Whether epidural analgesia for labor prolongs the active-first and second labor stages and increases the risk of vacuum-assisted delivery is a controversial topic. Our study was conducted to answer the question: does lumbar epidural analgesia with lidocaine affect the progress of labor in our obstetric population?395 healthy, nulliparous women, at term, presented in spontaneous labor with a singleton vertex presentation. These patients were randomized to receive analgesia either, epidural with bolus doses of 1% lidocaine or intravenous, with meperidine 25 to 50 mg when their cervix was dilated to 4 centimeters. The duration of the active-first and second stages of labor and the neonatal apgar scores were recorded, in each patient. The total number of vacuum-assisted and cesarean deliveries were also measured.197 women were randomized to the epidural group. 198 women were randomized to the single-dose intravenous meperidine group. There was no statistical difference in rates of vacuum-assisted delivery rate. Cesarean deliveries, as a consequence of fetal bradycardia or dystocia, did not differ significantly between the groups. Differences in the duration of the active-first and the second stages of labor were not statistically significant. The number of newborns with 1-min and 5-min Apgar scores less than 7, did not differ significantly between both analgesia groups.Epidural analgesia with 1% lidocaine does not prolong the active-first and second stages of labor and does not increase vacuum-assisted or cesarean delivery rate. Labor pain is a common phenomenon that can be mitigated by pharmacological and non-pharmacological methods. Epidural techniques can be used to provide complete analgesia during labor ,2, but sWe performed a controlled trial of 395 nulliparous women who were randomized to either epidural analgesia or single dose intravenous meperidine for pain relief during labor. Our primary purpose was to determine whether the use of 1% lidocaine for epidural analgesia during labor affects duration of second labor stage. Our secondary purposes were the rate of cesarean delivery and neonatal apgar score, vacuum-assisted delivery rate, and duration of active-first stage of labor.The study protocol was developed in collaboration with obstetricians at Kashan University of Medical Sciences (KAUMS) and approved by Kashan University of Medical Sciences ethics commitee. Between June 2004 and February 2005, an independent research assistant approched all nulliparous women that met our study inclusion criteria, when these patients were admitted to the maternity unit in Shabih-Khani Hospital, Kashan, Iran. Eligible patients were provided with written and verbal information about the study. Those patients that did not express interest in a particular form of analgesia were enrolled into the study, after they signed witnessed informed consent.The inclusion criteria included nulliparity, active labor, cervical dilatation < or = 4 centimeters, single fetus with vertex presentation, ASA Status = 1, and request for analgesia. Exclusion criteria included ASA status ≥2, age <19 years-old, receiving analgesia prior to enrollment, multiparity, probable cephalopelvic disproportion on pelvic examination, and cervical dilatation to >4 centimeters.Enrolled patients were randomly assigned to receive either epidural or single dose meperidine analgesia during labor. Odd and even numbers were used to allocate patients to either group. All parturients were managed by a nurse anesthetist, under the direct supervision of an anesthesiologist according to the study protocol.©) after each uterine contraction. The frequency and duration of uterine contractions were assessed with the palm of the hand on the uterus. Pelvic examination was performed every hour to evaluate the progress of labor. The aim was to produce a rate of cervical dilation of at least 1 cm/h. When the rate of dilation fell below this, coincidental with hypotonic uterine contractions, oxytocin in a concentration of 10 unit/liter was started at a rate of 6 milliunits/min, increased by 3 milliunits/min at 15-min intervals up to a maximum of 32 milliunits/min and titrated to allow no more than 3 uterine contractions with an acceptable resting duration every 10 minutes.Midwives conducted the obstetric management of all parturients during labor, under the direct supervision of an obstetrician according to the study protocol. Routine intrapartum management of all women included intravenous fluid management and auscultation of the fetal heart with fetal stethoscope or ultrasound fetal heart detector  was obtained before and epidural insertion and again at hourly intervals.Dystocia was diagnosed when adequate uterine activity did not result in progressive cervical dilation or descent of the fetal head. Indications for the use of vacuum were limited to inadequate voluntary pushing or fetal bradycardia. Inadequate voluntary pushing was determined at the bedside when lack of fetal descent due to inadequate maternal expulsive efforts was observed. In the presence of fetal bradycardia, delivery was expedited when the leading point of the fetal skull was at +2 station.In the epidural group, once cervical dilatation reached 4 cm, 500 ml of Ringer solution was administered intravenously, and the parturient was seated in the upright position for epidural placement. The low back was prepared and draped in a sterile fashion. An 18-gauge Tuohy needle was inserted at the L3–L4 or L4–L5 interspace with the loss of resistance to air and an epidural catheter  was advanced. A 3-ml test dose of 1.5% lidocaine containing epinephrine 15 μg was used to exclude intravascular or subarachnoid placement. A 10-ml bolus of 1% lidocaine was administered via the epidural catheter as a loading dose. Mini-doses of 1% lidocaine were then administered at a rate of 1 ml every 6 min. Additional boluses of 2-ml 1% lidocaine were injected to overcome inadequate analgesia and to achieve a bilateral block between the T-10 and T-8 sensory level. The study ended at the time of vaginal delivery, spontaneous or with vacuum extraction, or when the decision was made to perform a cesarean delivery for any reason. If additional analgesia was needed during labor, 2 ml of 1% lidocaine solution were administered epidurally. If anesthesia was needed for assisted delivery or episiotomy, 5 ml of 2% lidocaine was given. Hypotension, defined as a decrease in systolic arterial blood pressure below 110 mm/Hg, was managed with left uterine displacement, increased intravenous fluid replacement, or 5 mg ephedrine boluses. Following the induction and bolus doses of epidural lidocaine, blood pressures and sensory blockade levels were assessed every minute for 15 minutes and every 10 minutes, thereafter. Maternal oxyhemoglobin saturation and heart rate were monitored with pulse oximetry.In the single dose meperidine group, meperidine 25–50 mg (~0.5 mg/kg) was given intravenously at 4 cm uterine cervical dilatation.The mode of delivery  was recorded. Forceps delivery is not a common procedure in our center.U test was used to compare cervical dilatation rate and second-stage durations. Other outcomes were evaluated using unpaired student t test, chi-square analysis, and Fisher exact test as appropriate. P < 0.05 was considered significant.A power analysis was performed for the primary outcome of interest on the basis of our patients, ie, duration of the second labor stage. On the basis of institutional data, I estimated a sample size of 197 women by group with an assumed standard deviation of 100 minutes, based on my experience significant difference between mean of the groups at least 28 minutes, gives power of 80%, and a 2-tailed alpha error of 0.05. Demographic variables were assessed using descriptive statistics. The Mann-Whitney A total of 395 healthy, nulliparous parturients with a single gestation and an uneventful pregnancy in spontaneous labor were randomized in this investigation. All women completed the study as allocated. None of these patients crossed over to the epidural analgesia group. Women in both groups were similar in both obstetric and demographic characteristics as demonstrated in table There was no difference between groups in the incidence of vacuum-assisted or cesarean deliveriestable . The ratAs shown in table We had no crossover from meperidine to epidural analgesia group.No patient in meperidine group had lower extremity motor weakness at any time as measured by modified Bromage score. All patients from epidural group had a modified Bromage score of 2 throughout labor and delivery.vs. intravenous meperidine, 9 ± 1.3; P = 0.09). Women who received epidural analgesia reported lower pain scores during the first stage  and second stage  of labor. Women who received epidural analgesia had a significantly higher incidence of hypotension compared with women who received intravenous meperidine . There was no significant difference in the incidence of nausea and vomiting between the two groups . There were 5 accidental dural puncture among epidural group (2%). There was no case of incomplete analgesia or urinary retention.The preanalgesic visual analog pain scale scores were similar between the two study groups  patients did not want the physician to leave their bedside; (2) epidural pumps were not available; (3) bupivacaine was not available, when the study was initiated; and (4) obstetric co-workers lacked experience with epidurals.There are some points that make this study stand out from the other trials randomizing laboring women to epidural vs. opioid analgesia: The meperidine dose was quite small. In contrast, most of the randomized trials from the developed world use very large opioid dosage. The cervix was examined hourly. In the developed world, cervical exams are now infrequent. The reported first stage labor lengths therefore are quite accurate. Analgesia was initiated at a uniform cervical dilation. This is unusual in trials from the developed world. While crossovers confound many studies, this study was free from crossover.Even in those views, which in contrary to mine, insist that epidurals prolong the second stage of labor, it is stated that the length of the second stage, even in those lasting up to 6 hours or more, was not related to infant outcome .In summary, lumbar epidural analgesia with 1% lidocaine does not prolong the active-first and second stages of labor and does not increase the vacuum-assisted or cesarean delivery rate, when compared with intravenous meperidine. Epidural labor analgesia can be delivered, even in communities with significant economic, cultural and religious barriers to this technique. The higher intensity of the motor block of lidocaine was not associated with any harm in terms of obstetric outcome, duration of the second stage of labor, obstetric intervention or neonatal outcome.Several studies have helped to confirm the opinion of most anesthesiologists and obstetricians that epidural analgesia only minimally lengthens labor and does not increase the risk of cesarean section.The author(s) declare that they have no competing interests.SN designed and performed the study.The pre-publication history for this paper can be accessed here:


Still, estimates for the proportion of polygenic variance due to shared genes ran as high as 85% for some trait pairs. Univariate and multivariate linkage analyses replicate previous linkage results on chromosome 17 . In this study, multivariate analysis provides no increase in power; this is likely due to the strong positive correlation in systolic blood pressure measured at different ages.Multivariate variance-components analysis provides several advantages over univariate analysis when studying correlated traits. It can test for pleiotropy or  gene × age interaction. It can also have more power than univariate analyses to detect a quantitative trait locus influencing several traits. We apply multivariate variance components to longitudinal systolic blood pressure data from the Framingham Heart Study. We find evidence for a polygenic influence on blood pressure (heritabilities at different ages range from 27% to 38%). Tests based on a factor-analytic parameterization of the polygenic variance find significant ( High blood pressure is a complex disorder that results from environmental and genetic factors and their interactions. Levy et al.  found evde Andrade et al.  recentlyde Andrade et al.  defined We apply univariate and multivariate variance components to systolic blood pressure measurements on subjects from the Framingham Heart Study taken in four different age ranges. We consider models with a polygenic component and both a polygenic and major gene component. We describe and apply a test of the null hypothesis of complete pleiotropy versus the alternative of incomplete pleiotropy based on a factor-analytic parameterization of the polygenic variance component. We reject the null hypothesis of complete pleiotropy, suggesting a different set of genes influence systolic blood pressure at different ages. We find linkage signals on chromosome 17 consistent with the earlier report of Levy et al. [Phenotype data were available for 2885 subjects from 330 pedigrees  from the Framingham Heart Study. The data included age, systolic blood pressure (mm Hg), hypertension treatment (yes/no), sex, height, and weight measured at 2- to 4-year intervals. Data were not available on every subject at the same set of ages because of staggered entry, drop-out, and intermittent missing data.To ensure that we had phenotype data on comparable ages for as many subjects as possible, we averaged systolic blood pressure (SBP) and body mass index (BMI) over any measurements taken during four age intervals: younger than 35 years; between 35 and 50; between 50 and 55; and older than 65. On average, 1.6, 5.3, 6.3, and 5.9 SBP measurements were available on original cohort members in these four age intervals, respectively. The corresponding numbers for the offspring cohort were 1.8, 2.2, 2.2, and 1.6 . Interval-specific hypertension treatment phenotypes were defined to be "yes" if the subject received any hypertension treatment during the interval and "no" otherwise.We adjusted SBP for the effect of hypertension treatment using a procedure similar to that outlined by Levy et al.  for eachMarker genotype data were available on 1702 subjects. We used data on 16 markers along chromosome 17; the markers were roughly equidistant, one per 10 cM.j = 1,...,Ji, in family i = 1,...,I, let Yij be the p-dimensional vector of SBP residuals. We take p = 1, 2, or 4 for (respectively) the four age intervals considered separately, the two intervals 35–50 and 50–65 considered simultaneously, and all four intervals considered simultaneously.For each subject Yij = μ + X β + aij + gij + eij [X is a matrix of (possibly age-specific) covariates. For these analyses we fit no fixed effects as we regressed on relevant covariates when creating the age-interval-specific trait data. The aij, gij, and eij terms are multivariate normal random effects with mean 0. Here aij is an additive polygenic effect, gij is the additive effect due to a specific locus, and eij is individual-specific error.We use the variance components model ij + eij -10. HereY = (Y11' Y12' ... YIJi')' as the p (Σi Ji) × 1 = p n × 1 concatenated vector of all subjects' trait vectors,Writing Y,Y) = KA + ΠG + I E,Cov sharing probabilities for all possible pairs of subjects ; I is the n × n identity matrix; A is Cov = {σakl2}, with k and l indexing age intervals; G is Cov = {σgkl2}; and E is Cov = {σekl2}. Particular parametric forms for A, G or E – such as autoregressive or exponential decay – can be adopted [p is small, we used the general form for E .where K is the  adopted ,7. In thgij ≡ 0. To test the null hypothesis of complete polygenic pleiotropy, we first fit the constrained model ρkl ≡ 1 by writing A = Δ Δ', with Δ' = . This model is compared to the general model (with the q = p (p - 1)/2 correlation parameters allowed to range between -1 and 1) via a likelihood ratio test. Asymptotically, this test statistic is distributed as the mixture of υr2 variables r = 0,...,q, with mixing probabilities Binom 2q -[kl2 or 2 [Polygenic heritabilities can be estimated by fitting the reduced model with q,r) 2-q . The prokkσall)2 .10 of the likelihood ratio of the model, which estimates G to the polygenic model with gij ≡ 0. We use the general form for A and G. In principle, models with varying constraints on these variance matrices could be compared via Aikake's information criterion.LOD scores for linkage can be calculated by taking the logA = Λ Λ ', withWe used SIMWALK2  to calcu1 is a lower triangular s × s matrix (s <p) and Λ2 is a general (p - s) × s matrix. This amounts to parameterizing A in terms of s independent factors. Taking s = 1 leads to the parameterization A = Δ Δ' described above; taking s = p leads to a Cholesky decomposition of the general form for A.Here ΛBoth the univariate and multivariate analyses find evidence for a polygenic component to SBP. The estimates of polygenic variance for residual SBP from all models are more than 3 standard errors away from 0 Tables ,2,3. Unip-values of 2 × 10-3 and 2 × 10-4 for the two- and four-trait analyses, respectively). This suggests that the set of genes involved in regulating blood pressure differs with age. According to parameter estimates from the unconstrained four-trait analysis (Table Tests for incomplete pleiotropy are significant, however (test statistics of 8.4 and 21.8 with Univariate LOD scores for the 50–65 age range achieve a peak of 2.2 on chromosome 17 at approximately 80 cM. Bivariate lod scores considering the residuals from the 35–50 and 50–65 age ranges peak just over 2.4 at the same location. This position corresponds roughly to that found by Levy et al. [Our results suggest that different sets of genes regulate blood pressure at different ages. Havill and Mahaney  find eviWe also find linkage signals on chromosome 17 consistent with the earlier report .The small increase in multivariate LOD scores over univariate LOD scores likely reflects the fact that when traits are strongly positively correlated, univariate analyses are more powerful . This maIn contrast to our approach, which defines traits based on Framingham subjects' biological age, de Andrade and Olswold  define tA general drawback to the multivariate variance components approach sketched here is that missing observations on subjects with incomplete data are assumed to be missing at random  and henc


TNF-α) promoter genotype/haplotype markers. Each patient's disease activity was assessed by the disease activity score using 28 joint counts (DAS28) and functional capacity by the Health Assessment Questionnaire (HAQ) score. Systemic manifestations, radiological damage evaluated by the Sharp/van der Heijde (SvdH) score, disease-modifying anti-rheumatic drug use, joint surgeries, and work disability were also assessed. The promoter region of the TNF-α gene, between nucleotides -1,318 and +49, was sequenced using an automated platform. Five hundred fifty-four patients were evaluated and genotyped for 10 single-nucleotide polymorphism (SNP) markers, but 5 of these markers were excluded due to failure to fall within Hardy-Weinberg equilibrium or to monomorphism. Patients with more than 10 years of disease duration (DD) presented significant associations between the -857 SNP and systemic manifestations, as well as joint surgeries. Associations were also found between the -308 SNP and work disability in patients with more than 2 years of DD and radiological damage in patients with less than 10 years of DD. A borderline effect was found between the -238 SNP and HAQ score and radiological damage in patients with 2 to 10 years of DD. An association was also found between haplotypes and the SvdH score for those with more than 10 years of DD. An association was found between some TNF-α promoter SNPs and systemic manifestations, radiological progression, HAQ score, work disability, and joint surgeries, particularly in some classes of DD and between haplotypes and radiological progression for those with more than 10 years of DD.The objective of this study was to assess whether clinical measures of rheumatoid arthritis activity and severity were influenced by tumor necrosis factor-alpha ( TNF-α gene, where at least eight different single-nucleotide polymorphisms (SNPs) are concentrated, with the potential to affect the binding of transcriptor factors and thus to control the activity of the promoter and resulting mRNA and protein levels  , 1.5 mM MgCl2, 0.2 mM dNTPs, and 1 U of BioTaq polymerase (Bioline Ltd.). A 100-ng aliquot of genomic DNA was denatured for 5 minutes at 94°C followed by 35 cycles of amplification  and a final extension step at 72°C for 10 minutes. PCR products were subsequently purified trough incubation with 1 U of ExoSAP-IT  at 37°C for 15 minutes and then at 80°C for an additional 15 minutes. All of the thermal reactions took place in 96-well microtiter plates on a T1 Thermocycler .Genomic DNA was extracted from heparin anticoagulated whole blood, after sedimentation at room temperature, using the commercial kit QIAamp® Terminator version 3.1 Cycle Sequencing Kit  according to the manufacturer's instructions. A 300-ng aliquot of DNA template and the internal overlap primers F2: 5'-TGTGACCACAGCAATGGGTAGG-3', F3: 5'-CCAAACACAGGCCTCAGGACTC-3', and R5: 5'-GAAAGCTGAGTCCTTGAGGGAG-3' were used. Sequencing reactions were carried out with an initial step of 1 minute at 96°C, followed by one cycle of 96°C for 10 seconds and 60°C for 4 minutes (25 times). Purification of sequencing reactions was performed by ethanol precipitation. The purified reaction mixture was analyzed on a four-capillary automated sequencer ABI PRISMR 3100-Avant Genetic Analyzer (Applied Biosystems) with Sequencing Analysis Software version 5.2 (Applied Biosystems).DNA direct sequence analysis was carried out with a BigDyeMultiple linear regression models were used to assess the association between the five polymorphisms and DAS28 and HAQ score. For systemic manifestations, work disability, joint surgeries, and RF, logistic regression models were used instead. All the analyses were adjusted for the effects of socio-demographic and clinical covariates. Except for RF, the models were stratified by DD . In regression models, SNPs were separated into two groups: more prevalent homozygous versus heterozygous .i, i = 1, 2, 3, with i representing the groups. These βi values were supposed to be independent a priori with a non-informative distribution. The group distribution was considered to be categorical with probabilities π1, π2, and π3 following a prior Dirichlet distribution. These estimated probability curves  for the scores are essentially descriptive and should not be used for classification purposes. These curves are expected, one at a time as the SvdH score value increases, to exhibit higher values than the other two curves. The points at which the curves intersect can be seen as cutoff points. As can be seen in Figure SvdH score is known to be strongly associated with DD. To estimate the distribution of the SvdH score in the three different age groups, a hierarchical Bayesian model was built . For eac2 test. The public domain software R  [All SNP analyses, including conformance with Hardy-Weinberg equilibrium, linkage disequilibrium, and haplotype frequency estimation, were performed using Haploview version 3.32 . Conformre Team)  was usedThe 491 patients who were effectively included in the analysis had a mean age of 57 ± 13.3 years, 85% were women, mean DD was 12.7 ± 10.5 years, RF was detected in the serum of 319 patients (65%), systemic manifestations were present in 124 patients (25%), the mean DAS28 was 4.1 ± 1.5, the mean HAQ score was 1.2 ± 0.8, the mean modified SvdH score was 117.2 ± 77.8, 113 patients (23%) had been submitted to joint surgeries, and 282 (57%) were not working due to RA. The patients had been previously treated with 2.3 ± 1.6 DMARDs. The characteristics of the RA population studied were similar to the disease pattern previously described in Portugal  (Table 2p < 0.05), as well as joint surgeries (p < 0.01). Associations were also found between the -308 SNP and work disability in patients with 2 to 10 years (p < 0.05) and those with more than 10 years (p < 0.01) of DD. DAS28 was weakly associated (p < 0.10) with the -863 SNP for those with a DD of less than 2 years and with the -308 SNP for patients with from 2 to 10 years of DD.A total of 554 patients were evaluated and genotyped for 10 SNP markers, and five of these markers were excluded due to failure to fall within Hardy-Weinberg equilibrium or to monomorphism. For the remaining five markers , haplotypes were constructed using an accelerated EM algorithm implemented in the Haploview software. The subsequent analysis was carried out in a subgroup of 491 Caucasian patients with at least one of the five SNPs identified. In the analysis of the five SNPs (Table p = 0.052), and a weak association (p < 0.10) was detected between the -863, -857, and -238 SNPs and RF. Due to a lack of data in some of the covariates, the models for systemic manifestations, work disability, and joint surgery applied to the group of patients with less than 2 years of DD were not able to make an accurate estimation of the association (Table A borderline effect was found between the -238 SNP and HAQ score in patients with 2 to 10 years of DD (p < 0.05). For those with more than 10 years of DD, a weak association between the -238 SNP and SvdH score was detected (p < 0.10). Given that combinations of allelic variants at each of the five markers may provide valuable information regarding functional variation in terms of TNF-α transcription, we estimated haplotype frequencies for each particular subgroup of patients and evaluated its association with clinical covariates in addition to analyzing the association of individual SNPs with clinical covariates  and systemic manifestations, radiological progression, work disability, and joint surgeries. A weak association was also observed between other SNPs (-863 and -238) and DAS28, HAQ score, and RF, particularly in some categories of DD. In addition, an association between haplotypes and radiological progression for those with more than 10 years of DD was detected.TNF-α transcription [TNF-α promoter positions on the production of TNF-α, consistent with the association that we found with RA long-term outcome measures.Previous reports regarding the -308 position are contradictory, and different works suggest that both the -308GG  and -308cription . To concAnother nearby SNP, at position -863, is involved in NF-κB binding, and a report has suggested that the rare -863A allele is associated with a lower transcriptional activity , which cTNF-α gene polymorphisms is the one in position -238 (G→A). Different authors have associated both the -238A and -238G allelic forms to high TNF-α production but with clearly contrasting results [One of the most studied  results ,29. A si results . Regardl results ,32, so tTNF-α gene promoter polymorphisms influence the outcome of this chronic disease. Despite this evidence, the value of genotyping RA patients in order to define their clinical course will remain unproven until a proper prospective evaluation of this cohort of patients validates this hypothesis.Although genetic influences on RA outcome remain incompletely understood, our results suggest that ACR = American College of Rheumatology; DAS28 = disease activity score using 28 joint counts; DD = disease duration; DMARD = disease-modifying anti-rheumatic drug; EM = expectation-maximization; ESR = erythrocyte sedimentation rate; HAQ = Health Assessment Questionnaire; NF-κB = nuclear factor-kappa-B; PCR = polymerase chain reaction; RA = rheumatoid arthritis; RF = rheumatoid factor; SNP = single-nucleotide polymorphism; SvdH = Sharp/van der Heijde; TNF-α = tumor necrosis factor-alpha.The authors declare that they have no competing interests.JEF conceived the design of the study, coordinated all of the laboratorial work, personally observed most of the patients and coordinated and trained others to perform the same observations, coordinated and trained observers for the use of the SvdH method, participated in the statistical analysis, and coordinated all phases of manuscript writing. JC and JT carried out the molecular genetic studies, participated in the statistical analysis, and helped to draft the manuscript. ES carried out the x-ray evaluation using the SvdH method. VLA, MA, and MAA-T participated in the statistical analysis and helped to draft the manuscript. HC participated in the clinical data collection and helped to draft the manuscript. AFM, MS, PN, MJS, AM, MC, RM, A Braña, LM, JVP, A Barcelos, JCdS, LMS, GF, MR, HJ, and AQ participated in the clinical data collection. JL participated in the x-ray evaluation using the SvdH method. JC-L and PW participated in the molecular genetic studies. TC coordinated and designed part of the laboratorial work. JAPdS, JB, and MVQ participated in the design of the study and in the draft of the manuscript. All authors read and approved the final manuscript.


Recombination during meiosis is one of the most important biological processes, and the level of recombination rates for a given individual is under genetic control. In this study, we conducted genome-wide association studies to identify chromosomal regions associated with recombination rates. We analyzed genotype data collected on the pedigrees in the Collaborative Study on the Genetics on Alcoholism data provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 single-nucleotide polymorphisms from Affymetrix on 22 autosomal chromosomes were used in our association analysis. Genome-wide gender-specific recombination counts for family founders were inferred first and association analysis was performed using multiple linear regressions. We used the positive false discovery rate (pFDR) to account for multiple comparisons in the two genome-wide scans. Eight regions showed some evidence of association with recombination counts based on the single-nucleotide polymorphism analysis after adjusting for multiple comparisons. However, no region was found to be significant using microsatellites. In this study, seeking to identify regions potentially affecting recombination rates, we conducted genome-wide association studies based on microsatellites and single-nucleotide polymorphisms (SNPs) of the Collabroative Study on the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop 14 (GAW14). A total of 315 microsatellites and 10,081 SNPs from Affymetrix on 22 autosomal chromosomes were analyzed. We found eight regions/thirteen SNPs that showed some evidence of association with recombination counts. No region was found to be significant using microsatellites after adjusting for multiple comparisons based on the positive false discovery rate (pFDR) criterion.Recombination between two homologous chromosomes during meiosis generates novel gene combinations and creates genetic diversity among chromosomes. Furthermore, recombination is critical for proper segregation of homologous chromosomes, and is a major factor shaping linkage disequilibrium (LD) patterns in the genome . Much re-6 at these SNPs' genetic map positions to make them distinguishable. To estimate the number of both maternal and paternal recombination events for each female or male meiosis, we used the Best option in the haplotyping analysis in MERLIN [The COGA data consist of 143 pedigrees with 1,614 individuals, including 1,109 male and female meioses. Genetic maps for microsatellites and SNPs were both provided by GAW14. Some of the distinct SNPs have the same genetic map position, which made inferring recombination events between these SNPs impossible. Therefore, we added 1.0 × 10p ≤ 0.025 were excluded [Because genotyping error may lead to double recombinations within a short distance, it can significantly affect the overall recombination counts. To minimize this impact, the error-checking algorithm implemented in MERLIN, which identifies unlikely genotypes based on double recombination events, was applied and the erroneous genotypes were excluded before applying haplotyping analysis. We used the default parameter in MERLIN, where the erroneous genotypes with a likelihood ratio excluded . The samq-values [q-value is a measure of significance in terms of the pFDR, and it is defined to be the minimum pFDR at which the statistic can be called significant. A pFDR of 5% means that among all of the features that are called significant, 5% of them may correspond to the true null hypotheses on average. To get the q-value for each marker, we used the software QVALUE [p-values obtained from the multiple regressions.We used multiple linear regressions to evaluate the relation between recombination counts and markers across 22 autosomal chromosomes with adjustments for age and gender for both SNPs and microsatellites. Analysis was carried out based on Whites only to reduce potential confounding factors related to ethnic differences. To account for the multiple comparison problem in the two whole-genome scans, we used pFDR through q-values , where ae QVALUE  on the pThe genome-wide gender-specific recombination counts of the founders were obtained through averaging recombination counts in all meioses leading to his/her offspring. For SNPs, we inferred the founders' genome-wide recombination counts from the gametes of 1,334 offspring from 130 nuclear families. For microsatellites, we inferred the founders' genome-wide recombination counts from the gametes of 1,409 offspring from 111 nuclear families. This resulted in 189 founders  who were Whites with information for SNPs and 199 founders  who were Whites with information for microsatellies. Among these founders, 14 of them missed all microsatellite genotype information and 23 of them missed all SNP genotype information. They had no contribution in the multiple regression analysis. Therefore, the distributions of the founders' sex-specific recombination counts plotted in Figure We noted that our inferred female and male genome-wide recombination counts were slightly lower than that from previous studies . One reap-values for each marker, which were not adjusted for multiple comparisons. The corresponding q-values based on the pFDR were calculated using the software QVALUE. We applied the 0.05 q-value cut-off, which gave us 8 regions/13 SNPs that showed some evidence of association with recombination counts. The positions of those regions together with the raw p-values and q-values were summarized in Table q-value cut-off suggested that 1 out of these 13 SNPs may not be associated with recombination counts. For microsatellites, no region was found to be significant after adjusting for multiple comparisons using pFDR.Multiple linear regressions with adjustments for age and gender generated In summary, we have identified several candidate SNPs likely associated with recombination events, and further studies on these genes may help us gain valuable knowledge on recombination, better understand LD patterns, and lead to more efficient methods to map disease genes.COGA: Collaborative Study on the Genetics on AlcoholismGAW14: Genetic Analysis Workshop 14LD: Linkage disequilibriumpFDR: Positive false discovery rateSNP: Single-nucleotide polymorphismSH participated in the design of the study, performed the analysis, and drafted the manuscript. SW helped to obtain recombination counts and preparation of the manuscript. SW, NL, LC, and CO participated in the design and the discussion of the study. HZ conceived the study and helped to draft the manuscript. All authors read and approved the final manuscript.


Brain magnetic resonance imaging (MRI) and cognitive tests can identify heritable endophenotypes associated with an increased risk of developing stroke, dementia and Alzheimer's disease (AD). We conducted a genome-wide association (GWA) and linkage analysis exploring the genetic basis of these endophenotypes in a community-based sample.A total of 705 stroke- and dementia-free Framingham participants  who underwent volumetric brain MRI and cognitive testing (1999–2002) were genotyped. We used linear models adjusting for first degree relationships via generalized estimating equations (GEE) and family based association tests (FBAT) in additive models to relate qualifying single nucleotide polymorphisms  to multivariable-adjusted residuals of 9 MRI measures including total cerebral brain (TCBV), lobar, ventricular and white matter hyperintensity (WMH) volumes, and 6 cognitive factors/tests assessing verbal and visuospatial memory, visual scanning and motor speed, reading, abstract reasoning and naming. We determined multipoint identity-by-descent utilizing 10,592 informative SNPs and 613 short tandem repeats and used variance component analyses to compute LOD scores.SORL1  and abstract reasoning, and in GEE analyses between CDH4  and TCBV. SORL1 plays a role in amyloid precursor protein processing and has been associated with the risk of AD. Among the 50 strongest associations (25 each by GEE and FBAT) were other biologically interesting genes. Polymorphisms within 28 of 163 candidate genes for stroke, AD and memory impairment were associated with the endophenotypes studied at p < 0.001. We confirmed our previously reported linkage of WMH on chromosome 4 and describe linkage of reading performance to a marker on chromosome 18 (GATA11A06), previously linked to dyslexia (LOD scores = 2.2 and 5.1).The strongest gene-phenotype association in FBAT analyses was between Our results suggest that genes associated with clinical neurological disease also have detectable effects on subclinical phenotypes. These hypothesis generating data illustrate the use of an unbiased approach to discover novel pathways that may be involved in brain aging, and could be used to replicate observations made in other studies. PDE4D, ALOX5AP) [APOE4) [Age-related neurological diseases such as stroke and dementia represent a substantial population burden, and one in three persons will develop either stroke or dementia in their lifetime . Twin stALOX5AP) -11 or Al [APOE4) , in the Volumetric brain MRI and comprehensive cognitive testing have been used to define heritable, reproducible, quantitative endophenotypes which in turn relate to the risk of developing dementia or stroke -19. Twinth Offspring examination, did not attend this examination, declined or were unable to complete MRI or cognitive testing, 12 persons were excluded for prevalent stroke (n = 12) at the time of MRI and cognitive testing and 11 persons with neurological diseases such as multiple sclerosis or brain tumor that could impact study phenotypes were also excluded; all participants were screened, but none required exclusion for dementia at the time of MRI. Nine individuals were excluded because covariate information was not available. This study was approved by the Institutional Review Board of Boston University Medical Center; all participants provided written informed consent including consent for genetic studies.The study design, selection criteria and participant demographics of the Framingham Original and Offspring cohorts have been detailed in prior publications ,22. A toThe list of study phenotypes is shown in Column 1 of Table http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Digital information from the MRI scans was transferred to a central laboratory directed by one of the authors (C.D.) for processing and analysis. Analysis was done blind to the subjects' genotype, demographic and vascular risk factor data. Analyses were done using semi-automated measurements of pixel distributions based on mathematical modeling of MRI pixel intensity histograms for cerebrospinal fluid and brain matter (white matter and gray matter) to determine the optimal pixel intensity threshold that distinguished cerebrospinal fluid (CSF) from brain matter. Brain volume was determined in coronal sections by manually outlining the intracranial vault above the tentorium to determine the total cranial volume (TCV). Next, the skull and other non-brain tissues were removed from the image, followed by mathematical modeling to determine total brain volume (TBV). TBV included the supratentorial gray and white matter and excluded the CSF. We used the ratio of TBV to TCV  as a measure of brain volume to correct for differences in head size. Regional brain volumes were measured as the sum of the segmented right and left lobar volumes for that region indexed to the intracranial volume; frontal (FBV), parietal (PBV), occipital (OBV) and temporal (TBV) lobar brain volumes and the regional brain volume of the hippocampus (based on hand-drawn outlines) were assessed. Two measures of ventricular volume were used: the lateral ventricular volume, and the temporal horn volume each of which was measured as the sum of the volumes for two sides, log-normalized and indexed over TCV. Finally the white matter hyperintensity volume was measured as a z-score within 10-year age- and sex-specific categories of the logarithmically transformed continuous variable (WMH). All analyses were performed using a custom-designed image analysis package, QUANTA 6.2, operating on a Sun Microsystems  Ultra 5 workstation. The inter-rater reliabilities ranged between 0.90 and 0.94 for TCV, TCB, regional brain and ventricular volumes and white matter hyperintensities, and intra-rater reliabilities average 0.98 across all measures.Details of brain MRI acquisition parameters, blinded image analysis, definition of brain volumes  and the mean and standard deviation (SD) values for these measures in the larger sample of all Framingham subjects (n = 2259) who underwent brain MRI, have been published previously ,15,24-27Subjects were administered a neuropsychological test battery using standard administration protocols and trained examiners. Details of the tests administered and normative values for the Framingham Original and Offspring cohorts have been previously published ,28. Sinchttp://gmed.bu.edu/about/genotyping.html and the Marshfield short-tandem repeat genotyping performed by the Mammalian Genotyping Service http://research.marshfieldclinic.org/genetics. Only the SNP data were used for GWA studies whereas both SNP and STR data were combined for linkage analyses.The Overview  describeAs detailed in the Overview , we usedth Original cohort and the 6th Offspring examinations) and apolipoprotein E genotype (ε4 +ve/-ve). Unless otherwise specified, covariate data for all 15 phenotypic measures were drawn from the 26th Original cohort and the 7th Offspring examinations. Data from sex-specific regressions were pooled for the SNP-phenotype association and linkage analyses. Winsorized residuals  were used for linkage analysis of phenotypes with departures from normality as assessed by skewness and kurtosis .Multivariable-adjusted trait residuals for the phenotypic traits listed in Table http://genome.ucsc.edu/ [We used several strategies to explore the resulting phenotype-SNP association and linkage results. First, we used an unbiased approach and collated the 50 strongest phenotype-SNP associations  including 25 phenotype-SNP associations each for GEE and FBAT analyses, and all linkage results with a LOD score > 2.0. All SNPs were annotated using the UCSC genome browser tables csc.edu/ ,34 to exNext, we examined the data for genes with pleiotropic effects. We assessed if genes that were associated with TCBV or WMH at p < 0.001  were also associated with at least two of the other brain MRI measures (p < 0.01). We also evaluated if genes that were associated with lower scores on either F1 or F3 at p < 0.001  seemed associated with other cognitive test measures.http://www.alzforum.org/res/com/gen/alzgene [http://sageke.sciencemag.org/cgi/genesdb [Finally, we investigated SNP associations in candidate genes. There are few candidate genes that have been directly linked in prior studies to the endophenotypes described in these analyses. Hence, we investigated genes previously reported to be associated with stroke, Alzheimer's disease, brain aging and vascular dementia in established databases including the NCBI Gene, PubMed and OMIM databases , the Alz/alzgene , and the/genesdb . All SNPCDH4 and TCBV  and this was the only association that achieved genome-wide significance if we applied a conservative Bonferroni correction as detailed in the Overview (p < 5 × 10-8); in FBAT analyses the strongest phenotype-gene association was between a SNP on the gene SORL1  and performance in Sim, a test of abstract reasoning. Assuming an additive genetic model, a minor allele frequency of10% and a very conservative α of 1 × 10-8 we had an 80% power to detect an effect of 0.52 standard deviations (SD) in a given variable. For TCBV this translates to an effect size of 1.71% equivalent to8.5 years of brain aging.The brain aging phenotypic traits available in the Framingham Study 100K SNP resource with details of the sample size, statistical transformation and details of the covariates used for multivariable adjustment of each phenotype are provided in Table -5 and 9.3 × 10-5) but these SNPs are not within known genes. In GEE analyses, two SNPs on the biologically plausible gene CLDN10 or claudin 10, an integral membrane protein that is a component of the tight junction, were related to WMH . Other extragenic SNPs and SNPs on biologically interesting genes  were also associated with WMH with p values in the 10-5 to 10-4 range. We again observed the linkage between WMH and a region on chromosome 4 that we had previously reported [EVC and EVC1 related to the Ellis van Creveld syndrome and GRK4, previously related to salt-sensitive hypertension [We had previously reported high heritability for WMH. In the current analyses examining associations between individual SNPs and WMH there was one association that was in the top 50 and others that were in the top 100, but none were within the arbitrarily chosen cut-off for Table reported . Within rtension ,40.We observed that performance on the Wide-Range Achievement Test (WRAT), a test of reading ability, was linked to a region on chromosome 18p with a maximum LOD score of 5.1 at rs1846090. The 1.5 LOD support interval of this linkage peak includes an STR marker, D18S53, that has been associated with dyslexia in some prior studies  although2 > 0.80) results are only presented for the strongest phenotype-SNP association noted within the LD block. For the MRI parameters, GEE models identified 10 SNPs and FBAT models identified 7 SNPs using TCBV as the index phenotype and none using WMH as the index phenotype; among these were 4 SNPs on PDE3A and one each on PDE4B and SCN8. For the cognitive phenotypes GEE models identified 7 phenotype-SNP associations using F1 as the key phenotype and 4 using F3 as the key phenotype; FBAT models did not identify any phenotype-SNP associations meeting these prespecified criteria.Table We identified 163 potential candidate genes and looked for phenotype-SNP associations using all SNPs on the 100K Affymetrix gene chip that were within 60 kb of the candidate gene. 23 genes had no analyzable SNPs within the 100K Affymetrix gene chip while 140 genes had 1430 analyzable SNPs within 60 kb of the gene. Table http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. This resource has the potential to detect novel susceptibility genes for brain aging, to examine the relevance within humans of promising candidate gene associations with these diseases reported in animal models, and to replicate findings observed in other cohort studies. We used several strategies to prioritize phenotype-SNP associations, but there remain other unique ways of looking at these data that we and others will continue to explore.This is the first GWA study of volumetric brain MRI and cognitive phenotypes in a community-based sample of adults with data drawn from two generations of persons within the same families. The complete results of the association and linkage analyses are available at our website SORL1 and performance on tests of abstract reasoning . SORL1 is an apolipoprotein E receptor, binds alpha-2-macroglobulin, and is one component of a large multimeric complex, termed the retromer complex that is involved in retrograde transport of proteins from endosomes to the trans-Golgi network [APP) and β-site APP cleaving enzyme (BACE1). SORL1 protein is underexpressed in the frontal lobes of persons with AD compared to controls and the SORL1 gene has recently been associated with the risk of developing AD in 6 population samples [SORL1 gene were evaluated in the 100K Affymetrix gene chip. One of these SNPs on SORL1 that was associated with abstract reasoning  with SNPs  strongly associated with AD in these studies [In our untargeted approach of ranking SNP associations by the strength of the p-value, we found several phenotype-SNP associations within biologically interesting genes Table . The mos network ,44. This samples ,46. Only studies ,46.ERBB4, PDLIM5 and RFX4,  have each been previously associated with schizophrenia or mood disorders, conditions known to be associated with smaller frontal brain volumes and poorer performance on tests of executive function, even in unaffected family members [ERBB4 is a neuregulin (NRG1) receptor involved in forebrain development and N-methyl-D-aspartate (NMDA) receptor function. It has been associated with schizophrenia wherein excess of the IVS 12–15C > T has been noted (odds-ratio 2.98) [NRG1 itself has been associated with schizophrenia in the Icelandic DeCODE population [NRG1, like ERBB4, was associated with frontal brain volume (FBV) in our sample. PDLIM5 polymorphisms have been associated with schizophrenia (rs2433320 and rs2433322) [CDH4, VIPR2, CTNNB1 (GEE rank #1 and FBAT ranks #17 and 24) have been shown in animal studies to play an important role in neural tract and synaptic development [In unbiased analyses, we also identified 3 genes that were associated with measures of frontal or parietal brain volume and with tests of executive function and abstract reasoning. These 3 genes,  members ,48. ERBBio 2.98) ,50. NRG1pulation  and in opulation -54, withpulation , and witpulation ,56. As s2433322) ,55 and b2433322) . Additio2433322) . A finalelopment -61. UsinPDE3A, previously related to all aspects of thrombosis [SCN8A linked to cerebellar ataxia with mental retardation [PDE4B which has been associated with schizophrenia [We examined pleiotropic effects by identifying SNP associations across two sets of related phenotypes. In these analyses, we uncovered a different set of genes, none of which have been related to brain volumes, cognitive function, stroke or dementia in prior population studies. However, there are biologically interesting genes related to brain volumes including rombosis , SCN8A lardation , and PDEophrenia .PDE4D and LTA4H that have been previously related to stroke in several population samples [NGFB, NTRK2 and NTRK3  genes, previously associated with performance on memory tasks in animal studies [BACE1, PRNP and A2M, genes associated with AD in case-control or family-based association studies [VLDLR, a gene previously associated with an increased risk of dementia in the presence of vascular risk factors [LRRK2, a gene associated with an increased risk of Parkinson's disease in population samples [KIBRA) to one of the phenotypes  included in the current analyses. We did not have any SNPs in significant LD with the SNP (rs17070145) described in that study [PDE4D with ischemic stroke, SORL1 with AD) have described allelic heterogeneity. In these studies, multiple SNPs and haplotypes within the gene were associated with the phenotype, even within Caucasian populations [We also evaluated SNPs within some candidate genes previously reported to be associated with stroke and dementia in animal studies or in population samples, and observed that several of these SNPs were associated with MRI and cognitive endophenotypes that increase the risk of these conditions; this gene list is representative but not comprehensive. Among these genes are  samples ,10; NGFB studies ,66; BACE studies ,67,68, V factors  and LRRK samples , but als samples . There hat study . We haveulations -75.Our study had several limitations. A healthy survivor bias is likely as participants in this sample had to survive beyond 1990 to provide DNA. Further, persons undergoing MRI had to travel to an MRI center, provide informed consent, and have no contraindication to the study. We have previously shown that persons undergoing brain MRI were significantly healthier than the overall sample of Framingham participants alive at the time .related persons may have a limited power to uncover associations as compared to the larger sample that includes unrelated subjects (on whom 100K genotyping was not obtained). This is especially true for hippocampal volumes, which were computed based on hand-drawn hippocampal outlines; the number of persons in our study dataset with available hippocampal volumes was only 327. Further, we currently have only a single measure of brain MRI and cognitive tests in these subjects. However, all these participants are being restudied with a second cycle of MRI and cognitive testing. The genes associated with changes in these measures over time may be stronger candidate genes for usual and pathological brain aging processes than the genes related in current analyses to cross-sectional endophenotypes.Our sample of 705 APOE [-6) only tests with a p value < 5 × 10-8) would be considered significant; however others have argued that this is too stringent a threshold since it ignores correlation between individual SNPs [The 100K Affymetrix GeneChip provides limited (~30%) coverage of the genome, with no coverage of several gene rich areas and key candidate genes such as APOE . Howeverual SNPs -79. We eThe untargeted genome-wide approach to detect genetic associations with brain aging identified several biologically interesting genes (such as genes previously related to AD and schizophrenia) as possible novel candidates related to brain structure and function in middle-aged to elderly populations. Our data also suggest that genes previously associated with clinical disease may be associated with clinical endophenotypes known to increase the risk of developing these conditions. Finally, our database will serve as a resource for in silico replication of findings noted in other population-based samples, and in animal models of brain aging, stroke, and neurodegenerative diseases.GWAS = Genome-wide association study; FBAT = family based association testing; GEE = generalized estimating equations; LOD = logarithm of the odds; SNP = single nucleotide polymorphism; MRI = Magnetic resonance imaging; AD = Alzheimer's disease; TCBV = Total Cerebral Brain Volume; WMH = White matter hyperintensity volume; CSF = Cerebrospinal fluid.The authors declare that they have no competing interests.SS was involved in phenotype definition and data collection, planned the analyses, drafted and critically revised the manuscript. ALD planned and conducted the analyses and assisted in writing and critically revising the manuscript. RA was primarily responsible for the definition of cognitive phenotypes, supervised the collection of cognitive and MRI phenotypes and assisted in securing funding, planning the analyses and critically revising the manuscript. JMM and ASB were involved in phenotype definition, planning and conducting the analyses and critically revising the manuscript. CSK and MKH helped define and collect stroke-related phenotypic data and critically revised the manuscript. CD supervised the generation of MRI measurements, helped plan the analyses and reviewed the manuscript. RBD contributed to phenotypic definition, planning of analyses and helped critically revise the manuscript. LDA helped plan and conduct the analyses and critically revised the manuscript. PAW conceived of the Framingham 'MRI, Genetic and Cognitive Precursors of AD and Dementia' study, obtained funding for phenotype collection, helped plan the analyses and critically revised the manuscript. All authors read and approved the final manuscript.Details of test source and parameters used to define each individual test and factor are outlined in Additional Table 1. All SNPs within 60 kb of the candidate genes are listed in Additional Table 2.Click here for file


Increased circulating levels of hemostatic factors as well as anemia have been associated with increased risk of cardiovascular disease (CVD). Known associations between hemostatic factors and sequence variants at genes encoding these factors explain only a small proportion of total phenotypic variation. We sought to confirm known putative loci and identify novel loci that may influence either trait in genome-wide association and linkage analyses using the Affymetrix GeneChip 100K single nucleotide polymorphism (SNP) set.Plasma levels of circulating hemostatic factors  and hematological phenotypes  were obtained in approximately 1000 Framingham Heart Study (FHS) participants from 310 families. Population-based association analyses using the generalized estimating equations (GEE), family-based association test (FBAT), and multipoint variance components linkage analyses were performed on the multivariable adjusted residuals of hemostatic and hematological phenotypes.-16 for factor VII at SNP rs561241, a variant located near the F7 gene and in complete linkage disequilibrium (LD) (r2 = 1) with the Arg353Gln F7 SNP previously shown to account for 9% of total phenotypic variance. The lowest GEE p-value for hematological phenotypes was 7*10-8 at SNP rs2412522 on chromosome 4 for mean corpuscular hemoglobin concentration. We presented top 25 most significant GEE results with p-values in the range of 10-6 to 10-5 for hemostatic or hematological phenotypes. In relating 100K SNPs to known candidate genes, we identified two SNPs  in erythrocyte membrane protein band 4.1-like 2 (EPB41L2) associated with hematological phenotypes (GEE p < 10-3). In linkage analyses, the highest linkage LOD score for hemostatic factors was 3.3 for factor VII on chromosome 10 around 15 Mb, and for hematological phenotypes, LOD 3.4 for hemoglobin on chromosome 4 around 55 Mb. All GEE and FBAT association and variance components linkage results can be found at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007In association analysis, the lowest GEE p-value for hemostatic factors was p = 4.5*10Using genome-wide association methodology, we have successfully identified a SNP in complete LD with a sequence variant previously shown to be strongly associated with factor VII, providing proof of principle for this approach. Further study of additional strongly associated SNPs and linked regions may identify novel variants that influence the inter-individual variability in hemostatic factors and hematological phenotypes. The relationship of hemostasis and thrombosis with atherothrombotic cardiovascular disease has been extensively studied in the past decades. Elevated circulating levels of hemostatic factors, such as fibrinogen -3, plasmIn addition to coagulation proteins, the cellular and rheological components of circulating blood have been implicated in CHD, stroke and peripheral arterial disease, including hematological phenotypes such as hematocrit (HCT), hemoglobin (Hgb), red blood cell count (RBCC) and size, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) ,12, as wCis-acting sequence variants in the following genes – fibrinogen-β (FGB), fibrinogen-α (FGA), fibrinogen-γ (FGG), FVII (F7), and PAI-1 (SERPINE1) – have been associated with corresponding levels of circulating hemostatic factor. By comprehensively characterizing common genetic variation at each of these loci, we have recently clarified that cis-acting variants, in sum, explain a modest proportion of phenotypic variation, ranging from 1% – 10% [1% – 10% ,17. For 1% – 10% -20.Systematic searches for novel genes beyond the known genetic determinants influencing these phenotypes have been carried out using genome-wide linkage analyses with microsatellite markers: Chromosome regions that may harbor novel loci influencing fibrinogen, PAI-1 ,22, hemaThe recent completion of a genome-wide scan using the Affymetrix GeneChip Human Mapping 100K single nucleotide polymorphism (SNP) set on participants in the Framingham Heart Study offered the opportunity to conduct a genome-wide association study (GWAS) and linkage scan for variants that influence hemostatic factors and hematological phenotypes.The Framingham Heart Study design and the genotyping of the Affymetrix GeneChip Human Mapping 100K SNP set on Framingham Heart Study participants are detailed in the overview of this project . To avoiVenous blood samples of Framingham Heart Study Offspring Cohort taken at the first and second examination cycles  were used to measure Hgb, RBCC, MCV and MCH, and samples taken at the fifth examination cycle (1991–1995) were used to measure all the hemostatic factors, platelet aggregation, D-dimer, and viscosity. Fibrinogen was additionally measured at the sixth (1995–1998) and seventh (1998–2001) examination cycles, and PAI-I antigen levels at the sixth exam. Details of the assessment of hemostatic factor levels have been described previously ,26. PlasThe determination of hematological phenotypes has been detailed previously. Platelet aggregation was performed according to the method of Born . The reaHCT was measured by the Wintrobe method . Blood w2 < 0.5. Details of both association and linkage methods are described in the overview of this project [Standardized multivariable adjusted residuals of the hemostatic and hematological phenotypes were computed and used in all the linkage and association analyses. Covariates used in the adjustments were determined based upon what has been reported in the literature as potential risk factors for hemostatic factors or hematological phenotypes. Hardy-Weinberg equilibrium was examined using an exact chi-square test statistic . Associa project .In secondary analyses, we combined the GEE association tests results across multiple phenotypes that may share the common pathway to reduce the type I error rates, and possibly detect SNPs of smaller effect sizes. We ranked SNPs by the number of GEE test p-values less than 0.01, and then by the geometric mean of the GEE test p-values. We also examined the β coefficient from the GEE regression that is the change in the phenotype in one standardized deviation unit with an increment of a copy of the alphabetically second allele . This analysis was conducted for a phenotype assessed using multiple measurement methods such as the platelet aggregation with ADP-, collagen-, and Epi-induced platelet aggregation; or for a phenotype with serial measurements such as fibrinogen level measured at examination cycles 5, 6 and 7.F7, fibrinogen gene cluster , SERPINE1, plasminogen activator-tissue (PLAT), vWF and integrin beta 3 (ITGB3). For hematological phenotypes excluding platelet aggregation, we included erythropoietin receptor (EPOR), erythropoietin (EPO), erythrocyte membrane protein band 4.1-like 2 (EPB41L2), Kruppel-like factor 1(KLF1), heme binding protein 2 (HEBP2), the hemoglobin gene clusters on chromosome 11: hemoglobin-β chain complex (HBB), hemoglobin-δ (HBD), hemoglobin-γ A (HBG1), hemoglobin-γ G (HBG2), hemoglobin-ε 1 (HBE1), and the hemoglobin gene clusters on chromosome 16: hemoglobin-α 1 (HBA1), hemoglobin-α 2 (HBA2), hemoglobin-μ (HBM).We attempted to identify association of 100K SNPs in or within 60 kilo base pairs (kbp) of selected candidate genes previously reported to be associated with hemostatic factors or hematological phenotypes. For hemostatic factors and platelet aggregation phenotypes, we included the following candidate genes in the search: Table Among individuals who were included in the genotyping and had at least one hemostatic factor or platelet aggregation phenotype measured at examination cycle five, 52% were women, mean age was 52 years, and 6% had prevalent CVD. Among individuals who were included in the genotyping and had at least one hematological phenotype measured at examination cycle one or two, 52% were women, with a mean age over the two examinations of 36 years, and 2% had prevalent CVD.-16) for hemostatic factors was obtained from the test of association between circulating levels of FVII and rs561241; this SNP resides near the F7 gene on chromosome 13 and is in complete linkage disequilibrium (LD) (r2 = 1) with the Arg353Gln F7 SNP (rs6046) we previously reported to account for 9% of total phenotypic variance [-8) for hematological phenotypes was obtained in the test of association between MCH and rs1397048 on chromosome 11 near the olfactory receptors, olfactory receptor, family 5, subfamily AP, member 2 (OR5AP2), olfactory receptor, family 5, subfamily AR, member 1 (OR5AR1), olfactory receptor, family 9, subfamily G, member 1(OR9G1) and olfactory receptor, family 9, subfamily G, member 4 (OR9G4). The 25 SNPs with lowest FBAT association test p-values are presented in Additional file We report the 25 SNPs with lowest GEE association test p-values in Table Maximum multipoint LOD scores greater than 2 and the 1.5-LOD support intervals around the maximum LOD scores are presented in Table The top 10 SNPs with most number of p-values < 0.01 and lowest mean p-values are reported in Tables For fibrinogen, the top ranked SNP was rs4861952 on chromosome 4, which was also listed in the Table PLAT. Only SNPs in or near the rest of the candidate genes  are presented. Among all these associations, three reached nominal significance : rs561241 for factor VII, and rs6950982 and rs6956010 for PAI-1.100K SNPs residing in or near known candidate genes for hemostatic factors are presented in Table EPOR, EPO, KLF1, HBA1, HBA2, HBM. For the rest of the candidate genes, associations between hematological phenotypes and 100K SNPs in/near EPB41L2, the beta hemoglobin gene cluster on chromosome 11 , and HEBP2 are presented in Table EPB41L2 with hematocrit (p = 7.7 × 10-5), Hgb (p = 2.9 × 10-4), and RBCC (p = 3.9 × 10-4); SNP rs4897475 with hematocrit (p = 1.6 × 10-4) and Hgb (p = 6.0 × 10-4).Among the candidate genes for hematological traits, no 100K SNP was in or within 60 Kb of F7 SNP rs6046 (Arg353Gln) previously demonstrated to explain about 9% of total phenotypic variation. This association is significant after Bonferroni correction for multiple testing (we used a conservative α = 5 × 10-8), and confirms the strong association at this locus that has previously been reported by us and others. This SNP was also significant  at a nominal α level 0.05 for FBAT and linkage test , but not after Bonferroni correction. That may be explained by the well known fact that FBAT and linkage test are less powerful than population-based association tests.We conducted a GWAS and a genome-wide linkage analyses for hemostatic factors and hematological phenotypes measured in Framingham Heart Study Offspring participants. We identified a highly significant association between factor VII level and SNP rs561241 in complete LD with the FBAT lacks power to detect variants that explain small proportion of variance for this study. It is difficult to distinguish true positives from false ones among FBAT results because it was evident that few 100K SNPs explain a large proportion of variance for hemostatic factors or hematological phenotypes. Given that there is no evidence for major population substructure in FHS  and thercis-acting SNP rs561241 near F7 gene was associated with factor. For hematological phenotypes, we identified rs6811964 near PDGFC, platelet derived growth factor-C. It has been shown that PDGFC highly expressed in vascular smooth muscle cells, renal mesangial cells and platelets, and was likely involved in platelet biology [-5, Table Among the SNPs with top GEE p-values in single phenotype or multiple phenotypes analyses, only a few resided near genes that were known for a likely role in hemostasis and thrombosis and hematological biology. For hemostatic factors, the  biology . This SN-5, 28 SNPs had a GEE p-value greater than 10-5 with multivariable adjusted phenotypes. However, no age and sex adjusted GEE p-value for the 28 SNPs reached genome-wide significance , and no new highly plausible candidate genes resided within 60 Kb of these SNPs. The full disclosure results of all analyses, including the age-sex adjusted analyses, can be viewed at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.Although we only summarized results for multivariable adjusted phenotype, we have also conducted linkage and association analyses for age-sex adjusted phenotypes. It is possible that the effects of some loci may be mediated through the covariates included in multivariable adjustment, and thus only associated with age and sex adjusted phenotypes. Among the 52 SNPs that were associated with age and sex adjusted hemostatic factors or hematological phenotypes with a GEE p-value equal or less than 10There are some limitations to this study. The participants are Caucasian and thus the results may not be generalizable to other racial groups. The study sample size was relatively small, and as such, we may have insufficient power to detect small effects. To avoid worsening the multiple testing problem, we performed only sex-pooled and not sex-specific analyses. There may be some SNPs that are associated with some phenotypes only in female or male undetected in the current study. The advantages of this study are that we had family data, which enabled us to also apply family-based association tests that are robust to population admixture, and linkage analyses that can detect loci not in LD but in linkage with any 100K SNP. The study subjects were recruited without regarding to their phenotypic values, which makes the analyses of multiple phenotypes possible without the need to correct ascertainment bias.Finally, compared with studies focused only on SNPs within candidate genes, GWAS approaches are unbiased and as such they have the advantage of detecting novel genes or confirming genes that are not well-known to have an influence on a phenotype. However, since the current GWAS uses only a subset of all the SNPs in HapMap , it may In summary, we have tested for association and linkage using the Affymetrix 100K SNPs and a set of hemostatic factor and hematological phenotypes. We have confirmed a previously reported association, providing proof of principle (a "positive control") for the GWAS approach. Our results provide a set of hypotheses that warrant testing in additional studies.ADP = adenosine 5'-diphosphate; bp = base pair(s); CHD = coronary heart disease; CVD = cardiovascular disease; CHR = chromosome; EPB41L2 = erythrocyte membrane protein band 4.1-like 2; EPO = erythropoietin; EPOR = erythropoietin receptor; FBAT = family-based association test; FGB = fibrinogen-β; FGA = fibrinogen-α; FGG = fibrinogen-γ; FHS = Framingham Heart Study; FVII = factor VII; GAW = Genetic Analysis Workshop; GEE = generalized estimating equations; GWAS = genome-wide association study; HBA1 = hemoglobin-α 1; HBA2 = hemoglobin-α 2; HBB = hemoglobin-β chain complex; HBD = hemoglobin-δ; HBE1 = hemoglobin-ε 1; HBG1 = hemoglobin-γ A; HBG2 = hemoglobin-γ G; HBM = hemoglobin-μ; HCT = hematocrit; HEBP2 = heme binding protein 2; Hgb = hemoglobin; ITGB3 = integrin, beta 3 ; kb = kilo base pairs ; KLF1 = Kruppel-like factor 1; LD = linkage disequilibrium; MAF = minor allele frequency; MCV = mean corpuscular volume; MCH = mean corpuscular hemoglobin; Mb = mega base pairs ; OR5AP2 = olfactory receptor, family 5, subfamily AP, member 2; OR5AR1 = olfactory receptor, family 5, subfamily AR, member 1; OR9G1 = olfactory receptor, family 9, subfamily G, member 1; OR9G4 = olfactory receptor, family 9, subfamily G, member 4; PAI-1 = plasminogen activator inhibitor; PDGFC = platelet derived growth factor-C; RBCC = red blood cell count; SERPINE1 = serpin peptidase inhibitor, clade E, member 1; SNP = single nucleotide polymorphism; tPA = tissue plasminogen activator; vWF = von Willebrand factor; WBC = white blood cell.The authors declare that they have no competing interests.QY took a leading role in study design, results summarization and interpretation, and manuscript drafting. SK contributed to the study design, summarization and interpretation of the results, and the manuscript revision. JPL contributed to the study of hematological phenotypes. GHT contributed to the measurement and study of the hemostatic traits. COD contributed to study design, results summarization and manuscript drafting and revision. All authors read and approved the final manuscript.The 25 SNPs with lowest FBAT association test p-values are presented in Additional data file Click here for file


A large number of theoretical studies predict that the dynamics of spatially structured populations (metapopulations) can be altered by constant perturbations to local population size. However, these studies presume large metapopulations inhabiting noise-free, zero-extinction environments, and their predictions have never been empirically verified.Here we report an empirical study on the effects of localized perturbations on global dynamics and stability, using fruitfly metapopulations in the laboratory. We find that constant addition of individuals to a particular subpopulation in every generation stabilizes that subpopulation locally, but does not have any detectable effect on the dynamics and stability of the metapopulation. Simulations of our experimental system using a simple but widely applicable model of population dynamics were able to recover the empirical findings, indicating the generality of our results. We then simulated the possible consequences of perturbing more subpopulations, increasing the strength of perturbations, and varying the rate of migration, but found that none of these conditions were expected to alter the outcomes of our experiments. Finally, we show that our main results are robust to the presence of local extinctions in the metapopulation.Our study shows that localized perturbations are unlikely to affect the dynamics of real metapopulations, a finding that has cautionary implications for ecologists and conservation biologists faced with the problem of stabilizing unstable metapopulations in nature. Simple one-dimensional maps can exhibit a variety of dynamic behaviors ranging from stable points to limit cycles to chaos There are several reasons why experimental studies lag far behind the substantial body of theoretical predictions on the issue of metapopulation stabilization by perturbation. Most theoretical studies on the subject have explicitly concentrated on stability in terms of amelioration of chaos to get stable points or limit cycles Drosophila melanogaster metapopulations each. Each metapopulation contained 9 subpopulations, represented by single vial cultures, arranged on the periphery of a circle, with 30% migration in each generation to the two nearest neighbors. In the four pinnedpinned subpopulation) in every generation by adding a fixed number of flies from outside the system, whereas there were no such perturbations in the four control metapopulations. We show that although pinning affects the dynamics of the particular pinned subpopulation, it has no measurable effects on metapopulation dynamics. We also show that Ricker-based simulations capture the patterns observed in the data, indicating that our results are generalizable. We further demonstrate, via simulations, that our findings are robust to the various assumptions made in the experiment regarding the number of pinned patches per metapopulation, the strength of pinning, and migration rates. Finally, we investigate the effects of the interaction of extinction and pinning in shaping metapopulation dynamics and show that our results generally hold even in the absence of local extinctions. Since we explicitly focus on indicators of stability that are ecologically meaningful and can be measured relatively easily, our results are not only of interest to ecologists but have potential practical implications for conservation biologists trying to develop schemes for stabilizing a fragmented population.Here, we investigate whether pinning can stabilize the dynamics of real metapopulations that are generally noisy, finite -sized and prone to local extinctions. We begin by reporting a 21-generation long experiment on the effects of localized perturbations at the subpopulation level on local and global stability, using two sets of four replicate F1,3 = 180.95, p<0.0009) than the mean FI of the remaining eight subpopulations in the pinned metapopulations  between the average FI of the pinned group and the neighboring groups  and subpopulation  level. When we defined an extinct patch as one that remained empty during breeding after migration had taken place, the total number of subpopulation extinctions over 21 generations was considerably less in the pinned metapopulations (39) than in the controls (69). However, this is an artifact of the experimental protocol, as all three subpopulations in the pinned group of the pinned metapopulation were, by design, receiving flies from outside every generation , indicating that pinning did not affect the persistence of subpopulations. Together, these observations suggest that pinning had no effects on stability or any of the other measured attributes of the metapopulation.We then measured various attributes of metapopulation dynamics see , like metion see  and hencDrosophila life-history or ecology in the laboratory that ameliorate the effects of pinning. In case the second hypothesis were true, these results are not likely to be generalizable to other species, and hence would be of limited interest. One way to distinguish between these competing hypotheses is to simulate our experimental system with a biologically relevant model of population dynamics that is broadly applicable to several species and does not include any specific features of Drosophila life-history or laboratory ecology. If such a model were able to capture at least the general features seen in the experimental data, then one would expect our results to be valid for a wide spectrum of organisms.The above experimental observations could have arisen due to two possible reasons: either pinning, at least at the levels used here, genuinely does not affect the dynamics of metapopulations, or there are some unique features of Drosophila exhibit both features, we modeled subpopulation dynamics by the Ricker model rDrosophilaDrosophila specific, and widely applicable.It has been analytically demonstrated that populations with a random spatial distribution and scramble competition follow the Ricker dynamics r values , although there were no distinct patterns , which was again different from the effects under extinction  of D. melanogaster, whose ancestry and maintenance regime has been described elsewhere st day after collection of eggs, the adult flies were sexed, censused, subjected to migration (see below), and allowed to lay eggs for 24 hours in vials containing ∼1 ml banana-jaggery medium. After oviposition, the adults were discarded while the eggs formed the next generation. This maintenance regime  has been extensively studied and is known to induce large amplitude periodic oscillations in population numbers In this experiment we used eight replicate metapopulations of the fruit fly, Drosophila cultures) were arranged on the periphery of a circle, with each of them sending out and receiving migrants to and from the two nearest neighbors. This arrangement can also be visualized as a one-dimensional linear array with periodic boundary condition in terms of migration. Such one-dimensional systems can be found in nature on the shores of lakes or on forest edges. Migration (30%) was imposed by manually removing the required number of flies from a subpopulation and distributing them equally to the two neighboring vials, just prior to reproduction in every generation. Only mated females were migrated, as the dynamics of the population of a sexually reproducing organism is chiefly governed by the number of females. In order to calculate the number of migrant females, the total count in a vial was halved (i.e. a sex ratio of 1∶1 is assumed) and rounded upwards in case of fractions. This number was multiplied by 0.3 (i.e. the migration rate) and rounded off to the nearest even integer, to give the total number of female migrants. There were frequent extinctions in the subpopulations during the course of the experiment. Upon extinction, a vial remained empty until it was recolonized by migrants from a neighboring vial.Following an earlier study Pinning was imposed on four metapopulations by introducing eight mated females every generation to a designated (pinned) subpopulation just before the census. The flies required for this purpose were generated from backup vials that had excess (∼6 ml) food for larvae and yeast supplement for the adults, and were run in parallel with the experimental vials. It should be noted that for a particular metapopulation, the same subpopulation was pinned in every generation. The average subpopulation size in these experiments was found to be ∼26. Thus, the strength of pinning used in this experiment is ∼33% of the average population size. Given that only mated females were migrated, this represents a fairly strong perturbation. Since the pinning flies were introduced prior to the census, a 30% migration rate ensured that at least one female was migrated to each of the neighboring vials. Thus, the pinning strength can also be described as six flies to the pinned vial and one each to the two immediate neighbors. The remaining four metapopulations experienced 30% migration but no pinning and, thus, served as controls.sensutN is the population size at time t and N ¯ is the mean population size over T generations. Thus, FI measures the average one-step fluctuation in population size across generations, scaled by the mean population size. Since it is a dimensionless quantity, FI can be used to compare the dynamics of populations even if they vary widely in size. We measured synchrony as the cross-correlation at lag zero of the first differenced time series of log abundance  of the nearest neighboring subpopulations in a metapopulation We considered a population whose size fluctuates with higher amplitude across time to be less stable than one that has lower amplitude of fluctuation , treating replicate metapopulation as a random factor nested within treatment . All statistical analyses were performed using the commercially available software, STATISTICA ® v 5.0 (Statsoft Inc).tn+1 = tn exp (r(1−tn/K))] tn represents the subpopulation size at time t, and r and K refer to the intrinsic per capita growth rate of the subpopulation and carrying capacity of the patch, respectively. A metapopulation consisted of nine linearly arranged subpopulations, with nearest neighbor migration under periodic boundary condition K (25) and the initial subpopulation size (20) were kept constant, unless explicitly stated. All the subpopulations in a given run had the same value of r with a noise term ε  added to r for each subpopulation at every generation, to simulate stochastic variation in population growth rates. We simulated the experimental system for r values of the subpopulations ranging from 2.0 to 4.0 in increments of 0.1 and for each value of r, we plotted the means and standard errors of 10 independent runs. Estimates of r  and K  were derived by fitting the Ricker map to the individual, untransformed subpopulation time series from the experimental controls (see Parameter estimation below). Thus, the chosen parameter range includes the biologically relevant range for our laboratory populations of Drosophila.The simulation study was designed to be as close to the experimental system as possible. The subpopulation dynamics were modeled using the Ricker model [Coupled map lattices can have very long transients (supertransients) lasting for thousands of iterations pinned subpopulation), in every generation, prior to migration. Since a Ricker map does not take zero values, we stipulated extinction probabilities that were estimated from the time series of the controls (t+1), when the population sizes were low (<10), medium (≥10 and <70) or high (≥70) in the parent generation (t). At an r-value of 2.8, this set of extinction probabilities predicted an average of 5.02 out of 9 subpopulations going extinct per generation, which was higher than the corresponding estimation from the experimental controls (3.69). We also computed the extinction probability profile from the experimental data for bin sizes of <5, 5–70, and >70, and repeated all the simulations with these values of extinction probabilities (data not shown). This predicted an average subpopulation extinction rate of 3.3 out of 9 per generation but did not lead to any qualitatively different predictions at the subpopulation or metapopulation level from those shown in In the simulations seeking to imitate the experimental conditions, the rate of migration was kept constant at 30%. Pinning was modeled by adding 8 individuals to a particular subpopulation , pinning strengths  and migration rates  on the metapopulation dynamics. Since it is known that the distribution of pinned patches can affect the dynamics r and K, were obtained using the in-built Quasi-Newton algorithm of STATISTICA ® v 5.0 (Statsoft Inc) and, on an average, the model was able to explain ∼40% of the variation in the data. While this fraction does appear to be somewhat low, we note that the subpopulations were also undergoing migration in every generation, a fact that was ignored during the modeling procedure, when individual subpopulation time series data were fit to the model. Moreover, the sources of noise in our model are a) white noise in the parameter r, and b) experimentally derived extinction probabilities, whereas a model that explicitly incorporates demographic stochasticity The least-square estimates of the parameters