There is increasing evidence of the important role that small, isolated populations could play in finding genes involved in the etiology of diseases. For historical and political reasons, South Tyrol, the northern most Italian region, includes several villages of small dimensions which remained isolated over the centuries.The MICROS study is a population-based survey on three small, isolated villages, characterized by: old settlement; small number of founders; high endogamy rates; slow/null population expansion. During the stage-1 (2002/03) genealogical data, screening questionnaires, clinical measurements, blood and urine samples, and DNA were collected for 1175 adult volunteers. Stage-2, concerning trait diagnoses, linkage analysis and association studies, is ongoing. The selection of the traits is being driven by expert clinicians. Preliminary, descriptive statistics were obtained. Power simulations for finding linkage on a quantitative trait locus (QTL) were undertaken.Starting from participants, genealogies were reconstructed for 50,037 subjects, going back to the early 1600s. Within the last five generations, subjects were clustered in one pedigree of 7049 subjects plus 178 smaller pedigrees (3 to 85 subjects each). A significant probability of familial clustering was assessed for many traits, especially among the cardiovascular, neurological and respiratory traits. Simulations showed that the MICROS pedigree has a substantial power to detect a LOD score ≥ 3 when the QTL specific heritability is ≥ 20%.The MICROS study is an extensive, ongoing, two-stage survey aimed at characterizing the genetic epidemiology of Mendelian and complex diseases. Our approach, involving different scientific disciplines, is an advantageous strategy to define and to study population isolates. The isolation of the Alpine populations, together with the extensive data collected so far, make the MICROS study a powerful resource for the study of diseases in many fields of medicine. Recent successes and simulation studies give us confidence that our pedigrees can be valuable both in finding new candidates loci and to confirm existing candidate genes. The importance of isolated populations for revealing the genetic etiology of common diseases has been frequently highlighted in recent years: the homogeneity of the shared environmental factors and the limited number of recombination events in the DNA make isolates a valuable tool for linkage and association studies -5. CautiPerhaps surprisingly, population isolates are not as rare as it might be expected, and several isolates can be found even within nations of the "old Europe" -14, wherAlu markers [microisolate" was used to distinguish small subpopulations in remote, high valleys that have conserved an even higher degree of isolation than the cultural and linguistic South Tyrolean isolate, in which they are included [Bolzano is the northern-most province of Italy. This province is known as South Tyrol because it is the Southern part of the Tyrol region, split between Italy and Austria. The geographical structure, historical, and political events of this region resulted in the isolation of the population, with scattered villages often located at the ends of remote valleys. Two recent studies, one on the Y-chromosome and mtDNA and autosomal  markers , the oth markers , were deincluded .As a part of a larger, ongoing genetic healthcare research program on the South Tyrolean population (the GenNova project), in 2002–2003, an extensive survey (the MICROS study) was carried out on the populations of Stelvio, Vallelunga and Martello, three villages of the Val Venosta. Among them, Stelvio was already classified as a genetic microisolate [The present article is intended to introduce the MICROS study to the scientific community. We report some preliminary results, and future perspectives are discussed.The villages of Stelvio, Vallelunga and Martello were selected for their geographical and historical isolation and for the presence of highly collaborative local general practitioners (GPs). At the time of the survey, the adult population (aged 18+ year old) numbered 1043 people in Stelvio, 693 in Martell, and 339 in Vallelunga . The geoThe three villages were selected for study on the basis of four criteria for isolation: i) evidence of an old settlement; ii) a small number of founders; iii) high endogamy rates; iv) slow or null population expansion and negligible immigration. Of course, for verifying these criteria, another prerequisite was the availability and accessibility of historical documentation.Historical and demographic data on the evolution of the Val Venosta verify that these regions satisfy the criteria for isolation. Numerous archeological findings testify to a broader human presence in the Alpine zone of South Tyrol during the Mesolithic  . In 1991During this period the permanent settlement of the Val Venosta villages occurred, although there is evidence that the remote valleys were known and used for hunting and gathering already by the Raetians. The village of Stelvio represents a partial exception, as there is evidence of settlement and mining activity as early as the Bronze Age (2200-900 B.C.). Nevertheless, even in Stelvio the main expansion took place within the process of enlargement of the cropland, favored by the various landlords. Medieval documents identify a very restrict number of family names accounting for the entire population; some of them are still in use today .th and 16th century [th century. Some of these were farmers from the surroundings, but in Stelvio and Martello they were mostly mine workers from nearby Switzerland and northern Tyrol. This was the last wave of immigration to these villages before a period of prolonged emigration, caused by the severe poverty, started in the 19th century. People followed different migration patterns and moved to the nearby towns and cities, to other European countries, and to the United States [th century, before a steep decline in population began. The last big emigration occurred during the period under fascist rule that followed South Tyrol's annexation to Italy in 1919 [Major population bottleneck events recorded were the plague epidemics in Europe during the 14 century ,28. The d States . From thd States ,31. The  in 1919 . The preAnalysis of the marriage behavior during the past centuries reveals a strong tendency to endogamy together with a significant percentage of consanguineous unions. However, it should be noted that the vast majority of consanguineous marriages occurred between distant relatives, and that an avoidance of close relatives' marriage has been observed . The marIsolation still plays a role in these communities. Mining is no longer an industry in South Tyrol, and these villages have no economic inducements for immigration. Tourism is the leading resource and agriculture is declining. Population is stable or slightly decreasing , and versecondary isolate, that is an isolate that, after being detached from a larger population, "derived from a relatively small population sample, which then slowly expands, with very little recruitment from outside the group" [microisolates.The study of the history of Val Venosta population across the centuries suggested that the Val Venosta area could be classified as a e group" . Within Inhabitants of the target villages were invited, by public advertisement , to attend a meeting with the community mayor, the local GPs, and the scientists leading MICROS. The meeting was designed to give the population a clear understanding of the motivations and importance of MICROS, and to outline how the study would be performed, including the risks and possible benefits to research participants and the community. It was stressed in the meeting that participation was to be on a purely voluntary basis. During the event, the field team  was introduced to the population. A call center was established for scheduling appointments. During the appointment, the study was explained by a physician, and a detailed information sheet and a form for the written informed consent were provided to each prospective participant. The information sheet gave details regarding the aims and nature of the study, specimen handling and storage. In particular, the information clearly stated the rights of the participants to rigorous privacy measures  and the study's opt-out clause, guaranteeing that every participant may withdraw at any time from the study, for any reason and without consequences. To implement this right, the project has in place several information strategies to keep the communities updated about the development of the research. The study was approved by the Ethics Committee of the Autonomous Province of Bolzano. Every six months the Committee is informed of any changes or developments.A standardized, interviewer-administered questionnaire was submitted to the participants. The questionnaire was structured as follows: i) reconstruction of family history (pedigree); ii) questions about cardiology, angiology, neurology, psychiatry, ophthalmology, endocrinology, respirology, dermatology, allergy, internal medicine, hematology, oncology, gynecology (for women participants), and injuries due to accidents; iii) life-style exposures . The typical question occurring in any field of part ii of the questionnaire was a closed question, allowing a yes/no/don't know response. If the interviewing physician had knowledge of whether the subject was affected, this could be indicated, regardless the subject's answer. Subjects were asked the year or age of onset of their condition, they were asked to add a free text description of symptoms  and they were questioned to determine if any relative had the same trait.Subjects underwent anthropometric measurements , respiratory and ultrasound-based bone densitometry tests, dioptric measurement of eyeglasses, ECG, and blood pressure measurements. A urine sample was collected from each participant, and blood samples were taken for DNA and serum extraction, and for routine, biochemical analyses, including blood count/differential blood count, Quick, aPTT, ferritine, TSH, T4, cholesterol, HDL, LDL, triglycerides, electrolytes , GPT, GOT, GGT, glucose, and creatinine. A serum sample for each participant was prepared and stored at -80°C for subsequent analysis.Genomic DNA was isolated from 20 ml of EDTA-treated blood, according to standard procedures . DNA of © 2.1 .Genealogical information was reconstructed by means of church records  reaching back to the early 17th century and completed with municipality lists starting in 1924. We started by reconstructing single families, that in a second step were linked together to form genealogical trees of the whole villages. Genealogical data were managed using CyrillicSubjects are selected on the basis of a reported trait of interest, then they are asked for further examination. As far as possible, the information is extended to the pedigree of the targeted subjects, after all the ethical aspects are taken into account. In case of agreement, targeted patients and relatives are diagnosed by an expert specialist. After collecting the diagnoses, the genetics of the disease is under study, starting with linkage analysis and subsequent association studies.Data are entered in an enterprise-level database on a web-base front end providing a wide range of tools for entering and querying subject information. The database design was implemented in accordance with the Italian privacy law. Personal identifying data are physically separated from the other data and secured by anonymising methods. A unique code is given to each questionnaire and to the specimens, which are stored in a secure room. A role-based access policy guards access to the database. This system ensures that all information on individuals – genealogical, medical and genetic – is anonymised, identifiable only by authorized persons, who are assigned different levels of access rights to the database according to their role in the research. Transaction logs are maintained to record access to sensitive data. Servers are backed up weekly on tapes and stored off-site in a fire-proof safe.Furthermore, a java-api for the visual analysis of our complex data was implemented. It provides all the necessary functionality for the interactive exploration and analysis of our extended genealogies .birthplace was defined as the village in which the subject's mother was living at the time of the subject's birth. Educational level was classified as "no school attended", "primary school" (from 6 to 10 year old), "middle school" (11–14), "vocational school" , "secondary school" (15–19), and university. Subjects were asked for their occupations in the life: a maximum of four jobs could be reported, together with the respective working periods. For the smoking habits measure, subjects were defined as "never smokers" (never smoked or smoked for less than 1 year), "past smokers" (stopped smoking more than 1 year before the date of the interview) and "current smokers". Alcohol consumption frequency was classified as rarely (never or seldom), often (1 to 4 times per week), daily.For the purposes of this study, Prevalence and 95% exact confidence intervals (95%CIs) were estimated for the main binary traits. For each trait, the probability of familial clustering (PFC) was assessed through the estimation of a chi-square statistic, which compares the observed with the expected distribution of affected siblings ,39. To tInter-village homogeneity of subject's characteristics and study traits was assessed through chi-square test or F-test, depending on the variable being qualitative or quantitative. Homogeneity of quantitative traits between sex and age classes  was determined using the Kruskal-Wallis test.Thanks to the collection of extended genealogical trees for the three villages from church and municipal records, and from information on a small number of marriages between two of the three villages, we were able to build a pedigree including participants from all three villages. The accuracy of the pedigree structures was verified by checking available genotype data for Mendelian inconsistencies using Pedstats 0.6.8 .2) was assumed taking values from 0.10 to 0.60; QTL-specific heritability ranged from 0.05 to h2, with 0.05 increments. Since the average inter-marker distance was 4 cM, we assumed the marker be located at 0, 1 or 2 cM, respectively, from the QTL. Simulations were replicated on pedigrees from each single village separately and on the whole pedigree after splitting it with the Greffa software [The power of variance component linkage analysis to detect a QTL was assessed via simulation using the SOLAR software package . A biallsoftware . A LOD-ssoftware .Data were collected from September 2002 to December 2003. A total of 1175 volunteers were enrolled in the study, 509 from Stelvio , 351 from Martello (51%) and 315 from Vallelunga (93%). We obtained screening questionnaire, clinical measurements and genotypic data from each participant.Complete genealogy was reconstructed for 50,037 individuals, starting from the participants and going back to the 1600s (15 generations). When, for analysis issues, we limited the extension of the pedigree to five generations, 10,630 individuals were included, grouped into 178 pedigrees. One thousand, seven hundred and thirty-two singletons were discarded because it was impossible to include them in any pedigree. One of the 178 pedigrees accounted for 7049 subjects; in the remaining 177 pedigrees, the median number of subjects per pedigree was 8 (range: 3–85). Overall, mean inbreeding coefficient was 0.000565  and mean kinship coefficient was 0.001777 (sd = 0.014439). When considering village-specific pedigrees, for Vallelunga, Martello, and Stelvio, the mean inbreeding coefficient was 0.000432 (sd = 0.002213), 0.000172 (sd = 0.001367) and 0.000112 (sd = 0.003625), respectively, and the mean kinship coefficient was 0.002228 (sd = 0.014822), 0.002317 (sd = 0.015773), and 0.000806 (sd = 0.012370), respectively.The birthplace of participants, their parents and grandparents was recorded: 85% of subjects from Martello, 77% from Vallelunga and 59% from Stelvio were born from a mother who was living in the same village where they were residing at the time of the interview. When extending the area to the Val Venosta, we observed that the percentage of people born in the valley was 89% (Stelvio) to 99% . A similar pattern was observed for the parent's and grandparent's generations Figure .Distribution of subject's characteristics is reported in Table Overall, 52.1% of participants went to school until they were 13 years old, 17.4% completed secondary school and 3.9% had a higher degree. The educational level was non homogeneous between villages (p = 0.0003). On average, males had a higher level of education than females  and education was related to age : secondary school or university were attended by 3% of the 70+ year old participants, and by 49% of the 18–30 year old participants.Overall, 19.4% were employed in tourism, with noticeable differences between villages . In Vallelunga, the leading sector was agriculture (18.8%). Many people (in particular women) worked at home as housewives. The second most common category of occupation (13.9%) was manual trades, typical of small villages: bricklayers, carpenters, electricians, etc. Ninety-five percent of participants held the same occupation throughout life.We observed a differential distribution of smoking habits between the two sexes (p < 0.001). Percentage of never smokers was lower for younger males than for older males, but higher for younger females than older females. Overall the smoking prevalence decreased with decreasing age. Smoking habits were homogeneous across villages (p = 0.1635).Eighty-one percent of women and 41% of men reported an alcohol consumption frequency of never to seldom; 27% of males and 7% of females reported daily frequency; males consumed alcohol more often than females (p < 0.001). Alcohol consumption varied by age  and village .Distribution of qualitative traits, by village, is reported in Table Elevated blood pressure, cardiac arrhythmias, and angina pectoris were the most common disorders; all the three with a non-homogeneous distribution across villages. Prevalence of these symptoms steeply increased with age. The percentage of women reporting symptoms was higher than males, but this difference varied with age. Cardiac disease was reported by 2.5% 95%CI: 1.7–3.6%) of subjects, with 1.2% (95%CI: 0.6–2.0%) having had a myocardial infarction. Interestingly, 36 subjects reported heart defects, such as heart failure and coronary heart disease . The last three symptoms were homogeneously distributed across villages. For all the CVD symptoms, Martello had the highest prevalence. For sex-age specific distribution of the main CVD symptoms [see additional file .7–3.6% oThe most frequent neurological symptoms were vertigo and migraine. Migraine prevalence was higher in Martello and Vallelunga than in Stelvio (p = 0.0244). Symptoms compatible with carpal tunnel syndrome were reported by 14.8% (95%CI: 12.7–17.1%), with Vallelunga having much higher prevalence than the other two villages (p = 0.0017). Prevalence of restless leg's syndrome (RLS) was 12.8% (95%CI: 10.9–14.8%). Three percent (95%CI: 2.0–4.0%) had vascular problems in the carotid arteries, 1.7% had a stroke (95%CI: 1.0–2.6%) and 2.0% (95%CI: 1.3–3.0%) suffered from epilepsy.Any episode of asthma in life was reported by 4.2% 95%CI: 3.1–5.5%) of subjects; 5.2% (95%CI: 4.0–6.6%) reported to have had chronic bronchitis and 8.3% (95%CI: 6.8–10.0%) reported hay fever. At younger ages asthma prevalence was higher in women than in men, with the percentage increasing with age until more males than females were affected by asthma after the age of 70. A similar pattern of increasing prevalence with age was observed for chronic bronchitis, but with females always having a higher prevalence than males. An almost parallel decreasing trend was observed for hay fever [see additional file –5.5% of Diabetes prevalence was 3.6% (95%CI: 2.6–4.9%). Goitre was present in 6.4% (5.1–7.9%) and gout or hyperuricemia in 3.5% (95%CI: 2.5–4.7%) of the sample. Eleven percent (95%CI: 9.0–12.6%) reported gallbladder disease, 9.3% (95%CI: 7.7–11.1%) had ulcer and 6.3% (95%CI:5.0–7.9%) had liver disease. Between village heterogeneity of liver disease was observed (p = 0.0102). Kidney disease was reported by 13.6% (95%CI: 11.7–15.7%) and anemia by 6.5% (95%CI: 5.1–8.0%).The hypothesis of random distribution of a trait across the pedigree was rejected for all the traits related to CVD: the p-values ranged from < 0.000001 for elevated blood pressure to 0.0295 for cardiac arrhythmias. When assessing the likelihood of familial clustering within village, certain heterogeneity was observed. Elevated blood pressure clustered in families in each of the three villages, but cardiac arrhythmias was significantly clustered in families only in Martello, and myocardial infarction as well. Familial clusters of myocardial infarction and heart defects were observed in Stelvio and Vallelunga, but not in Martello. Clusters of angina pectoris were assessed in Stelvio. Among the neurological traits, vascular problems at the level of carotid arteries had the strongest evidence of familial clustering (p < 0.000001). Other traits clustering in families were stroke, apoplexy, migraine, Alzheimer's disease, CTS-like symptoms, and RLS. Similar to the case of CVDs, some inter-village differences were observed. While some symptoms were clustering in families in all of the three villages , in other cases this evidence was related to one single village (e.g.: stroke). Asthma, chronic bronchitis, and diabetes showed evidence of familial clustering in Stelvio and Martello. Ulcer and liver disease were clustering in families in Martello and Vallelunga. No evidence of familial clustering was observed for kidney and gallbladder diseases. More results are reported in Table Body Mass Index (BMI) differed between sexes and increased with age . Waist circumference was related to age, with young males having bigger waist circumference than young females. Among the other determinants for CVD, diastolic and systolic blood pressure increased with age . Cholesterol increased with age; in the elderly, females had slightly higher values than males. Mean High Density Lipoprotein (HDL) was 53 mg/dl (sd = 13) in males, 64 mg/dl (sd = 14) in females. Low Density Lipoproteins (LDL) increased with age: mean = 104 mg/dl (sd = 32) in 18–30 year old subjects, mean = 136 (sd = 31) in 70+ year old subjects. Triglycerides were heterogeneous between groups , with median within 75–120 mg/dl. For distribution of CVD traits by age and sex [see additional file Within the CVD/kidney related traits, median blood sodium level was 138–139 mEq/l and median potassium level was in 4.2–4.3 mEq/l. Higher calcium levels were observed in younger subjects and in males. Hemoglobin was higher in males than in females. Mean creatinine was 0.98 mg/dl (sd = 0.15) in males and 0.79 mg/dl (sd = 0.15) in females. Regarding the intermediate phenotypes for renal function, the distribution of albuminuria was similar across sex-age groups , with a peak in 70+ year old males, who had the highest values . Creatinuria was lower for the elderly and for females: maximum median was 188 mg/dl in 18–30 year old males; minimum median was 74 mg/dl in 70+ year old females.Fasting glucose was sex-age related, with median within 77–87 mg/dl; among 50+ year old subjects the variability increased as well as hyperglycemia cases. Liver transaminases  and gamma glutamyl-transpeptidase (GGT) had similar patterns: before the age of 70, they were higher in males than in females, after which levels converged to similar values [see additional file Among anemia related traits, ferritin was higher for males than females: in males the median ranged in 109–142 ng/ml, with no age trend; in females the values increased from a median of 22 in 18–30 year old subjects to 76 in the 70+ year old participants (the variability increasing as well). Mean corpuscular hemoglobin concentration (MCHC) decreased slightly with age . Red cell distribution width (RDW) was constant until 50 year of age (median 12.3–12.4%), then increased (median ≥ 12.90% after 70 years). Mean corpuscular volume (MCV) was homogeneous between groups .Immune system indicators exhibited little heterogeneity. Neutrophils were homogeneous between sexes, with small variations and no age trend . Lymphocytes were decreasing with age , with females having higher values than males. Heterogeneity (p = 0.00156) but no age trend was observed for monocytes, with males having always higher values than females (p < 0.00001). Median percentage of eosinophils was within 2.3–3.1, with significant sex-age heterogeneity. Basophils had median of 0.7% (interquartile range: 0.5–1.0).Statistically significant, between-village heterogeneity was observed for all the studied traits but waist circumference, GOT, and several hematological parameters .We ran power simulation for the pooled pedigree (the three villages together), for each one of the three villages separately, and for the total pedigree after pedigree splitting.When considering the pooled data, the power for localizing a QTL was substantial Figure . When thBy pooling all the samples together, we were making the assumption that people from the three microisolates share the same variants with the same frequencies and, consequently, the same effects. In the case that the three microisolates are genetically differentiated, this assumption could be unrealistic. In addition, running a linkage analysis on pedigrees with size 7049 could be unfeasible for computational power limitations. For these reasons we ran more simulations for each microisolate separately.2 = 0.60. Red and blue lines indicate the probability to find a LOD-score ≥ 3 and ≥ 2, respectively. Power curves for markers placed 0 (continuous line) and 2 (shaped line) cM apart from the QTL are plotted. The grey area within the curves indicates the differential power at different marker-QTL distances. As long as the average microsatellite spacing is 4 cM, it is worth noting that placing the marker at 2 cM from the QTL corresponds to the worst scenario, while 0 cM corresponds to the best one. Depending on location, we observed 80% probability to find a LOD-score ≥ 3 for QTLs with specific heritability from 0.31 (0 cM) to 0.35 (2 cM). When the QTL specific heritability is 25 (0 cM) to 28 (2 cM), the Vallelunga pedigree has 80% power to find a suggestive linkage signal (LOD-score ≥ 2).We obtained the best results when analyzing the Vallelunga pedigree, probably because the collection of the 93% of the total inhabitants allowed a better pedigree reconstruction. In Figure When simulating power for QTLs with different minor allele frequencies (MAF), we did not observe any particular relationship between MAF and power.Finally, we considered the option to analyze the pooled dataset after splitting the big pedigrees into smaller ones. Under the constraints of pedigree size ≥ 3 and kinship coefficient ≥ 0.0000125, we obtained the entire sample be split into 78 pedigrees, with 4 to 397 subjects each . The average mean kinship coefficient was 0.13 . This approach had the advantage to consider all the three villages together while enormously reducing the computational time. Unfortunately, despite these major advantages, the estimated power was lower than considering the Vallelunga microisolate alone, for all the described scenarios.MICROS is an extensive, population-based survey on three German-speaking microisolates of South Tyrol, with the goal of genetic characterization of the most relevant traits for this population. In this paper we described the first stage of the survey, the study design and the data collection. As described in the methods section, a second stage is necessary to study the traits in a greater detail and, possibly, to recruit subjects that did not participate in the first phase, so that families could be completed. The second assessment phase requires contacting the GPs and the targeted families, in order to narrowly define specific phenotypes. To narrow the phenotype definition, subjects are then re-examined by specialists for the medical fields of interest.Among the strengths of the MICROS study, the interdisciplinary scientific team is one of the most important. The interconnection between historians and biologists-geneticists was the first step toward the targeting of the microisolates. Moreover, geneticists, ethicists and epidemiologists had a key role in the design of the survey. The correct data processing is being guaranteed by the bioinformatics core. At the same time, research is ongoing on all of these fields.After demonstrating the historical isolation of South Tyrol across the centuries, we concluded that this isolation still plays an important role in the microisolates; the analysis of demographical records highlighted that the population is very stable and the absolute number of inhabitants almost the same across the years . We demoAll these features lead us to consider the possibility of upper Val Venosta itself as a secondary isolate  with sevWe have listed a number of traits, classifying qualitative symptoms by those categories which will be the most relevant for the involved clinicians. Interestingly, many common traits showed a significant probability of familial clustering. On the one hand, we observed some traits clustering in families within all three villages ; on the other hand, familial clusters were observed only in one or two villages as, for example, the case of angina pectoris, migraine, asthma, and liver disease. Familial clustering can be driven by allelic homogeneity or common life style. The combination of these two factors must be carefully evaluated for each single trait under study and simplyfing assumption on the allelic distribution cannot be done.Until now, we have not performed further investigations on the qualitative traits obtained through the screening questionnaire, because these traits are intended to serve as indicators of the possible presence of specific phenotypes, which will be investigated in the second stage of the study. At that stage, clinical diagnoses will be performed, and pedigrees will be reconstructed starting from affected subjects. This was the case for RLS which was assessed in a greater detail with clinical diagnosis by a movement disorder specialist and successive linkage analysis . A genomRegarding the power to detect QTLs, the MICROS study has a potentially high power when pooling data from the three villages. Unfortunately, this potentially big power is bounded by the limited computational power of linkage analysis softwares. Nevertheless, the power resulting from the Vallelunga pedigree of finding LOD-scores ≥ 2 is encouraging and suggests that the villages of Martello and Stelvio could be considered as replication samples for common variants. The results also suggest that some efforts should be spent in second phase studies for phenotyping and genotyping new individuals, related to the current participants, especially from Martello and Stelvio. Currently, this strategy is being pursued.The collected sample will also serve for association studies, while controlling for disease specific determinant and effect confounders. We expect that the assessment of phenotype-genotype association could be favoured by the high extent of linkage disequilibriumobserved in the villages . MethodsIn the case of Vallelunga, where the sample was 93% of all the inhabitants, data will allow researchers to assess the global health of population living in the village, with interesting issues for public health. The observed heterogeneity in the distribution of several qualitative and quantitative traits suggests that, for both linkage and association studies, covariates such as age, sex and environmental factors should be taken into account. Adjustment or stratification may be required, depending on the specific situation.Within the neurological diseases, further assessment on the RLS is ongoing. In addition, as one out of five participants was affected by migraine, a new research branch on this disease is to be started. At the moment, a questionnaire for the assessment of migraine is under validation. In case of positive response, it will be used to assess migraine in the study subjects. Subjects reporting migraine have been targeted together with relatives which did not participate in the first phase. Following this, a comprehensive study of the genetic epidemiology of this disease and genome-wide linkage analyses will be undertaken.Genome-wide scans will allow detailed investigation of nephrological diseases, and of gall bladder disease, in particular gall stone formation. Chronic kidney disease (CKD) will be studied by means of a quantitative trait, the glomerular filtration rate (GFR), obtained from serum and urine creatinine measurements. Epidemiology of CKD will be explored particularly in the isolated village of Vallelunga, where we have a sample which is representative of the population. Moreover, linkage analysis will be performed together with an association study with environmental and genetic factors.Our approach, involving different scientific disciplines, seems an advantageous strategy to define and study small population isolates. Priming of the population and support by the local GPs is a prerequisite to ensure a high participation rate. The isolation of the Alpine populations that we included in our study together with the extensive data collection make the MICROS study a powerful resource for the study of diseases in many fields of medicine. It is a promising strategy to ascertain extended pedigrees ensuring a homogeneous environment. Linkage disequilibrium and historical data from the South Tyrol region, and the small isolated villages suggest that individuals may share a small number of disease alleles. Recent successes and simulation studies give us confidence that our pedigrees can be valuable both in finding new candidates loci and to confirm existing candidate genes.The author(s) declare that they have no competing interests.All authors contributed to drafting the manuscript. The main text was written by PC, MF, and RA with comments and amendments made by all authors. PPP was the study coordinator. PPP, MT, VFD, PGK, and PI conceived of the study and participated in its design. DGA and BVC participated in the study design. PC and MF did the statistical analyses and power simulations. RA, DCU, GM and PGK collected the genealogical data and helped in the draft of the historical paragraphs. MD, EC and FC wrote all the parts related to ethics, privacy issues and database administration. SSA designed the database and the data entry tool, and was involved in the study design. DGA, BVC, PI and PS carried out the DNA extraction, quality controls, sample storage management, and the molecular genetic studies. EA was responsible for data collection and database quality control. Clinical data were commented by WCJ and PPP. All authors have read and approved the final manuscript.The pre-publication history for this paper can be accessed here:Distribution of cardiovascular and respiratory traits. Prevalence of the main cardiovascular disease symptoms (upper panel) and respiratory traits (lower panel), by age and sex , with 95% confidence intervals.Click here for fileDistribution of cardiovascular disease's determinants. Differential distribution of six quantitative traits by age and sex . Age is reported on the x-axis.Click here for fileDistribution of liver-related intermediate phenotypes. Distribution of glutamic-oxaloacetic transaminase (GOT), glutamate pyruvate transaminase (GPT) and gamma glutamyl-transpeptidase (GGT), by age and sex .Click here for file


Theconsortium  bringstogether expertise in genetics, epidemiology, biostatistics, andbioinformatics to develop and test innovative, informative, and cost-effectivestudy designs and analytical strategies for performing genome-wideassociation studies on complex diseases. Available resourcessuch as the International Haplotype Mapping (HapMap) data and single nucleotidepolymorphism (SNP) discoveries along with improvements in genometechnologies have increased the feasibility of genome-wide associationstudies for complex diseases. However, it has become increasinglyapparent that a major barrier to successfully completing these studiesis a lack of both appropriate analytical tools and understanding ofwhich study designs and computational methods are most appropriate forparticular study scenarios.The NIH has recently established a highly interactive consortium of 11 researchgroups on Enhancing Development of Genome-wide Association MThe NIEHS is most interested in the development of analytical tools andapproaches that would allow identification of environmental componentsor covariates of complex diseases in genome-wide association studies. Althoughmost common chronic diseases are the result of complex interactionsbetween genes (G) and environmental (E) factors, most analyticalapproaches adopted for whole genome scans do not incorporate interactiveeffects with environmental factors. Studies have indicated thatfailure to account for G × E interactions in complex diseaseassociation analyses can decrease the power to find genetic disease lociand underestimate both the genetic and environmental effects of thedisease. The NIEHS is therefore co-funding with the NCI two applicationsin this consortium, led by Dr. Duncan Thomas of the University ofSouthern California and Dr. Charles Kooperberg of the Fred HutchinsonCancer Center, that specifically focus on identifying study designs andanalytical methods that will enhance the possibility of identifyinggene–gene and gene–environment interactions. All strategiesand tools developed through this consortium will be made availableto the entire scientific community. The long-term goals of ENDGAMEare to accelerate the identification of genetic susceptibility factorsin human disease and the ultimate development of novel and individualdisease prevention and treatment strategies through the advancement ofgenome-wide association study methodologies.Kimberly A. McAllister, Ph.D. | mcallis2@niehs.nih.gov


We use the Genetic Analysis Workshop 14 simulated data to explore the effectiveness of a two-stage strategy for mapping complex disease loci consisting of an initial genome scan with confidence interval construction for gene location, followed by fine mapping with family-based tests of association on a dense set of single-nucleotide polymorphisms. We considered four types of intervals: the 1-LOD interval, a basic percentile bootstrap confidence interval based on the position of the maximum Zlr score, and asymptotic and bootstrap confidence intervals based on a generalized estimating equations method. For fine mapping we considered two family-based tests of association: a test based on a likelihood ratio statistic and a transmission-disequilibrium-type test implemented in the software FBAT. In two of the simulation replicates, we found that the bootstrap confidence intervals based on the peak Zlr and the 1-LOD support interval always contained the true disease loci and that the likelihood ratio test provided further strong confirmatory evidence of the presence of disease loci in these regions. A primary goal of many current human genetic studies is to identify disease susceptibility loci for complex diseases. When there are no obvious candidate genes, a linkage genome scan is usually conducted to select regions for further study. In these regions, further genotyping can be carried out in order to narrow down the possible locations of disease loci using linkage disequilibrium mapping. Given significant genome scan results, the size of the region under a linkage peak on which to concentrate further mapping efforts is not immediately obvious. Confidence intervals (CIs) for the locations of susceptibility genes provide a natural way to determine regions for follow up, yet this is rarely done in practice. Several types of intervals may be considered, such as the 1-LOD score support interval .Once a region is selected, linkage disequilibrium mapping can be carried out using association methods for family-based designs, which are often preferred over case-control designs because they are immune to potential population stratification. However, standard family-based tests of association such as the transmission-disequilibrium test (TDT) do not utilize all information available in nuclear families, incurring a potential loss of power. Lewinger and Bull  recentlyIn this paper we use the simulated Genetic Analysis Workshop 14 (GAW14) data to explore the effectiveness of a two-stage strategy for mapping complex disease loci: an initial genome scan with confidence interval construction for disease gene location, followed by fine mapping with family-based association (FBAT) analysis using the likelihood ratio test of Lewinger and Bull  and FBATpairs scoring function. We identified regions with Zlr peaks exceeding 4.09 corresponding to a genome-wide significance of 2.2 × 10-5 and constructed CIs based on two different estimators of disease gene location: an estimator based on the GEE method proposed by Liang et al. [We performed initial multipoint linkage genome scans separately for each of the four populations using Kofendrerd Personality Disorder (KPD) as the phenotype and both the microsatellite and single-nucleotide polymorphism (SNP) marker maps. Analyses of affected sib pairs for Danacaa, Karangar, and Aipotu (the populations with only nuclear family data) were performed with ALLEGRO  using thX denote all the genotypes and the disease status of all family members in a sample of nuclear families. Let S denote the genotypes of the parents only and the disease status of all family members. Under the null hypothesis that the examined marker is not linked to any disease predisposing locus, the distribution of the children's genotypes conditional on S is independent of the phenotypes and completely determined by Mendelian inheritance. Given any test statistic, randomization of the children's genotypes according to Mendelian probabilities yields a valid test of linkage independent of the distribution of parental genotypes and phenotypes; in particular, tests obtained in this manner are immune to population stratification. The choice of test statistic determines the power of the resulting test. Lewinger and Bull proposed a test statistic based on the standard single locus two-point linkage model with recombination fraction parameter θ, penetrance parameters f0, f1, f2, disease allele frequency p, marker allele frequency q, and a parameter measuring the degree of allelic association between the marker and disease loci, ψ. The test statistic is the conditional likelihood ratio based on this model and is given byBased on the answers, we purchased SNP packets spanning each of the regions containing the disease loci D1-D4. In the populations with significant linkage scan findings, we tested the purchased SNPs for association with KPD status using the test proposed by Lewinger and Bull  and FBATwhere parameters in the numerator are estimated from the conditional likelihoodf0, f1, f2, p, q, ψ) = PrL were obtained in all of the regions spanned by the SNP packets with very similar patterns of p-value "peaks and valleys" for the likelihood ratio test and FBAT. However, the likelihood ratio test gave smaller p-values than the FBAT/TDT for most SNPs and in particular the SNPs flanking disease loci gave significant results with the likelihood ratio test but were not found to be associated by FBAT/TDT. This is consistent with simulations that showed that the likelihood ratio test can be more powerful than the TDT for many scenarios [We performed family-based association tests on all the purchased SNP packets including the ones on chromosome 1, which did not have linkage disequilibrium built into the simulation. Figure cenarios . We wereWhen compared to the Zlr linkage peaks, the FBATs did not succeed in "getting closer" to the true disease loci, but to gauge the full potential narrowing of chromosomal regions with FBAT methods, some form of interval estimate would be required. This is beyond the scope of this study. However, the likelihood ratio test provided strong confirmatory evidence of the presence of disease loci.p-values and the p-values obtained using an asymptotic normal approximation (results not shown), particularly for the likelihood ratio test. This shows the importance of accurate computation of p-values.It is noteworthy that we found considerable discrepancies between exact The initial genome scan identified the four main disease loci in replicates 1 and 66 of the GAW14 simulated data. The regions for follow-up determined by the bootstrap CI based on the peak Zlr and and the 1-LOD support always contained the true disease loci in these two replicates and the likelihood ratio test of Lewinger and Bull  providedCI: Confidence intervalFBAT: Family-based association testGAW14: Genetic Analysis Workshop 14GEE: Generalized estimating equationsIBD: Identity-by-descentKPD: Kofendrerd Personality DisorderSNP: Single-nucleotide polymorphismJPL designed the study, conducted the family-based association analyses, and drafted the manuscript. SSFL assisted in the design of the study, computed the 1-LOD and bootstrap Zlr confidence intervals, and prepared the figures. JB computed the GEE confidence intervals and assisted in designing the study and drafting the manuscript. LYW bootstrapped families to obtain resampling estimates of location. HSS conducted the genome scans and assisted with the association analyses. SBB assisted in designing the study and revising the manuscript. All authors read and approved the final manuscript.


About one quarter of adults are hypertensive and high blood pressure carries increased risk for heart disease, stroke, kidney disease and death. Increased arterial stiffness is a key factor in the pathogenesis of systolic hypertension and cardiovascular disease. Substantial heritability of blood-pressure (BP) and arterial-stiffness suggests important genetic contributions.In Framingham Heart Study families, we analyzed genome-wide SNP (Affymetrix 100K GeneChip) associations with systolic (SBP) and diastolic (DBP) BP at a single examination in 1971–1975 (n = 1260), at a recent examination in 1998–2001 (n = 1233), and long-term averaged SBP and DBP from 1971–2001  and with arterial stiffness measured by arterial tonometry . In primary analyses we used generalized estimating equations in models for an additive genetic effect to test associations between SNPs and phenotypes of interest using multivariable-adjusted residuals. A total of 70,987 autosomal SNPs with minor allele frequency ≥ 0.10, genotype call rate ≥ 0.80, and Hardy-Weinberg equilibrium p ≥ 0.001 were analyzed. We also tested for association of 69 SNPs in six renin-angiotensin-aldosterone pathway genes with BP and arterial stiffness phenotypes as part of a candidate gene search.-5. The lowest p-values for SBP and DBP respectively were rs10493340 (p = 1.7 × 10-6) and rs1963982 (p = 3.3 × 10-6). For the five tonometry phenotypes, five SNPs had p values < 10-5; lowest p-values were for reflected wave  and carotid-brachial pulse wave velocity  in MEF2C, a regulator of cardiac morphogenesis. We found only weak association of SNPs in the renin-angiotensin-aldosterone pathway with BP or arterial stiffness.In the primary analyses, none of the associations attained genome-wide significance. For the six BP phenotypes, seven SNPs yielded p values < 10These results of genome-wide association testing for blood pressure and arterial stiffness phenotypes in an unselected community-based sample of adults may aid in the identification of the genetic basis of hypertension and arterial disease, help identify high risk individuals, and guide novel therapies for hypertension. Additional studies are needed to replicate any associations identified in these analyses. Hypertension affects about one quarter of adults in industrialized countries  and carrThe search for genetic variants contributing to hypertension and arterial stiffness has focused on complementary approaches: linkage applied to rare Mendelian blood pressure disorders and to large family-based studies to identify positional candidate genes, and the study of biologically plausible candidate genes selected by virtue of their role in blood pressure regulation or vascular properties. A great deal is known about mutations responsible for Mendelian blood pressure disorders , but neiGenome-wide association offers the opportunity to conduct analysis of common genetic variants unconstrained by prior knowledge of biological pathways in relation to phenotypes of interest. This approach succeeded in identifying the association of complement factor H with age-related macular degeneration . The FraIn this report we provide results of a genome-wide association study of blood pressure and arterial stiffness, including results of generalized estimating equation (GEE) association testing, FBAT, and linkage, as well as a summary of associations of these phenotypes with candidate genes in the renin-angiotensin-aldosterone pathways.The Framingham Heart Study began in 1948 when 5209 men and women from Framingham, Mass, who were between 28 and 62 years of age were recruited to participate in an observational study . SubjectAt each clinic visit, the examining physician measured the systolic and diastolic BP in the left arm using a mercury column sphygmomanometer. BP was measured twice at each visit, with the exception of the first Offspring Cohort clinic visit, when it was measured once in about half the participants. Systolic and diastolic pressures were determined by the first and fifth Korotkoff sounds, respectively, and the two BP measurements were averaged to derive the systolic and diastolic pressures for that examination.Examination cycles for the two cohorts were overlaid temporally as follows [offspring cohort/original cohort (earliest - latest year)]: examination 1/examination 12 (1971–1975), examination 2/examination 16 (1979–1983), examination 3/examination 18 (1983–1987), examination 4/examination 20 (1986–1991), examination 5/examination 22 (1990–1995), examination 6/examination 24 (1995–1998) and examination 7/examination 26 (1998–2001). Referring to offspring cycle numbers, the six BP phenotypes analyzed for this investigation were residuals for SBP and DBP at Examination 1, at Examination 7, and average of residuals from available Examinations 1 to 7. BP was imputed for treated observations as previously described [2, height, weight, to generate sex-specific residuals.Arterial tonometry for assessment of arterial stiffness was conducted on Offspring Cohort participants attending their 7th clinic examination. Five primary tonometry phenotypes were analyzed: carotid-femoral and carotid-brachial pulse wave velocity, forward and reflected pressure wave amplitude, and mean arterial pressure. Tonometry was performed in the supine position after 5 minutes of rest. Arterial tonometry with simultaneous ECG recording was obtained from brachial, radial, femoral and carotid arteries using a commercially available tonometer . Carotid-brachial, carotid-radial and carotid-femoral PWV were calculated as previously described . Mean arDetails of the genotyping methods are available in the Executive Summary . BrieflyStandardized multivariable-adjusted blood pressure and tonometry residuals were generated as described above. Table GEE and FBAT additive genetic effect models were run for SNPs in or near 6 genes in the renin-angiotensin-aldosterone pathways. These genes were selected a priori because of a substantial body of literature implicating them in hypertension and altered vascular properties. All SNPs from 200 Kb proximal to the start and extending to 200 kb of the terminus of each gene were included in analysis providing the minor allele frequency was >= 0.1, the genotype call rate was 0.8, and the Hardy-Weinberg equilibrium p value was >= 0.001.http://web.ncbi.nlm.nih.gov/projects/gap/framingham/cgi-bin/study.cgi?id=phs000007.The six primary BP phenotypes were examination 1 SBP and DBP (n = 1260), examination 7 SBP and DBP (n = 1233), and long-term averaged SBP and DBP (n = 1327). The five primary arterial stiffness phenotypes were carotid-femoral and carotid-brachial pulse wave velocity, forward and reflected pressure wave amplitude, and mean arterial pressure (n = 644). The study sample available for BP phenotypes included up to 1327 individuals . The complete list of blood pressure and arterial stiffness phenotypes analyzed and the covariates used in generating sex-specific standardized residuals for each phenotype are listed in Table -6), rs10493340 (p = 1.7 × 10-6), and rs6063312 (p = 2.1 × 10-6 for reflected wave amplitude). For the same three phenotype groups the number of associations with p values < 10-5 were 6, 1, and 5, respectively.Results of primary GEE models for an additive genetic effect for DBP, SBP, and arterial stiffness phenotypes are presented in Table -5. Of note, rs10520569 in ADAMTSL3 was associated with DBP (4.2 × 10-5) and SBP (1.4 × 10-4). For arterial stiffness phenotypes there were 2 p values < 10-5, including rs792833 in COL8A1.FBAT models for an additive genetic effect are presented in Table 2 = 0.55) and SBP (h2 = 0.57), and intermediate for the other BP phenotypes (h2 = 0.28–0.45). Among the arterial stiffness phenotypes, heritability was high for the reflected arterial waveform (h2 = 0.66), low for carotid-brachial PWV (h2 = 0.02), and intermediate for the other phenotypes (h2 = 0.22–0.43). These heritability results are consistent with our prior findings [Linkage analyses Table  yielded m h2 = 0.6, low fo h2 = 0.5, and intCCL20  and for SBP was in CDH13 . Of note, SNPs in CDH13, CCL20, and WDR69 were associated with DBP and SBP. GEE general effects models for the tonometry phenotypes identified association of mean arterial pressure with TGFBR2 .Secondary analyses using the GEE general genetic effects model  are presented in Table -5). Geometric means of association results for the 5 tonometry phenotypes considered concurrently yielded its lowest p value  for DCK.Geometric means of GEE association results (additive genetic effect model) for SBP and DBP considered jointly are summarized in Table SNPs in 6 renin-angiotensin-aldosterone pathway genes were analyzed for association with the BP and tonometry phenotypes Table . A total-5 for blood pressure and 5 for arterial stiffness phenotypes. Among the GEE additive effect model results the most likely candidate genes were MEF2C, SYNE1, and TNFSF11, which were associated with arterial stiffness. We have not yet attempted replication of our results. Follow-up genotyping of the top SBP and DBP SNPs reported in our study sample in additional Framingham participants is planned; additional replication attempts will be needed in independent samples to confirm any of the association results we report.We provide results of genome-wide association study for 6 blood pressure and 5 arterial stiffness phenotypes in a carefully characterized study sample. Association analyses and linkage reveal a number of intriguing results. For the GEE model of additive genetic effects Table  there weCOL8A1 with arterial stiffness . This gene codes for type VIII collagen, which is produced by aortic endothelial cells [FBAT Table  identifial cells , suggestLinkage yielded a LOD score of 3, approaching genome-wide significance, for long-term SBP on chromosome 15. A meta-analysis of blood pressure and hypertension linkage studies did not identify this as a region of interest . The lowFor tonometry phenotypes, we found LOD scores for reflected wave amplitude of 5.0 (chromosome 8 at 19 Mb) and 3.2  near peaks for this phenotype that we previously reported in a largely overlapping study sample . Similar-6. Differences in model results may be due to the greater sensitivity of the general model to detect recessive genotype effects. SNPs in CCL20, CDH13, and LPP were associated with both long-term SBP and DBP. GEE general genetic effects models for arterial stiffness phenotypes yielded the lowest p value (p = 1.99 × 10-7) for rs3773643 in TGFBR2, which has been implicated in aortic aneurysm and Loeys-Dietz syndrome [Compared with the primary GEE model for additive genetic effects Table , a diffesyndrome ,19. DisrLOXL2, SYNE1, and MEF2C as attractive candidates. LOXL2 is a member of the lysyl oxidase family of enzymes that initiate cross-linking of collagens and elastin, and alter arterial elasticity [LOX) die perinatally from aortic aneurysm [MEF2C is involved in cardiac morphogenesis and extracellular matrix remodeling [SYNE1 is involved in aortic vascular smooth muscle differentiation [Due to high correlations of SBP and DBP , joint analyses of SBP and DBP added little to what was identified in individual phenotype analyses. In contrast, joint analyses of the five tonometry phenotypes, which are less highly correlated, identified asticity . Collageaneurysm . MEF2C imodeling . SYNE1 intiation . To our Since none of the primary associations attained genome-wide significance, this investigation should be viewed as hypothesis generating. Association analyses for SNPs in six renin-angiotensin-aldosterone pathway genes showed weak evidence of association. Negative results for these candidate genes may be due in part to incomplete linkage disequilibrium coverage of these genes by the SNPs in this genome-wide scan. It is likely that the vast majority of low p values from association analyses are due to chance. Replication studies in other populations, using a genome-wide approach or selective genotyping is needed to establish if any of our results are indicative of true positive associations.We provide results of genome-wide association testing for blood pressure and arterial stiffness phenotypes obtained in a carefully described community-based sample of adults who were recruited without regard to disease status. Additional studies are needed to validate these results. Finding genetic variants associated with hypertension or altered arterial properties may aid in the identification of high risk individuals and in the development of new targeted therapies for hypertension. Our report is one of the earlier genome-wide association studies of blood pressure. Several additional studies, some with larger sample size and others with more dense genome-wide coverage of common variation will follow. In that regard, a 550 k SNP genome-wide association study in approximately 9400 Framingham Heart Study participants across three generations is underway and results from that study will help in the interpretation of the findings we report in this manuscript.DBP = diastolic blood pressure; FBAT = family based association test; GEE = generalized estimating equation; LOD = log of the odds; SBP = systolic blood pressure; SNP = single nucleotide polymorphism.GFM is owner of Cardiovascular Engineering, Inc, a company that designs and manufactures devices that measure vascular stiffness. All other authors declare that they have no competing interests.EJB secured funding for tonometry measurements, assisted in planning the analyses, and critically revised the manuscript. CNC contributed to design, analysis, and critical review of the manuscript. SJH generated the phenotype data, participated in the analysis and interpretation of results. MGL assisted to secure funding for tonometry measurements, generated phenotype data, assisted in planning analyses, and critically revised the manuscript. DL conceived of the FHS tonometry project and assisted in securing funding, planned the analyses, interpreted the results, and drafted the manuscript. GFM conceived of the FHS tonometry project and assisted in securing funding, planned the analyses, and critically revised the manuscript. RSV provided critical input in conceiving the project, securing the funding, planning the analyses and critically revising the manuscript. TJW contributed to design, analysis, and critical review of the manuscript.


Tachycardia is commonly observed in hypertensive patients, predominantly mediated by regulatory mechanisms integrated within the autonomic nervous system. The genetic loci and genes associated with increased heart rate in hypertension, however, have not yet been identified.An F2 intercross of Spontaneously Hypertensive Rats (SHR) × Brown Norway (BN) linkage analysis of quantitative trait loci mapping was utilized to identify candidate genes associated with an increased heart rate in arterial hypertension.Basal heart rate in SHR was higher compared to that of normotensive BN rats . A total genome scan identified one quantitative trait locus in a 6.78 cM interval on rat chromosome 8 (8q22–q24) that was responsible for elevated heart rate. This interval contained 241 genes, of which 65 are known genes.Our data suggest that an influential genetic region located on the rat chromosome 8 contributes to the regulation of heart rate. Candidate genes that have previously been associated with tachycardia and/or hypertension were found within this QTL, strengthening our hypothesis that these genes are, potentially, associated with the increase in heart rate in a hypertension rat model. Heart rate is primarily determined by regulatory mechanisms that are integrated within the autonomic nervous system. In arterial hypertension, an increased heart rate is commonly observed as a compensatory response to decreased stroke volume  ,2. AlthoEven though QTLs for complex diseases have been identified, little progress has been made towards the identification of the genes related to complex traits such as increased heart rate associated with hypertension. Recently, Jaworski et al.  reported23Dd, Hato Rey, Puerto Rico). The pulse pressure signal from the transducer was fed to an amplifier  and then to a 10-bit analog-to-digital converter . Basal heart rate was derived from pulsatile blood pressure. All animal experimental procedures were followed in accordance to the guidelines for animal care and use of laboratory of the University of São Paulo, Brazil and the protocol approved by designated ethics committee.To construct a detailed linkage map of all chromosomes and determine the genetic traits for tachycardia associated with hypertension we analyzed an intercross (F2) progeny (N = 188 rats) of the BN and the SHR strains, and normotensive  and hypertensive  rats as parental strains. All animals were genotyped for 179 polymorphic markers distributed along the rat genome, according to a standard protocol . Basal hThe 188 animals from the F2 progeny were previously genotyped for 179 genetic markers distributed along the whole genome . AlthougTo assess the contribution of a putative QTL to the heart rate trait, we used the composite interval mapping method -28 in thGenes presented on the mapped heart rate QTL were listed by consulting the public database of the rat genome . The selAnnotation of functional classes was carried out using information provided by the Gene Ontology database , accesset test. Correlation between blood pressure and heart rate values was performed calculating the Pearson r correlation coefficient. Statistical significance was establish according to the P value (p < 0.05) for a two-tailed distribution. All data are reported as mean ± SEM.Kolmogorov-Smirnov test (with Lilliefors' correction) was used to test data for normality of the estimated underlying population using SigmaStat 3.11 . Normality was assumed for all parametric tests and regression procedures. All phenotype measurements (blood pressure and heart rate) were compared between each parental strain (SHR × BN) using the Student As expected, inbred parental strains had different levels of basal blood pressure and heart rate Table . SystoliThe linkage signals for the composite interval mapping based on 179 markers along the 21 rat chromosomes  that is concordant with the additive effect obtained by composite interval mapping (-27.3 bpm). These data suggest that the putative QTL, mapped on rat chromosome 8, harbours gene(s) that negatively influence the heart rate levels. Moreover, it should be taken into consideration that the algorithm of the composite interval mapping method considers the influence of other chromosomal regions or genes on the effect of the mapped QTL.Total genome scan analysis has contributed to the discovery of several QTLs associated with different phenotypes in complex diseases. However, although relative success has been achieved in the identification of monogenic diseases ,35, limiIn humans, loci associated with the autonomic nervous system's control of the heart rate have also been mapped . Singh e+ type 4); Chrna3, Chrna5, and Chrnb4 . Interestingly, association studies and genetic engineered mice data support the involvement of some of the heart rate candidate genes in arterial hypertension.In the present investigation, interval mapping revealed 17 genes that are potentially implicated in basal heart rate in the SHR. Among these candidate genes, just 4 families of genes were identified that maintained the association with the heart rate phenotype after the use of certain selection criteria; these genes were Scn2b ; Htr3a and Htr3b (5-Hydroxytryptamine receptors type 3); Hcn4  gene have cardiac cells with "mature" pacemaker potentials, demonstrating that this gene that encodes a hyperpolarization-activated channel is essential for the proper generation of pacemaker potentials in the sinoatrial node [Knockout mice for the hyperpolarization-activated, cyclic nucleotide – gated Kial node . In patiial node . In addiial node . These dThe neuronal nicotinic acetylcholine receptor (Chrn) subtypes α3, α5, and β4 are members of a ligand-gated ion channel family that affect sodium and potassium transients. Knockout mice strains lacking the genes encoding the subtypes α5 and β4 of the nicotinic receptors showed cardiac autonomic dysfunction, affecting the heart rate response to vagal electrical stimulation. In humans, although intragenic polymorphisms on the nicotinic receptor subunits have been identified -45, thesOne potential study limitation is the number of genetic markers used in the genome-mapping experiment. Although a large number of F2 analyses have been reported, genes responsible for SHR hypertension and/or heart rate related phenotypes have not been yet identified. One of the possible explanations for this fact is the small number of markers genotyped in these studies. We used 179 informative markers in our mapping experiment using an intercross between the SHR and BN strains. Although genetic distances and locations of these markers were updated before conducting our analysis, it is possible that QTLs important for the phenotype studied remain unidentified.Our data suggest that genetic markers on the rat chromosome 8 contribute to heart rate control in experimental hypertension. The recognition of genetic determinants of heart rate may help gain insights into the pathophysiology of the autonomic nervous system and offer clues as to its modulation in hypertension. Further studies will be necessary to identify the specific gene(s) in chromosome 8 that are involved in hypertension-related tachychardia and characterize the molecular mechanisms underlying the genetic influence on heart rate in hypertensive rats.The author(s) declare that they have no competing interests.GJJS carried out the molecular genetic studies, participated in the sequence alignment and drafted the manuscript. ACP carried out the molecular genetic studies and drafted the manuscript. EMK conceived of the study, and participated in its design and coordination. JEK conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


BMC Genet 2003, 4(Suppl 1):S82] proposed to improve the power of the Haseman-Elston algorithm by weighting for information available from marker genotypes. The authors did not show, however, the validity of the employed asymptotic distribution. In this paper, we use the simulated data provided for GAW 14 and show that weighting Haseman-Elston by marker information results in increased type I error rates. Specifically, we demonstrate that the number of significant findings throughout the chromosome is significantly increased with weighting schemes. Furthermore, we show that the classical Haseman-Elston method keeps its nominal significance level when applied to the same data. We therefore recommend to use Haseman-Elston with marker informativity weights only in conjunction with empirical p-values. Whether this approach in fact yields an increase in power needs to be investigated further.In the Haseman-Elston approach the squared phenotypic difference is regressed on the proportion of alleles shared identical by descent (IBD) to map a quantitative trait to a genetic marker. In applications the IBD distribution is estimated and usually cannot be determined uniquely owing to incomplete marker information. At Genetic Analysis Workshop (GAW) 13, Jacobs et al. [ The Haseman and Elston (HE) method  is the bp-value dropped from approximately 10-2 to 10-4 on chromosome 5 and from 10-3 to 10-5 on chromosome 12 for the mean systolic blood pressure. However, Jacobs et al. [A different approach has been followed by Jacobs et al.  at the GHere, we pick up their work. We use the same distance metric as Jacobs et al. as well as a different simplex-based weighting scheme and show that the HE regression with weights according to this measure of marker informativity suffers from inflated type I error rates. We illustrate the effect of the weighted HE regression using data from chromosome 4 of the Aipotu population as available for GAW 14. For the analyses, we required and utilized the correct answers for the simulated data.d between the current available identity-by-descent (IBD) marker information and the IBD information for uninformative markers.In the simplex weighting scheme, families are weighted according to their degree of informativity for linkage. The degree of informativity is determined by the Euclidian distance f = , the sum of the perpendiculars fi from f to the sides of the triangle equals the triangle's height, i.e., f0 + f1 + f2 = 1. Therefore, any point in Figure  between IBD points f =  and g = , the IBD distributions f and g have to be mapped to the Cartesian coordinates  and , respectively. The required mapping function can easily be deduced from Figure The calculation of the simplex weights can be illustrated by an equilateral triangle of height 1  and a non-informative marker byIf a genetic marker is non-informative, the IBD distribution equals . We therefore define simplex weights D defined asJacobs et al.  proposedOther measures of informativity might be preferable but are beyond the scope of this paper.n independent sib pairs. Then, the classical HE regression ignores a possible dominance effect and fits the linear modelFor simplicity, we consider a sample of Δ2 = Xγ + ε, whereΩ. The squared phenotypic difference of the ith sib pair is represented by , and πi denotes the proportion of alleles IBD (πi = fi2 + fi1 / 2) of the ith sib pair. In practice, a t-test statistic is employed to test the null hypothesis of no linkage. Under H0 the test statisticand the identity matrix tn-1, where n - 1 denotes the degrees of freedom. The parameter estimators for β and  are given byis asymptotically distributed as Ω no longer represents the identity matrix. Specifically, we aim to use simplex weights as described above, so that Ω becomes a diagonal matrix with elements wi(f). Since the IBD distribution is estimated from the available marker data,  replaces the true matrix Ω. Test statistics and estimates are obtained from equations (3) and (4) where Ω is replaced by its estimate .As soon as weights are introduced, We illustrate the effect of the weighted HE regression by using data from chromosome 4 of Aipotu. The ethnic group as well as the chromosome was randomly chosen. No positive finding in the microsatellite scan can be expected for chromosome 4.We employed a development branch of SIBPAL and GENIBD from S.A.G.E.  to compup < 0.05 was tested. Generalized estimating equations with an autoregressive (AR(1)) working correlation structure, using the replicate as class level indicator, was used for each weighting scheme to investigate whether the proportion of significant findings across the chromosome exceeded 5%. This model adequately adjusts for inter-marker correlations on a chromosome. The estimated proportion of significant findings across the whole chromosome is reported with its corresponding asymptotic 95% confidence interval (CI).We wished to investigate the validity of the asymptotic distribution of the weighted HE methods. This can be achieved by testing whether the number of significant findings across a chromosome is significantly increased. For sake of simplicity, we used the 5% test level for all further investigations. For each genetic marker position, each of the 100 replicates and each weighting scheme, p <0.05 for both weighted and the classical HE regression across the whole chromosome. The Wilcoxon signed rank tested was computed across the 100 independent chromosomes. If the weighted HE methods was too liberal, the number of significant findings would be significantly increased compared with the classical HE method.Furthermore, we want to show that the number of significant findings is greater using the weighted HE methods compared with the classical HE approach. To this end, a Wilcoxon signed rank test was employed. Specifically, we counted the number of microsatellite positions where  = 0.0685; 95% CI: 0.0546–0.0823 for the simplex weighting scheme;  = 0.0634; 95% CI: 0.0562–0.0706 for the weighting scheme proposed by Jacobs et al. [ = 0.0552; 95% CI: 0.0431–0.0673). Furthermore, the number of markers with positive linkage is greater for both weighted HE regressions compared with the classical HE method (p = 3.3 × 10-7 for the simplex weight (Eq. (1)), p = 7.1 × 10-6 for the weights proposed by Jacobs et al. [Table Weighting HE regression models by informativity is an appealing approach. However, some care is needed when applying this approach to real data. If phenotypes are weighted appropriately within families, this may result in substantial gain in statistical power ,3. Instep-values are computed asymptotically from weighted regression models only, the t-test statistic maybe distorted.In this paper, using simulated data available for GAW14, we demonstrated that the HE method with family-wise weights according to marker informativity suffers from inflated type I error levels when we measure informativity as the distance between current IBD marker information and the IBD information for uninformative markers. If p-values until a theoretical solution to the detected problem is found. Furthermore, it needs to be investigated whether empirical p values in fact yield an increase in statistical power for weighted HE compared to the classical HE regression.We therefore recommend the use of weighting functions in conjunction only with empirically computed CI: Confidence intervalGAW: Genetic Analysis WorkshopHE: Haseman and ElstonIBD: Identical by descentAZ had the original idea for the study. DF and AK did the statistical analyses. RCE was an intellectual consultant on the study. AZ and DF wrote the report. All authors read and approved the final manuscript.


The genetic regulation of variation in intra-individual fluctuations in systolic blood pressure over time is poorly understood. Analysis of the magnitude of the average fluctuation of a person's systolic blood pressure around his or her age-adjusted trend line, however, shows moderate, albeit significant, family resemblance in Cohort 1 of the Framingham Heart Study. To determine whether genomic regions affecting this phenotype could be identified, we pursued a "model-free" multipoint quantitative linkage analysis.Two different linkage methods revealed multiple nominally significant signals, two to four of which are "replicated" in Cohort 2. When both cohorts are assembled into extended pedigrees, three linkage signals remain nominally significant by one or both methods.Any or all of the genomic regions in the vicinity of D5S1456, D11S2359, and D20S470 may contain elements that regulate systolic blood pressure homeostasis. Our strategy was to: 1) document family resemblance for the variability of SBP in Cohort 1; 2) use multipoint quantitative linkage analysis to identify genomic regions that may harbor genes that affect within-person variability in SBP; and 3) replicate any findings in Cohort 2. Despite the inherent differences between the two cohorts, if one or more linkage signals are replicated, we will combine the cohorts and repeat the analysis on the extended pedigrees.We used the rich longitudinal Framingham Heart Study data to explore the hypothesis that there is a heritable component to intra-individual magnitude of the fluctuations about each individual's age-related mean SBP. Since we anticipate that different individuals may have unique age-related trends  and since we are not interested in the age-related means, per se, but rather the size of the fluctuations, we defined the phenotype as the average of absolute residual (AVGRES) obtained by regressing each person's SBP measurements on his or her age when the measurements were obtained. Because we wanted to be assured of having a sufficient number of measurements per person, we restricted our Cohort 1 analysis to subjects whose SBP was measured on a minimum of ten occasions. The mean number of SBP measurements for the subsets of Cohort 1 was 19 (SD = 3.5). See Dawber [We are particularly interested in assessing variation in the context of homeostatic regulation. It has been shown that a locus influencing age-adjusted SBP maps to chromosome 17q in these data [e Dawber  for a dep = 0.35, and for females, p = 0.46). All subsequent analyses were carried out on the transformed values of AVGRES.First we evaluated the distribution of AVGRES and found it to be significantly skewed and leptokurtotic in both males and females. A logarithmic (base 10) transformation rendered AVGRES normally distributed in both genders  were genotyped. The distribution of genotyped sibs is as follows: 38 pairs, 6 trios, and 1 quintet.To perform multipoint quantitative linkage analysis for log AVGRES, we chose two methods implemented in the GENEHUNTER linkage program: nonparametric (NP) and expectation maximization Haseman-Elston (EM-HE). The NP method performs a Wilcoxon rank-sum test by first summing the ranks of absolute trait difference from sib pairs, multiplied by a simple weight based on the number of alleles shared identically by descent (IBD). Z scores are then obtained by the usual method .The EM-HE procedure is based on the traditional Haseman-Elston method of regressing the squared sib-pair trait difference on the proportion of alleles shared IBD. In addition, when genotype information is missing, the EM algorithm infers the probability of alleles shared IBD by taking into account the allele sharing distribution as well as the regression parameters estimated from the real data points . SimilarTable The members of Cohort 2 have a maximum of only five SBP assessments (taken over a period of about 20 years). We felt that performing the regression analysis on subjects with fewer than five measurements could not adequately capture the sort of variation we are interested in. Indeed, the relative paucity of Cohort 2 measures may render it fundamentally different from Cohort 1. Restricting the analysis to genotyped sibs who were older than age 20 and had all five SBP assessments, however, yielded a much larger sample size compared to Cohort 1: 151 pairs, 84 trios, 35 quartets, 7 quintets, 4 sextets, and 2 septets.p-value < 0.05 obtained under the same weighting and statistical methodology, then there are two replications: D5S1456 for the NP approach and D11S2359 for the EM-HE approach. Two additional markers (D1S1653 and D20S470) revealed a "reversal". Both of these markers gave a nominally significant signal in Cohort 1 with the EM-HE statistic, but in Cohort 2 it was the NP statistic that achieved nominal "replication".Table Combining available individuals from both cohorts results in 17 new families that were not analyzed in either cohort. Specifically, there are two half-sib pair families, two sib-pair families (that straddle the cohorts), and 13 families with additional avuncular or cousin members. Five large pedigrees were disassembled at a marriage node into smaller units. A total of 305 families were used for the combined analyses.Table Physiological homeostasis is the process whereby a narrow range of phenotypes develops in the presence of wide variation in genotypes and environments. A classic example is the ability of homeothermic mammals to maintain a constant body temperature despite substantial genetic variation and fluctuating ambient air temperatures. The concept of physiological homeostasis bears some similarity to Waddington's notion of "canalization"  althoughDiagnostic and Statistical Manual of Mental Disorders (4th ed.) criteria for major depression are the following: a significant decrease or increase in appetite, insomnia or hypersomnia, psychomotor agitation or psychomotor retardation.That a breakdown in physiological homeostasis can result in either an increase or decrease in a phenotype — thus increasing the variance — is recognized in the operational definitions of many disorders. For example, among the While the preponderance of genetic studies concentrate on the first moment of various phenotypes — and for medical reasons are usually interested in understanding deviantly high  phenotypes — it is reasonable to suppose that the magnitude of an individual's phenotypic variation is to some extent under genetic control . Indeed,r = 0.51) and Cohort 2 (r = 0.29) in the Framingham Heart Study. This previously unrecognized relationship between the use of hypertension medication and AVGRES is likely to have confounded our linkage analysis in an unknown fashion. Second, we did not adjust the data for body mass index (BMI)-a covariate known to show familial resemblance and to affect SBP. Since our phenotype was defined as the average residual from each subject's unique regression line it is unclear what, if any, effect the failure to adjust for BMI had on our results. Third, we did not test whether curvilinear regression would have provided an improved fit compared to simple linear regression. This decision was based on two considerations. First, if for some of the subjects, a quadratic or cubic function were found to fit significantly better than the linear component, its use in a subset of the data would have created a heterogeneous definition of the phenotype. Second, we were aware that the maximum number of available SBP measures on the Cohort 2 subjects was five, and it seemed frivolous to fit a high order function to such meager data.We used the rich longitudinal Framingham Heart Study data to study the genetics of long term variation in SBP. Our findings indicate that there is moderate familial resemblance for the magnitude of the deviation of a person's SBP from his or her unique age-related trend line. At least three caveats need to be mentioned. First, we restricted our Cohort 1 analysis to individuals with 10 or more measurements. Because this cohort was relatively small, we chose to include all subjects who met the above criterion, regardless of whether they were under treatment for hypertension. Subsequent to the Genetic Analysis Workshop 13, however, we carried out an analysis to determine if there exists a relationship between medication usage and AVGRES. We quantified medication usage as the proportion of SBP assessments where the subject was reported to be medicated. Unexpectedly, there is a strong positive correlation for both cohort 1  plots . An additional two signals were found to be nominally significant by EM-HE in Cohort 1 and "replicated" with NP in Cohort 2. Three of these four signals remained nominally significant when extended pedigrees were analyzed.