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Nan M Laird

Publications and source records attributed to Nan M Laird.

At least 19 recordsLinked to original sources
PubMed2007

LongCriSP: a test for bump hunting in longitudinal data.

We propose an extension of the Harezlak and Heckman (J. Comput. Graph. Statist. 2001; 10(4): 713-729) test for detecting local extrema to the longitudinal data setting. We use penalized spline regression techniques (Statist. Sci. 1996; 11:89-102) to provide a computationally efficient method of testing for relatively large data sets. We estimate the p-values of our test, LongCriSP, with a smoothed bootstrap. Our simulation studies indicate that the test is generally conservative and has power exceeding 70 per cent at the alpha = 0.1 nominal level in most considered settings. Finally, we apply our testing procedure to the longitudinal measurements of body mass index of former prisoners of war in Vietnam and conclude that the mean population curve exhibits non-monotone behaviour.

Body Mass Index↗
PubMed2007

Incorporating missingness for estimation of marginal regression models with multiple source predictors.

Multiple informant data refers to information obtained from different individuals or sources used to measure the same construct; for example, researchers might collect information regarding child psychopathology from the child's teacher and the child's parent. Frequently, studies with multiple informants have incomplete observations; in some cases the missingness of informants is substantial. We introduce a Maximum Likelihood (ML) technique to fit models with multiple informants as predictors that permits missingness in the predictors as well as the response. We provide closed form solutions when possible and analytically compare the ML technique to the existing Generalized Estimating Equations (GEE) approach. We demonstrate that the ML approach can be used to compare the effect of the informants on response without standardizing the data. Simulations incorporating missingness show that ML is more efficient than the existing GEE method. In the presence of MCAR missing data, we find through a simulation study that the ML approach is robust to a relatively extreme departure from the normality assumption. We implement both methods in a study investigating the association between physical activity and obesity with activity measured using multiple informants (children and their mothers).

Bias↗
PubMed2007

A new multimarker test for family-based association studies.

We propose a new multimarker test for family-based studies in candidate genes. We use simulations under different genetic models to assess the performance of competing testing strategies, characterized in this study as combinations of the following important factors: genes, statistical tests, tag single nucleotide polymorphisms (SNP) methods, number of tag SNPs and family designs. An ANOVA model is employed to provide descriptive summaries of the effects on power of the above-mentioned factors. We find that tag SNP methods, gene characteristics and family designs have minimal impact on the best testing strategy. The familywise error rate (FWER) controlling multiple comparison procedure and the new multimarker test offer the highest power followed by the asymptotic global haplotype test. Both the FWER and the multimarker test are invariant to family designs and gain power as we increase the number of tag SNPs. However, the performance of the global haplotype test is slightly degraded when analyzing larger numbers of tag SNPs. Within the framework of our study, the best strategy for family-based studies in candidate genes that emerged from our analysis is to use the FWER or the multimarker test and select 6-10 tag SNPs using any of the tag SNP methods considered. We confirm the conclusions of our study with an application to Alzheimer's disease data.

Alzheimer Disease↗
PubMed2006

Marginal regression models with a time to event outcome and discrete multiple source predictors.

Information from multiple informants is frequently used to assess psychopathology. We consider marginal regression models with multiple informants as discrete predictors and a time to event outcome. We fit these models to data from the Stirling County Study; specifically, the models predict mortality from self report of psychiatric disorders and also predict mortality from physician report of psychiatric disorders. Previously, Horton et al. found little relationship between self and physician reports of psychopathology, but that the relationship of self report of psychopathology with mortality was similar to that of physician report of psychopathology with mortality. Generalized estimating equations (GEE) have been used to fit marginal models with multiple informant covariates; here we develop a maximum likelihood (ML) approach and show how it relates to the GEE approach. In a simple setting using a saturated model, the ML approach can be constructed to provide estimates that match those found using GEE. We extend the ML technique to consider multiple informant predictors with missingness and compare the method to using inverse probability weighted (IPW) GEE. Our simulation study illustrates that IPW GEE loses little efficiency compared with ML in the presence of monotone missingness. Our example data has non-monotone missingness; in this case, ML offers a modest decrease in variance compared with IPW GEE, particularly for estimating covariates in the marginal models. In more general settings, e.g., categorical predictors and piecewise exponential models, the likelihood parameters from the ML technique do not have the same interpretation as the GEE. Thus, the GEE is recommended to fit marginal models for its flexibility, ease of interpretation and comparable efficiency to ML in the presence of missing data.

Biometry↗
PubMed2006

A common genetic variant is associated with adult and childhood obesity.

Obesity is a heritable trait and a risk factor for many common diseases such as type 2 diabetes, heart disease, and hypertension. We used a dense whole-genome scan of DNA samples from the Framingham Heart Study participants to identify a common genetic variant near the INSIG2 gene associated with obesity. We have replicated the finding in four separate samples composed of individuals of Western European ancestry, African Americans, and children. The obesity-predisposing genotype is present in 10% of individuals. Our study suggests that common genetic polymorphisms are important determinants of obesity.

Adult↗
PubMed2006

Variance calculations for identity-by-descent estimation.

Nonparametric linkage strategies often involve estimation of identity by descent (IBD) with the use of affected sibling pairs. Methods for IBD estimation are well established and have been successful for mapping complex traits. However, the majority of linkage approaches involving IBD have focused on statistical testing, rather than on the effect estimates themselves. Through a bootstrap procedure developed for linkage-scan data sets, we provide standard errors for the estimated mean IBD that are broadly applicable. Applications that benefit from the availability of standard errors include effect-size estimates and confidence intervals; meta-analyses, including tests for heterogeneity; and discordant-sibling-pair evaluation. We demonstrate the use of estimated mean IBD and its standard errors in the National Institute of Mental Health Human Genetics Initiative linkage samples for bipolar disorder and Alzheimer disease. Mean IBD and its standard errors are valuable tools for the further assessment and evaluation of linkage-scan samples involving complex disease.

Alzheimer Disease↗
PubMed2006

Binge-eating disorder as a distinct familial phenotype in obese individuals.

CONTEXT: Binge-eating disorder (BED)-a syndrome that only recently has attracted scientific attention-is often seen in obese individuals, especially those with severe obesity. However, it remains unclear whether BED represents an etiologically distinct behavioral phenotype of obesity or simply a nonspecific eating pattern sometimes seen in obese individuals. OBJECTIVE: To test whether BED aggregates in families independently of obesity, and if so, whether familial factors for BED also independently increase the risk of obesity. DESIGN, PATIENTS, AND SETTING: Blinded family interview study of overweight or obese probands with (n = 150) and without (n = 150) BED, and their first-degree relatives (n = 888) in a community setting evaluated between October 2002 and July 2004. MAIN OUTCOME MEASURES: Lifetime diagnosis of BED; current and highest lifetime body mass index (calculated as the weight in kilograms divided by the square of the height in meters). RESULTS: Binge-eating disorder aggregated strongly in families independently of obesity (odds ratio, 2.2; 95% confidence interval, 1.4-3.6; P<.001). Furthermore, relatives of probands with BED displayed a markedly higher prevalence of severe obesity in adulthood (body mass index >/=40) than relatives of probands without BED even when controlling for proband body mass index (odds ratio, 2.5; 95% confidence interval, 1.4-4.4; P = .002). CONCLUSIONS: Binge-eating disorder is a familial disorder caused in part by factors distinct from other familial factors for obesity. Furthermore, these BED-specific familial factors may independently increase the risk of obesity, especially severe obesity. It follows that targeted interventions capable of preventing or treating traits influenced by these BED-specific familial factors could reduce the public health burden of obesity.

Adolescent↗
PubMed2006

Family-based designs in the age of large-scale gene-association studies.

Both population-based and family-based designs are commonly used in genetic association studies to locate genes that underlie complex diseases. The simplest version of the family-based design--the transmission disequilibrium test--is well known, but the numerous extensions that broaden its scope and power are less widely appreciated. Family-based designs have unique advantages over population-based designs, as they are robust against population admixture and stratification, allow both linkage and association to be tested for and offer a solution to the problem of model building. Furthermore, the fact that family-based designs contain both within- and between-family information has substantial benefits in terms of multiple-hypothesis testing, especially in the context of whole-genome association studies.

Case-Control Studies↗
PubMed2005

Genomic screening in family-based association testing.

Due to the recent gains in the availability of single-nucleotide polymorphism data, genome-wide association testing has become feasible. It is hoped that this additional data may confirm the presence of disease susceptibility loci, and identify new genetic determinants of disease. However, the problem of multiple comparisons threatens to diminish any potential gains from this newly available data. To circumvent the multiple comparisons issue, we utilize a recently developed screening technique using family-based association testing. This screening methodology allows for the identification of the most promising single-nucleotide polymorphisms for testing without biasing the nominal significance level of our test statistic. We compare the results of our screening technique across univariate and multivariate family-based association tests. From our analyses, we observe that the screening technique, applied to different settings, is fairly consistent in identifying optimal markers for testing. One of the identified markers, TSC0047225, was significantly associated with both the ttth1 (p = 0.004) and ttth1-ttth4 (p = 0.004) phenotype(s). We find that both univariate- and multivariate-based screening techniques are powerful tools for detecting an association.

Family↗
PubMed2005

Comparison of linkage and association strategies for quantitative traits using the COGA dataset.

Genome scans using dense single-nucleotide polymorphism (SNP) data have recently become a reality. It is thought that the increase in information content for linkage analysis as a result of the denser scans will help refine previously identified linkage regions and possibly identify new regions not identifiable using the sparser, microsatellite scans. In the context of the dense SNP scans, it is also possible to consider association strategies to provide even more information about potential regions of interest. To circumvent the multiple-testing issues inherent in association analysis, we use a recently developed strategy, implemented in PBAT, which screens the data to identify the optimal SNPs for testing, without biasing the nominal significance level. We compare the results from the PBAT analysis to that of quantitative linkage analysis on chromosome 4 using the Collaborative Study on the Genetics of Alcoholism data, as released through Genetic Analysis Workshop 14.

Alcoholism↗
PubMed2005

Combined analysis from eleven linkage studies of bipolar disorder provides strong evidence of susceptibility loci on chromosomes 6q and 8q.

Several independent studies and meta-analyses aimed at identifying genomic regions linked to bipolar disorder (BP) have failed to find clear and consistent evidence of linkage regions. Our hypothesis is that combining the original genotype data provides benefits of increased power and control over sources of heterogeneity that outweigh the difficulty and potential pitfalls of the implementation. We conducted a combined analysis using the original genotype data from 11 BP genomewide linkage scans comprising 5,179 individuals from 1,067 families. Heterogeneity among studies was minimized in our analyses by using uniform methods of analysis and a common, standardized marker map and was assessed using novel methods developed for meta-analysis of genome scans. To date, this collaboration is the largest and most comprehensive analysis of linkage samples involving a psychiatric disorder. We demonstrate that combining original genome-scan data is a powerful approach for the elucidation of linkage regions underlying complex disease. Our results establish genomewide significant linkage to BP on chromosomes 6q and 8q, which provides solid information to guide future gene-finding efforts that rely on fine-mapping and association approaches.

Bipolar Disorder↗
PubMed2005

The corticotropin-releasing hormone gene and behavioral inhibition in children at risk for panic disorder.

BACKGROUND: Behavioral inhibition to the unfamiliar (BI) is a heritable temperamental phenotype involving the tendency to display fearful, avoidant, or shy behavior in novel situations. BI is a familial and developmental risk factor for panic and phobic anxiety disorders. We previously observed an association between BI and a microsatellite marker linked to the corticotropin releasing hormone (CRH) gene in children at risk for panic disorder. To evaluate this further, we genotyped additional families for this marker and a panel of markers encompassing the CRH locus. METHODS: Sixty-two families that included parents with panic disorder and children who underwent laboratory-based behavioral observations were studied. Family-based association tests and haplotype analysis were used to evaluate the association between BI and polymorphisms spanning the CRH locus. RESULTS: We examined a set of markers which we found to reside in a block of strong linkage disequilibrium encompassing the CRH locus. The BI phenotype was associated with the microsatellite marker (p=.0016) and three single nucleotide polymorphisms (SNPs), including a SNP in the coding sequence of the gene (p=.023). Haplotype-specific tests revealed association with a haplotype comprising all of the markers (p=.015). CONCLUSIONS: These results suggest that the CRH gene influences inhibited temperament, a risk factor for panic and phobic anxiety disorders. Genetic studies of anxiety-related temperament represent an important strategy for identifying the genetic basis of anxiety disorders.

Child↗
PubMed2005

Genomic screening and replication using the same data set in family-based association testing.

The Human Genome Project and its spin-offs are making it increasingly feasible to determine the genetic basis of complex traits using genome-wide association studies. The statistical challenge of analyzing such studies stems from the severe multiple-comparison problem resulting from the analysis of thousands of SNPs. Our methodology for genome-wide family-based association studies, using single SNPs or haplotypes, can identify associations that achieve genome-wide significance. In relation to developing guidelines for our screening tools, we determined lower bounds for the estimated power to detect the gene underlying the disease-susceptibility locus, which hold regardless of the linkage disequilibrium structure present in the data. We also assessed the power of our approach in the presence of multiple disease-susceptibility loci. Our screening tools accommodate genomic control and use the concept of haplotype-tagging SNPs. Our methods use the entire sample and do not require separate screening and validation samples to establish genome-wide significance, as population-based designs do.

Asthma↗
PubMed2004

A family-based association test for repeatedly measured quantitative traits adjusting for unknown environmental and/or polygenic effects.

We propose a family-based association test, FBAT-PC, for studies with quantitative traits that are measured repeatedly. The traits may be influenced by partially or completely unknown factors that may vary for each measurement. Using generalized principal component analysis, FBAT-PC amplifies the genetic effects of each measurement by constructing an overall phenotype with maximal heritability. Analytically, and in the simulation studies, we compare FBAT-PC with standard methodology and assess both the heritability of the overall phenotype and the power of FBAT-PC. Compared to univariate analysis, FBAT-PC achieves power gains of up to 200%. Applications of FBAT-PC to an osteoporosis study and to an asthma study show the practical relevance of FBAT-PC. FBAT-PC has been implemented in the software package PBAT and is freely available at http://www.biostat.harvard.edu/~clange/default.htm.

Journal Article↗
PubMed2004

Family-based association tests for survival and times-to-onset analysis.

In this paper, we discuss family-based association test (FBATs) relating genetic data to survival and time-to-onset data. We show how the standard logrank and Wilcoxon statistics can be used with family data to develop tests of association. We prove that the FBAT-logrank approach can be identical to the proportional hazard approach discussed in Mokliatchouk et al. (2000). Further, using simulation studies, we compare the power of the logrank, Wilcoxon and an approach developed for censored exponential data (Euro J Hum Gen 2001; 9:301-306). Based on the results of the simulation study, we suggest rules of thumb about which statistics to use in a given situation. An application of all three tests to an Alzheimer study illustrates the practical relevance of our discussion.

Age of Onset↗
PubMed2004

Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics.

We provide a general purpose family-based testing strategy for associating disease phenotypes with haplotypes when phase may be ambiguous and parental genotype data may be missing. These tests for linkage and association can be used in candidate gene studies with tightly linked markers. Our proposed weighted conditional approach extends the method described in Rabinowitz and Laird to multiple markers. It is attractive because it provides haplotype tests for family-based studies that are efficient and robust to population admixture, phenotype distribution specification, and ascertainment based on phenotypes. It can handle missing parental genotypes and/or missing phase in both offspring and parents. It yields either haplotype-specific (univariate) tests or multi-haplotype (global) tests. This extension has been implemented in the freely available software haplotype FBAT. We used the haplotype FBAT program to test for associations between asthma phenotypes and single nucleotide polymorphisms (SNPs) in the beta-2 adrenergic receptor gene. Whereas no single SNP showed significant association with asthma diagnosis or bronchodilator responsiveness (quantitative trait), a haplotype-based global test found a highly significant association with asthma diagnosis (P value <0.00005) and the measure of bronchodilator responsiveness (P value =0.016).

Algorithms↗
PubMed2003

Association of a genetic marker at the corticotropin-releasing hormone locus with behavioral inhibition.

BACKGROUND: Behavioral inhibition to the unfamiliar (BI), a heritable temperamental profile involving an avoidant response to novel situations, may be an intermediate phenotype in the development of anxiety disorders. Corticotropin-releasing hormone (CRH) is a key mediator of the stress response through its effects on the hypothalamic-pituitary-adrenal axis and limbic brain systems. Transgenic mice overexpressing CRH exhibit BI-like behaviors, implicating this gene in the development of the phenotype. METHODS: We genotyped a marker tightly linked to the CRH locus in 85 families of children who underwent laboratory-based behavioral assessments of BI and performed family-based association analyses. RESULTS: We observed an association between an allele of the CRH-linked locus and BI (p =.015). Among offspring of parents with panic disorder, this association was particularly marked (p =.0009). We further demonstrate linkage disequilibrium between this marker and single nucleotide polymorphisms encompassing the CRH gene. CONCLUSIONS: These results are consistent with the possibility that variants in the CRH gene are associated with anxiety proneness.

Anxiety Disorders↗