6 and TRPM7 on diabetes risk.
We therefore conducted a prospective nested case-control study among postmenopausal women in the Women's Health Study to investigate the association between common variations of the TRPM6 and TRPM7 genes and the risk of type 2 diabetes. We specifically tested the following hypotheses: (1) common genetic variants in TRPM6 and TRPM7 are associated with risk of developing type 2 diabetes because of their critical functions on maintaining magnesium homeostasis; and (2) The genetic association, if any, is more pronounced among women with inadequate magnesium intake.
The Women's Health Study, begun in 1993, is a randomized, double-blind, placebo-controlled, 2 × 2 factorial trial of low-dose aspirin and vitamin E for the primary prevention of CVD and cancer among 39,876 US female health professionals aged 45 years and older free of cancer (other than nonmelanoma skin cancer), and cardiovascular disease [19, 20] at baseline. Of the 39,876 participants, 28,345 (71%) provided baseline blood samples. Of these 28,345, we restricted our study to 12,304 women free of diabetes at baseline who were not currently receiving hormone replacement therapy and were postmenopausal at the time of blood collection. By February 2005 with 10 years of follow up, 366 of these initially healthy women had developed incident diabetes. Using the principle of risk-set sampling to randomly select controls from the cohort person-time , controls providing baseline blood samples were matched in 1:1 ratio to cases by age (within 1 year), duration of follow up (within 1 month), race, and fasting status at time of blood draw (72% provided fasting blood, defined as ≥ 10 hrs since last meal). Based on these criteria, a total of 359 cases and 359 controls were selected. This study was conducted according to the ethical guidelines of Brigham and Women's Hospital.
On the baseline questionnaire, we collected detailed information on demographics, medical history, and lifestyle factors. In the enrollment questionnaire, participants provided information on lifestyle exposures including cigarette smoking status (never, past, current), aerobic physical activity (never, <1/week, 1/week, 2–3 times/week, 4–6 times/week, daily), total intakes of beer, wine, and liquor (g/day), use of aspirin before the trial (yes, no), use of multivitamin (yes, no), and use of postmenopausal hormone therapy (never, past, current), Moreover, participants reported their medical history characteristics such as age, height, weight, history of type 2 diabetes, and family history of diabetes in first-degree relatives.
As described previously, a semiquantitative food-frequency questionnaire (SFFQ) at baseline was used to assess dietary nutrient intake . Each nutrient was adjusted for total energy using the residual method . In populations of nurses and health professionals, this SFFQ has demonstrated reasonably good validity as a measure of long-term average dietary intakes . The Pearson correlation coefficient between magnesium intake assessed by SFFQ and 2 weeks of diet records was 0.76 . The use of multivitamin supplements was taken into account to assess intake of supplemental magnesium. Total magnesium represents the sum of magnesium intake from both dietary and supplemental sources.
Ascertainment of diabetes
After excluding prevalent cases at baseline, all eligible participants were asked annually whether and when they had been diagnosed with diabetes since completing the previous questionnaire. Women self-reporting incident diabetes in annual follow-up were mailed supplementary questionnaires to confirm diabetes symptoms, diagnostic tests, and treatments. Diabetes was confirmed according to the American Diabetes Association (ADA) criteria of 1997 . Previous WHS diabetes validation via physician-led telephone interviews and self-administered supplementary questionnaires both yielded positive predictive values >91%, and confirmation of diabetes via combined supplementary questionnaire and medical records was 99% [27, 28].
SNP selection and genotyping
The location, minor allele frequency, and crude association analyses of 25 SNPs spanning both TRPM6 and TRPM7 loci
Minor allele frequency (MAF)
P values for crude associationb
C>T (in only European whites)
DNA was extracted from the buffy coat fraction of centrifuged blood using a QIAmp blood kit (Qiagen, Chatsworth, CA) at the Dana Farber/Harvard Cancer Center High Throughput Genotyping Core (David J. Hunter, MD, Director). DNA samples were genotyped using Taqman single nucleotide polymorphism allelic discrimination by means of an ABI 7900 HT (Applied Biosystems, Foster City, CA). The primers and probes were custom-designed by the ABI Taqman system (PE Biosystems, Foster City, CA). Following PCR amplification, end-point fluorescence was read with the Applied Biosystems Primer 7900 HT instrument and genotypes were assigned using SDS2.2.2 Allelic Discrimination Software (Applied Biosystems, Foster City, CA) by a technician blinded to sample identification numbers. Replicate quality control samples (10%) were included and genotyped with >99% concordance.
Distributions or proportions of baseline characteristics of study subjects were examined according to case-control status. Student's t-test and chi-square test were used for comparisons of means and proportions. We assessed each SNP for the Hardy-Weinberg equilibrium (HWE) test using the chi-square test.
Pairwise LD between SNPs was assessed using Lewontin's D' statistic and the squared correlation statistic r 2. The Haploview program was used to calculate the LD coefficient and define haplotype blocks [30, 31]. For each SNP, we tested for allelic association with diabetes risk under dominant, recessive, and additive models. We used unconditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for each SNP with diabetes risk for each genetic model, respectively. We made adjustments for multiple confounding variables including matching factors (age and ethnicity), BMI (continuous), smoking (current, former, and never), alcohol use (rarely/never drinkers, 1–3 drinks/month, 1–6 drinks/week, and 1 or more drinks/day), exercise (rarely/never, 1, 2–3, 4–6 and ≥ 7 times/week), and family history of diabetes (yes, no).
Haplotypes inferred from all SNPs and all haplotypes within each LD block were tested for association with type 2 diabetes. We estimated haplotype frequency from phase-unknown genotype data using the expectation-maximization (EM) algorithm . For each individual and each haplotype, h, the haplotype dosage estimate (i.e. an estimate of the number of copies of haplotype h) was computed using the individual's genotype data and haplotype frequency estimates obtained from the combined (cases+controls) data set [32, 33]. Only haplotypes with estimated frequencies ≥ 1% in the combined cases and controls were included for analyses. We first performed global likelihood ratio tests to examine whether the frequency distributions of the common haplotypes differed between cases and controls by comparing a model with additive effects on the log odds scale for each common haplotype (using the most common halpotype as the reference) to the intercept-only model. As compared to the corresponding non-carriers of each haplotype, we also calculated haplotype-specific ORs and 95% CIs for carriers of each specific haplotype from logistic regression analyses.
A sliding window (window width = 2) haplotype-based analysis was performed to identify a "sub-haplotype" that would be most significantly associated with the disease outcome. The omnibus likelihood ratio test was used to test the association significance in the total samples, and in subsamples stratified by levels of magnesium intake. A -log10p > 2.88 (p < 0.0013) was used as the global significance threshold by Bonferroni correction for 19 window frames.
Stratified analyses were conducted to examine whether the association between the TRPM6 and TRPM7 genotypes and risk of type 2 diabetes was modified by magnesium intake. The cutpoints for magnesium levels were prespecified by those for quintiles among controls. Interactions between the genotype/haplotype and magnesium intake were assessed by comparison of regression models with and without the product terms between the genotype and stratifying variables using a likelihood ratio test.
All reported P-values were 2-tailed, and statistical significance was defined at the α = 0.05 level. Statistical analyses were performed using SAS statistical package (version 9.0 for window; SAS Institute, Cary, NC). In addition, we performed bioinformatics analyses to address the potential functional relevance of three common coding SNPs in TRPM6 and TRPM7. Several algorithms including PMut , PolyPhen , and SIFT  were employed to predict the impact of these amino acid substitutions on protein structure and activity. The principles for these algorithms are sequence conservation over evolutionary time, the physical and chemical properties of the exchanged residues, and/or protein structural domain information. Different algorithms emphasize different aspects of assumptions, quantitative measures, and dynamic databases, but these algorithms are highly capable of predicting the impact of amino acid substitutions on function, consistent with results from biochemical analyses (>80% concordance rate). Polymorphisms within highly conserved regions are likely to be of greater functional significance than those within more diverged regions. Our cross-species comparison approach for TRPM6 and TRPM7 was based solely on the availability of relevant information from the NCBI Genbank database. To provide some indirect evidence for protein sequence changes among different species, cross-species comparison of the protein sequences was performed for two TRPM6 segments encompassing the two non-synonymous SNPs, V1393I and K1584E in human, chimpanzee, and mouse and a TRPM7 segment encompassing the non-synonymous SNP, T1482I in human, Sheep, mouse, and zebra fish. Protein sequence alignments were performed by use of ClustalX, version 1.81 .
Baseline characteristics of patients with type 2 diabetes and control participants.
n = 359
n = 359
60.3 ± 6.1
60.3 ± 6.1
Race (Caucasian), %
30.9 ± 6.1
26.0 ± 5.0
Cigarette smoking (%)
Physical activity, %
1 to 3 times per month
> 1 time per week
Alcohol consumption, %
1 to 3 times per month
>1 time per week
Family history of diabetes (%)
Total calorie intake, kcal/d
1809 ± 592
1722 ± 554
Magnesium intake, mg/d d
329 ± 70.5
348 ± 83.0
Haplotype analyses of the 20 SNPs spanning the TRPM6 region did not reveal any significant associations. There was no evidence for haplotypic associations with all derived haplotypes based on all 20 TRPM6 SNPs and 5 TRPM7 SNPs. Haplotype analysis in each of these blocks did not provide evidence of statistically significant associations with diabetes risk (data not shown). Overall, a global test for differences in risk according to common haplotypes (≥ 1%) inferred from 20 SNPs over TRPM6 was not statistically significant (P = 0.31). There was no significant association with haplotypes in any single block: p values for global testing of haplotype associations were 0.69 for block 1, 0.96 for block 2, 0.12 for block 3, 0.92 for block 4, 0.85 for block 5, and 0.33 for block 6. Neither was there any evidence of association between common TRPM7 haplotypes and diabetes risk (P for global test = 0.43).
Association between three non-synonymous polymorphisms in TRPM6 and TRPM7 and the risk of type 2 diabetes among women stratified by magnesium intake
(≥ 250 mg/day)
Age, race, and BMI-adjusted OR (95% CI)
Multivariate-adjusted OR (95% CI)b
Age, race, and BMI-adjusted OR
Multivariate-adjusted OR (95% CI)b
Computational predictions of the functional significances of 3 non-synonymous SNPs in the TRPM6 and TRPM7 genes
Functional Prediction Algorithm
Overall, we observed no significant associations between the TRPM6 and TRPM7 genetic variants, individually or jointly, with risk of type 2 diabetes in women. However, women who were homozygous carriers of both the rare alleles of two non-synonymous SNPs in TRPM6 and had low magnesium intake had a significantly higher risk of type 2 diabetes, although these findings may have been due to chance and further large-scale studies are needed to confirm these subgroup analyses.
To the best of our knowledge, this is the first population study to comprehensively investigate TRPM6 and TRPM7 as potential candidate genes for the common form of type 2 diabetes. On the basis of the publicly accessible dataset, we focused on common SNPs including intronic SNPs, synonymous SNPs, and non-synonymous SNPs to characterize genetic variation spanning the TRPM6 gene, including at least 30 kb upstream and downstream of the coding regions. There may be some unknown causal SNPs including splicing SNPs and SNPs in the promoter region of TRPM6 that likely regulate gene expression. However, we could not identify regulatory SNPs without functional data and did not find any common SNP in the promoter region. Although we did not sequence genomic regions in TRPM6 in our study samples, empirical data have demonstrated that tSNPs selected from the HapMap population samples can effectively capture common variation and provide good power to detect a modest association in many other independent samples . We cannot completely rule out the possibility of rare but functional variants (with an allele frequency <5%). Nevertheless, such rare alleles may not be major contributors to the most common late-onset form of type 2 diabetes with a high prevalence and incidence in the general population.
Our most significant statistical finding focused on two coding SNPs in TRPM6 among women with insufficient magnesium intake (at the lowest 20 percentile) (i.e. <250 mg/d), close to three quarters of the RDA for adult women. Among women, only those with low magnesium intake were found to have increased risk of type 2 diabetes. In our study population, approximately 33% are heterozygous for the rare G allele of rs2274924 and 18% heterozygous for the allele A of rs3750425. Both SNPs are also very common in populations with various genetic backgrounds; the HapMap population frequencies are 0.07–0.21 for the rs3750425 A allele (1393 Ile) and 0.15–0.36 for the rs2274924 G allele (1584Glu) in four ethnic groups. Two changed amino acids are located between the coiled region and kinase near the C-terminal . Although such two amino acid substitutions in this region are unlikely responsible for direct regulation of the TRPM6 channel trans-membrane structures and kinase activity, they may alter protein conformation and thus reduce TRPM6 channel activity. Our bioinformatics approaches indicated that these coding SNPs in highly conserved region might be functionally significant, although further functional analysis should be warranted. There is also the possibility that these SNPs are in high LD with untyped SNPs that directly affect channel activity. Alternatively, two amino acid substitutions may be necessary to influence channel protein structure and function. Individuals with both rare alleles, whose TRPM6 channel function is deficient in magnesium absorption, may be more prone to magnesium deficiency through low intake and in turn to an increased risk of diabetes. If our finding is replicated in future studies, it will suggest that common genetic variation in the TRPM6 locus known to harbor severe mutations causing monogenic magnesium deficiency confers a modest susceptibility to the risk of type 2 diabetes in a small subgroup of the general population.
We found no significant evidence for an association between TRPM7 genetic variants and diabetes. Given the limited number of genotyped SNPs (n = 5) across TRPM7 (128 kb) from the reference panel (HapMap database), it is likely that they are not sufficient to capture the vast majority of the genetic variability of TRPM7. Biologically, TRPM7 is ubiquitously expressed and its constitutive activation is required for cellular survival. Animal studies showed that dietary magnesium restriction did not alter TRPM7 mRNA expression in mouse kidney and colon . We therefore hypothesize that TRPM7, as a housekeeping gene regulating cellular magnesium metabolism, may truly have limited genetic variability.
The precise mechanisms for the regulatory role of TRPM6 and TRPM7 in magnesium homeostasis remain largely undefined. TRPM6 is a magnesium-permeable channel protein localized to the apical domain of the distal convoluted tubule and the brush-border membrane of the absorptive cells in intestine and plays an important role in active magnesium handling in the small intestine and kidney. Rare mutations in TRPM6, either incomplete or complete loss-of-function, have been associated with cases of autosomal-recessive hypomagnesemia with secondary hypocalcemia [12, 13]. Clinical data show that sufficient magnesium intake can partially compensate for the severe magnesium deficiency caused by genetic defect in the TRPM6 gene . It has been hypothesized that intestinal magnesium absorption occurs via two different pathways: an active transcellular transport and a passive paracellular passive transport . High magnesium concentrations in intestinal tracts may overcome the genetic defects of magnesium absorption and independently increase magnesium absorption via the passive paracellular pathway. In contrast, when dietary magnesium intake is inadequate, the function of TRPM6 in active intestinal magnesium absorption and renal reabsorption become very important. Further well-designed functional studies are needed to investigate the molecular consequences of common genetic variability of TRPM6 and TRPM7 on mRNA expression, splicing, and degradation and metabolic profiles.
The strengths of our study include a prospective study design with up to 10 years of follow-up, careful collection of DNA samples and baseline variables, and an effective matching strategy used in a well-characterized study population. However, several limitations deserve further consideration. First, our study had relatively small sample size and did not have the power to assess modest genetic effects or interactions. The associations we identified could have been caused by chance due to statistical fluctuation; however, consistency with laboratory findings makes our results plausible. Second, population stratification may be a concern but unlikely to explain our findings, because our populations are racially homogeneous, with the majority of the participants being white (>92.5%). Third, we lacked stable and reliable measures of extracellular or intracellular magnesium status. Serum magnesium may be a good indicator for severe magnesium deficiency but is not sensitive to suboptimal magnesium status. Finally, findings in this cohort of women of predominantly European descent could not address population-specific genetic effects.
Our results suggest that two common non-synonymous TRPM6 coding region variants might interact with magnesium intake in determining the risk of type 2 diabetes. However, our findings could be caused by chance due to multiple comparisons and insufficient statistical power; additional studies are needed to confirm these results and to further characterize the effects of genetic variation in TRPM6 on magnesium homeostasis.
This ancillary study of diabetes was supported by grant DK066401 from the National Institutes of Health, Bethesda, MD. The Women's Health Study is supported by grants HL 043851, HL 080467 and CA 047988 from the National Institutes of Health, Bethesda, MD. Dr. Song is supported by a grant (K01-DK078846) from the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institutes of Health, Bethesda, MD. The funding sources had no role in the study conduct and analysis. We thank Dr. Lixia Yue at University of Connecticut Health Center for invaluable advice and comments.
- Saris NE, Mervaala E, Karppanen H, Khawaja JA, Lewenstam A: Magnesium. An update on physiological, clinical and analytical aspects. Clin Chim Acta. 2000, 294 (1–2): 1-26.View ArticlePubMedGoogle Scholar
- Barbagallo M, Dominguez LJ, Galioto A, Ferlisi A, Cani C, Malfa L, Pineo A, Busardo A, Paolisso G: Role of magnesium in insulin action, diabetes and cardio-metabolic syndrome X. Mol Aspects Med. 2003, 24: 39-52.View ArticlePubMedGoogle Scholar
- Song Y, Ridker PM, Manson JE, Cook NR, Buring JE, Liu S: Magnesium intake, C-reactive protein, and the prevalence of metabolic syndrome in middle-aged and older U.S. women. Diabetes Care. 2005, 28 (6): 1438-1444.View ArticlePubMedGoogle Scholar
- Song Y, Li TY, van Dam RM, Manson JE, Hu FB: Magnesium intake and plasma concentrations of markers of systemic inflammation and endothelial dysfunction in women. Am J Clin Nutr. 2007, 85 (4): 1068-1074.PubMedGoogle Scholar
- Song Y, Ford ES, Manson JE, Liu S: Relations of magnesium intake with metabolic risk factors and risks of type 2 diabetes, hypertension, and cardiovascular disease: A critical appraisal. Current Nutrition & Food Science. 2005, 1 (3): 231-243.View ArticleGoogle Scholar
- Song Y, Manson JE, Buring JE, Liu S: Dietary magnesium intake in relation to plasma insulin levels and risk of type 2 diabetes in women. Diabetes Care. 2004, 27 (1): 59-65.View ArticlePubMedGoogle Scholar
- Chubanov V, Waldegger S, Mederos y, Schnitzler M, Vitzthum H, Sassen MC, Seyberth HW, Konrad M, Gudermann T: Disruption of TRPM6/TRPM7 complex formation by a mutation in the TRPM6 gene causes hypomagnesemia with secondary hypocalcemia. Proc Natl Acad Sci USA. 2004, 101 (9): 2894-2899.View ArticlePubMedPubMed CentralGoogle Scholar
- Montell C: The TRP superfamily of cation channels. Sci STKE. 2005, 2005 (272): re3-PubMedGoogle Scholar
- Takezawa R, Schmitz C, Demeuse P, Scharenberg AM, Penner R, Fleig A: Receptor-mediated regulation of the TRPM7 channel through its endogenous protein kinase domain. Proc Natl Acad Sci USA. 2004, 101 (16): 6009-6014.View ArticlePubMedPubMed CentralGoogle Scholar
- Voets T, Nilius B, Hoefs S, Kemp van der AW, Droogmans G, Bindels RJ, Hoenderop JG: TRPM6 forms the Mg2+ influx channel involved in intestinal and renal Mg2+ absorption. J Biol Chem. 2004, 279 (1): 19-25.View ArticlePubMedGoogle Scholar
- Schlingmann KP, Sassen MC, Weber S, Pechmann U, Kusch K, Pelken L, Lotan D, Syrrou M, Prebble JJ, Cole DE, Metzger DL, Rahman S, Tajima T, Shu SG, Waldegger S, Seyberth HW, Konrad M: Novel TRPM6 mutations in 21 families with primary hypomagnesemia and secondary hypocalcemia. J Am Soc Nephrol. 2005, 16 (10): 3061-3069.View ArticlePubMedGoogle Scholar
- Walder RY, Landau D, Meyer P, Shalev H, Tsolia M, Borochowitz Z, Boettger MB, Beck GE, Englehardt RK, Carmi R, Sheffield VC: Mutation of TRPM6 causes familial hypomagnesemia with secondary hypocalcemia. Nat Genet. 2002, 31 (2): 171-174.View ArticlePubMedGoogle Scholar
- Schlingmann KP, Weber S, Peters M, Niemann Nejsum L, Vitzthum H, Klingel K, Kratz M, Haddad E, Ristoff E, Dinour D, Syrrou M, Nielsen S, Sassen M, Waldegger S, Seyberth HW, Konrad M: Hypomagnesemia with secondary hypocalcemia is caused by mutations in TRPM6, a new member of the TRPM gene family. Nat Genet. 2002, 31 (2): 166-170.View ArticlePubMedGoogle Scholar
- Schlingmann KP, Konrad M, Seyberth HW: Genetics of hereditary disorders of magnesium homeostasis. Pediatr Nephrol. 2004, 19 (1): 13-25.View ArticlePubMedGoogle Scholar
- Ryazanova LV, Dorovkov MV, Ansari A, Ryazanov AG: Characterization of the protein kinase activity of TRPM7/ChaK1, a protein kinase fused to the transient receptor potential ion channel. J Biol Chem. 2004, 279 (5): 3708-3716.View ArticlePubMedGoogle Scholar
- Schmitz C, Dorovkov MV, Zhao X, Davenport BJ, Ryazanov AG, Perraud AL: The channel kinases TRPM6 and TRPM7 are functionally nonredundant. J Biol Chem. 2005, 280 (45): 37763-37771.View ArticlePubMedGoogle Scholar
- Groenestege WM, Hoenderop JG, Heuvel van den L, Knoers N, Bindels RJ: The epithelial Mg2+ channel transient receptor potential melastatin 6 is regulated by dietary Mg2+ content and estrogens. J Am Soc Nephrol. 2006, 17 (4): 1035-1043.View ArticlePubMedGoogle Scholar
- Shalev H, Phillip M, Galil A, Carmi R, Landau D: Clinical presentation and outcome in primary familial hypomagnesaemia. Arch Dis Child. 1998, 78 (2): 127-130.View ArticlePubMedPubMed CentralGoogle Scholar
- Buring JE, Hennekens CH: The Women's Health Study: summary of the study design. J Myocard Ischemia. 1992, 4: 27-29.Google Scholar
- Rexrode KM, Lee IM, Cook NR, Hennekens CH, Buring JE: Baseline characteristics of participants in the Women's Health Study. J Womens Health Gend Based Med. 2000, 9 (1): 19-27.View ArticlePubMedGoogle Scholar
- Wacholder S, Rothman N, Caporaso N: Population stratification in epidemiologic studies of common genetic variants and cancer: quantification of bias. J Natl Cancer Inst. 2000, 92 (14): 1151-1158.View ArticlePubMedGoogle Scholar
- Watt BK, Merrill AL: Composition of Foods: Raw, Processed, Prepared, 1963–1992. Agriculture Handbook no. 8. 1993, US Department of Agriculture, DC, US Govt Printing OfficeGoogle Scholar
- Willett WC, Stampfer MJ: Total energy intake: implications for epidemiologic analyses. Am J Epidemiol. 1986, 124: 17-27.PubMedGoogle Scholar
- Willett WC: Nutritional Epidemiology. 1998, New York: Oxford University PressView ArticleGoogle Scholar
- Ascherio A, Hennekens C, Willett WC, Sacks F, Rosner B, Manson J, Witteman J, Stampfer MJ: Prospective study of nutritional factors, blood pressure, and hypertension among US women. Hypertension. 1996, 27 (5): 1065-1072.View ArticlePubMedGoogle Scholar
- Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 1997, 20 (7): 1183-1197.Google Scholar
- Song Y, Manson JE, Buring JE, Liu S: A prospective study of red meat consumption and type 2 diabetes in middle-aged and elderly women: the women's health study. Diabetes Care. 2004, 27 (9): 2108-2115.View ArticlePubMedGoogle Scholar
- Ding EL, Song Y, Manson JE, Pradhan AD, Buring JE, Liu S: Accuracy of administrative coding for type 2 diabetes in children, adolescents, and young adults. Diabetes Care. 2007, 30 (9): e98-author reply e99.View ArticlePubMedGoogle Scholar
- Hwang DL, Yen CF, Nadler JL: Insulin increases intracellular magnesium transport in human platelets. J Clin Endocrinol Metab. 1993, 76: 549-553.PubMedGoogle Scholar
- Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D: The structure of haplotype blocks in the human genome. Science. 2002, 296 (5576): 2225-2229.View ArticlePubMedGoogle Scholar
- Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21 (2): 263-265.View ArticlePubMedGoogle Scholar
- Kraft P, Cox DG, Paynter RA, Hunter D, De Vivo I: Accounting for haplotype uncertainty in matched association studies: a comparison of simple and flexible techniques. Genet Epidemiol. 2005, 28 (3): 261-272.View ArticlePubMedGoogle Scholar
- Stram DO, Leigh Pearce C, Bretsky P, Freedman M, Hirschhorn JN, Altshuler D, Kolonel LN, Henderson BE, Thomas DC: Modeling and E-M estimation of haplotype-specific relative risks from genotype data for a case-control study of unrelated individuals. Hum Hered. 2003, 55 (4): 179-190.View ArticlePubMedGoogle Scholar
- Ferrer-Costa C, Gelpi JL, Zamakola L, Parraga I, de la Cruz X, Orozco M: PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics. 2005, 21 (14): 3176-3178.View ArticlePubMedGoogle Scholar
- Sunyaev S, Ramensky V, Koch I, Lathe W, Kondrashov AS, Bork P: Prediction of deleterious human alleles. Hum Mol Genet. 2001, 10 (6): 591-597.View ArticlePubMedGoogle Scholar
- Ng PC, Henikoff S: SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003, 31 (13): 3812-3814.View ArticlePubMedPubMed CentralGoogle Scholar
- Jeanmougin F, Thompson JD, Gouy M, Higgins DG, Gibson TJ: Multiple sequence alignment with Clustal X. Trends Biochem Sci. 1998, 23 (10): 403-405.View ArticlePubMedGoogle Scholar
- De Bakker PI, Graham RR, Altshuler D, Henderson BE, Haiman CA: Transferability of tag SNPs to capture common genetic variation in DNA repair genes across multiple populations. Pac Symp Biocomput. 2006, 478-486.Google Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/10/4/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.