Linkage Analysis Using Co-Phenotypes in the BRIGHT Study Reveals Novel Potential Susceptibility Loci for Hypertension
2006; Elsevier BV; Volume: 79; Issue: 2 Linguagem: Inglês
10.1086/506370
ISSN1537-6605
AutoresChris Wallace, Mingzhan Xue, Stephen Newhouse, Ana Carolina B. Marçano, Abiodun Onipinla, Beverley Burke, Johannie Gungadoo, Richard Dobson, Morris J. Brown, John Connell, Anna F. Dominiczak, G.M. Lathrop, John Webster, Martin Farrall, Charles A. Mein, Nilesh J. Samani, Mark J. Caulfield, David Clayton, Patricia B. Munroe,
Tópico(s)Renin-Angiotensin System Studies
ResumoIdentification of the genetic influences on human essential hypertension and other complex diseases has proved difficult, partly because of genetic heterogeneity. In many complex-trait resources, additional phenotypic data have been collected, allowing comorbid intermediary phenotypes to be used to characterize more genetically homogeneous subsets. The traditional approach to analyzing covariate-defined subsets has typically depended on researchers’ previous expectations for definition of a comorbid subset and leads to smaller data sets, with a concomitant attrition in power. An alternative is to test for dependence between genetic sharing and covariates across the entire data set. This approach offers the advantage of exploiting the full data set and could be widely applied to complex-trait genome scans. However, existing maximum-likelihood methods can be prohibitively computationally expensive, especially since permutation is often required to determine significance. We developed a less computationally intensive score test and applied it to biometric and biochemical covariate data, from 2,044 sibling pairs with severe hypertension, collected by the British Genetics of Hypertension (BRIGHT) study. We found genomewide-significant evidence for linkage with hypertension and several related covariates. The strongest signals were with leaner-body-mass measures on chromosome 20q (maximum LOD=4.24) and with parameters of renal function on chromosome 5p (maximum LOD=3.71). After correction for the multiple traits and genetic locations studied, our global genomewide P value was .046. This is the first identity-by-descent regression analysis of hypertension to our knowledge, and it demonstrates the value of this approach for the incorporation of additional phenotypic information in genetic studies of complex traits. Identification of the genetic influences on human essential hypertension and other complex diseases has proved difficult, partly because of genetic heterogeneity. In many complex-trait resources, additional phenotypic data have been collected, allowing comorbid intermediary phenotypes to be used to characterize more genetically homogeneous subsets. The traditional approach to analyzing covariate-defined subsets has typically depended on researchers’ previous expectations for definition of a comorbid subset and leads to smaller data sets, with a concomitant attrition in power. An alternative is to test for dependence between genetic sharing and covariates across the entire data set. This approach offers the advantage of exploiting the full data set and could be widely applied to complex-trait genome scans. However, existing maximum-likelihood methods can be prohibitively computationally expensive, especially since permutation is often required to determine significance. We developed a less computationally intensive score test and applied it to biometric and biochemical covariate data, from 2,044 sibling pairs with severe hypertension, collected by the British Genetics of Hypertension (BRIGHT) study. We found genomewide-significant evidence for linkage with hypertension and several related covariates. The strongest signals were with leaner-body-mass measures on chromosome 20q (maximum LOD=4.24) and with parameters of renal function on chromosome 5p (maximum LOD=3.71). After correction for the multiple traits and genetic locations studied, our global genomewide P value was .046. This is the first identity-by-descent regression analysis of hypertension to our knowledge, and it demonstrates the value of this approach for the incorporation of additional phenotypic information in genetic studies of complex traits. Hypertension (MIM 145500) is a major risk factor for kidney failure, stroke, and cardiovascular disease and is estimated to cause 4.5% of the global disease burden.1World Health Organization International Society of Hypertension Writing Group 2003 World Health Organization (WHO)/International Society of Hypertension (ISH) statement on management of hypertension.J Hypertens. 2003; 21: 1983-1992Crossref PubMed Scopus (2252) Google Scholar A familial disposition to high levels of systolic and diastolic blood pressure has been demonstrated,2Cusi D Bianchi G A primer on the genetics of hypertension.Kidney Int. 1998; 54: 328-342Crossref PubMed Scopus (22) Google Scholar which implies that there is genetic susceptibility to human hypertension. The British Genetics of Hypertension (BRIGHT) study has collected a resource of 1,634 families with at least two affected siblings (i.e., having severe hypertension) drawn from the upper 5% of the U.K. blood pressure distribution. A genomewide linkage scan was performed and identified regions of interest on chromosomes 2, 5, 6, and 9.3Caulfield M Munroe P Pembroke J Samani N Dominiczak A Brown M Benjamin N Webster J Ratcliffe P O’Shea S Papp J Taylor E Dobson R Knight J Newhouse S Hooper J Lee W Brain N Clayton D Lathrop GM Farrall M Connell J MRC British Genetics of Hypertension Study Genome-wide mapping of human loci for essential hypertension.Lancet. 2003; 361: 2118-2123Abstract Full Text Full Text PDF PubMed Scopus (217) Google Scholar Follow-up work has focused attention on chromosome 5q13.4Munroe PB, Wallace C, Xue M, Marçano ACB, Dobson RJ, Onipinla AK, Burke B, Gungadoo J, Newhouse SJ, Pembroke J, Brown M, Dominiczak AF, Samani NJ, Lathrop M, Connell J, Webster J, Clayton D, Farrall M, Mein CA, Caulfield M, MRC British Genetics of Hypertension Study. Increased support for linkage of a novel locus on chromosome 5q13 for essential hypertension in the BRIGHT Study. Hypertension (in press)Google Scholar In common with other complex-trait resources, a variety of phenotypic covariate data, including biometric and biochemical measurements, were collected from these severely affected siblings (see BRIGHT Web site). The aim of a primary genome scan in affected sibling pairs is the detection of regions of excess identical-by-descent (IBD) genetic sharing, but, in complex traits, the presence of genetic heterogeneity and phenocopies may dilute linkage signals. Phenotypic covariate data may carry information about comorbid characteristics, which offers the opportunity to reduce genetic heterogeneity and to identify novel linked loci. Researchers could select a comorbid characteristic, such as body mass, and choose to study leaner individuals with hypertension who might be expected to possess stronger genetic predisposition. This method could augment or unmask linkage signals, but it uses only a portion of the data set and relies upon dichotomization of a quantitative variable, on the basis of an often arbitrary threshold. In addition, application of more-stringent selection thresholds (which lead to higher expected proportions of genetic cases) leads to smaller data subsets, which may, in turn, lead to a corresponding attrition in power. The optimal threshold for a covariate is usually unknown, which leads to the temptation to try multiple thresholds and incur additional penalties due to multiple testing. An approach known as “ordered-subset analysis”5Hauser ER Watanabe RM Duren WL Bass MP Langefeld CD Boehnke M Ordered subset analysis in genetic linkage mapping of complex traits.Genet Epidemiol. 2004; 27: 53-63Crossref PubMed Scopus (144) Google Scholar can be used to identify the optimal threshold, by ranking families by some covariate and by finding the subset that maximizes the LOD score. However, it remains unclear how easily this methodology can be extended to multiple related covariates, such as anthropometric measures. An interesting alternative strategy to subset analysis is to include the quantitative covariate directly in the linkage analysis.6Holmans P Detecting gene-gene interactions using affected sib pair analysis with covariates.Hum Hered. 2002; 53: 92-102Crossref PubMed Scopus (36) Google Scholar, 7Rice JP Rochberg N Neuman RJ Saccone NL Liu KY Zhang X Culverhouse RC Covariates in linkage analysis.Genet Epidemiol. 1999; 17: S691-S695Crossref PubMed Scopus (29) Google Scholar This strategy offers the potential advantage that the within–sib pair covariate similarity and the mean covariate levels may be jointly studied. The results of such maximum-likelihood–based analysis can be conveniently expressed as a LOD score. However, the level at which this LOD corresponds to genomewide significance is not established, and, in practice, permutations of the covariate data are required to determine statistical significance.8Holmans P Zubenko GS Crowe RR DePaulo JR Scheftner WA Weissman MM Zubenko WN Boutelle S Murphy-Eberenz K MacKinnon D McInnis MG Marta DH Adams P Knowles JA Gladis M Thomas J Chellis J Miller E Levinson DF Genomewide significant linkage to recurrent, early-onset major depressive disorder on chromosome 15q.Am J Hum Genet. 2004; 74: 1154-1167Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar This determination requires the repeated maximization of a likelihood at each of many locations across the genome and is computationally slow. Indeed, computational burden becomes an increasing problem as more covariates are considered. In contrast to maximum likelihood, score tests do not require estimation of the full model, so they are considerably faster to implement while maintaining the same local power as likelihood-ratio tests.9Cox DR Hinkley DV Asymptotic theory.in: Theoretical statistics. 1st ed. Chapman and Hall, London1974: 273-363Crossref Google Scholar Thus, they present a particularly attractive method when permutation is a consideration. In this article, we describe the development of a score test for the Rice-Holmans model and its application to multiple phenotypic covariates and genome-scan data from the affected sibling pairs in the BRIGHT study. This application offers the opportunity to fully exploit the extensive phenotypic characterization of this hypertensive resource while controlling for multiple statistical comparisons. The likelihood ratio for observed IBD sharing at any genetic location among a sample of affected sib pairs may be written as LR=Πi∑jzjfˆijfj,where fj and fˆij are the prior and posterior IBD probabilities, respectively, that sib pair i shares j alleles IBD, and where zj is the unknown probability that an affected sib pair shares j alleles IBD. If the IBD sharing of maternal and paternal alleles are assumed to be independent, then zj may be expressed as a function of p, the probability that an affected sib pair share the allele they inherit from a given parent IBD. Assuming no parent-of-origin effect, we write z0=(1-p)2, z1=2p(1-p), and z2=p2. Covariates may be incorporated in the model by setting p=eα+βX1+eα+βX,where X denotes some vector of covariates and α and β are standard regression parameters for the intercept and slope, respectively. Holmans6Holmans P Detecting gene-gene interactions using affected sib pair analysis with covariates.Hum Hered. 2002; 53: 92-102Crossref PubMed Scopus (36) Google Scholar discusses two statistics, T=2ln(LR(αˆ,βˆ)LR(α=0,β=0))and S=2ln(LR(αˆ,βˆ)LR(αˆ,β=0)),(1) where T is a test of linkage allowing for the effects of covariates and S is a test for dependence of IBD sharing on covariates. We consider it likely that a general test of linkage (without covariates) would be performed before a covariate analysis, in which case the latter statistic (a specific test for dependence of IBD sharing on covariate measures) would more likely be of interest. We shall, therefore, focus on developing a score test for this approach, although T is, in fact, a special case. The likelihood for the Rice-Holmans model is10Olson JM A general conditional-logistic model for affected-relative-pair linkage studies.Am J Hum Genet. 1999; 65: 1760-1769Abstract Full Text Full Text PDF PubMed Scopus (113) Google ScholarL∝Πi=1n∑j=02pij(1-pi)(2-j)fˆij/fjwith pi=logit(eα+βXi), where Xi is some vector of covariates measured for sib pair i. We wish to test the null hypothesis H0:β=0 against an alternative H1:β∈C⊆ℜn, where n is the number of covariates under testing, treating α as a nuisance parameter. Note that testing the null hypothesis corresponding to Holmans's T statistic H′0:α=β=0 can be expressed as a special case, with α′=0 and β′=(α,β). Let θ=(α,β) and X′i=(1,Xi). The first and second derivatives of the log likelihood under H0 are, then, dldθ=∑Sθ,i=∑eα(2fˆi2-fˆi1)eα+fˆi1-2fˆi0(1+eα)(fˆi2e2α+fˆi1eα+fˆi0)Xi'and d2ldθ2=∑eα(1+eα)2AiXi′Xi′T,where Ai=[fˆi0(fˆi1-2fˆi0)+2eαfˆi0(2fˆi2-fˆi1)+e2α(fˆi1fˆi0-2fˆi12+4fˆi2fˆi0+fˆi1fˆi2)+2e3αfˆi2(2fˆi0-fˆi1)+e4αfˆi2(fˆi1+-2fˆi2)]÷[(e2αfˆi2+eαfˆi1+fˆi0)2] . Explicit forms for a generalized score test of H1 against H0, with allowance for parameter constraints and nuisance parameters, have been derived.11Boos DD On generalized score tests.Am Stat. 1992; 46: 327-333Google Scholar, 12Lin D Zou F Assessing genomewide statistical significance in linkage studies.Genet Epidemiol. 2004; 27: 202-214Crossref PubMed Scopus (21) Google Scholar, 13Silvapulle M Silvapulle P A score test against one sided alternatives.J Am Stat Assoc. 1995; 90: 342-349Crossref Scopus (97) Google Scholar For the allowance of a nuisance parameter, the likelihood must be maximized under H0 to find the maximum-likelihood estimate of α, α˜. The vector Sθ,i is partitioned into Sα,i and Sβ,i, according to the partitioning of θ. Let A=-1nd2ldθ2|θ=(α˜,0),and similarly partition A, so that A=(Aα,αAα,βAβ,αAβ,β) .Then, the score statistic is given by W=1nUTV-1U-min b∈C [(1nU-Rb)TV-1(1nU-Rb)],where U=ΣUi=ΣSβ,i( α˜,0)-Aα,βA−1α,αSα,i( α˜,0), V=n−1ΣUiUTi, and R=Aβ,β-Aβ,αA−1α,αAα,β. Although the minimization may appear to negate the attractive properties of the score test stated above, this is a special case for which fast algorithms exist14Wollan P Dykstra R Minimizing linear inequality constrained Mahalanobis distances.Appl Stat. 1987; 36: 234-240Crossref Google Scholar and can be solved much more quickly than a general minimization problem. Additionally, the minimum is always 0 when no constraints are placed on β. Rather than assume a distribution for the score statistic (which might asymptotically be χ2 or a mixture of χ2, depending on the constraints15Self SG Liang KY Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions.J Am Stat Assoc. 1987; 82: 605-610Crossref Scopus (1898) Google Scholar), we estimated it empirically by permuting the rows of the covariate matrix. Note that α˜ at any locus is invariate to permutation and, so, needs to be calculated only once. To determine genomewide significance, we compared the maximum observed score statistic with its empirical distribution. Studies will often collect multiple covariates, many of which will be correlated. The permutation procedure described above will generate a genomewide P value only for a single (set of) covariate(s). When multiple tests are being performed, we must take further action to maintain control of the familywise error rate (the probability of at least one false-positive result). A commonly used method is the application of a Bonferroni correction—multiplying each P value by the number of analyses undertaken—but this method is very conservative. A disadvantage common to this method and the more powerful sequential step-down and step-up procedures that have been suggested16Hochberg Y A sharper Bonferroni procedure for multiple tests of significance.Biometrika. 1988; 75: 800-802Crossref Scopus (3741) Google Scholar, 17Holm S A simple sequentially rejective multiple test procedure.Scand J Stat. 1979; 6: 65-70Crossref Google Scholar is that each test is assumed to be independent. In practice, researchers may conduct multiple related tests (for example, in the case of a hypertension study, both of the related covariates serum creatinine and urea may be of interest). We chose to calculate a global P value for the global null hypothesis—that genetic sharing among affected siblings is independent of any covariate—on the basis of the maximum observed score statistic across all genetic loci and covariates. Its distribution is easily estimated empirically (if permutations for each covariate are initiated with the same random seed) by the set of maximum score statistics across all covariates and genetic positions under each permuted data set. One attraction of this method is that it accounts naturally for any correlation structure between the covariates. If a significant result is found, it is of interest to know whether the increased IBD sharing is associated with increased or decreased levels of a covariate. In the context of score tests, this can be indicated by Zk=Uk/nVk for each covariate Xk. Zk may be referred to a standard normal distribution, and its sign corresponds to the gradient of the likelihood surface at the null, so that a positive (or negative) Zk indicates IBD-sharing increases with increasing (or decreasing) covariate k. The 1,634 pedigrees in the BRIGHT study contain 2,044 affected full sibling pairs (3,376 individuals) from whom additional phenotypic covariate data were collected, including biochemical and biometric measures. Ascertainment and methods used for phenotyping and biochemical and urinary analyses are described elsewhere3Caulfield M Munroe P Pembroke J Samani N Dominiczak A Brown M Benjamin N Webster J Ratcliffe P O’Shea S Papp J Taylor E Dobson R Knight J Newhouse S Hooper J Lee W Brain N Clayton D Lathrop GM Farrall M Connell J MRC British Genetics of Hypertension Study Genome-wide mapping of human loci for essential hypertension.Lancet. 2003; 361: 2118-2123Abstract Full Text Full Text PDF PubMed Scopus (217) Google Scholar (BRIGHT Web site). Other measures, including waist/hip ratio and BMI, were derived from these data, with the use of standard formulas. Although total serum calcium is conveniently measured, it is ionized calcium that is physiologically active. We used published formulas to estimate ionized calcium (“corrected calcium”)18Orrell DH Albumin as an aid to the interpretation of serum calcium.Clin Chim Acta. 1971; 35: 483-489Crossref PubMed Scopus (91) Google Scholar and the glomerular filtration rate (GFR),19Levey AS Bosch JP Lewis JB Greene T Rogers N Roth D Modification of Diet in Renal Disease Study Group A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation.Ann Intern Med. 1999; 130: 461-470Crossref PubMed Scopus (13028) Google Scholar which is generally considered to be a better index of renal function than is serum creatinine concentration. Since many of the covariates under study vary with age and sex, each was regressed on age and sex (allowing for an age-sex interaction), and the residuals were used as adjusted covariates in all subsequent analyses. For each adjusted covariate X measured on sibs 1 and 2, we defined Xsum=(X1- X¯)+(X2- X¯) and Xdiff=|X1-X2| as pairwise covariates for the regression model, where X¯ is the mean of X in the entire sample. Thus, we are testing for dependence of IBD sharing on mean covariate levels and/or covariate similarity within a sibling pair. Xsum represents the mean covariate level for the sibling pair, so that βsum≠0 would indicate dependence of genetic similarity on covariate values. Xdiff represents the within–sibling pair covariate difference. If a covariate influences the propensity of a sib pair to exhibit linkage at a particular locus, genetic sharing would also be expected to be higher among siblings with more similar covariates (and would be identified by βdiff<0). The regression parameters were constrained so that α≥0 and βdiff≤0. This means that we did not allow mean genetic sharing to fall below that expected under the null or allow increasing covariate similarity to relate to decreased genetic sharing. A problem common to all quantitative regressions is how to deal with outlying observations that may have a large influence on the test statistic. We decided it was inappropriate to drop outliers, since they may represent individuals with genuine but rare (in our sample) particular disease phenotypes. However, we also do not want to follow up results that depend on just a few families. Therefore, we conducted analyses of raw (Xdiff and Xsum, as defined above) and ranked (Xdiff and Xsum, replaced by their ranks) data that were interpreted in parallel. Only a minority of individuals had complete data for all covariates. It has been shown that statistical inference is more efficient if missing data are replaced by their expectation, given observed data, than if missing observations are dropped.22Dempster AP Laird NM Rubin DB Maximum likelihood from incomplete data via the EM algorithm.J Royal Stat Soc B. 1977; 39: 1-38Google Scholar Since intervariable correlations mean that information about the missing data exists in the complete data set, we imputed values for missing observations, using best-subset regression. Adjusted covariate data and age and sex for each sibling pair were used as explanatory variables, and the sum and difference variables were imputed. Inference using this mixture of observed and imputed data is valid, provided that the variance of any statistic is estimated appropriately, as with the robust-variance estimator described above. A total of 3,254 individuals (60% female) were included in this analysis. The mean age at recruitment was 60 (±SD 9.1) years, and 3,037 (93%) individuals were undergoing some kind of antihypertensive or lipid-lowering treatment, with 1,105 (34%), 398 (12%), and 131 (4%) taking two, three, or four or more distinct medications, respectively. A breakdown of antihypertensive medications is given in table 1. Summary statistics for all covariates studied are shown in table 2. Two covariates, urine albumin and albumin/creatinine ratio, showed strong positive skew and were log transformed.Table 1Antihypertensive/Lipid-Lowering Drug Treatment of Subjects in the BRIGHT StudyMedicationNaNumber of subjects who reported each medication.Proportion (%)β-Blocker1,41743.5Thiazide diuretic1,09933.8ACE inhibitor1,02031.3Dihydropyridine Ca-channel antagonist79424.4Statin34010.4Loop diuretic3039.3Other Ca-channel antagonist1564.9α-Blocker1454.5Centrally acting agent501.5A2-receptor antagonist17.5a Number of subjects who reported each medication. Open table in a new tab Table 2Summary Statistics for Covariates StudiedCovariateaGGT = γ-glutamyl transpeptidase; SBP = systolic blood pressure; DBP = diastolic blood pressure.NbNumber of observations.MeanSDMedianInterquartile RangeSerum biochemistry: Sodium (mmol/liter)3,062138.533.06139.00137.00–140.00 Chloride (mmol/liter)3,062102.033.13102.00100.00–104.00 Urea (mmol/liter)3,0826.071.715.905.00–6.90 Creatinine (μmol/liter)3,08389.5620.4787.0077.00–99.00 Calcium (mmol/liter)3,0792.43.132.432.35–2.51 Corrected calcium (mmol/liter)3,0822.34.132.342.27–2.41 Albumin (g/liter)3,07144.472.8444.0043.00–46.00 GGT (U/liter)3,07134.4430.7526.0019.00–38.00 Urate (mmol/liter)3,071.31.08.31.26–.37 Total cholesterol (mmol/liter)3,1385.581.015.524.90–6.20 Tryglyceride (mmol/liter)3,1382.131.341.801.29–2.57 HDL cholesterol (mmol/liter)3,1381.36.371.321.11–1.58 GFR (ml/min per 1.73 m2)3,07072.2914.2372.1963.19–81.01Urine biochemistry: Sodium (24-h excretion) (mmol)2,45384.2636.2778.0058.00–105.00 Potassium (24-h excretion) (mmol)2,45341.6116.2739.0031.00–49.00 Creatinine (24-h excretion) (mmol)2,4526.393.035.604.30–7.80 Sodium concentration (mmol/liter)2,440141.3359.89132.60100.30–172.65 Potassium concentration (mmol/liter)2,43969.9026.6567.6052.50–83.70 Creatinine concentration (mmol/liter)2,43910.393.869.697.80–12.48 Creatinine clearance (ml/min)2,30882.3329.8079.1763.33–96.81 Albumin (mg/liter)2,45213.7368.475.003.00–8.00 Sodium/potassium ratio2,4532.16.962.001.53–2.61 Urinary albumin/serum creatinine ratio (mg/mmol)2,4504.9520.861.56.90–3.11Biometric measurements: Triceps (cm)2,83118.848.7718.0012.00–25.00 Biceps (cm)2,93014.657.4613.009.00–20.00 Subscapular (cm)2,82719.286.4819.0015.00–23.00 Suprailiac (cm)2,88618.916.4518.0014.00–23.00 Height (m)3,2501.66.091.651.59–1.72 Weight (kg)3,24476.1313.4075.0866.50–85.00 BMI3,24027.613.9327.0025.00–30.00 Mean waist (cm)3,07090.6011.6391.0082.00–99.00 Mean hip (cm)3,069103.838.26103.7098.20–109.00 Waist/hip ratio3,069.87.09.87.80–.94Pulse and blood pressure measurements: Pulse (beats/min)3,25068.4411.9368.0060.00–76.00 SBP at phenotyping (mm Hg)3,254155.8521.15154.00141.00–169.00 DBP at phenotyping (mm Hg)3,25493.5611.3993.0086.00–101.00 SBP at diagnosis (mm Hg)3,101172.4218.36170.00160.00–180.00 DBP at diagnosis (mm Hg)3,101104.578.83103.00100.00–110.00a GGT = γ-glutamyl transpeptidase; SBP = systolic blood pressure; DBP = diastolic blood pressure.b Number of observations. Open table in a new tab We calculated genomewide significance levels, using score statistics and 10,000 permutations for each covariate. On a computer with a 2.6 GHz processor, maximizing the genomewide likelihood one time for a single covariate took ∼70 s. In contrast, permuting the data and calculating score statistics genomewide 10,000 times took only ∼35 min. Several covariates obtained a genomewide significant result (P<.05). To aid interpretation for geneticists who are more familiar with the LOD score in linkage, we also calculated LOD scores, defined by LOD = log10S, where S is as defined in equation (1). We present, in table 3, both P values and LOD scores for those results that were significant under both the raw and ranked analyses. More-complete results—all those that were significant under either analysis—are shown in table 4. Global P values (adjusted for the multiple traits and genetic locations studied) were .049 and .046 for the raw and ranked analyses, respectively.Table 3Results for Traits That Displayed Genomewide Significance (P<.05) for Both Raw and Ranked AnalysesRaw DataRanked DataCovariateLocationMarkerLODZ (Sum)Z (Difference)Minimum PLODZ (Sum)Z (Difference)Minimum PAnthropometric: BMI20q12D20S1072.71−3.09−2.49.02023.04−2.88−2.81.0232 Hip circumference9q21D9S273-D9S1753.09−.91−3.56.02122.91−.83−3.50.0403 Hip circumference20q11-20q13D20S195-D20S1193.26−2.80−3.21.00354.25−1.84−4.11.0015 Weight20q11-20q13D20S195-D20S1783.70−3.33−3.23.00233.96−2.95−3.60.0026Serum chemistry: Creatinine5p13-5q12D5S426-D5S4273.29−4.60−3.19.00153.51−3.77−2.41.0070 GFR5p13-5q12D5S426-D5S4273.703.80−1.29.00323.713.90−1.38.0050 Urea5p13-5q11D5S426-D5S19693.29−4.24−1.68.00593.15−3.89−.76.0149Urine chemistry: 24-h Creatinine13q22D13S156-D13S18122.491.58−2.40.04722.832.22−2.53.0252Note.—Z statistics for the sum and difference covariates may be referred to a standard normal distribution. Open table in a new tab Table 4Results for Traits That Displayed Genomewide Significance (P<.05) for Either Raw or Ranked AnalysisRaw DataRanked DataCovariateLocationMarkerLODZ (Sum)Z (Difference)Minimum PLODZ (Sum)Z (Difference)Minimum PAnthropometric: BMI20q12D20S1072.71−3.09−2.49.02023.04−2.88−2.81.0232 Height21q22.3D21S266…………3.18−1.05−3.84.0145 Hip circumference9q21D9S273-D9S1753.09−.91−3.56.02122.91−.83−3.50.0403 Hip circumference20q11-20q13D20S195-D20S1193.26−2.80−3.21.00354.25−1.84−4.11.0015 Waist circumference9q21.13D9S1752.84−2.30−2.91.0225………… Weight20q11-20q13D20S195-D20S1783.70−3.33−3.23.00233.96−2.95−3.60.0026Serum chemistry: Albumin/creatinine ratio9q33.1D9S1776…………3.05−3.36−2.33.0229 Chloride concentration9q34.3D9S312-D9S1826…………3.43−2.37−2.90.0070 Chloride concentration14q11.2D14S283…………3.15−2.03−2.69.0315 Total cholesterol16p12.3D16S3103…………3.10−3.59−1.23.0285 Corrected calcium2p22-2p21D2S367-D2S2259…………2.85−1.12−3.53.0203 Creatinine5p13-5q12D5S426-D5S4273.29−4.60−3.19.00153.51−3.77−2.41.0070 GFR5p13-5q12D5S426-D5S4273.703.80−1.29.00323.713.90−1.38.0050 Sodium15q26.2D15S657…………2.563.59−.89.0350 Tryglyceride13q31.3D13S2652.65−2.57−4.67.0259………… Tryglyceride13q33.1D13S1583.04−2.03−4.42.0473………… Urea5p13-5q11D5S426-D5S19693.29−4.24−1.68.00593.15−3.89−.76.0149Blood pressure phenotypic measures: SBP at diagnosis15q12D15S9862.32−3.09−.71.0454………… DBP at phenotyping3q13.31D3S12782.76−1.39−3.48.0202………… DBP at phenotyping15q23D15S1312.943.73.11.0136………… Pulse at phenotyping6q27D6S264-D6S5033.00−1.18−3.78.0143………… Pulse at phenotyping15q26.2D15S130…………2.86−2.64−3.12.0273Urine chemistry: Albumin/creatinine ratio1q43D1S2785-D1S28422.65−3.94−1.16.0260………… Albumin/creatinine ratio9q33.2D9S1682…………2.74−3.53−1.55.0298 24-h Creatinine13q22D13S156-D13S18122.491.58−2.40.04722.832.22−2.53.0252 Urine creatinine concentration13q22D13S156-D13S1812…………2.942.22−2.97.0255 24-h Sodium4p16.3D4S4123.24.05−3.75.0106…………Note.—Z statistics for the sum and difference covariates may be referred to a standard normal distribution. Open table in a new tab Note.— Z statistics for the sum and difference covariates may be referred to a standard normal distribution. Note.— Z statistics for the sum and difference covariates may be referred to a standard normal distribution. Follow-up work on our primary genome scan revealed a region of suggestive linkage (LOD=2.5) on chromosome 5q13.4Munroe PB, Wallace C, Xue M, Marçano ACB, Dobson RJ, Onipinla AK, Burke B, Gungadoo J, Newhouse SJ, Pembroke J, Brown M, Dominiczak AF, Samani NJ, Lathrop M, Connell J, Webster J, Clayton D, Farrall M, Mein CA, Caulfield M, MRC British Genetics of Hypertension Study. Increased support for linkage of a novel locus on chromosome 5q13 for essential hypertension in the BRIGHT Study. Hypertension (in press)Google Scholar Inclusion of at-phenotyping systolic (but not diastolic) blood pressure led to an increase in evidenc
Referência(s)