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Human Molecular Genetics Advance Access originally published online on August 4, 2004
Human Molecular Genetics 2004 13(19):2325-2332; doi:10.1093/hmg/ddh237
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Human Molecular Genetics, Vol. 13, No. 19 © Oxford University Press 2004; all rights reserved

Meta-analysis of genome-wide scans for hypertension and blood pressure in Caucasians shows evidence of susceptibility regions on chromosomes 2 and 3

Liisa Koivukoski1, Sheila A. Fisher2, Timo Kanninen3, Cathryn M. Lewis2, Fredrik von Wowern1, Steven Hunt4, Sharon L.R. Kardia5, Daniel Levy6, Markus Perola7, Tuomo Rankinen8, Dabeeru C. Rao9, Treva Rice10, Bonnie A. Thiel11 and Olle Melander1,*

1Department of Endocrinology, Malmö University Hospital, Lund University, SE 205 02 Malmö, Sweden, 2Guy's, King's and St Thomas' School of Medicine, King's College London, Division of Genetics and Development, London, UK, 3Biocomputing Platforms Ltd, Espoo, Finland, 4Cardiovascular Genetics Division, University of Utah School of Medicine, Salt Lake City, Utah, USA, 5Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA, 6NHLBI's Framingham Heart Study, Framingham, Massachusetts, USA, 7Department of Molecular Medicine, KTL, MOLS, Helsinki, Finland, 8Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA, 9Division of Biostatistics and Department of Genetics, Department of Psychiatry, and Department of Mathematics, Washington University in St Louis, St Louis, Missouri, USA, 10Division of Biostatistics, Washington University School of Medicine, St Louis, Missousi, USA and 11Case Western Reserve University, School of Medicine, Cleveland, Ohio, USA

Received June 1, 2004; Revised July 1, 2004; Accepted July 23, 2004


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Individual genome-wide scans of blood pressure (BP) and hypertension (HT) have shown inconsistent results. The aim of this study was to investigate whether there was any consistent evidence of linkage across multiple studies with similar ethnicity. We applied the genome-search meta-analysis method (GSMA) to nine published genome-wide scans of BP (n=5) and HT (n=4) from Caucasian populations. For each study, the genome was divided into 120 bins and ranked according to the maximum evidence of linkage within each bin. The ranks were summed and averaged across studies and significance levels were estimated, on the basis of a distribution function of summed ranks or permutation tests without (PU) or with (PW) a study sample size weighting factor. Chromosome 3p14.1–q12.3 showed consistent evidence of linkage to HT (PU=0.0001 and PW=0.0001), diastolic BP (DBP) (PU=0.007 and PW=0.02), HT and DBP pooled (PU=0.00002 and PW=0.0001) and HT and systolic BP (SBP) pooled (PU=0.0003 and PW=0.0005). Chromosome 2p12–q22.1 showed evidence of linkage to HT (PU=0.003 and PW=0.009), DBP (PU=0.05 and PW=NS), HT and DBP pooled (PU=0.001 and PW=0.004) and HT and SBP pooled (PU=0.001 and PW=0.005). The summed ranks of the HT analysis correlated significantly with those of the DBP (r=0.20, P=0.03) but not with those of the SBP. Both loci showed clustering of significant bins in the analysis of HT and DBP. We conclude that modest or non-significant linkage on chromosomes 3p14.1–q12.3 and 2p12–q22.1 in each individual study translates into genome-wide significant or highly suggestive linkages to HT and DBP in our GSMA analysis.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Human hypertension and blood pressure (BP) variation represent examples of complex genetic traits. Health complications stemming from high blood pressure are well known, but mapping of the genetic loci involved has proven to be a difficult task. To map loci of importance for complex diseases, several different approaches have been undertaken. One of the most promising techniques in clarifying the genetics of complex diseases, such as type 2 diabetes (1), has been genome-wide linkage analysis. This approach has been undertaken in several studies of hypertension and blood pressure variation. However, attempts to find chromosomal regions showing consistent evidence of linkage in multiple studies have been unsuccessful. One potential explanation for this could be differences in the underlying pathophysiological, environmental and genetic factors between the various ethnicities studied. Furthermore, as indicated by the normal distribution of blood pressure in the population, the genetic component of hypertension and blood pressure variation is likely to be composed by the sum of many genes, each of which has only a small to moderate effect. A second possibility for the inconsistent results could thus be that many of the individual studies are underpowered to detect such small gene effects. The inconsistent results may further be explained by differences in analytical methods and study design.

Genome-wide scans of blood pressure variation, as opposed to scans using the qualitative trait of hypertension as the phenotype, assume that genes of importance for population blood pressure variation are also of importance for the risk of hypertension. This is reasonable to believe, as common hypertension susceptibility gene variants can be assumed to be frequently present and to affect blood pressure also within the normotensive segment of the population (2).

A family history of hypertension has been shown to be strongly associated with development of hypertension with early age at onset (3). The strength of the association decreases with increase in age at onset and is negligible when the disease makes its debut after the age of 70 years (3). Thus, in genome-wide scans using the qualitative trait of hypertension as the phenotype, it seems instrumental to include only patients with an age at onset <60 years in order to ensure a significant genetic component in the disease pathogenesis. Additionally, in this age group isolated systolic hypertension, which is likely to have a different etiology than ‘normal’ primary hypertension, is less common than in older populations.

We hypothesized that one reason for the inconsistent results of previous genome-wide scans for blood pressure and hypertension is insufficient power in many of the individual scans in detecting small to moderate gene effects. The aim of our study was to find evidence of linkage occurring consistently across studies using populations of similar ethnicity. To address this we applied the genome-search meta-analysis method (GSMA) (4,5). The power of this method to assess linkage across studies has been supported with simulation studies (5) and several GSMAs (6–11) of other complex diseases.

In addition to analysing hypertension scans and blood pressure scans separately, we pooled the results of published genome scans of blood pressure variation with those of hypertension, assuming that genes affecting blood pressure variation to a large extent are the same as those increasing the risk of hypertension (2).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
The point-wise significant P-values (P<0.05) of the summed ranks and locations of the particular bins for all performed analyses are listed in Tables 1–3. Also shown are the original linkage results of the nine individual genome scans for these regions.


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  		Table 1. P-values for the summed ranks with P<0.05 in the weighted or unweighted combined analysis of hypertension and diastolic blood pressure and hypertension and systolic blood pressure and maximum linkage scores of the original individual genome screens for these regions
 

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  		Table 3. P-values for summed ranks with P<0.05 in the weighted or unweighted analysis of diastolic blood pressure and systolic blood pressure and maximum linkage scores of the original individual genome screens for these regions
 
Chromosome 3p14.1–q12.3 (bin 3.4) obtained genome-wide significant evidence of linkage to hypertension (HT) in both unweighted and weighted analysis (Table 2) and suggestive evidence of linkage to diastolic blood pressure (DBP) in unweighted analysis (Table 3). In accordance with our priori hypothesis that genes affecting the risk of hypertension are largely the same as those affecting blood pressure level we pooled the scans for hypertension with those for blood pressure. We found that chromosome 3p14.1–q12.3 showed genome-wide significant evidence of linkage to hypertension pooled with diastolic blood pressure (HT+DBP) (Table 1 and Fig. 1) and to hypertension pooled with systolic blood pressure (HT+SBP) in the unweighted analysis (Table 1). The evidence of linkage between the 3p14.1–q12.3 locus and HT+DBP is further strengthened by the fact that an adjacent bin (bin 3.5) shows suggestive evidence of linkage (5) (Table 1 and Fig. 1).


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  		Table 2. P-values for the summed ranks with P<0.05 in the weighted or unweighted analysis of hypertension, and maximum linkage scores of the original individual genome screens for these regions
 


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  		Figure 1. Summed ranks for each bin in the unweighted (A) and weighted (B) analysis of HT+DBP. 90%, 95% and 99% confidence limits are shown.

 
In addition, chromosome 2p12–q22.1 (bin 2.5) showed suggestive evidence of linkage to HT, HT+DBP, HT+SBP (Tables 1 and 2) and nominal evidence of linkage to DBP (Table 3). Although generally less significant than the chromosome 3p14.1–q12.3 locus, this region is also supported by some evidence of linkage in the adjacent bin 2.4.

An ordered rank analysis was performed giving a POrd value for each bin. The results of the ordered rank analysis for weighted summed ranks of HT+DBP are presented in Figure 2. The strongest result in bin 3.4 is significant (POrd=0.012), confirming that this bin is likely to harbor susceptibility loci. The POrd value for bin 3.5 (third highest result) was significant, whereas the POrd for bin 2.5 (second highest result) was of borderline significance. The discrepancy between the order of simulated summed ranks and the POrd for bins 3.5 and 2.5 is caused by random variation but both these bins can be considered to have at least suggestive evidence of linkage.



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  		Figure 2. Results of the ordered rank analysis for the combined weighted analysis of HT+DBP, showing observed results (solid line) and the distribution of simulated results for the highest (120), second highest (119), etc, summed rank in the GSMA simulation. Also shown for each bin is the simulated distribution with median, interquartiles and range of values.

 
Since the two adjacent bins 3.4 and 3.5 both showed evidence of linkage (significant and suggestive, respectively), we performed an experimental fused bin analysis of the HT+DBP dataset to refine the location of the susceptibility locus. Bins 3.4 and 3.5 were each divided equally to create two new ‘fusion-bins’, one bin where the outer bin halves of bins 3.4 and 3.5 are merged and one new central bin with the original boundary marker of the two bins in the middle. Reanalysis with these fused bins replacing bins 3.4 and 3.5 show that the central bin is the most significant (P_sumrnk=0.00013). This P-value is of the same magnitude as bin 3.4 in the previous analysis and would indicate that the susceptibility locus is central to this region located in the boundary region of original bins 3.4 and 3.5.

To determine which of the two blood pressure phenotypes (SBP, DBP) provides the strongest evidence for common regions of linkage with hypertension (HT), we tested for correlation between the summed ranks. The summed ranks of the HT and DBP analyses correlated significantly with each other (r=0.20, P=0.03) whereas those of the HT and SBP did not (r=0.07, P=0.42).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
By performing a meta-analysis of genome-wide scans for blood pressure variation and hypertension in Caucasians using the GSMA method, we found strong evidence of linkage to chromosome 3p14.1–q12.3 and some evidence of linkage to chromosome 2p12–q22.1. Interestingly, each of the individual scans showed no or only modest linkage to these two loci, suggesting that the genetic effect mediating the linkage is too weak to be identified by any of the individual scans.

The strength of the GSMA is that it can identify regions that give weak but consistent linkage signals in multiple genome scans. The GSMA cannot and is not designed to identify linked regions that are present only in a subset of scans, for example, because of population specific effects. We therefore focused our meta-analysis only on studies of Caucasians and did not include studies on genetically isolated populations. Thus, other genome scans may identify further linked regions.

Previous observations have shown that a family history of hypertension is of no importance for the development of hypertension after the age of 70 years, but starts to be increasingly important when disease onset occurs below the age of 60 years (3). Thus, in order to ensure a significant genetic component in the disease pathogenesis, we only included hypertension scans in which all patients had an age at onset below 60 years.

The results of the hypertension scans correlated with the diastolic blood pressure results but not with those of systolic blood pressure. One potential explanation for this could be that within the study populations included in the present study, there is a greater overlap between hypertension and diastolic blood pressure genes than between hypertension and systolic blood pressure genes. This seems logical as all patients in the four hypertension scans were diagnosed with hypertension before the age of 60 years (12–15) and in two of the studies were diagnosed before the age of 50 years, with a mean age at onset of around 40 years (12,15). In this age group the prevalence of isolated systolic hypertension is less common than in the elderly, and in the majority of cases the diagnosis is thus based on diastolic blood pressure, i.e., either only elevation in diastolic blood pressure or simultaneous elevations in systolic and diastolic blood pressures. However, it is important to bear in mind that a subset of the subjects in the blood pressure genome-wide scans belongs to the elderly. As diastolic blood pressure starts to decline between the ages of 50 and 60 years, systolic blood pressure continues to increase and part of the linkage to systolic and diastolic blood pressure may be related to age-dependent stiffening of the large arteries and thus reflecting a different pathophysiological mechanism than in the younger parts of the study populations.

Using meta-analysis for combining the results of hypertension and blood pressure genome-wide scans has been pursued previously with a different method [Fisher's (16) modified method (17)] by the Family Blood Pressure Program investigators (18). The analysis was performed on all ethnicities of the four FBPP studies and then separately for the three ethnic groups (whites, African Americans and Asians). However, this study found no regions showing uniformly large effects on hypertension/blood pressure in all populations but several modest peaks, including a region on the short arm of chromosome 2, were identified. This peak overlaps with the region identified in the current GSMA (2p12–q22.1). Evidence for linkage in this region has also been found in other individual genome-wide scans (19,20) not included in the current meta-analysis. Interestingly, the largest genome-wide scan for hypertension performed so far, the BRIGHT study (21), showed nominal evidence of linkage to the chromosome 3 region corresponding to bin 3.4, adding further support to this region as a hypertension susceptibility locus. Furthermore, we obtained nominal evidence of linkage to bin 6.6 in the weighted analysis of the combination of hypertension and systolic blood pressure (Table 1), corresponding to the strongest region of linkage in the BRIGHT study (21). Due to the large size of the BRIGHT study, it is possible that bin 6.6 would have reached a higher level of significance if included in the present study.

We acknowledge that there are limitations of our study and the GSMA method. The markers and their distribution differed between the studies, introducing some bias in the analysis. To minimize that bias we did not include any second-stage fine mapping data produced by a subset of the included scans, thus losing some available genetic information.

Although there is a solid theoretical background indicating that genes regulating population blood pressure variation (i.e. promoting both high and low blood pressure) are the same as the genes affecting the risk of developing hypertension, thereby justifying the pooling of hypertension and blood pressure scans, this hypothesis can be criticized. For example, it is possible that a genetic variant may have a different impact on blood pressure depending on whether individuals belong to the upper (pre-hypertensive and hypertensive) or lower (low and normal blood pressure) population blood pressure distribution, due to factors such as differing capacities in counter-regulatory systems and vascular function.

However, we believe that the limitations mentioned can be overcome by using a large, ethnically homogenous sample and a reasonably uniform phenotype with a substantial genetic component. In fact, hypertension and blood pressure variation could be described as almost ideal diseases for application of the GSMA, since in simulation studies it was concluded that GSMA is probably most useful when applied to traits with many weakly linked loci (5).

Our definition of genome-wide significant and suggestive linkages, as opposed to point-wise P-values, was derived from a Bonferroni correction for multiple comparisons (5). More evidence for genome-wide significance of bin 3.4, in the combined analysis of hypertension and diastolic blood pressure, was achieved by ordered rank analysis, which confirmed its significance. Furthermore, this analysis confirmed suggestive evidence of linkage of hypertension and diastolic blood pressure to bins 2.5 and 3.5.

As reported in previous GSMA studies (11), 1.2 bins are expected to achieve the 99% threshold of significance by chance in an unweighted analysis. In our analysis, three bins achieved this threshold value in the combined unweighted as well as in the weighted analysis of hypertension and diastolic blood pressure. In the simulation studies based on 20 schizophrenia genome-wide scans (5), it was observed that in the three sets of 1000 unlinked GSMA replicates, 5% of data sets had 11 bins or more with a point-wise P<0.05. The weighted analysis of hypertension and diastolic blood pressure pooled came closest to this value with 10 bins exceeding the threshold. Since this does not meet the criterion of 11 bins or more, we do not achieve genome-wide significance on the basis of this particular empirical criterion (5), thus weakening the total evidence of linkage of bin 3.4. On the other hand, the two most significant bins 3.4 and 2.5 across the combined analyses of blood pressure and hypertension and hypertension alone also showed adjacent bins (3.5 and 2.4) with at least nominal significance. As previously reported (11), this could be expected if a susceptibility locus for a complex disease is present in these regions, since linkage can extend up to 30 cM from the true locus position (22). In the simulation studies of GSMA (5), it was observed that when two adjacent bins achieve significant P-values in both the summed rank analysis and in the ordered rank analysis (5), false positives are extremely rare. Indications of this phenomenon can be observed with the cluster of bins 3.4–3.5. (Table 1 and Figs 1 and 2).

Finally, the consistent finding of at least nominal evidence of linkage for bins 3.4 and 2.5 in all analysis groups except for systolic blood pressure could be regarded as additional evidence against the possibility that these findings would be false positives, as the blood pressure scans and the hypertension scans consist of unique sets of individuals and thus can be regarded as independent samples replicating each other.

In conclusion, our GSMA analysis reveals two new loci on chromosomes 3p14.1–q12.3 and 2p12–q22.1 that appear to have a weak but consistent effect on the risk of hypertension and on diastolic blood pressure in Caucasians. These two loci are candidates for future positional analysis in order to unravel genes of importance for hypertension and blood pressure variation. We suggest that this meta-analysis could be used as a scientific reference for upcoming genome scans in Caucasians and also that it is repeated once additional genome scans have been published in the future.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Study populations
In an attempt to decrease genetic and environmental heterogeneity, only studies of Caucasians were included. Studies of genetic isolates were excluded. In order to ensure a significant genetic component, inclusion of hypertension genome-wide scans was restricted to those who studied patients with hypertension with an age at onset <60 years of age. The studies included in the meta-analysis were identified through publications available at the time of recruitment and the investigators were contacted personally to obtain linkage results throughout the genome. Nine out of 11 eligible studies agreed to participate, five of which were blood pressure scans (23–27) and four of which were hypertension scans (12–15). Among the hypertension scans, the upper limit for age at onset was 50 years in two studies (12,15) and 60 years in the remaining two studies (13,14). Some of the individual studies (13,14,25,27) have included both Caucasian and non-Caucasian families but since the results were reported separately for the different ethnicities, the results for Caucasians could easily be extracted. All linkage analyses were based on multipoint identical by descent estimates, and LOD scores were computed using non-parametric methods. In accordance with the conditions of the GSMA method (4), linkage results for basic genome-wide scans were used, i.e., no fine-mapping results. The main characteristics of the nine genome-wide scans are presented in Table 4.


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  		Table 4. Characteristics of the hypertension and blood pressure genome-wide scans
 
GSMA method and statistics
The theoretical basis and statistical power of the GSMA method have been described in detail in previous articles (4,5). Simply put, this method enables statistics of genome-wide scans for each chromosomal region to be combined. The autosomes are divided into 120 bins, the width of which depends on the used marker map. In our analysis, as recommended by the developers of the method, the autosomal chromosomes were divided into bins with an average width of 29.1 cM on the Marshfield map (http://research.marshfieldclinic.org/genetics/). Markers and marker maps differed between studies and accordingly all maps were adjusted to Marshfield map with a method described in a previous meta-analysis of type 2 diabetes (10). This publication introduced a formula, according to which given marker locations were transformed into Marshfield map locations on the basis of the first and last genotyped marker of the chromosome. This formula was applied to all studies except two (24,25), which used a map with physical distances. These maps were divided into bins with the help of the KTL cartographer (http://www.bioinfo.helsinki.fi/cartographer/) and the UCSC Genome Browser (http://genome.ucsc.edu/cgi-bin/hgGateway).

Each marker was placed in one of the 120 bins according to its location on the map and the most significant result within a bin was obtained. Bins were then ranked within each study, giving the bin with the highest LOD score the best within study rank (Rstudy) value.

As a complement to unweighted Rstudy we introduced a weighting factor in order to also take into account differences in individual study sample size. For the weighted analysis, the Rstudy value was multiplied with a weight factor calculated from the square root of the number of affected/included subjects. In each case, this value is divided by the average value of the studies, giving a mean weight of 1.0. The range of weights ranged between 0.5 and 1.7, indicating that the contribution of the largest study was approximately three times the contribution of smallest included study. In both the weighted and unweighted analyses, the Rstudy values were summed and a point-wise P-value for each bin was calculated. This value describes the probability of observed summed rank arising by chance for a particular bin. The point-wise P-values are determined from the theoretical distribution of the GSMA in the unweighted analysis (PU) (4) and by simulation of the observed ranks for the weighted analysis (PW) (5). By applying a Bonferroni correction for multiple comparisons on the basis of 120 bins, a point-wise P-value of P<0.0004 was defined as genome-wide significant and P<0.008 as genome-wide suggestive evidences of linkage as suggested previously (5).

As a further indicator of what to regard as genome-wide significant, ordered rank analysis was completed by simulating complete GSMA replicates based on the observed ranks (5). Correlations between the summed ranks of the analysis of hypertension and those of diastolic and systolic blood pressure, respectively, were calculated using Kendall/Spearman non-parametric rank correlation coefficients.


   ACKNOWLEDGEMENTS
 
The study was supported by grants from the Swedish Medical Research Council, the Swedish Heart and Lung Foundation, the Medical Faculty of Lund University, Malmö University Hospital, Skaraborg Institute, the Skaraborg County Council, the Albert Påhlsson Research Foundation, the Crafoord Foundation, the Ernhold Lundströms Research Foundation, National Heart, Lung and Blood (NHLBI) grants HL45508 and HL47910 and the Region Skane.


   FOOTNOTES
 
* To whom correspondence should be addressed. Tel: +46 40336023, Fax: +46 40337042, Email: olle.melander{at}endo.mas.lu.se


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Horikawa, Y., Oda, N., Cox, N.J., Li, X., Orho-Melander, M., Hara, M., Hinokio, Y., Lindner, T.H., Mashima, H., Schwarz, P.E. et al. (2000) Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat. Genet., 26, 163–175.[CrossRef][ISI][Medline]

  2. Lifton, R.P. and Jeunemaitre, X. (1993) Finding genes that cause human hypertension. J. Hypertens., 11, 231–236.[CrossRef][ISI][Medline]

  3. Hunt, S.C., Williams, R.R. and Barlow, G.K. (1986) A comparison of positive family history definitions for defining risk of future disease. J. Chronic Dis., 39, 809–821.[CrossRef][ISI][Medline]

  4. Wise, L.H., Lanchbury, J.S. and Lewis, C.M. (1999) Meta-analysis of genome-searches. Ann. Hum. Genet., 63, 263–272.[CrossRef][ISI][Medline]

  5. Levinson, D.F., Levinson, M.D., Segurado, R. and Lewis, C.M. (2003) Genome scan meta-analysis of schizophrenia and bipolar disorder, part I: methods and power analysis. Am. J. Hum. Genet., 73, 17–33.[CrossRef][ISI][Medline]

  6. Williams, C.N., Kocher, K., Lander, E.S., Daly, M.J. and Rioux, J.D. (2002) Using a genome-wide scan and meta-analysis to identify a novel IBD locus and confirm previously identified IBD loci. Inflamm. Bowel Dis., 8, 375–381.[CrossRef][ISI][Medline]

  7. Chiodini, B.D. and Lewis, C.M. (2003) Meta-analysis of 4 coronary heart disease genome-wide linkage studies confirms a susceptibility locus on chromosome 3q. Arterioscler. Thromb. Vasc. Biol., 23, 1863–1868.[Abstract/Free Full Text]

  8. Lewis, C.M., Levinson, D.F., Wise, L.H., DeLisi, L.E., Straub, R.E., Hovatta, I., Williams, N.M., Schwab, S.G., Pulver, A.E., Faraone, S.V. et al. (2003) Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: schizophrenia. Am. J. Hum. Genet., 73, 34–48.[CrossRef][ISI][Medline]

  9. Segurado, R., Detera-Wadleigh, S.D., Levinson, D.F., Lewis, C.M., Gill, M., Nurnberger, J.I., Jr., Craddock, N., DePaulo, J.R., Baron, M., Gershon, E.S. et al. (2003) Genome scan meta-analysis of schizophrenia and bipolar disorder, part III: bipolar disorder. Am. J. Hum. Genet., 73, 49–62.[CrossRef][ISI][Medline]

  10. Demenais, F., Kanninen, T., Lindgren, C.M., Wiltshire, S., Gaget, S., Dandrieux, C., Almgren, P., Sjogren, M., Hattersley, A., Dina, C. et al. (2003) A meta-analysis of four European genome screens (GIFT consortium) shows evidence for a novel region on chromosome 17p11.2–q22 linked to type 2 diabetes. Hum. Mol. Genet., 12, 1865–1873.[Abstract/Free Full Text]

  11. Fisher, S.A., Lanchbury, J.S. and Lewis, C.M. (2003) Meta-analysis of four rheumatoid arthritis genome-wide linkage studies: confirmation of a susceptibility locus on chromosome 16. Arthritis Rheum., 48, 1200–1206.[CrossRef][ISI][Medline]

  12. Perola, M., Kainulainen, K., Pajukanta, P., Terwilliger, J.D., Hiekkalinna, T., Ellonen, P., Kaprio, J., Koskenvuo, M., Kontula, K. and Peltonen, L. (2000) Genome-wide scan of predisposing loci for increased diastolic blood pressure in Finnish siblings. J. Hypertens., 18, 1579–1585.[CrossRef][ISI][Medline]

  13. Kardia, S.L., Rozek, L.S., Krushkal, J., Ferrell, R.E., Turner, S.T., Hutchinson, R., Brown, A., Sing, C.F. and Boerwinkle, E. (2003) Genome-wide linkage analyses for hypertension genes in two ethnically and geographically diverse populations (GENOA). Am. J. Hypertens., 16, 154–157.[CrossRef][ISI][Medline]

  14. Rao, D.C., Province, M.A., Leppert, M.F., Oberman, A., Heiss, G., Ellison, R.C., Arnett D.K., Eckfeldt, J.H., Schwander, K., Mockrin, S.C., Hunt, S.C. and HyperGEN Network. (2003) A genome-wide affected sibpair linkage analysis of hypertension: the HyperGEN network. Am. J. Hypertens., 16, 148–150.[CrossRef][ISI][Medline]

  15. von Wowern, F., Bengtsson, K., Lindgren, C.M., Orho-Melander, M., Fyhrquist, F., Lindblad, U., Rastam, L., Forsblom, C., Kanninen, T., Almgren, P. et al. (2003) A genome-wide scan for early onset primary hypertension in Scandinavians. Hum. Mol. Genet., 12, 2077–2081.[Abstract/Free Full Text]

  16. Fisher, R.A. (1925) Statistical Methods for Research Workers, 13th edn. Oliver and Loyd, Ltd, London, pp. 99–101.

  17. Province, M.A. (2001) The significance of not finding a gene. Am. J. Hum. Genet., 69, 660–663.[CrossRef][ISI][Medline]

  18. Province, M.A., Kardia, S.L., Ranade, K., Rao, D.C., Thiel, B.A., Cooper, R.S., Risch, N., Turner, S.T., Cox, D.R., Hunt, S.C. et al. (2003) National Heart, Lung and Blood Institute Family Blood Pressure Program. A meta-analysis of genome-wide linkage scans for hypertension: the National Heart, Lung and Blood Institute Family Blood Pressure Program. Am. J. Hypertens., 16, 144–147.[CrossRef][ISI][Medline]

  19. Atwood, L.D., Samollow, P.B., Hixson, J.E., Stern, M.P. and MacCluer, J.W. (2001) Genome-wide linkage analysis of blood pressure in Mexican Americans. Genet. Epidemiol., 20, 373–382.[CrossRef][ISI][Medline]

  20. Kristjansson, K., Manolescu, A., Kristinsson, A., Hardarson, T., Knudsen, H., Ingason, S., Thorleifsson, G., Frigge, M.L., Kong, A., Gulcher, J.R. and Stefansson, K. (2002) Linkage of essential hypertension to chromosome 18q. Hypertension, 39, 1044–1049.[Abstract/Free Full Text]

  21. Caulfield, M., Munroe, P., Pembroke, J., Samani, N., Dominiczak, A., Brown, M., Benjamin, N., Webster, J., Ratcliffe, P., O'Shea, S. et al. (2003) Genome-wide mapping of human loci for essential hypertension. Lancet, 361, 2118–2123.[CrossRef][ISI][Medline]

  22. Cordell, H.J. (2001) Sample size requirements to control for stochastic variation in magnitude and location of allele-sharing linkage statistics in affected sibling pairs. Ann. Hum. Genet., 65, 491–502.[CrossRef][ISI][Medline]

  23. Levy, D., DeStefano, A.L., Larson, M.G., O'Donnell, C.J., Lifton, R.P., Gavras, H., Cupples, L.A. and Myers, R.H. (2000) Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension, 36, 477–483.[Abstract/Free Full Text]

  24. Rice, T., Rankinen, T., Province, M.A., Chagnon, Y.C., Perusse, L., Borecki, I.B., Bouchard, C. and Rao, D.C. (2000) Genome-wide linkage analysis of systolic and diastolic blood pressure: the Quebec Family Study. Circulation, 102, 1956–1963.[Abstract/Free Full Text]

  25. Rice, T., Rankinen, T., Chagnon, Y.C., Province, M.A., Perusse, L., Leon, A.S., Skinner, J.S., Wilmore, J.H., Bouchard, C. and Rao, D.C. (2002) Genomewide linkage scan of resting blood pressure: HERITAGE Family Study. Health, risk factors, exercise training, and genetics. Hypertension, 39, 1037–1043.[Abstract/Free Full Text]

  26. Hunt, S.C., Ellison, R.C., Atwood, L.D., Pankow, J.S., Province, M.A. and Leppert, M.F. (2002) Genome scans for blood pressure and hypertension: the National Heart, Lung, and Blood Institute Family Heart Study. Hypertension, 40, 1–6.[Abstract/Free Full Text]

  27. Thiel, B.A., Chakravarti, A., Cooper, R.S., Luke, A., Lewis, S., Lynn, A., Tiwari, H., Schork, N.J. and Weder, A.B. (2003) Genome-wide linkage analysis investigating the determinants of blood pressure in whites and African Americans (GenNet). Am. J. Hypertens., 16, 151–153.[CrossRef][ISI][Medline]


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