Quantitative Trait Linkage Research Articles

Genetic analysis of serum osteocalcin and bone mineral in multigenerational Afro-Caribbean families

Osteocalcin is a major component of bone matrix. Concentrations of total, carboxylated, and uncarboxylated osteocalcin, are highly heritable and genetically correlated with bone mineral content (BMC) within African ancestry families. Osteocalcin (OC) is a protein constituent of bone matrix and a marker of bone formation. We characterized the heritability of serum OC measures and identified genomic regions potentially involved in the regulation of OC via high-density genome-wide linkage analysis in African ancestry individuals. African ancestry individuals (n = 459) were recruited, without regard to health status, from seven probands (mean family size = 66; 4,373 relative pairs). Residual heritability of serum OC measures was estimated and multipoint quantitative trait linkage analysis was performed using pedigree-based maximum likelihood methods. Residual heritabilities of total OC, uncarboxylated OC, carboxylated OC and percent uncarboxylated OC were 0.74 ± 0.10, 0.89 ± 0.08, 0.46 ± 0.10 and 0.41 ± 0.09, respectively. All OC measures were genetically correlated with whole body BMC. We obtained strong evidence of bivariate linkage for percent uncarboxylated OC and whole body BMC on chromosome 17 (logarithm of the odds [LOD] = 3.15, 99 cM). All forms of OC were highly heritable and genetically correlated with total body BMC in these African ancestry families. The identified linkage region contains several candidate genes for bone and energy metabolism including COL1A1 and TNFRSF11A. Further studies of this genomic region may reveal novel insight into the genetic regulation of OC and bone mineralization.

Unifying Ideas for Non-Parametric Linkage Analysis

Objectives: Non-parametric linkage analysis (NPL) exploits marker allele sharing among affected relatives to map genes influencing complex traits. Computational barriers force approximate analysis on large pedigrees and the adoption of a questionable perfect data assumption (PDA) in assigning p values. To improve NPL significance testing on large pedigrees, we examine the adverse consequences of missing data and PDA. We also introduce a novel statistic, Q-NPL, appropriate for NPL analysis of quantitative traits. Methods: Using simulated and real data sets with qualitative traits, we compare NPL analysis results for four testing procedures and various degrees of missing data. The simulated data sets vary from all nuclear families, to all large pedigrees, to a mix of pedigrees of different sizes. We implemented the Kong and Cox linear adjustment of p values in the software packages Mendel and SimWalk. We perform similar analysis with Q-NPL on quantitative traits of various heritabilities. Results: The Kong and Cox extension for significance testing is robust to realistic missing data patterns, greatly improves p values in approximate analyses, and works equally well for qualitative and quantitative traits and small and large pedigrees. The Q-NPL statistic is robust to missing data and shows good power to detect linkage for quantitative traits with a wide spectrum of heritabilities. Conclusions: The Kong and Cox extension should be a standard tool for calculating NPL p values. It allows the combination of exact and estimated analyses into a single significance score. Q-NPL should be a standard statistic for NPL analysis of quantitative traits. The new statistics are implemented in Mendel and SimWalk.

Human Plasma Glycome in Attention-Deficit Hyperactivity Disorder and Autism Spectrum Disorders

Over a half of all proteins are glycosylated, and their proper glycosylation is essential for normal function. Unfortunately, because of structural complexity of nonlinear branched glycans and the absence of genetic template for their synthesis, the knowledge about glycans is lagging significantly behind the knowledge about proteins or DNA. Using a recently developed quantitative high throughput glycan analysis method we quantified components of the plasma N-glycome in 99 children with attention-deficit hyperactivity disorder (ADHD), 81 child and 5 adults with autism spectrum disorder, and a total of 340 matching healthy controls. No changes in plasma glycome were found to associate with autism spectrum disorder, but several highly significant associations were observed with ADHD. Further structural analysis of plasma glycans revealed that ADHD is associated with increased antennary fucosylation of biantennary glycans and decreased levels of some complex glycans with three or four antennas. The design of this study prevented any functional conclusions about the observed associations, but specific differences in glycosylation appears to be strongly associated with ADHD and warrants further studies in this direction.

The Retinoic Acid Receptor Beta (Rarb) Region of Mmu14 Is Associated with Prion Disease Incubation Time in Mouse

In neurodegenerative conditions such as Alzheimer's and prion disease it has been shown that host genetic background can have a significant effect on susceptibility. Indeed, human genome-wide association studies (GWAS) have implicated several candidate genes. Understanding such genetic susceptibility is relevant to risks of developing variant CJD (vCJD) in populations exposed to bovine spongiform encephalopathy (BSE) and understanding mechanisms of neurodegeneration. In mice, aspects of prion disease susceptibility can be modelled by examining the incubation period following experimental inoculation. Quantitative trait linkage studies have already identified multiple candidate genes; however, it is also possible to take an individual candidate gene approach. Rarb and Stmn2 were selected as candidates based on the known association with vCJD. Because of the increasing overlap described between prion and Alzheimer's diseases we also chose Clu, Picalm and Cr1, which were identified as part of Alzheimer's disease GWAS. Clusterin (Clu) was considered to be of particular interest as it has already been implicated in prion disease. Approximately 1,000 heterogeneous stock (HS) mice were inoculated intra-cerebrally with Chandler/RML prions and incubation times were recorded. Candidate genes were evaluated by sequencing the whole transcript including exon-intron boundaries and potential promoters in the parental lines of the HS mice. Representative SNPs were genotyped in the HS mice. No SNPs were identified in Cr1 and no statistical association with incubation time was seen for Clu (P = 0.96) and Picalm (P = 0.91). Significant associations were seen for both Stmn2 (P = 0.04) and Rarb (P = 0.0005), however, this was only highly significant for Rarb. This data provides significant further support for a role for the Rarb region of Mmu14 and Stmn2 in prion disease.

Genome-wide linkage and peak-wide association study of obesity-related quantitative traits in Caribbean Hispanics

Although obesity is more prevalent in Hispanics than non-Hispanic whites in the United States, little is known about the genetic etiology of the related traits in this population. To identify genetic loci influencing obesity in non-Mexican Hispanics, we performed a genome-wide linkage scan in 1,390 subjects from 100 Caribbean Hispanic families on six obesity-related quantitative traits: body mass index (BMI), body weight, waist circumference, waist-to-hip ratio, abdominal and average triceps skinfold thickness after adjusting for significant demographic and lifestyle factors. We then carried out an association analysis of the linkage peaks and the FTO gene in an independent community-based Hispanic subcohort (N = 652, 64% Caribbean Hispanics) from the Northern Manhattan Study. Evidence of linkage was strongest on 1q43 with multipoint LOD score of 2.45 (p = 0.0004) for body weight. Suggestive linkage evidence of LOD > 2.0 was also identified on 1q43 for BMI (LOD = 2.03), 14q32 for abdominal skinfold thickness (LOD = 2.17), 16p12 for BMI (LOD = 2.27) and weight (LOD = 2.26), and 16q23-24 for average triceps skinfold thickness (LOD = 2.32). In the association analysis of 6,440 single nucleotide polymorphisms (SNPs) under 1-LOD unit down regions of our linkage peaks on chromosome 1q43 and 16p12 as well as in the FTO gene, we found that two SNPs (rs6665519 and rs669231) on 1q43 and one FTO SNP (rs12447427) were significantly associated with BMI or body weight after adjustment for multiple testing. Our results suggest that in addition to FTO, multiple genetic loci, particularly those on 1q43 region, may contribute to the variations in obesity-related quantitative traits in Caribbean Hispanics.

Genomewide Linkage and Peakwide Association Analyses of Carotid Plaque in Caribbean Hispanics

Atherosclerosis is a complex subclinical cardiovascular disorder with a substantial genetic component. This study sought to identify genetic loci influencing carotid plaque in 2 independent samples. B-mode ultrasound was performed to determine the presence and area of carotid plaque. Variance components analysis was used to test for linkage using 383 autosomal microsatellite markers in 1308 subjects from 100 Dominican families. Multiple linear and logistic regression models were used to investigate the association between plaque traits and 18,904 single nucleotide polymorphisms under the 1-logarithm of odds unit down regions of linkage peaks in an independent community-based data set (N = 941, 41% Dominicans) from the Northern Manhattan Study. After adjustment for age, hypertension, diabetes mellitus, cigarette pack-years, body mass index, and waist-to-hip ratio, significant heritability was detected for plaque presence (h² = 0.50 ± 0.14, P < 0.0001) and plaque area (h²=0.17 ± 0.04, P < 0.0001). Quantitative and dichotomous trait linkage analyses obtained similar results and identified 4 regions with multipoint logarithm of odds scores ≥ 2.00 on 7q36, 11p15, 14q32, and 15q23. In the association analysis of the 4 linkage peaks, several single nucleotide polymorphisms in or near SOX6, FSD2, AP3S2, EFTUD1, and MYOD1 were associated with carotid plaque traits with a nominal P ≤ 0.0005 in the Northern Manhattan Study data set and with a P ≤ 0.01 in Northern Manhattan Study Dominican subset. Carotid plaque has considerable heritability and may be influenced by loci on chromosomes 11p15, 14q32, and 15q23. The SOX6 gene within the bone morphogenic protein pathway could be a candidate for carotid plaque. Larger independent studies are needed to validate these findings.

Localization of genes for V+LDL plasma cholesterol levels on two diets in the opossum Monodelphis domestica

Plasma cholesterol levels among individuals vary considerably in response to diet. However, the genes that influence this response are largely unknown. Non-HDL (V+LDL) cholesterol levels vary dramatically among gray, short-tailed opossums fed an atherogenic diet, and we previously reported that two quantitative trait loci (QTLs) influenced V+LDL cholesterol on two diets. We used hypothesis-free, genome-wide linkage analyses on data from 325 pedigreed opossums and located one QTL for V+LDL cholesterol on the basal diet on opossum chromosome 1q [logarithm of the odds (LOD) = 3.11, genomic P = 0.019] and another QTL for V+LDL on the atherogenic diet (i.e., high levels of cholesterol and fat) on chromosome 8 (LOD = 9.88, genomic P = 5 x 10(-9)). We then employed a novel strategy involving combined analyses of genomic resources, expression analysis, sequencing, and genotyping to identify candidate genes for the chromosome 8 QTL. A polymorphism in ABCB4 was strongly associated (P = 9 x 10(-14)) with the plasma V+LDL cholesterol concentrations on the high-cholesterol, high-fat diet. The results of this study indicate that genetic variation in ABCB4, or closely linked genes, is responsible for the dramatic differences among opossums in their V+LDL cholesterol response to an atherogenic diet.

A SSR-based composite genetic linkage map for the cultivated peanut (Arachis hypogaea L.) genome

BackgroundThe construction of genetic linkage maps for cultivated peanut (Arachis hypogaea L.) has and continues to be an important research goal to facilitate quantitative trait locus (QTL) analysis and gene tagging for use in a marker-assisted selection in breeding. Even though a few maps have been developed, they were constructed using diploid or interspecific tetraploid populations. The most recently published intra-specific map was constructed from the cross of cultivated peanuts, in which only 135 simple sequence repeat (SSR) markers were sparsely populated in 22 linkage groups. The more detailed linkage map with sufficient markers is necessary to be feasible for QTL identification and marker-assisted selection. The objective of this study was to construct a genetic linkage map of cultivated peanut using simple sequence repeat (SSR) markers derived primarily from peanut genomic sequences, expressed sequence tags (ESTs), and by "data mining" sequences released in GenBank.ResultsThree recombinant inbred lines (RILs) populations were constructed from three crosses with one common female parental line Yueyou 13, a high yielding Spanish market type. The four parents were screened with 1044 primer pairs designed to amplify SSRs and 901 primer pairs produced clear PCR products. Of the 901 primer pairs, 146, 124 and 64 primer pairs (markers) were polymorphic in these populations, respectively, and used in genotyping these RIL populations. Individual linkage maps were constructed from each of the three populations and a composite map based on 93 common loci were created using JoinMap. The composite linkage maps consist of 22 composite linkage groups (LG) with 175 SSR markers (including 47 SSRs on the published AA genome maps), representing the 20 chromosomes of A. hypogaea. The total composite map length is 885.4 cM, with an average marker density of 5.8 cM. Segregation distortion in the 3 populations was 23.0%, 13.5% and 7.8% of the markers, respectively. These distorted loci tended to cluster on LG1, LG3, LG4 and LG5. There were only 15 EST-SSR markers mapped due to low polymorphism. By comparison, there were potential synteny, collinear order of some markers and conservation of collinear linkage groups among the maps and with the AA genome but not fully conservative.ConclusionA composite linkage map was constructed from three individual mapping populations with 175 SSR markers in 22 composite linkage groups. This composite genetic linkage map is among the first "true" tetraploid peanut maps produced. This map also consists of 47 SSRs that have been used in the published AA genome maps, and could be used in comparative mapping studies. The primers described in this study are PCR-based markers, which are easy to share for genetic mapping in peanuts. All 1044 primer pairs are provided as additional files and the three RIL populations will be made available to public upon request for quantitative trait loci (QTL) analysis and linkage map improvement.

Development of a set of SNP markers present in expressed genes of the apple

Molecular markers associated with gene coding regions are useful tools for bridging functional and structural genomics. Due to their high abundance in plant genomes, single nucleotide polymorphisms (SNPs) are present within virtually all genomic regions, including most coding sequences. The objective of this study was to develop a set of SNPs for the apple by taking advantage of the wealth of genomics resources available for the apple, including a large collection of expressed sequenced tags (ESTs). Using bioinformatics tools, a search for SNPs within an EST database of approximately 350,000 sequences developed from a variety of apple accessions was conducted. This resulted in the identification of a total of 71,482 putative SNPs. As the apple genome is reported to be an ancient polyploid, attempts were made to verify whether those SNPs detected in silico were attributable either to allelic polymorphisms or to gene duplication or paralogous or homeologous sequence variations. To this end, a set of 464 PCR primer pairs was designed, PCR was amplified using two subsets of plants, and the PCR products were sequenced. The SNPs retrieved from these sequences were then mapped onto apple genetic maps, including a newly constructed map of a Royal Gala×A689-24 cross and a Malling 9×Robusta 5, map using a bin mapping strategy. The SNP genotyping was performed using the high-resolution melting (HRM) technique. A total of 93 new markers containing 210 coding SNPs were successfully mapped. This new set of SNP markers for the apple offers new opportunities for understanding the genetic control of important horticultural traits using quantitative trait loci (QTL) or linkage disequilibrium analysis. These also serve as useful markers for aligning physical and genetic maps, and as potential transferable markers across the Rosaceae family.

Phenotype Matters: The Case for Careful Characterization of Relevant Traits

Phenotype Matters: The Case for Careful Characterization of Relevant Traits

A robust QTL mapping procedure

In quantitative trait linkage studies using experimental crosses, the conventional normal location-shift model or other parameterizations may be unnecessarily restrictive. We generalize the mapping problem to a genuine nonparametric setup and provide a robust estimation procedure for the situation where the underlying phenotype distributions are completely unspecified. Classical Wilcoxon–Mann–Whitney statistics are employed for point and interval estimation of QTL positions and effects.

Genotype-by-diet effects on co-variation in Lp-PLA2 activity and LDL-cholesterol concentration in baboons fed an atherogenic diet

Both lipoprotein-associated phospholipase A(2) (Lp-PLA(2)) activity, a biomarker of inflammation, and concentration of its primary associated lipoprotein, LDL, are correlated with adverse coronary outcomes. We previously reported a quantitative trait locus (QTL) corresponding to HSA2p24.3-p23.2 with pleiotropic effects on Lp-PLA(2) activity and LDL-cholesterol (LDL-C) concentration in baboons fed a basal diet. Here, our goal was to locate pleiotropic QTLs influencing both traits in the same baboons fed a high-cholesterol, high-fat (HCHF) diet, and to assess whether shared genetic effects on these traits differ between diets. We assayed Lp-PLA(2) activity and LDL-C concentration in 683 baboons fed the HCHF diet. We used a bivariate maximum likelihood-based variance components approach in whole-genome linkage screens to locate a QTL [logarithm of odds (LOD) = 3.13, genome-wide P = 0.019] corresponding to HSA19q12-q13.2 with pleiotropic effects on Lp-PLA(2) activity and LDL-C levels in the HCHF diet. We additionally found significant evidence of genetic variance in response to diet for Lp-PLA(2) activity (P = 0.0017) and for LDL-C concentration (P = 0.00001), revealing a contribution of genotype-by-diet interaction to covariation in these two traits. We conclude that the pleiotropic QTLs detected at 2p24.3-p23.2 and 19q12-q13.2 on the basal and HCHF diets, respectively, exert diet-specific effects on covariation in Lp-PLA(2) activity and LDL-C concentration.

EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis

Motivation: We developed an EM-random forest (EMRF) for Haseman–Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data.Results: Using simulations, we find that the new VI indices in EMRF performed better than the original RF VI index and performed similarly or better than EM-Haseman–Elston regression LOD score for various genetic models. Moreover, tree size and markers subset size evaluated at each node are important considerations in RFs.Availability: The source code for EMRF written in C is available at www.infornomics.utoronto.ca/downloads/EMRFContact: bull@mshri.on.caSupplementary information: Supplementary data are available at www.infornomics.utoronto.ca/downloads/EMRF

The lipoprotein subfraction profile: heritability and identification of quantitative trait loci

The HDL and LDL subclass profile is an emerging cardiovascular risk factor. Yet, the biological and genetic mechanisms controlling the lipoprotein subclass distribution are unclear. Therefore, we aimed 1) to determine the heritability of the entire spectrum of LDL and HDL subclass features and 2) to identify gene loci influencing the lipoprotein subfraction pattern. Using NMR spectroscopy, we analyzed the lipoprotein subclass distribution in 1,275 coronary artery disease patients derived from the Regensburg Myocardial Infarction Family Study. We calculated heritabilities, performed a microsatellite genome scan, and calculated linkage. HDL and LDL subclass profiles showed heritabilities ranging from 23% to 67% (all P < 10(-3)) of traits using univariate calculation. After multivariate adjustment, we found heritabilities of 27-48% (all P < 0.05) for HDL and 21-44% for LDL traits. The linkage analysis revealed a significant logarithm of the odds (LOD) score (3.3) for HDL particle concentration on chromosome 18 and a highly suggestive signal for HDL particle size on chromosome 12 (2.9). After multivariate adjustment, we found a significant maximum LOD score of 3.7 for HDL size. Our study is the first to analyze heritability and linkage for the entire spectrum of LDL and HDL subclass features. Our findings may lead to the identification of genes controlling the lipoprotein subclass distribution.

Genome‐wide linkage scans for renal function and albuminuria in Type 2 diabetes mellitus: the Diabetes Heart Study

Glomerular filtration rate (GFR), end-stage renal disease and albuminuria are highly heritable. We performed a genome-wide linkage scan in 416 Diabetes Heart Study (DHS) families to detect loci that contributed to renal function and albuminuria. A total of 1067 individuals (900 with Type 2 diabetes mellitus) from 348 European American and 68 African American DHS families had measures of urine albumin : creatinine ratio (ACR), serum creatinine concentration and Modification of Diet in Renal Disease estimated GFR (eGFR). Variance components quantitative trait linkage analysis (using SOLAR) was computed. Participants had mean +/- sd age 61.4 +/- 9.4 years; diabetes duration 10.5 +/- 7.4 years; eGFR 1.15 +/- 0.32 ml/sec; and urine ACR 15.8 +/- 67.2 mmol/l (median 1.4). In all families, significant evidence for linkage of GFR was observed on chromosome 2p16 (log of the odds; LOD = 4.31 at 72.0 cM, ATA47C04P/D2S1352) and 1p36 (LOD = 3.81 at 45.0 cM, D1S3669/D1S3720), with suggestive evidence on 7q21 (LOD = 2.42 at 99.0 cM, D7S820/D7S821) and 13q13 (LOD = 2.28 at 28.0 cM, D13S1493/D13S894). The evidence for linkage to ACR was far weaker, on 13q21-q22 (LOD = 1.84 at 50 cM, D13S1807/D13S800), 3p24-p23 (LOD = 1.81 at 58 cM, D3S3038/D3S2432) and 10p11 (LOD = 1.78 at 71.0 cM, D10S1208/D10S1221). The eGFR linkage peaks on 2p16, 7q21 and 13q13 closely overlap with nephropathy peaks identified in family studies enriched for severe kidney disease. These diabetes-enriched families provide an opportunity to map genes regulating renal function, potentially leading to the identification of genes producing nephropathy susceptibility in subjects with Type 2 diabetes.

Variance Components Linkage Analysis with Repeated Measurements

Background: When subjects are measured multiple times, linkage analysis needs to appropriately model these repeated measures. A number of methods have been proposed to model repeated measures in linkage analysis. Here, we focus on assessing the impact of repeated measures on the power and cost of a linkage study. Methods: We describe three alternative extensions of the variance components approach to accommodate repeated measures in a quantitative trait linkage study. We explicitly relate power and cost through the number of measures for different designs. Based on these models, we derive general formulas for optimal number of repeated measures for a given power or cost and use analytical calculations and simulations to compare power for different numbers of repeated measures across several scenarios. We give rigorous proof for the results under the balanced design. Results: Repeated measures substantially improve power and the proportional increase in LOD score depends mostly on measurement error and total heritability but not much on marker map, the number of alleles per marker or family structure. When measurement error takes up 20% of the trait variability and 4 measures/subject are taken, the proportional increase in LOD score ranges from 38% for traits with heritability of ~20% to 63% for traits with heritability of ~80%. An R package is provided to determine optimal number of repeated measures for given measurement error and cost. Variance component and regression based implementations of our methods are included in the MERLIN package to facilitate their use in practical studies.

Identification and Replication of a Novel Obesity Locus on Chromosome 1q24 in Isolated Populations of Cilento

Obesity is a complex trait with a variety of genetic susceptibility variants. Several loci linked to obesity and/or obesity-related traits have been identified, and relatively few regions have been replicated. Studying isolated populations can be a useful approach to identify rare variants that will not be detected with whole-genome association studies in large populations. Random individuals were sampled from Campora, an isolated village of the Cilento area in South Italy, phenotyped for BMI, and genotyped using a dense microsatellite marker map. An efficient pedigree-breaking strategy was applied to perform genome-wide linkage analyses of both BMI and obesity. Significance was assessed with ad hoc simulations for the two traits and with an original local false discovery rate approach to quantitative trait linkage analysis for BMI. A genealogy-corrected association test was performed for a single nucleotide polymorphism located in one of the linkage regions. A replication study was conducted in the neighboring village of Gioi. A new locus on chr1q24 significantly linked to BMI was identified in Campora. Linkage at the same locus is suggested with obesity. Three additional loci linked to BMI were also detected, including the locus including the INSIG2 gene region. No evidence of association between the rs7566605 variant and BMI or obesity was found. In Gioi, the linkage on chr1q24 was replicated with both BMI and obesity. Overall, our results confirm that successful linkage studies can be accomplished in these populations both to replicate known linkages and to identify novel quantitative trait linkages.

A unified framework for linkage and association analysis of quantitative traits.

We give a unified treatment of the statistical foundations of population based association mapping and of family based linkage mapping of quantitative traits in humans. A central ingredient in the unification involves the efficient score statistic. The discussion focuses on generalized linear models with an additional illustration of the Cox (proportional hazards) model for age of onset data. We give analytic expressions for noncentrality parameters and show how they give qualitative insight into the loss of power that occurs if the scientist's assumed genetic model differs from nature's "true" genetic model. Issues to be studied in detail in the future development of this approach are discussed.

Sex, age and generation effects on genome-wide linkage analysis of gene expression in transformed lymphoblasts

Many traits differ by age and sex in humans, but genetic analysis of gene expression has typically not included them in the analysis. We used Genetic Analysis Workshop 15 Problem 1 data to determine whether gene expression in lymphoblasts showed differences by age and/or sex using generalized estimating equations (GEE). We performed quantitative trait linkage analysis of these genes including age and sex as covariates to determine whether the linkage results changed when they were included as covariates. Because the families included in the study all contain three generations, we also determined what effect inclusion of generation in the model had on the age effects. When controlling the false-discovery rate at 1%, using GEE we identified 30 transcripts that showed significant differences in expression by sex, while 1950 transcripts showed differences in expression associated with age. When subjected to linkage analysis, there were 37 linkages that disappeared, while 17 appeared when sex was included as a covariate. All these genes were, as expected, on the sex chromosomes. In contrast, when age was included in the linkage analysis, 462 linkage signals were no longer significant, while 223 became significant. When generation was included in the model with age, all but 6 of the GEE age effects were no longer significant. However, there were minimal changes in the linkage results. The effect of age on linkage analyses was apparent for the expression of many genes, which appear to be mostly due to differences between the generations.

Handling linkage disequilibrium in linkage analysis using dense single-nucleotide polymorphisms

The presence of linkage disequilibrium violates the underlying assumption of linkage equilibrium in most traditional multipoint linkage approaches. Studies have shown that such violation leads to bias in qualitative trait linkage analysis when parental genotypes are unavailable. Appropriate handling of marker linkage disequilibrium can avoid such false positive evidence. Using the rheumatoid arthritis simulated data from Genetic Analysis Workshop 15, we examined and compared the following three approaches to handle linkage disequilibrium among dense markers in both qualitative and quantitative trait linkage analyses: a simple algorithm; SNPLINK, methods for marker selection; and MERLIN-LD, a method for modeling linkage disequilibrium by creating marker clusters. In analysis ignoring linkage disequilibrium between markers, we observed LOD score inflation only in the affected sib-pair linkage analysis without parental genotypes; no such inflation was present in the quantitative trait locus linkage analysis with severity as our phenotype with or without parental genotypes. Using methods to model or adjust for linkage disequilibrium, we found a substantial reduction of inflation of LOD score in affected sib-pair linkage analysis. Greater LOD score reduction was observed by decreasing the amount of tolerable linkage disequilibrium among markers selected or marker clusters using MERLIN-LD; the latter approach showed most reduction. SNPLINK performed better with selected markers based on the D' measure of linkage disequilibrium as opposed to the r2 measure and outperformed the simple algorithm. Our findings reiterate the necessity of properly handling dense markers in linkage analysis, especially when parental genotypes are unavailable.