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1.  Nicotinic Acetylcholine Receptor Variation and Response to Smoking Cessation Therapies 
Pharmacogenetics and genomics  2013;23(2):94-103.
Evaluate nicotinic acetycholine receptor (nAChR) single nucleotide polymorphism (SNP) association with seven day point prevalence abstinence (abstinence) in randomized clinical trials of smoking cessation therapies (RCTs) in individuals grouped by pharmacotherapy randomization to inform the development of personalized smoking cessation therapy.
We quantified association of four SNPs at three nAChRs with abstinence in eight RCTs. Participants were 2,633 outpatient treatment-seeking, self-identified European ancestry individuals smoking ≥10 cigarettes per day, recruited via advertisement, prescribed pharmacotherapy, and provided with behavioral therapy. Interventions included nicotine replacement therapy (NRT), bupropion, varenicline, placebo or combined NRT and bupropion, and five modes of group and individual behavioral therapy. Outcome measures tested in multivariate logistic regression were end of treatment (EOT) and six month (6MO) abstinence, with demographic, behavioral and genetic covariates.
“Risk” alleles previously associated with smoking heaviness were significantly (P<0.05) associated with reduced abstinence in the placebo pharmacotherapy group (PG) at 6MO [for rs588765 OR (95%CI) 0.41 (0.17–0.99)], and at EOT and at 6MO [for rs1051730, 0.42 (0.19–0.93) and 0.31 (0.12–0.80)], and with increased abstinence in the NRT PG at 6MO [for rs588765 2.07 (1.11–3.87) and for rs1051730 2.54 (1.29–4.99)]. We observed significant heterogeneity in rs1051730 effects (F=2.48, P=0.021) between PGs.
chr15q25.1 nAChR SNP risk alleles for smoking heaviness significantly increase relapse with placebo treatment and significantly increase abstinence with NRT. These SNP-PG associations require replication in independent samples for validation, and testing in larger sample sizes to evaluate whether similar effects occur in other PGs.
PMCID: PMC3563676  PMID: 23249876
logistic regression; mediation analysis; nAChR variation; nicotine dependence; pharmacotherapy; randomized clinical trials
2.  A regression model for risk difference estimation in population-based case–control studies clarifies gender differences in lung cancer risk of smokers and never smokers 
Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. Yet technical challenges have limited the consideration of additive risk models in case–control studies.
Using a flexible risk regression model that allows additive and multiplicative components to estimate absolute risks and risk differences, we report a new analysis of data from the population-based case–control Environment And Genetics in Lung cancer Etiology study, conducted in Northern Italy between 2002–2005. The analysis provides estimates of the gender-specific absolute risk (cumulative risk) for non-smoking- and smoking-associated lung cancer, adjusted for demographic, occupational, and smoking history variables.
In the multiple-variable lexpit regression, the adjusted 3-year absolute risk of lung cancer in never smokers was 4.6 per 100,000 persons higher in women than men. However, the absolute increase in 3-year risk of lung cancer for every 10 additional pack-years smoked was less for women than men, 13.6 versus 52.9 per 100,000 persons.
In a Northern Italian population, the absolute risk of lung cancer among never smokers is higher in women than men but among smokers is lower in women than men. Lexpit regression is a novel approach to additive-multiplicative risk modeling that can contribute to clearer interpretation of population-based case–control studies.
PMCID: PMC3840559  PMID: 24252624
Additive risk; Absolute risk; Case–control study; EAGLE; Lung cancer; Risk assessment; Sex factors; Smoking
3.  Gene-Centric Analysis of Serum Cotinine Levels in African and European American Populations 
Neuropsychopharmacology  2011;37(4):968-974.
To date, most genetic association studies of tobacco use have been conducted in European American subjects using the phenotype of smoking quantity (cigarettes per day). However, smoking quantity is a very imprecise measure of exposure to tobacco smoke constituents. Analyses of alternate phenotypes and populations may improve our understanding of tobacco addiction genetics. Cotinine is the major metabolite of nicotine, and measuring serum cotinine levels in smokers provides a more objective measure of nicotine dose than smoking quantity. Previous genetic association studies of serum cotinine have focused on individual genes. We conducted a genetic association study of the biomarker in African American (N=365) and European American (N=315) subjects from the Coronary Artery Risk Development in Young Adults study using a chip containing densely-spaced tag SNPs in ∼2100 genes. We found that rs11187065, located in the non-coding region (intron 1) of insulin-degrading enzyme (IDE), was the most strongly associated SNP (p=8.91 × 10−6) in the African American cohort, whereas rs11763963, located on chromosome 7 outside of a gene transcript, was the most strongly associated SNP in European Americans (p=1.53 × 10−6). We then evaluated how the top variant association in each population performed in the other group. We found that the association of rs11187065 in IDE was also associated with the phenotype in European Americans (p=0.044). Our top SNP association in European Americans, rs11763963 was non-polymorphic in our African American sample. It has been previously shown that psychostimulant self-administration is reduced in animals with lower insulin because of interference with dopamine transmission in the brain reward centers. Our finding provides a platform for further investigation of this, or additional mechanisms, involving the relationship between insulin and self-administered nicotine dose.
PMCID: PMC3280653  PMID: 22089314
cotinine; nicotine; IDE; CARDIA; MORF4L1; IMAT-Broad-CARe; behavioral science; neurogenetics; addiction & substance abuse; pharmacogenetics/pharmacogenomics; cotinine; nicotine; IDE; CARDIA; MORF4L1
4.  Genetic Association of Recovery from Eating Disorders: The Role of GABA Receptor SNPs 
Neuropsychopharmacology  2011;36(11):2222-2232.
Follow-up studies of eating disorders (EDs) suggest outcomes ranging from recovery to chronic illness or death, but predictors of outcome have not been consistently identified. We tested 5151 single-nucleotide polymorphisms (SNPs) in approximately 350 candidate genes for association with recovery from ED in 1878 women. Initial analyses focused on a strictly defined discovery cohort of women who were over age 25 years, carried a lifetime diagnosis of an ED, and for whom data were available regarding the presence (n=361 ongoing symptoms in the past year, ie, ‘ill') or absence (n=115 no symptoms in the past year, ie, ‘recovered') of ED symptoms. An intronic SNP (rs17536211) in GABRG1 showed the strongest statistical evidence of association (p=4.63 × 10−6, false discovery rate (FDR)=0.021, odds ratio (OR)=0.46). We replicated these findings in a more liberally defined cohort of women age 25 years or younger (n=464 ill, n=107 recovered; p=0.0336, OR=0.68; combined sample p=4.57 × 10−6, FDR=0.0049, OR=0.55). Enrichment analyses revealed that GABA (γ-aminobutyric acid) SNPs were over-represented among SNPs associated at p<0.05 in both the discovery (Z=3.64, p=0.0003) and combined cohorts (Z=2.07, p=0.0388). In follow-up phenomic association analyses with a third independent cohort (n=154 ED cases, n=677 controls), rs17536211 was associated with trait anxiety (p=0.049), suggesting a possible mechanism through which this variant may influence ED outcome. These findings could provide new insights into the development of more effective interventions for the most treatment-resistant patients.
PMCID: PMC3176559  PMID: 21750581
GABA; anorexia nervosa; recovery from eating disorders; genetic association; single nucleotide polymorphisms; eating/metabolic disorders; GABA; eating/metabolic disorders; neurogenetics; biological psychiatry; genetic association; anorexia nervosa; recovery from eating disorders; single-nucleotide polymorphisms; phenomic association
5.  Resequencing of Nicotinic Acetylcholine Receptor Genes and Association of Common and Rare Variants with the Fagerström Test for Nicotine Dependence 
Neuropsychopharmacology  2010;35(12):2392-2402.
Common single-nucleotide polymorphisms (SNPs) at nicotinic acetylcholine receptor (nAChR) subunit genes have previously been associated with measures of nicotine dependence. We investigated the contribution of common SNPs and rare single-nucleotide variants (SNVs) in nAChR genes to Fagerström test for nicotine dependence (FTND) scores in treatment-seeking smokers. Exons of 10 genes were resequenced with next-generation sequencing technology in 448 European-American participants of a smoking cessation trial, and CHRNB2 and CHRNA4 were resequenced by Sanger technology to improve sequence coverage. A total of 214 SNP/SNVs were identified, of which 19.2% were excluded from analyses because of reduced completion rate, 73.9% had minor allele frequencies <5%, and 48.1% were novel relative to dbSNP build 129. We tested associations of 173 SNP/SNVs with the FTND score using data obtained from 430 individuals (18 were excluded because of reduced completion rate) using linear regression for common, the cohort allelic sum test and the weighted sum statistic for rare, and the multivariate distance matrix regression method for both common and rare SNP/SNVs. Association testing with common SNPs with adjustment for correlated tests within each gene identified a significant association with two CHRNB2 SNPs, eg, the minor allele of rs2072660 increased the mean FTND score by 0.6 Units (P=0.01). We observed a significant evidence for association with the FTND score of common and rare SNP/SNVs at CHRNA5 and CHRNB2, and of rare SNVs at CHRNA4. Both common and/or rare SNP/SNVs from multiple nAChR subunit genes are associated with the FTND score in this sample of treatment-seeking smokers.
PMCID: PMC3055324  PMID: 20736995
Fagerström test for nicotine dependence; single-nucleotide polymorphism; candidate gene association scan; treatment-seeking smokers; addiction & substance abuse; clinical pharmacology; clinical trials; neurogenetics; acetylcholine
6.  Clinical trial participant characteristics and saliva and DNA metrics 
Clinical trial and epidemiological studies need high quality biospecimens from a representative sample of participants to investigate genetic influences on treatment response and disease. Obtaining blood biospecimens presents logistical and financial challenges. As a result, saliva biospecimen collection is becoming more frequent because of the ease of collection and lower cost. This article describes an assessment of saliva biospecimen samples collected through the mail, trial participant demographic and behavioral characteristics, and their association with saliva and DNA quantity and quality.
Saliva biospecimens were collected using the Oragene® DNA Self-Collection Kits from participants in a National Cancer Institute funded smoking cessation trial. Saliva biospecimens from 565 individuals were visually inspected for clarity prior to and after DNA extraction. DNA samples were then quantified by UV absorbance, PicoGreen®, and qPCR. Genotyping was performed on 11 SNPs using TaqMan® SNP assays and two VNTR assays. Univariate, correlation, and analysis of variance analyses were conducted to observe the relationship between saliva sample and participant characteristics.
The biospecimen kit return rate was 58.5% among those invited to participate (n = 967) and 47.1% among all possible COMPASS participants (n = 1202). Significant gender differences were observed with males providing larger saliva volume (4.7 vs. 4.5 ml, p = 0.019), samples that were more likely to be judged as cloudy (39.5% vs. 24.9%, p < 0.001), and samples with greater DNA yield as measured by UV (190.0 vs. 138.5, p = 0.002), but reduced % human DNA content (73.2 vs. 77.6 p = 0.005) than females. Other participant characteristics (age, self-identified ethnicity, baseline cigarettes per day) were associated with saliva clarity. Saliva volume and saliva and DNA clarity were positively correlated with total DNA yield by all three quantification measurements (all r > 0.21, P < 0.001), but negatively correlated with % human DNA content (saliva volume r = -0.148 and all P < 0.010). Genotyping completion rate was not influenced by saliva or DNA clarity.
Findings from this study show that demographic and behavioral characteristics of smoking cessation trial participants have significant associations with saliva and DNA metrics, but not with the performance of TaqMan® SNP or VNTR genotyping assays.
Trial registration
COMPASS; registered as NCT00301145 at
PMCID: PMC2776600  PMID: 19874586
7.  SLC6A3 and body mass index in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial 
BMC Medical Genetics  2009;10:9.
To investigate the contribution of the dopamine transporter to dopaminergic reward-related behaviors and anthropometry, we evaluated associations between polymorphisms at the dopamine transporter gene(SLC6A3) and body mass index (BMI), among participants in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.
Four polymorphisms (rs6350, rs6413429, rs6347 and the 3' variable number of tandem repeat (3' VNTR) polymorphism) at the SLC6A3 gene were genotyped in 2,364 participants selected from the screening arm of PLCO randomly within strata of sex, age and smoking history. Height and weight at ages 20 and 50 years and baseline were assessed by questionnaire. BMI was calculated and categorized as underweight, normal, overweight and obese (<18.5, 18.5–24.9, 25.0–29.9, or ≥ 30 kg/m2, respectively). Odds ratios (ORs) and 95% confidence intervals (CIs) of SLC6A3 genotypes and haplotypes were computed using conditional logistic regression.
Compared with individuals having a normal BMI, obese individuals at the time of the baseline study questionnaire were less likely to possess the 3' VNTR variant allele with 9 copies of the repeated sequence in a dose-dependent model (** is referent; OR*9 = 0.80, OR99 = 0.47, ptrend = 0.005). Compared with individuals having a normal BMI at age 50, overweight individuals (A-C-G-* is referent; ORA-C-G-9 = 0.80, 95% CI 0.65–0.99, p = 0.04) and obese individuals (A-C-G-* is referent; ORA-C-G-9 = 0.70, 95% CI 0.49–0.99, p = 0.04) were less likely to possess the haplotype with the 3'variant allele (A-C-G-9).
Our results support a role of genetic variation at the dopamine transporter gene, SLC6A3, as a modifier of BMI.
PMCID: PMC2640369  PMID: 19183461
8.  Environment And Genetics in Lung cancer Etiology (EAGLE) study: An integrative population-based case-control study of lung cancer 
BMC Public Health  2008;8:203.
Lung cancer is the leading cause of cancer mortality worldwide. Tobacco smoking is its primary cause, and yet the precise molecular alterations induced by smoking in lung tissue that lead to lung cancer and impact survival have remained obscure. A new framework of research is needed to address the challenges offered by this complex disease.
We designed a large population-based case-control study that combines a traditional molecular epidemiology design with a more integrative approach to investigate the dynamic process that begins with smoking initiation, proceeds through dependency/smoking persistence, continues with lung cancer development and ends with progression to disseminated disease or response to therapy and survival. The study allows the integration of data from multiple sources in the same subjects (risk factors, germline variation, genomic alterations in tumors, and clinical endpoints) to tackle the disease etiology from different angles. Before beginning the study, we conducted a phone survey and pilot investigations to identify the best approach to ensure an acceptable participation in the study from cases and controls. Between 2002 and 2005, we enrolled 2101 incident primary lung cancer cases and 2120 population controls, with 86.6% and 72.4% participation rate, respectively, from a catchment area including 216 municipalities in the Lombardy region of Italy. Lung cancer cases were enrolled in 13 hospitals and population controls were randomly sampled from the area to match the cases by age, gender and residence. Detailed epidemiological information and biospecimens were collected from each participant, and clinical data and tissue specimens from the cases. Collection of follow-up data on treatment and survival is ongoing.
EAGLE is a new population-based case-control study that explores the full spectrum of lung cancer etiology, from smoking addiction to lung cancer outcome, through examination of epidemiological, molecular, and clinical data. We have provided a detailed description of the study design, field activities, management, and opportunities for research following this integrative approach, which allows a sharper and more comprehensive vision of the complex nature of this disease. The study is poised to accelerate the emergence of new preventive and therapeutic strategies with potentially enormous impact on public health.
PMCID: PMC2464602  PMID: 18538025
9.  Linkage analysis of anti-CCP levels as dichotomized and quantitative traits using GAW15 single-nucleotide polymorphism scan of NARAC families 
BMC Proceedings  2007;1(Suppl 1):S107.
Rheumatoid arthritis is a clinically and genetically heterogeneous disease. Anti-cyclic citrullinated (anti-CCP) antibodies have a high specificity for rheumatoid arthritis and levels correlate with disease severity. The focus of this study was to examine whether analyzing anti-CCP levels could increase the power of linkage analysis by identifying a more homogeneous subset of rheumatoid arthritis patients. We also wanted to compare linkage signals when analyzing anti-CCP levels as dichotomized (CCP_binary), categorical (CCP_cat), and continuous traits, with and without transformation (log_CCP and CCP_cont). Illumina single-nucleotide polymorphism scans of the North American Rheumatoid Arthritis Consortium families were analyzed for four chromosomes (6, 7, 11, 22) using nonparametric linkage (NPL) (rheumatoid arthritis and CCP_binary), regress (CCP_cat and Log_CCP), and deviates (CCP_cont) analysis options as implemented in Merlin. Similar linkage results were obtained from analyses of rheumatoid arthritis, CCP_binary, and CCP_cont. The only exception was that we observed improved linkage signals and a narrower region for CCP_binary as compared to a clinical diagnosis of rheumatoid arthritis alone on chromosome 7, a region which previously showed variation in linkage results with rheumatoid arthritis according to anti-CCP levels. Analyses of CCP_cat and Log_CCP had little power to detect linkage. Our data suggested that linkage analyses of anti-CCP levels may facilitate identification of rheumatoid arthritis genes but quantitative analyses did not further improve power. Our study also highlighted that quantitative trait linkage results are highly sensitive to phenotype transformation and analytic approaches.
PMCID: PMC2367471  PMID: 18466447
10.  cis sequence effects on gene expression 
BMC Genomics  2007;8:296.
Sequence and transcriptional variability within and between individuals are typically studied independently. The joint analysis of sequence and gene expression variation (genetical genomics) provides insight into the role of linked sequence variation in the regulation of gene expression. We investigated the role of sequence variation in cis on gene expression (cis sequence effects) in a group of genes commonly studied in cancer research in lymphoblastoid cell lines. We estimated the proportion of genes exhibiting cis sequence effects and the proportion of gene expression variation explained by cis sequence effects using three different analytical approaches, and compared our results to the literature.
We generated gene expression profiling data at N = 697 candidate genes from N = 30 lymphoblastoid cell lines for this study and used available candidate gene resequencing data at N = 552 candidate genes to identify N = 30 candidate genes with sufficient variance in both datasets for the investigation of cis sequence effects. We used two additive models and the haplotype phylogeny scanning approach of Templeton (Tree Scanning) to evaluate association between individual SNPs, all SNPs at a gene, and diplotypes, with log-transformed gene expression. SNPs and diplotypes at eight candidate genes exhibited statistically significant (p < 0.05) association with gene expression. Using the literature as a "gold standard" to compare 14 genes with data from both this study and the literature, we observed 80% and 85% concordance for genes exhibiting and not exhibiting significant cis sequence effects in our study, respectively.
Based on analysis of our results and the extant literature, one in four genes exhibits significant cis sequence effects, and for these genes, about 30% of gene expression variation is accounted for by cis sequence variation. Despite diverse experimental approaches, the presence or absence of significant cis sequence effects is largely supported by previously published studies.
PMCID: PMC2077339  PMID: 17727713
11.  Identification of susceptibility loci for complex diseases in a case-control association study using the Genetic Analysis Workshop 14 dataset 
BMC Genetics  2005;6(Suppl 1):S102.
Although current methods in genetic epidemiology have been extremely successful in identifying genetic loci responsible for Mendelian traits, most common diseases do not follow simple Mendelian modes of inheritance. It is important to consider how our current methodologies function in the realm of complex diseases. The aim of this study was to determine the ability of conventional association methods to fine map a locus of interest. Six study populations were selected from 10 replicates (New York) from the Genetic Analysis Workshop 14 simulated dataset and analyzed for association between the disease trait and locus D2. Genotypes from 45 single-nucleotide polymorphisms in the telomeric region of chromosome 3 were analyzed by Pearson's chi-square tests for independence to test for association with the disease trait of interest. A significant association was detected within the region; however, it was found 3 cM from the documented location of the D2 disease locus. This result was most likely due to the method used for data simulation. In general, this study showed that conventional case-control association methods could detect disease loci responsible for the development of complex traits.
PMCID: PMC1866837  PMID: 16451558
12.  Linkage analysis of the GAW14 simulated dataset with microsatellite and single-nucleotide polymorphism markers in large pedigrees 
BMC Genetics  2005;6(Suppl 1):S14.
Recent studies have suggested that a high-density single nucleotide polymorphism (SNP) marker set could provide equivalent or even superior information compared with currently used microsatellite (STR) marker sets for gene mapping by linkage. The focus of this study was to compare results obtained from linkage analyses involving extended pedigrees with STR and single-nucleotide polymorphism (SNP) marker sets. We also wanted to compare the performance of current linkage programs in the presence of high marker density and extended pedigree structures. One replicate of the Genetic Analysis Workshop 14 (GAW14) simulated extended pedigrees (n = 50) from New York City was analyzed to identify the major gene D2. Four marker sets with varying information content and density on chromosome 3 (STR [7.5 cM]; SNP [3 cM, 1 cM, 0.3 cM]) were analyzed to detect two traits, the original affection status, and a redefined trait more closely correlated with D2. Multipoint parametric and nonparametric linkage analyses (NPL) were performed using programs GENEHUNTER, MERLIN, SIMWALK2, and S.A.G.E. SIBPAL. Our results suggested that the densest SNP map (0.3 cM) had the greatest power to detect linkage for the original trait (genetic heterogeneity), with the highest LOD score/NPL score and mapping precision. However, no significant improvement in linkage signals was observed with the densest SNP map compared with STR or SNP-1 cM maps for the redefined affection status (genetic homogeneity), possibly due to the extremely high information contents for all maps. Finally, our results suggested that each linkage program had limitations in handling the large, complex pedigrees as well as a high-density SNP marker set.
PMCID: PMC1866796  PMID: 16451599
13.  Effects of DNA mass on multiple displacement whole genome amplification and genotyping performance 
BMC Biotechnology  2005;5:24.
Whole genome amplification (WGA) promises to eliminate practical molecular genetic analysis limitations associated with genomic DNA (gDNA) quantity. We evaluated the performance of multiple displacement amplification (MDA) WGA using gDNA extracted from lymphoblastoid cell lines (N = 27) with a range of starting gDNA input of 1–200 ng into the WGA reaction. Yield and composition analysis of whole genome amplified DNA (wgaDNA) was performed using three DNA quantification methods (OD, PicoGreen® and RT-PCR). Two panels of N = 15 STR (using the AmpFlSTR® Identifiler® panel) and N = 49 SNP (TaqMan®) genotyping assays were performed on each gDNA and wgaDNA sample in duplicate. gDNA and wgaDNA masses of 1, 4 and 20 ng were used in the SNP assays to evaluate the effects of DNA mass on SNP genotyping assay performance. A total of N = 6,880 STR and N = 56,448 SNP genotype attempts provided adequate power to detect differences in STR and SNP genotyping performance between gDNA and wgaDNA, and among wgaDNA produced from a range of gDNA templates inputs.
The proportion of double-stranded wgaDNA and human-specific PCR amplifiable wgaDNA increased with increased gDNA input into the WGA reaction. Increased amounts of gDNA input into the WGA reaction improved wgaDNA genotyping performance. Genotype completion or genotype concordance rates of wgaDNA produced from all gDNA input levels were observed to be reduced compared to gDNA, although the reduction was not always statistically significant. Reduced wgaDNA genotyping performance was primarily due to the increased variance of allelic amplification, resulting in loss of heterozygosity or increased undetermined genotypes. MDA WGA produces wgaDNA from no template control samples; such samples exhibited substantial false-positive genotyping rates.
The amount of gDNA input into the MDA WGA reaction is a critical determinant of genotyping performance of wgaDNA. At least 10 ng of lymphoblastoid gDNA input into MDA WGA is required to obtain wgaDNA TaqMan® SNP assay genotyping performance equivalent to that of gDNA. Over 100 ng of lymphoblastoid gDNA input into MDA WGA is required to obtain optimal STR genotyping performance using the AmpFlSTR® Identifiler® panel from wgaDNA equivalent to that of gDNA.
PMCID: PMC1249558  PMID: 16168060
14.  A genome-wide linkage scan for body mass index on Framingham Heart Study families 
BMC Genetics  2003;4(Suppl 1):S97.
Genome-wide scan data from a community-based sample was used to identify the genetic factors that affect body mass index (BMI). BMI was defined as weight (kg) over the square of height (m), where weight and height were obtained from the first measurement available between the ages of 40 and 50 years.
Significant familial correlations were observed in mother:father (spouse) relative pairs and in all relative pairs examined except parent:daughter pairs. Single-point sib-pair regression analysis provided nominal evidence for linkage (p < 0.05) of loci to BMI at 23 markers. Multi-point sib-pair regression analysis provided nominal evidence for linkage to BMI at 42 loci on 12 chromosomes. Empirical p-values showed results consistent with the multi-point results; all but three of the loci identified by multi-point analysis were also significant.
The largest regions of nominally significant linkage were found on chromosomes 2, 3, and 11. The most significant evidence for linkage was obtained with markers D2S1788, D2S1356, D2S1352, D3S1744, and D11S912 from multi-point sib-pair single-trait regression analysis. Our results are in agreement with some of the recently published reports on BMI using various data sets including the Framingham Heart Study data.
PMCID: PMC1866538  PMID: 14975165
15.  Genomic regions linked to alcohol consumption in the Framingham Heart Study 
BMC Genetics  2003;4(Suppl 1):S101.
Pedigree, demographic, square-root transformed maximum alcohol (SRMAXAPD) and maximum cigarette (MAXCPD) consumption, and genome-wide scan data from the Framingham Heart Study (FHS) were used to investigate genetic factors that may affect alcohol and cigarette consumption in this population-based sample.
A significant sister:sister correlation greater than spouse correlation was observed for MAXCPD only. Single-point sib-pair regression analysis provided nominal evidence for linkage of loci to both SRMAXAPD and MAXCPD consumption traits, with more significant evidence of linkage to SRMAXAPD than to MAXCPD. One genomic region, chr9q21.11, exhibits significant multi-point sib-pair regression to SRMAXAPD.
SRMAXAPD exhibits greater evidence for genetic linkage than does MAXCPD in the FHS sample. Four regions of the genome exhibiting nominal evidence for linkage to SRMAXAPD in the FHS sample correspond to regions of the genome previously identified as linked to alcoholism or related traits in the family data set ascertained on individuals affected with alcohol dependence known as COGA.
PMCID: PMC1866439  PMID: 14975169
16.  Performance of high-throughput DNA quantification methods 
BMC Biotechnology  2003;3:20.
The accuracy and precision of estimates of DNA concentration are critical factors for efficient use of DNA samples in high-throughput genotype and sequence analyses. We evaluated the performance of spectrophotometric (OD) DNA quantification, and compared it to two fluorometric quantification methods, the PicoGreen® assay (PG), and a novel real-time quantitative genomic PCR assay (QG) specific to a region at the human BRCA1 locus. Twenty-Two lymphoblastoid cell line DNA samples with an initial concentration of ~350 ng/uL were diluted to 20 ng/uL. DNA concentration was estimated by OD and further diluted to 5 ng/uL. The concentrations of multiple aliquots of the final dilution were measured by the OD, QG and PG methods. The effects of manual and robotic laboratory sample handling procedures on the estimates of DNA concentration were assessed using variance components analyses.
The OD method was the DNA quantification method most concordant with the reference sample among the three methods evaluated. A large fraction of the total variance for all three methods (36.0–95.7%) was explained by sample-to-sample variation, whereas the amount of variance attributable to sample handling was small (0.8–17.5%). Residual error (3.2–59.4%), corresponding to un-modelled factors, contributed a greater extent to the total variation than the sample handling procedures.
The application of a specific DNA quantification method to a particular molecular genetic laboratory protocol must take into account the accuracy and precision of the specific method, as well as the requirements of the experimental workflow with respect to sample volumes and throughput. While OD was the most concordant and precise DNA quantification method in this study, the information provided by the quantitative PCR assay regarding the suitability of DNA samples for PCR may be an essential factor for some protocols, despite the decreased concordance and precision of this method.
PMCID: PMC280658  PMID: 14583097

Results 1-16 (16)