Published online before print February 7, 2005, doi: 10.1161/01.HYP.0000154685.54766.2d
(Hypertension. 2005;45:793.)
© 2005 American Heart Association, Inc.
Original Articles |
Genomic Susceptibility Loci for Brain Atrophy in Hypertensive Sibships From the GENOA Study
From the Division of Nephrology and Hypertension (S.T.T.), Department of Internal Medicine; the Division of Biostatistics (M.d.), Department of Health Sciences Research; and the Department of Diagnostic Radiology (C.R.J.), Mayo Clinic and Foundation, Rochester, Minn; the Division of Geriatrics (T.H.M.), Department of Medicine, University of Mississippi Medical Center, Jackson; the Department of Epidemiology (S.L.R.K.), University of Michigan, Ann Arbor; and the Human Genetics Center and Institute of Molecular Medicine (M.F., E.B.), University of Texas-Houston Health Science Center, Houston.
Correspondence to Stephen T. Turner, MD, Division of Nephrology and Hypertension, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. E-mail turner.stephen{at}mayo.edu
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Abstract |
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We measured 366 microsatellite markers genome-wide to search for loci contributing to subcortical white matter ischemic damage (leukoariosis) and brain atrophy in 488 non-Hispanic white subjects (193 men, 295 women; mean age±SD=64.1±7.7 years; 79% hypertensive) from 223 sibships recruited through
2 members with essential hypertension diagnosed before age 60. Leukoariosis was quantitated by magnetic resonance imaging (MRI), brain atrophy by the difference between intracranial and brain volumes, and calculated mean arterial pressure and pulse pressure provided measures of steady-state level and pulsatile components of blood pressure. After adjustment for sex and age, variance components models estimated significant heritability of leukoariosis (0.72), brain atrophy (0.52), mean arterial pressure (0.084), and pulse pressure (0.294) (P<0.0001 for each trait). Univariate maximum logarithm of odds scores (MLS) were observed for leukoariosis on chromosome 5 (MLS=1.91; P=0.00150); for brain atrophy on 1q and 17p (MLS=2.76, P=0.00018); for mean arterial pressure on 11p (MLS=1.57; P=0.00354); and for pulse pressure on 11p (MLS=3.02; P=0.00070). Bivariate linkage analyses provided evidence of loci with pleiotropic effects on brain atrophy and pulse pressure on chromosomes 11p (MLS = 5.07 at 16 cM; P=0.00001) and 16q (MLS of 4.56 at 124 cM; P=0.00003). These results demonstrate usefulness of multivariate linkage analyses to detect loci with pleiotropic effects on genetically correlated traits and suggest overlap between the genes influencing blood pressure and those contributing to brain atrophy.
Key Words: blood pressure • brain • genetics
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Introduction |
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Hypertension in midlife contributes to cognitive dysfunction late in life, largely caused by to ischemic brain injury.1 Genetic susceptibility to hypertension-related brain injury has been demonstrated,2 but it is unknown whether the genes responsible are the same as those influencing blood pressure (BP). Multivariate linkage analysis provides an approach to identify genes influencing 2 or more correlated traits.3 A major advantage over separate univariate analyses is greater statistical power to identify loci whose effects are too small to be detected in single-trait analyses. Mapping of related traits to the same chromosomal region may represent pleiotropic effects of a single gene or of tightly-clustered loci that each influence different traits.4
The goal of this study was to scan the human genome for regions influencing ischemic damage to the subcortical white matter (leukoariosis) and brain atrophy. Both brain traits have been associated with hypertension and with cognitive dysfunction.5,6 Moreover, measures of brain anatomy and ischemic brain injury appear to be heritable.2,7 In addition to performing genome-wide univariate linkage analyses, we leveraged the power of multivariate linkage analyses by also conducting bivariate linkage analyses of each brain measure with a measure of steady-state BP level, ie, mean arterial pressure (MAP), and a measure of the pulsatile component of BP, ie, pulse pressure (PP). The bivariate linkage analyses further provided an opportunity to formally assess whether there may be overlap between the genes influencing BP and those determining susceptibility to hypertension-related brain injury.
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Methods |
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Subjects
The 488 non-Hispanic white subjects in the present study (193 men and 295 women) were members of sibships initially enrolled in Rochester, Minn between July 1997 and August 1999 in the Genetic Epidemiology Network of Arteriopathy (GENOA) of the Family Blood Pressure Program (FBPP).8 The FBPP, sponsored by the National Heart, Lung, and Blood Institute, is designed to identify and characterize genetic determinants of hypertension and its associated cardiac and renal complications. An ancillary study, Genetics of Microangiopathic Brain Injury (GMBI), was designed to extend the assessment of target organ damage to include ischemic damage to the subcortical white matter of the brain determined by magnetic resonance imaging (MRI), referred to as leukoariosis. For the GENOA–Rochester cohort, the Mayo Clinic diagnostic index and medical record linkage system of the Rochester Epidemiology Project were used to identify non-Hispanic white residents of Olmsted County with a diagnosis of essential hypertension made before age 60. When an eligible proband had at least 1 full sibling who also reported hypertension, all available members of the sibships were invited to the Mayo Clinic for an initial study visit. Between December 2000 and October 2002, 815 of the original 1583 GENOA–Rochester participants returned for a second study visit; and between August 2001 and August 2004, participants also underwent MRI of the brain if they had no history of stroke or neurological disease, implanted metal devices, and at least 1 sibling who also qualified and agreed to undergo MRI. Of the subjects who had undergone MRI at the time of the present analysis, we excluded only those in whom the MRI detected unexpected cerebral infarctions, masses, or metallic artifacts, or measurements of other analysis variables were missing. Study protocols were approved by the human studies review board of the Mayo Clinic, and informed consent was obtained from all participants.
Study Protocol
Height was measured by a wall stadiometer, weight by electronic balance, and body mass index was calculated in units of kg · m–2. Blood pressure measurements were made with random zero sphygmomanometers (Hawksley and Sons, West Sussex, England) and cuffs appropriate for arm size. Three readings were taken from the right arm after the participant rested in the sitting position for at least 5 minutes; the last 2 readings were averaged for the analysis. Each prescription medication recorded at the study visit was assigned a code based on mechanism of action. The MAP, calculated as ([systolic BP +2 · diastolic BP]/3), and PP, calculated as (systolic BP – diastolic BP), provided measures of the steady-state level and pulsatile components of BP. The diagnosis of hypertension was confirmed if a previous diagnosis of hypertension and use of prescription antihypertensive medication were reported, or if the average systolic or diastolic BP was 140 mm Hg or 90 mm Hg, respectively.8
Brain MRI and Determination of Leukoariosis Volume
All scans were performed on identically equipped Signa 1.5-Tesla MRI scanners (GE Medical Systems, Waukesha Wisc) under the supervision of Mayo Clinic neuroradiologists. Symmetric head positioning with respect to orthogonal axes was verified by a series of short scout scans. Total intracranial volume was measured from T1-weighted sagittal images, each set consisting of 192 contiguous 5-mm-thick slices with no interslice gap, obtained with the following sequence: repetition time=500 ms, echo time=20 ms, repetitions=2, time=2.5 min, and field of view=24 cm. Brain and leukoariosis volumes were determined from axial fluid-attenuated inversion recovery images, each set consisting of 192 contiguous 3-mm interleaved slices with no interslice gap, obtained with the following sequence: echo time=144.8 ms, inversion time=2600 ms, repetition time=11,000 ms, bandwidth=±32 kHz, echo train length=22, time=8 min, field of view=24 cm, and matrix=256x192. A fluid-attenuated inversion recovery image is a T2-weighted image with the signal of cerebrospinal fluid (colony-stimulating factor) nulled, such that brain pathology appears as the brightest intracranial tissue. Interactive image processing steps were performed by a research associate who had no knowledge of the subjects’ personal or medical histories or biological relationships. A fully automated algorithm was used to segment each slice of the edited multislice fluid-attenuated inversion recovery sequence into voxels assigned to 1 of 3 categories—brain, colony-stimulating factor, or leukoariosis. The mean absolute error of this method is 1.4% for brain volume and 6.6% for leukoariosis volume, and the mean test–retest coefficient of variation is 0.3% for brain volume and 1.4% for leukoariosis volume. The difference between total intracranial volume and brain volume provided a measure of brain atrophy.
Laboratory Measurements
Blood was drawn after an overnight fast of at least 8 hours. A set of 381 microsatellite markers distributed across the 22 autosomes (CHLC/Weber screening set 9.0) were genotyped by standard polymerase chain reaction methods by the Mammalian Genotyping Center of the Marshfield Medical Research Foundation (Marshfield, Wisc), which provided the ordering of markers and their genetic map distances.9 Inconsistencies of the genotypes with pedigree structure were identified by the Lange and Goradia algorithm as implemented in the PedCheck software.10,11 Instances that could not be resolved as genotyping errors were considered as missing data.
Statistical Analyses
Because the distribution of leukoariosis was positively skewed the values were log-transformed for the analyses. The logarithm transformed measure of leukoariosis, and the measures of brain atrophy, MAP, and PP were adjusted for sex and age by incorporating these covariates in the genetic models. Genetic and environmental correlations between the age- and sex-adjusted measures of brain anatomy and BPs were estimated by variance decomposition using maximum likelihood methods,12 and phenotypic correlations between traits were calculated based on the genetic and environmental correlations.13
Univariate and bivariate quantitative linkage analyses were performed using the Expectation Maximization Variance Components software program,14 which uses a multivariate variance components approach that is an extension of the univariate approach described by Amos15 and de Andrade et al.16 The multipoint identity-by-descent sharing among pairs of relatives were calculated using the SIMWALK2 software program, which estimates multipoint identity-by-descent probabilities using an alternate Markov chain Monte Carlo algorithm that accommodates large sibships and provides estimates in agreement with deterministic methods.17 Calculations were based on the pedigree relationships for 1413 of the original GENOA–Rochester participants in whom measurements of genome-wide markers were available.
Additional details regarding implementation of the multivariate linkage analysis methods have been previously published.3 To test for genetic linkage, a likelihood ratio test (LRT) was used, in which the LRT is defined as –2 · ([log likelihood under the null hypothesis] – [log likelihood under the alternative hypothesis]). Under the null hypothesis, the linked gene parameter(s) are restricted to equal and (
g.ij are zero for all traits i and j). The asymptotic distribution of the multivariate test is a mixture of 
2 tests under the null hypothesis.18 For the bivariate linkage analysis, the distribution of the bivariate test that the linked gene components and covariance are zero is a mixture of 1/4 02, 1/2 12, and 1/4 32, as described by Self and Liang.18
All logarithm of odds (LOD) scores for the multipoint linkage analyses were calculated from the LRT values as LRT/(2 · log10). In the univariate analyses, we considered multipoint maximum LOD scores (MLS) 3.00 as statistically significant evidence of linkage, 2.00 as suggestive evidence, and 1.30 as tentative evidence of linkage.19,20 These MLS thresholds correspond to probability values of 0.0001, 0.001, and 0.007, respectively. Bivariate quantitative linkage analyses were then performed for pair-wise combinations between traits. To achieve levels of statistical significance comparable to the univariate thresholds, we considered a bivariate the MLS 4.00 as statistically significant evidence of linkage (ie, P
0.0001), 2.87 as suggestive evidence (ie, P0.001), and 2.06 as tentative evidence of linkage (P0.007). The higher bivariate thresholds were calculated using asymptotic values of a mixture of 1/4 02, 1/2 12, and 1/4 32. We inferred evidence of chromosomal regions with pleiotropic effects when the bivariate MLS met the criteria for at least tentative evidence of linkage and its nominal probability value was less than the univariate maxima at the same location.
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Results |
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Consistent with the sampling of sibships with
2 hypertensive members, 79.1% of subjects had diagnosed hypertension and 75.6% were treated with antihypertensive medications. These and other descriptive characteristics are summarized by sex in Table 1.
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Estimated heritability was highest for the log transformed measure of leukoariosis (0.716), followed by brain atrophy (0.522), PP (0.294), and MAP (0.084); each heritability estimate was significantly greater than zero (P<0.0001). The log transformed leukoariosis volume was more genetically correlated with MAP (0.624) than with PP (–0.107), whereas brain atrophy was more genetically correlated with PP (0.593) than with MAP (0.011). In contrast to the relatively strong genetic correlation between log leukoariosis and MAP or between brain atrophy and PP, the genetic correlations were weaker between log leukoariosis and brain atrophy (0.064) and between MAP and PP (0.294).
The univariate linkage analyses of log leukoariosis demonstrated tentative evidence of linkage, defined by maximum MLSs of 1.30 to 1.99, on chromosomes 1, 5, 9, 11, 12, 13, and 15 (Table 2). For brain atrophy, there was tentative evidence of linkage on chromosome 6 and suggestive evidence of linkage, defined by MLSs of 2.00 to 2.99, on chromosomes 1, 16, and 17. For MAP, there was tentative evidence of linkage only on chromosome 11. For PP, there was tentative evidence of linkage on chromosome 7 and significant evidence of linkage, defined by MLS 3.00, on chromosome 11.
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The bivariate analyses demonstrated 2 chromosomal regions in which there was significant evidence of genes with pleiotropic effects on brain atrophy and PP (Table 2 and Figures 1 and 2
). On chromosome 11 where the univariate linkage analyses had shown significant evidence of linkage for PP (MLS=3.02), the bivariate MLS for brain atrophy and PP increased to a maximum value of 5.07 (at 16 cM, P=0.00001). In addition, on chromosome 16 where the univariate linkage analyses had shown suggestive evidence of linkage for brain atrophy (MLS=2.70), the bivariate MLS for atrophy and PP increased to a maximum value of 4.56 (at 124 cM, P=0.00003). Both bivariate MLSs exceeded the threshold for significant evidence of linkage, defined by bivariate MLSs 4.00, and both achieved greater statistical significance than the separate univariate MLSs (Table 2).
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There were 5 regions in which the bivariate MLSs satisfied the criteria for suggestive evidence of linkage (ie, bivariate MLS=2.87 to 3.99 and greater statistical significance than the separate univariate MLSs) for log leukoariosis and brain atrophy on chromosomes 12 and 16, for log leukoariosis and MAP on chromosome 5, and for brain atrophy and MAP on chromosomes 1 and 11 (Table 2). In addition, there were 3 regions in which the bivariate MLS satisfied the criteria for tentative evidence of linkage (ie, bivariate MLS=2.06 to 2.86 and greater statistical significance than the separate univariate MLSs) for log leukoariosis and MAP on chromosomes 10 and 15; for log leukoariosis and PP on chromosome 1, and for brain atrophy and PP on chromosome 5. In all other chromosomal regions, the bivariate MLSs did not achieve the bivariate threshold for tentative evidence of linkage or the associated probability values of the bivariate MLSs were less statistically significant than the univariate MLSs (not shown).
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Discussion |
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To our knowledge, there are no previous reports of genome-wide linkage analyses for leukoariosis or brain atrophy. Most genetic studies of quantitative MR measures of brain anatomy have been conducted in relatively small samples of twins.21 Although twin studies have indicated genetic influences on several measures of brain anatomy,7 only recently has the genetic influence on leukoariosis been estimated in large population-based samples.22 We believe this study provides the first estimate of the heritability of brain atrophy, which is another recognized risk factor for cognitive impairment in the elderly.5,23,24
The bivariate linkage analyses of brain atrophy and PP provided the strongest evidence of genes with pleiotropic effects on measures of BP and brain injury. Of the regions on chromosomes 11 and 16 that satisfied the bivariate criteria for significant evidence of linkage, the chromosome 11p15 region was detected in the univariate linkage analyses of PP but not brain atrophy, whereas the chromosome 16q region was detected in the univariate linkage analyses of brain atrophy but not PP. Results of these analyses are consistent with simulations demonstrating greater statistical power of multivariate than univariate linkage analyses25 and suggest that bivariate linkage analyses may improve the ability to localize genes for BP and BP-related brain injury beyond what is possible by univariate linkage analyses. More specifically, they support the pulsatile component of BP, measured by PP, as an "intermediate phenotype" for brain atrophy by indicating that some of the genes contributing to this measure of brain injury may overlap with those influencing BP.
On chromosome 11p15, the univariate MLS for PP achieved statistical significance on a genome-wide basis (Table 2 and Figure 1). Several candidate genes hypothesized to influence BP are located in this region, including insulin (at 4 cM from 11pter), calcitonin gene-related peptide (at 21 cM), and adrenomedullin (at 15 cM), a hypotensive peptide with homology to calcitonin gene-related peptide.26 Previous genome scans have identified evidence of linkage in this region for total and high-density lipoprotein cholesterol, but not for PP or other measures of BP.27 The chromosome 16q23–24 region demonstrated suggestive univariate linkage evidence for brain atrophy, but no evidence of linkage for PP (Table 2 and Figure 2). To our knowledge, this region has not been previously implicated in linkage analyses of BP or other risk factors for vascular disease, and we are unaware of reports of associations of genes in this region with measures of brain injury. The analyses we performed do not distinguish whether the bivariate evidence of linkage reflects a single locus with effects on 2 different traits or 2 loci in the same region each influencing a different trait.4
The more modest MLSs that provided only "suggestive" or "tentative" evidence of linkage for leukoariosis and for MAP are a limitation of the present study. This is not surprising in view of the complexity of the phenotypes, which are undoubtedly influenced by many genetic and environmental factors, and is consistent with results of genome-wide linkage analyses for other quantitative traits.28 Variation at multiple genetic loci may have only small effects on these phenotypes, and the magnitude of effects may also be context dependent, differing across backgrounds of other genetic and environmental factors. Because our sample only included non-Hispanic white subjects from sibships with 2 members with essential hypertension diagnosed before age 60 years, extension of inferences to groups of different ethnicity or background risk for vascular disease may be inappropriate. Replication of findings in independent samples or confirmation in a larger GENOA cohort would be prudent before undertaking the arduous task of attempting to determine which among the numerous positional candidate genes may be responsible for the linkage peaks on chromosomes 11 and 16.
Perspective
Results of the present study suggests that multivariate linkage analyses of measures of BP-associated brain injury and genetically correlated measures of BP may improve the ability to localize genes acting through pathways of known risk factors or through novel pathways that have not or cannot be directly measured in vivo.
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Acknowledgments |
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This work was supported by United States Public Health Service Grants from the National Institutes of Health U01 HL 54464, U01 HL 54457, U01 HL 54481, R01 HL 71917, and M01 RR 00585. We appreciate the expert technical support provided by Jodie Van De Rostyne, Debra Gearhart, Maria Shiung, and Curtis Olswold.
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Footnotes |
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This paper was sent to Curt D. Sigmund, associate editor, for review by expert referees, editorial decision, and final disposition.
Received October 12, 2004; first decision November 8, 2004; accepted December 17, 2004.
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