Model-free Linkage Research Articles

Model-Free Linkage Analysis of a Binary Trait.

Genetic linkage analysis aims to detect chromosomal regions containing genetic variants that influence risk of specific inherited diseases. The presence of linkage is indicated when a disease or trait cosegregates through the families with genetic markers at a particular region of the genome. Two main types of genetic linkage analysis are in common use, namely model-based linkage analysis and model-free linkage analysis. In this chapter, we focus solely on the latter type and specifically on binary traits or phenotypes, such as the presence or absence of a specific disease. Model-free linkage analysis is based on allele-sharing, where patterns of genetic similarity among affected relatives are compared to chance expectations. Because the model-free methods do not require the specification of the inheritance parameters of a genetic model, they are preferred by many researchers at early stages in the study of a complex disease. We introduce the history of model-free linkage analysis in Subheading 1. Table 1 describes a standard model-free linkage analysis workflow. We describe three popular model-free linkage analysis methods, the nonparametric linkage (NPL) statistic, the affected sib-pair (ASP) likelihood ratio test, and a likelihood approach for pedigrees. The theory behind each linkage test is described in this section together with a simple example of the relevant calculations. Table 4 provides a summary of popular genetic analysis software packages that implement model-free linkage models. In Subheading 2, we work through the methods on a rich example providing sample software code and output. Subheading 3 contains notes with additional details on various topics that may need further consideration during analysis.

Homozygosity Analysis in Autoimmunity Affected Individuals and Multiplex Autoimmune Disease Families

Autoimmune diseases (AD) are responsible for a substantial amount of disability and morbidity worldwide. Research generally focuses on a single disease, although autoimmune phenotypes could represent pleiotropic outcomes of non-specific disease genes underlying similar immunogenetic mechanisms. This report examined the effect and importance of the homozygosity status, using genome-wide interspersed markers, in individuals and multiplex families affected with AD. This study presented two approaches: (I) a case-control comparison and evaluation on the effect of homozygosity at the genome-wide level and per marker, including 453 unrelated individuals (121 late-, 79 early-onset AD, 40 polyautoimmunity (PolyA), 30 multiple autoimmune syndrome (MAS) and 183 healthy control individuals); and (II) a model-free affected pair linkage approach which included 35 MAS, 49 polyA, 104 late-, and 83 early-onset multiplex families. A total of 372 genome-wide markers were used in the analysis. The standardized observed homozygosity (SOH) was calculated and the association of the homozygosity status and the autoimmune trait was evaluated. The multipoint model-free linkage analysis was applied by using RELPAL from S.A.G.E v6.3. Results for the SOH showed significant differences between controls and early-onset individuals, where early-onset affected individuals showed lower homozygosity relative to controls. No differences were observed relative to controls for MAS, polyA and late-onset disease at the genome-wide level. The local marker homozygosity effect showed share and specific risk and/or protective effects for 24 markers. The model-free affected pair linkage approach lacked any suggestive linkage signals, but marginal signals displayed excess allele sharing for extreme phenotypes in autoimmunity. This study presumed autoimmunity as a trait rather than a clinical phenotype and tried to approach AD as a continuous trait presenting extreme phenotypes. Future approaches would be expected to dwell on the data presented here to corroborate and expand on sample size, marker coverage and their effects.

Linkage and related analyses of Barrett's esophagus and its associated adenocarcinomas.

BackgroundFamilial aggregation and segregation analysis studies have provided evidence of a genetic basis for esophageal adenocarcinoma (EAC) and its premalignant precursor, Barrett's esophagus (BE). We aim to demonstrate the utility of linkage analysis to identify the genomic regions that might contain the genetic variants that predispose individuals to this complex trait (BE and EAC).MethodsWe genotyped 144 individuals in 42 multiplex pedigrees chosen from 1000 singly ascertained BE/EAC pedigrees, and performed both model‐based and model‐free linkage analyses, using S.A.G.E. and other software. Segregation models were fitted, from the data on both the 42 pedigrees and the 1000 pedigrees, to determine parameters for performing model‐based linkage analysis. Model‐based and model‐free linkage analyses were conducted in two sets of pedigrees: the 42 pedigrees and a subset of 18 pedigrees with female affected members that are expected to be more genetically homogeneous. Genome‐wide associations were also tested in these families.ResultsLinkage analyses on the 42 pedigrees identified several regions consistently suggestive of linkage by different linkage analysis methods on chromosomes 2q31, 12q23, and 4p14. A linkage on 15q26 is the only consistent linkage region identified in the 18 female‐affected pedigrees, in which the linkage signal is higher than in the 42 pedigrees. Other tentative linkage signals are also reported.ConclusionOur linkage study of BE/EAC pedigrees identified linkage regions on chromosomes 2, 4, 12, and 15, with some reported associations located within our linkage peaks. Our linkage results can help prioritize association tests to delineate the genetic determinants underlying susceptibility to BE and EAC.

Identification of susceptibility loci in hereditary prostate cancer families using copy number variation and linkage analysis.

232 Background: Family history is one of the most significant predictor of prostate cancer risk. With approximately 10% of prostate cancer cases attributable to inheritable genetic factors. However, disease gene identification for prostate cancer has been extremely challenging due to both disease and genetic heterogeneity. Therefore, the goal of this study was to employ an array of genetic tools in order to characterize germline variants in seven high-risk families with prostate cancer. Methods: To overcome the effects of ethnic disparity, genetic heterogeneity, incomplete penetrance, and missing heritability, we utilized a comprehensive approach that combines both array comparative genomic hybridization (aCGH) analysis and linkage analysis to identify genetic components of prostate cancer. Seven prostate cancer cases were studied using aCGH to search for germ-line copy number variants (CNVs) associated with hereditary prostate cancer. The study subjects were from 7 large, high-risk, clinically homogenous families with European ancestry from Southern Louisiana. Genotyping for linkage analyses was done using Illumina Infinium II SNP HumanLinkage-12 panel. Results: Three novel regions of CNVs were identified: 16q23, 11q22, and 2q22 in all 7 prostate cancer cases from high-risk families. Both model-based and model-free linkage analyses were performed on 4 of the most informative families (≥5 prostate cancer patients per family). Suggestive evidence of linkage was obtained at 2q14 (HLOD score of 1.94). Using aCGH, a 68 kb duplication was observed in this region in all 7 hereditary prostate cancer cases. Conclusions: These genetic findings, which were identified in multiple large, well-characterized families, provide new insight into hereditary prostate cancer. A combination of future fine mapping of the 2q14 region in a larger cohort of patients, confirmation of these novel CNVs in additional subjects, and use of next-generation sequencing approaches to search for potential disease-associated genetic alterations are needed to provide further characterization of these prostate cancer associated susceptibility loci.

Exome sequencing of 75 individuals from multiply affected coeliac families and large scale resequencing follow up.

Coeliac disease (CeD) is a highly heritable common autoimmune disease involving chronic small intestinal inflammation in response to dietary wheat. The human leukocyte antigen (HLA) region, and 40 newer regions identified by genome wide association studies (GWAS) and dense fine mapping, account for ∼40% of the disease heritability. We hypothesized that in pedigrees with multiple individuals with CeD rare [minor allele frequency (MAF) <0.5%] mutations of larger effect size (odds ratios of ∼ 2–5) might exist. We sequenced the exomes of 75 coeliac individuals of European ancestry from 55 multiply affected families. We selected interesting variants and genes for further follow up using a combination of: an assessment of shared variants between related subjects, a model-free linkage test, and gene burden tests for multiple, potentially causal, variants. We next performed highly multiplexed amplicon resequencing of all RefSeq exons from 24 candidate genes selected on the basis of the exome sequencing data in 2,248 unrelated coeliac cases and 2,230 controls. 1,335 variants with a 99.9% genotyping call rate were observed in 4,478 samples, of which 939 were present in coding regions of 24 genes (Ti/Tv 2.99). 91.7% of coding variants were rare (MAF <0.5%) and 60% were novel. Gene burden tests performed on rare functional variants identified no significant associations (p<1×10−3) in the resequenced candidate genes. Our strategy of sequencing multiply affected families with deep follow up of candidate genes has not identified any new CeD risk mutations.

Abstract 3648: Unraveling phenotype heterogeneity in prostate cancer susceptibility in Finland utilizing covariate-based analysis.

Abstract Prostate cancer is the most common male cancer in developed countries. Previously, we reported a genome-wide linkage scan in 69 Finnish Hereditary Prostate Cancer (HPC) families, which replicated the HPC9 locus on 17q21-q22 and a locus on 2q37. We used ordered subset analysis (OSA) to detect other loci linked to HPC in subsets of families to detect other loci linked to HPC incorporating mean family age of onset as a trait-related covariate to address genetic heterogeneity. The overall mean age of onset across the families was 66.2±8.8 years while the range of individual onset ages ranged from 46-98 years and age of onset varies within families. The highest OSA LOD score was 2.876 (ΔLOD p=0.02) on 15q26.2-q26.3 in a subset of 40 families ascending by age at onset, suggesting that a subset of early age at onset families may be segregating a risk allele(s) at this locus. No other ΔLOD scores were significant after permutation testing. To better capture the effect of age on the linkage signals, we used LODPAL to perform a model-free linkage analysis in affected relative pairs, while adjusting for the age of each individual family member as a single covariate. Preliminary results revealed strong evidence of linkage to HPC on chromosome 15q (LOD=4.9, 132cM) and 8q (LOD=3.1, 157cM). Future analyses are planned which will use age of onset, PSA levels and other clinical characteristics to prioritize regions which may harbor genetic variants of large effect on PRCA. Citation Format: Cheryl D. Cropp, Claire L. Simpson, Tiina Wahlfors, Asha George, MaryPat S. Jones, Ursula Harper, Damaris Ponciano-Jackson, Teuvo Tammela, Johanna Schleutker, Joan E. Bailey-Wilson. Unraveling phenotype heterogeneity in prostate cancer susceptibility in Finland utilizing covariate-based analysis. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3648. doi:10.1158/1538-7445.AM2013-3648

Germline mutations in MSR1, ASCC1, and CTHRC1 in patients with Barrett esophagus and esophageal adenocarcinoma.

Barrett esophagus (BE) occurs in 1% to 10% of the general population and is believed to be the precursor of esophageal adenocarcinoma (EAC). The incidence of EAC has increased 350% in the last 3 decades without clear etiology. Finding predisposition genes may improve premorbid risk assessment, genetic counseling, and management. Genome-wide multiplatform approaches may lead to the identification of genes important in BE/EAC development. To identify risk alleles or mutated genes associated with BE/EAC. Model-free linkage analyses of 21 concordant-affected sibling pairs with BE/EAC and 11 discordant sibling pairs (2005-2006). Significant germline genomic regions in independent prospectively accrued series of 176 white patients with BE/EAC and 200 ancestry-matched controls (2007-2010) were validated and fine mapped. Integrating data from these significant genomic regions with somatic gene expression data from 19 BE/EAC tissues yielded 12 "priority" candidate genes for mutation analysis (2010). Genes that showed mutations in cases but not in controls were further screened in an independent prospectively accrued validation series of 58 cases (2010). Identification of germline mutations in genes associated with BE/EAC cases. Functional interrogation of the most commonly mutated gene. Three major genes, MSR1, ASCC1, and CTHRC1 were associated with BE/EAC (all P < .001). In addition, 13 patients (11.2%) with BE/EAC carried germline mutations in MSR1, ASCC1, or CTHRC1. MSR1 was the most frequently mutated, with 8 of 116 (proportion, 0.069; 95% confidence interval [CI], 0.030-0.130; P < .001) cases with c.877C>T (p.R293X). An independent validation series confirmed germline MSR1 mutations in 2 of 58 cases (proportion, 0.035; 95% CI, 0.004-0.120; P = .09). MSR1 mutation resulted in CCND1 up-regulation in peripheral-protein lysate. Immunohistochemistry of BE tissues in MSR1-mutation carriers showed increased nuclear expression of CCND1. MSR1 was significantly associated with the presence of BE/EAC in derivation and validation samples, although it was only present in a small percentage of the cases.

Assessing the Contribution Family Data Can Make to Case-Control Studies of Rare Variants

When pathogenic variants are rare then even among cases the proportion of subjects possessing a variant might be low, meaning that very large samples might be required to conclusively demonstrate evidence of an effect. Relatives of subjects within a case-control sample might provide useful additional information. The method of model-free linkage analysis implemented in MFLINK was adapted to incorporate linkage disequilibrium (LD) parameters in order to test for an effect of a putative pathogenic variant in complete LD with a disease locus. The effect of adding in to the analysis relatives of cases and controls found to carry the variant was investigated. When affected siblings or cousins of cases possessing the variant were incorporated they had a large effect on the results obtained. The evidence for involvement increased or reduced as expected, depending on whether or not the relatives themselves were found to possess the variant. The size of the effect was large relative to that expected from just increasing the size of a standard case-control sample. Affected relatives offer a valuable resource to assist the interpretation of case-control studies of rare variants. The method is capable of including other relative types and can deal with complex pedigrees.

The Maximum-Likelihood-Binomial method revisited: a robust approach for model-free linkage analysis of quantitative traits in large sibships

Model-free linkage analysis methods, based on identity-by-descent allele sharing, are commonly used for complex trait analysis. The Maximum-Likelihood-Binomial (MLB) approach, which is based on the hypothesis that parental alleles are binomially distributed among affected sibs, is particularly popular. An extension of this method to quantitative traits (QT) has been proposed (MLB-QTL), based on the introduction of a latent binary variable capturing information about the linkage between the QT and the marker. Interestingly, the MLB-QTL method does not require the decomposition of sibships into constituent sibpairs and requires no prior assumption about the distribution of the QT. We propose a new formulation of the MLB method for quantitative traits (nMLB-QTL) that explicitly takes advantage of the independence of paternal and maternal allele transmission under the null hypothesis of no linkage. Simulation studies under H₀ showed that the nMLB-QTL method generated very consistent type I errors. Furthermore, simulations under the alternative hypothesis showed that the nMLB-QTL method was slightly, but systematically more powerful than the MLB-QTL method, whatever the genetic model, residual correlation, ascertainment strategy and sibship size considered. Finally, the power of the nMLB-QTL method is illustrated by a chromosome-wide linkage scan for a quantitative endophenotype of leprosy infection. Overall, the nMLB-QTL method is a robust, powerful, and flexible approach for detecting linkage with quantitative phenotypes, particularly in studies of non Gaussian phenotypes in large sibships.

Allele‐Sharing Statistics Using Information on Family History

When conducting genetic studies for complex traits, large samples are commonly required to detect new genetic factors. A possible strategy to decrease the sample size is to reduce heterogeneity using available information. In this paper we propose a new class of model-free linkage analysis statistics which takes into account the information given by the ungenotyped affected relatives (positive family history). This information is included into the scoring function of classical allele-sharing statistics. We studied pedigrees of affected sibling pairs with one ungenotyped affected relative. We show that, for rare allele common complex diseases, the proposed method increases the expected power to detect linkage. Allele-sharing methods were applied to the symptomatic osteoarthritis GARP study where taking into account the family-history increased considerably the evidence of linkage in the region of the DIO2 susceptibility locus.

Genome‐wide Linkage Analysis of Multiple Metabolic Factors: Evidence of Genetic Heterogeneity

The metabolic syndrome is a highly complex disease and has become one of the major public-health challenges worldwide. We sought to identify genetic loci with potential influence on multiple metabolic factors in a white population in Beaver Dam, Wisconsin, and to explore the possibility of genetic heterogeneity by family history of diabetes (FHD). Three metabolic factors were generated using principal-component factor analysis, and they represented: (i) glycemia, (ii) blood pressure, and (iii) combined (BMI, high-density lipoprotein (HDL) cholesterol, and serum uric acid) factors. Multipoint model-free linkage analysis of these factors with 385 microsatellite markers was performed on 1,055 sib-pairs, using Haseman-Elston regression. Genome-wide suggestive evidence of linkage was found at 30 cM on chromosome 22q (empirical P (P(e)) = 0.0002) for the glycemia factor, at 188-191 cM on chromosome 1q (P(e) = 0.0007) for the blood pressure factor, and at 82 cM on chromosome 17q (P(e) = 0.0007) for the combined factor. Subset analyses of the families by FHD showed evidence of genetic heterogeneity, with divergent linkage signals in the subsets on at least four chromosomes. We found evidence of genetic heterogeneity by FHD for the three metabolic factors. The results also confirmed findings of previous studies that mapped components of the metabolic syndrome to a chromosome 1q region.

Genetic linkage of region containing the CREB1 gene to depressive disorders in families with recurrent, early‐onset, major depression: A re‐analysis and confirmation of sex‐specific effect

A previously published model-free linkage analysis of chromosome 2q33-35, highlighted by previous case-control studies and supported by within-family analyses employing the transmission disequilibrium test, revealed evidence of sex-specific linkage of the CREB1-containing region of 2q to unipolar mood disorders among women in 81 recurrent, early-onset, major depressive disorder (RE-MDD) families. Since it has been reported that the LODPAL program from S.A.G.E. v.4.0 used to conduct this previous linkage analysis suffers from an increased type I error rate that is exacerbated by covariates such as sex, we re-analyzed the evidence for this sex-specific linkage result using a simulation approach to estimate the empirical significance of our previous results. The results continue to support sex-specific linkage of the CREB1 region to mood disorders among women from families with RE-MDD. Moreover, these results have been supported by a host of additional published findings that implicate sequence variations in CREB1 in the sex-dependent development of syndromic mood disorders, as well as related clinical features and disorders.

Contrasting identity-by-descent estimators, association studies, and linkage analyses using the Framingham Heart Study data

We explored the utility of population- and pedigree-based analyses using the Framingham Heart Study genome-wide 50 k single-nucleotide polymorphism marker data provided for Genetic Analysis Workshop 16. Our aims were: 1) to compare identity-by-descent sharing estimates from variable amounts of data; 2) to apply each of these estimates to a case-control association study designed to control for relatedness among samples; and 3) to contrast these results to those obtained using model-based and model-free linkage analysis methods.

Advances in genetics of restless legs syndrome

Recent advances in genetic studies on restless legs syndrome (RLS) have led to a series of important discoveries that greatly expand our knowledge on the molecular basis of this disease. RLS is a common and complex disease. Several genetic epidemiological studies and twin studies have characterized the genetic components of RLS and suggest that it is a highly heritable trait with heritability estimates of about 50%. The five chromosomal positions (12q13-23, 14q13-21, 9p24-22, 2q33, and 20p13) have been determined either by model-based linkage analysis or by model-free linkage analysis, which have provided the linkage maps for positional cloning of the underlying genes or susceptibility genes for this disorder. Most recent genome-wide association studies based on high-throughput SNP genotyping platforms have also defined three significantly associated RLS regions (6p21.2, 2p 14, and 15q23). By integrating with our recent genetic studies for this neurological disorder, we reviewed the most important findings achieved in genetic studies of RLS.

SNP HiTLink: a high-throughput linkage analysis system employing dense SNP data

BackgroundDuring this recent decade, microarray-based single nucleotide polymorphism (SNP) data are becoming more widely used as markers for linkage analysis in the identification of loci for disease-associated genes. Although microarray-based SNP analyses have markedly reduced genotyping time and cost compared with microsatellite-based analyses, applying these enormous data to linkage analysis programs is a time-consuming step, thus, necessitating a high-throughput platform.ResultsWe have developed SNP HiTLink (SNP High Throughput Linkage analysis system). In this system, SNP chip data of the Affymetrix Mapping 100 k/500 k array set and Genome-Wide Human SNP array 5.0/6.0 can be directly imported and passed to parametric or model-free linkage analysis programs; MLINK, Superlink, Merlin and Allegro. Various marker-selecting functions are implemented to avoid the effect of typing-error data, markers in linkage equilibrium or to select informative data.ConclusionThe results using the 100 k SNP dataset were comparable or even superior to those obtained from analyses using microsatellite markers in terms of LOD scores obtained. General personal computers are sufficient to execute the process, as runtime for whole-genome analysis was less than a few hours. This system can be widely applied to linkage analysis using microarray-based SNP data and with which one can expect high-throughput and reliable linkage analysis.

Genome screen in familial intracranial aneurysm

BackgroundIndividuals with 1st degree relatives harboring an intracranial aneurysm (IA) are at an increased risk of IA, suggesting genetic variation is an important risk factor.MethodsFamilies with multiple members having ruptured or unruptured IA were recruited and all available medical records and imaging data were reviewed to classify possible IA subjects as definite, probable or possible IA or not a case. A 6 K SNP genome screen was performed in 333 families, representing the largest linkage study of IA reported to date. A 'narrow' (n = 705 definite IA cases) and 'broad' (n = 866 definite or probable IA) disease definition were used in multipoint model-free linkage analysis and parametric linkage analysis, maximizing disease parameters. Ordered subset analysis (OSA) was used to detect gene × smoking interaction.ResultsModel-free linkage analyses detected modest evidence of possible linkage (all LOD < 1.5). Parametric analyses yielded an unadjusted LOD score of 2.6 on chromosome 4q (162 cM) and 3.1 on chromosome 12p (50 cM). Significant evidence for a gene × smoking interaction was detected using both disease models on chromosome 7p (60 cM; p ≤ 0.01). Our study provides modest evidence of possible linkage to several chromosomes.ConclusionThese data suggest it is unlikely that there is a single common variant with a strong effect in the majority of the IA families. Rather, it is likely that multiple genetic and environmental risk factors contribute to the susceptibility for intracranial aneurysms.

Genome Scan of M. tuberculosis Infection and Disease in Ugandans

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is an enduring public health problem globally, particularly in sub-Saharan Africa. Several studies have suggested a role for host genetic susceptibility in increased risk for TB but results across studies have been equivocal. As part of a household contact study of Mtb infection and disease in Kampala, Uganda, we have taken a unique approach to the study of genetic susceptibility to TB, by studying three phenotypes. First, we analyzed culture confirmed TB disease compared to latent Mtb infection (LTBI) or lack of Mtb infection. Second, we analyzed resistance to Mtb infection in the face of continuous exposure, defined by a persistently negative tuberculin skin test (PTST-); this outcome was contrasted to LTBI. Third, we analyzed an intermediate phenotype, tumor necrosis factor-alpha (TNFα) expression in response to soluble Mtb ligands enriched with molecules secreted from Mtb (culture filtrate). We conducted a full microsatellite genome scan, using genotypes generated by the Center for Medical Genetics at Marshfield. Multipoint model-free linkage analysis was conducted using an extension of the Haseman-Elston regression model that includes half sibling pairs, and HIV status was included as a covariate in the model. The analysis included 803 individuals from 193 pedigrees, comprising 258 full sibling pairs and 175 half sibling pairs. Suggestive linkage (p<10−3) was observed on chromosomes 2q21-2q24 and 5p13-5q22 for PTST-, and on chromosome 7p22-7p21 for TB; these findings for PTST- are novel and the chromosome 7 region contains the IL6 gene. In addition, we replicated recent linkage findings on chromosome 20q13 for TB (p = 0.002). We also observed linkage at the nominal α = 0.05 threshold to a number of promising candidate genes, SLC11A1 (PTST- p = 0.02), IL-1 complex (TB p = 0.01), IL12BR2 (TNFα p = 0.006), IL12A (TB p = 0.02) and IFNGR2 (TNFα p = 0.002). These results confirm not only that genetic factors influence the interaction between humans and Mtb but more importantly that they differ according to the outcome of that interaction: exposure but no infection, infection without progression to disease, or progression of infection to disease. Many of the genetic factors for each of these stages are part of the innate immune system.

Genome-wide Scan of African-American and White Families for Linkage to Myopia

To identify myopia susceptibility genes influencing common myopia in 94 African-American and 36 White families. A prospective study of families with myopia consisting of a minimum of two individuals affected with myopia. Extended families consisting of at least two siblings affected with myopia were ascertained. A genome-wide linkage scan using 387 markers was conducted by the Center for Inherited Disease Research. Linkage analyses were conducted with parametric and nonparametric methods. Model-free linkage analysis was performed maximizing over penetrance and over dominance (that is, fitting a wide range of both dominant and recessive models). Under the model-free analysis, the maximum two point heterogeneity logarithm of the odds score (MALOD) was 2.87 at D6S1009 in the White cohort and the maximum multipoint MALOD was 2.42 at D12S373-D12S1042 in the same cohort. The nonparametric linkage (NPL) maximum multipoint at D6S1035 had a P value of .005. An overall multipoint NPL score was obtained by combining NPL scores from both populations. The highest combined NPL score was observed at D20S478 with a significant P value of .008. Suggestive evidence of linkage in the White cohort mapped to a previously mapped locus on chromosome 11 at D11S1981 (NPL = 2.14; P = .02). Suggestive evidence of linkage to myopia in both African Americans and Whites was seen on chromosome 20 and became more significant when the scores were combined for both groups. The locus on chromosome 11 independently confirms a report by Hammond and associates mapping a myopia quantitative trait locus to this region.

Replication of linkage with bipolar disorder on chromosome 16p in the eastern Quebec population

In a previous study [Maziade et al. (2005); Mol Psychiatry 10:486-499], we provided evidence for linkage (parametric lod score of 4.05) on chromosome 16p for bipolar affective disorder (BP) in 21 kindreds from Eastern Quebec, a population characterized by a founder effect. Using a stringent design, we performed a replication study in a second sample of 27 kindreds (sample 2) collected from the same population and assessed with the same methodologies as in our original sample (sample 1), that is with the same diagnostic procedure and using a common set of 23 markers studied with model-based (parametric) and model-free (nonparametric) linkage analyses. We replicated our initial finding with P values <0.001. Indeed, maximum NPL(all) scores of 3.7 and 3.52 were found at marker D16S3060 in sample 2 for the narrow and broad BP phenotype definition, respectively. For the latter definition, the nonparametric score reached 3.87 in the combined sample, a value that exceeded the maximum NPL score obtained in each individual sample (NPL(all) = 2.32 in sample 1; NPL(all) = 3.52 in sample 2). Moreover, a refined phenotype restricted to BP associated with psychosis yielded significant evidence for linkage in each individual sample (NPL(all) = 2.38 in sample 1; NPL(all) = 2.72) while yielding the best result (NPL(all) score = 3.90) in the combined sample (samples 1 and 2), despite an important reduction in the number of affected individuals. It is also noteworthy that the use of the refined phenotype provided a location of the maximum linkage peak shared by both samples, that is, at marker D16S668 in 16p13.12, suggesting consistency across samples. Our study provided one of the strongest pieces of evidence for linkage with BP in 16p and illustrated the heuristic potential of a replication study in a second sample ascertained from the same population and using homogeneous methodologies.

Allelic frequencies and heterozygosities of microsatellite markers covering the whole genome in the Korean

Microsatellite markers are an essential tool for genetic linkage analysis because of their high polymorphism content. Four hundred commercially available markers covering the entire genome were genotyped from 578 sib individuals from 249 Korean families. Allelic frequencies and heterozygosities were determined for each marker loci and compared between Korean, Taiwanese, Japanese and Caucasian populations. In the three Asian populations, 10-13% of the markers had less than 0.6 heterozygosity, whereas in the Caucasian population, only 0.5% of the markers had less than 0.6 heterozygosity. Mean identical by descent (IBD) values were calculated for 578 sib individuals. Analysis of IBD values greater than 0.5 suggested that markers with low heterozygosity can also provide positive linkages, at least for the IBD sharing method of model-free linkage analysis. The data presented in this study will be a useful reference for genome-wide screens of Koreans and comparative studies with other ethnic populations.