Model-based Linkage Analysis Research Articles

Model-Based Linkage Analysis of a Quantitative Trait.

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

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.

Model-Based Linkage Analysis of a Binary Trait.

Linkage analysis is a statistical genetics method to localize disease and trait genes to specific chromosome regions. The analysis requires pedigrees with members who vary among each other in the trait of interest and who have been genotyped with known genetic markers. Linkage analysis tests whether any of the marker alleles cosegregate with the disease or trait within the pedigree. Evidence of cosegregation is then combined across the families. We describe here the background and methods to conduct a linkage analysis for a binary trait, such as a disease, when the model of the gene contributing to the trait can be formulated. There are a number of statistical genetics software packages that allow you conduct a model-based linkage analysis of a binary trait. We describe in great detail how to run one of the programs, the LODLINK program of the Statistical Analysis for Genetic Epidemiology (S.A.G.E.) package. We provide directions for making the four input files and information on how to access and interpret the output files. We then discuss more complex analyses that can be conducted. We discuss the MLOD program for multipoint linkage analysis, including its relation to LODLINK and the additional file needed. Notes to improve your ability to run the program are included.

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.

A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma.

We propose a 2-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma. First, we fit segregation models by formulating the likelihood for a person to have a bivariate phenotype, affection status and age of onset, along with other covariates, and from these we estimate population trait allele frequencies and penetrance parameters as a function of age (N = 281 multiplex glioma pedigrees). Second, the best fitting models are used as trait models in multipoint linkage analysis (N = 74 informative multiplex glioma pedigrees). To correct for ascertainment, a prevalence constraint is used in the likelihood of the segregation models for all 281 pedigrees. Then the trait allele frequencies are reestimated for the pedigree founders of the subset of 74 pedigrees chosen for linkage analysis. Using the best-fitting segregation models in model-based multipoint linkage analysis, we identified 2 separate peaks on chromosome 17; the first agreed with a region identified by Shete and colleagues who used model-free affected-only linkage analysis, but with a narrowed peak: and the second agreed with a second region they found but had a larger maximum log of the odds (LOD). Our approach was able to narrow the linkage peak previously published for glioma. We provide a practical solution to model-based linkage analysis for disease affection status with variable age of onset for the kinds of pedigree data often collected for linkage analysis.

Old lessons learned anew: family-based methods for detecting genes responsible for quantitative and qualitative traits in the Genetic Analysis Workshop 17 mini-exome sequence data

Family-based study designs are again becoming popular as new next-generation sequencing technologies make whole-exome and whole-genome sequencing projects economically and temporally feasible. Here we evaluate the statistical properties of linkage analyses and family-based tests of association for the Genetic Analysis Workshop 17 mini-exome sequence data. Based on our results, the linkage methods using relative pairs or nuclear families had low power, with the best results coming from variance components linkage analysis in nuclear families and Elston-Stewart model-based linkage analysis in extended pedigrees. For family-based tests of association, both ASSOC and ROMP performed well for genes with large effects, but ROMP had the advantage of not requiring parental genotypes in the analysis. For the linkage analyses we conclude that genome-wide significance levels appear to control type I error well but that “suggestive” significance levels do not. Methods that make use of the extended pedigrees are well powered to detect major loci segregating in the families even when there is substantial genetic heterogeneity and the trait is mainly polygenic. However, large numbers of such pedigrees will be necessary to detect all major loci. The family-based tests of association found the same major loci as the linkage analyses and detected low-frequency loci with moderate effect sizes, but control of type I error was not as stringent.

ADHD in Dutch adults: Heritability and linkage study

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental phenotype that persists into adulthood. This study investigated the heritability of inattentive and hyperactive symptoms and of total ADHD symptomatology load (ADHD index) in adults and performed linkage scans for these dimensions. Data on sibling pairs and their family members from the Netherlands Twin Register with genotype and phenotype data for inattention, hyperactivity and ADHD index (∼750 sib-pairs) were analyzed. Phenotypes were assessed with the short self-report form of the Conners' Adult ADHD Rating Scales (CAARS). Heritabilities were estimated in SOLAR under polygenic models. Genome-wide linkage scans were performed using variance components (VC) in MERLIN and MINX and model-based linkage analysis was carried out in MENDEL with empirical evaluation of the results via simulations. Heritability estimates for inattention, hyperactivity and ADHD index were 35%, 23%, and 31%, respectively. Chromosomes 18q21.31-18q21.32 (VC LOD = 4.58, p(emp) = 0.0026) and 2p25.1 (LOD = 3.58, p(emp) = 0.0372) provided significant evidence for linkage for inattention and the ADHD index, respectively. The QTL on chromosome 2p25.1 also showed suggestive linkage for hyperactivity. Two additional suggestive QTLs for hyperactivity and the ADHD index shared the same location on chromosome 3p24.3-3p24.1. Finally, a suggestive QTL on 8p23.3-8p23.2 for hyperactivity was also found. Heritability of inattention, hyperactivity and total ADHD symptoms is lower in adults than in children. Chromosomes 18q and 2p are likely to harbor genes that influence several aspects of adult ADHD.

Some Capabilities for Model-Based and Model-Free Linkage Analysis using the Program Package S.A.G.E. (Statistical Analysis for Genetic Epidemiology)

For both model-free and model-based linkage analysis the S.A.G.E. (Statistical Analysis for Genetic Epidemiology) program package has some unique capabilities in analyzing both continuous traits and binary traits with variable age of onset. Here we highlight model-based linkage analysis of a quantitative trait (plasma dopamine β hydroxylase) that is known to be largely determined by monogenic inheritance, using a prior segregation analysis to produce the best fitting model for the trait. For a binary trait with variable age of onset (schizophrenia), we illustrate how using age of onset information to obtain a quantitative susceptibility trait leads to more statistically significant linkage signals, suggesting better power.

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.

Genetic Predisposition to Familial Neuroblastoma: Identification of Two Novel Genomic Regions at 2p and 12p

Objectives: The rarity of familial neuroblastoma (NB) has allowed only a few linkage studies, most of which did not show any evidence of linkage to regions involved in somatic alterations or to genes implicated in other neurocristopathies seldom associated with NB. We screened a highly informative family with recurrent NB by genome-wide linkage analysis aimed at identifying chromosomal regions for NB predisposing genes. Methods: A genome-wide screen was performed using 382 microsatellite markers. Multipoint model-based linkage analysis was carried out under a dominant mode of inheritance for the disease using the ‘affected only’ approach. Results: Our analysis identified two haplotypes co-segregating with the disease on chromosomes 2p and 12p, and yielded maximum lod-score values of 3.01 (p < 0.0001) for markers on both intervals. Conclusions: Evidence of linkage was reported at 16p in North American families, whereas our studies excluded this interval and indicated other loci for disease predisposition, thus confirming the remarkable genetic heterogeneity of NB. These results suggest an oligogenic inheritance in NB involving more loci in genetic determination of the disease.

Genetic characterization and fine mapping of susceptibility loci for sarcoidosis in African Americans on chromosome 5

Sarcoidosis, a systemic granulomatous disease, likely results from both environmental agents and genetic susceptibility. Sarcoidosis is more prevalent in women and, in the United States, African Americans are both more commonly and more severely affected than Caucasians. We report a follow up of the first genome scan for sarcoidosis susceptibility genes in African Americans. Both the genome scan and the present study comprise 229 African American nuclear families ascertained through two or more sibs with sarcoidosis. Regions studied included those which reached a significance in the genome scan of 0.01 (2p25, 5q11, 5q35, 9q34, 11p15 and 20q13), 0.05 (3p25 and 5p15-13) or which replicated previous findings (3p14-11). We performed genotyping with additional markers in the same families used in the genome scan. We examined multi-locus models for epistasis and performed model-based linkage analysis on subsets of the most linked families to characterize the underlying genetic model. The strongest signal was at marker D5S407 (P=0.005) on 5q11.2, using both full and half sibling pairs. Our results support, in an African American population, a sarcoidosis susceptibility gene on chromosome 5q11.2, and a gene protective for sarcoidosis on 5p15.2. These fine mapping results further prioritize the importance of candidate regions on chromosomes 2p25, 3p25, 5q35, 9q34, 11p15 and 20q13 for African Americans. Additionally, our results suggest joint action of the effects of putative genes on chromosome 3p14-11 and 5p15.2. We conclude that multiple susceptibility loci for sarcoidosis exist in African Americans and that some may have interdependent effects on disease pathogenesis.

A Novel Mutation of POU3F4 Causes Congenital Profound Sensorineural Hearing Loss in a Large Chinese Family

It is known that approximately 5% of congenital profound hearing impaired cases are inherited in X-linked inheritance. This study aimed at identifying its underlying molecular determinant(s) using a large, five-generation Chinese family with multiple familial cases. Model-based linkage analysis and positional cloning. Model-based genetic linkage analyses were performed with the use of microsatellite polymorphisms to determine disease locus. Mutation screening was performed within the family and unrelated population-based controls to establish molecular evidence as to what caused the specific X-linked inheritance pattern in the family. Clinical investigations of the pedigree demonstrated the extremely high penetrance in the male members but no penetrance in the female members. Linkage analyses mapped the disease to the chromosomal region Xq13.I-Xq23 (maximum X-linkage logarithm of odds score = 3.27). Mutation screening of the candidate genes in the linkage region by direct sequencing revealed a de novo missense substitution (925T>C) in the well-known deaf gene. POU3F4. Direct sequencing on 240 unrelated controls did not detect any mutation. Multiple analysis approaches demonstrated that these disorders in the family were caused by a founder mutation in the POU3F4 gene. Our findings provided confirmatory molecular evidence to support that development of congenital profound sensorineural hearing loss in the Chinese population results from a novel mutation in the same gene.

Allele frequency misspecification: effect on power and Type I error of model-dependent linkage analysis of quantitative traits under random ascertainment

BackgroundStudies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate. An increase in Type I error rate is seen when marker related parameters (e.g., allele frequencies) are misspecified and ascertainment is through the trait, but lod-score methods are expected to be robust when ascertainment is random (as is often the case in linkage studies of quantitative traits). In previous studies, the power of lod-score linkage analysis using the "correct" generating model for the trait was found to increase when the marker allele frequencies were misspecified and parental data were missing. An investigation of Type I error rates, conducted in the absence of parental genotype data and with misspecification of marker allele frequencies, showed that an inflation in Type I error rate was the cause of at least part of this apparent increased power. To investigate whether the observed inflation in Type I error rate in model-based LOD score linkage was due to sampling variation, the trait model was estimated from each sample using REGCHUNT, an automated segregation analysis program used to fit models by maximum likelihood using many different sets of initial parameter estimates.ResultsThe Type I error rates observed using the trait models generated by REGCHUNT were usually closer to the nominal levels than those obtained when assuming the generating trait model.ConclusionThis suggests that the observed inflation of Type I error upon misspecification of marker allele frequencies is at least partially due to sampling variation. Thus, with missing parental genotype data, lod-score linkage is not as robust to misspecification of marker allele frequencies as has been commonly thought.

The role of parametric linkage methods in complex trait analyses using microsatellites

Many investigators of complexly inherited familial traits bypass classical segregation analysis to perform model-free genome-wide linkage scans. Because model-based or parametric linkage analysis may be the most powerful means to localize genes when a model can be approximated, model-free statistics may result in a loss of power to detect linkage. We performed limited segregation analyses on the electrophysiological measurements that have been collected for the Collaborative Study on the Genetics of Alcoholism. The resulting models are used in whole-genome scans. Four genomic regions provided a model-based LOD > 2 and only 3 of these were detected (p < 0.05) by a model-free approach. We conclude that parametric methods, using even over-simplified models of complex phenotypes, may complement nonparametric methods and decrease false positives.

Genetic linkage of systemic lupus erythematosus to 13q32 in African American families with affected male members

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder involving genetic and environmental factors. Previously, our group showed that SLE females with affected male relatives have higher prevalence of renal disease than SLE females with no affected male relatives in a sample of 372 individuals from 159 families. By adding 392 individuals from 181 new families, we replicated this finding in the largest collection of families with affected males, confirming our hypothesis that multiplex SLE families with at least one affected male member ("male families") comprise a distinct subpopulation of SLE multiplex families. We studied 64 male families by a genome-wide scan for SLE and found the largest signal (lod=3.08) at 13q32 in 18 African American male families using an affected-relative-pair model-free linkage method. Closer examination of IBD sharing at this region suggested a dominant mode of inheritance. Multipoint model-based linkage analysis generated a lod score of 3.13 in the same chromosomal region with a low-disease allele frequency of 0.0004 and a disease penetrance of 0.5 for the 18 African American male families. We performed fine mapping in these and three additional African American male families and the SLE predisposing locus was localized to a region tightly linked to the marker D13S892. We have therefore confirmed the linkage of SLE to 13q32, which was reported previously, and suggested that an SLE susceptibility gene in this region is specific to predisposition of African Americans to a specific form of SLE, with males at high risk.

Software for quantitative trait analysis

This paper provides a brief overview of software currently available for the genetic analysis of quantitative traits in humans. Programs that implement variance components, Markov Chain Monte Carlo (MCMC), Haseman-Elston (H-E) and penetrance model-based linkage analyses are discussed, as are programs for measured genotype association analyses and quantitative trait transmission disequilibrium tests. The software compared includes LINKAGE, FASTLINK, PAP, SOLAR, SEGPATH, ACT, Mx, MERLIN, GENEHUNTER, Loki, Mendel, SAGE, QTDT and FBAT. Where possible, the paper provides URLs for acquiring these programs through the internet, details of the platforms for which the software is available and the types of analyses performed.

A genome wide linkage study of obesity as secondary effect of antipsychotics in multigenerational families of eastern Quebec affected by psychoses.

Antipsychotics can induce in schizophrenic (SZ) and bipolar disorder (BP) patients serious body weight changes that increase risk for noncompliance to medication, and risk for cardiovascular diseases and diabetes. A genetic origin for this susceptibility to weight changes has been hypothesized because only a proportion of treated patients are affected, the degree of affection differing also in rates and magnitudes. In a first genome scan on obesity under antipsychotics in SZ and BP, we analyzed 21 multigenerational kindreds (508 family members) including several patients treated for a minimum of 3 years mainly with haloperidol or chlopromazine. Obesity was defined from medical files and was shown to be 2.5 times more frequent in patients treated with antipsychotics than in untreated family members (30 vs 12%). The nine pedigrees that showed at least two occurrences of obesity under antipsychotics were submitted to model-based linkage analyses. We observed a suggestive linkage with a multipoint Lod score (MLS) of 2.74 at 12q24. This linkage finding vanished when we used as phenotypes, obesity unrelated to antipsychotics, and when we used SZ or BP. This suggests that this positive linkage result with obesity is specific to the use of antipsychotics. A potential candidate gene for this linkage is the pro-melanin-concentrating hormone (PMCH) gene located at less then 1 cM of the linkage. PMCH encodes a neuropeptide involved in the control of food intake, energy expenditure, and in anxiety/depression. This first genome scan targeting the obesity side effect of antipsychotics identified 12q24 as a susceptibility region.

Genomewide Linkage Scan Identifies a Novel Susceptibility Locus for Restless Legs Syndrome on Chromosome 9p

Restless legs syndrome (RLS) is a common neurological disorder that affects 5%-12% of all whites. To genetically dissect this complex disease, we characterized 15 large and extended multiplex pedigrees, consisting of 453 subjects (134 affected with RLS). A familial aggregation analysis was performed, and SAGE FCOR was used to quantify the total genetic contribution in these families. A weighted average correlation of 0.17 between first-degree relatives was obtained, and heritability was estimated to be 0.60 for all types of relative pairs, indicating that RLS is a highly heritable trait in this ascertained cohort. A genomewide linkage scan, which involved >400 10-cM-spaced markers and spanned the entire human genome, was then performed for 144 individuals in the cohort. Model-free linkage analysis identified one novel significant RLS-susceptibility locus on chromosome 9p24-22 with a multipoint nonparametric linkage (NPL) score of 3.22. Suggestive evidence of linkage was found on chromosome 3q26.31 (NPL score 2.03), chromosome 4q31.21 (NPL score 2.28), chromosome 5p13.3 (NPL score 2.68), and chromosome 6p22.3 (NPL score 2.06). Model-based linkage analysis, with the assumption of an autosomal-dominant mode of inheritance, validated the 9p24-22 linkage to RLS in two families (two-point LOD score of 3.77; multipoint LOD score of 3.91). Further fine mapping confirmed the linkage result and defined this novel RLS disease locus to a critical interval. This study establishes RLS as a highly heritable trait, identifies a novel genetic locus for RLS, and will facilitate further cloning and identification of the genes for RLS.