Linkage Statistics Research Articles

Identification of Chromosomal Regions Linked to Diabetic Nephropathy: A Meta-Analysis of Genome-Wide Linkage Scans

Diabetic nephropathy (DN) has become a serious public health problem. Genetic factors are involved in the pathogenesis of DN, but the exact mode of inheritance is still unknown. Genome-wide linkage scans (GWLS) have produced inconclusive or inconsistent results. In an effort to test consistency and provide more conclusive results, we applied a heterogeneity-based genome search meta-analysis (HEGESMA) to GWLS regarding DN. We combined results from eight GWLS in the primary analysis and nine GWLS for a conditional analysis about DN for both diabetes types, as well as in each type of diabetes and ethnicity in subgroup analyses. HEGESMA identified cytogenetic locations that rank highly on average in terms of linkage statistics across multiple genome scans, taking into consideration the magnitude of heterogeneity of the results between scans. Main analyses: Our meta-analysis identified 13 cytogenetic locations (bins) with statistical significance (Prank ≤ 0.05), 11 of which were significant in both weighted and unweighted analyses located on chromosomes 1q, 3q, 4p, 5q, 7q, 15q, 16p, 17q, 19q, and 22p. In addition, four novel regions (5q11.2-5q14.3, 5q23.2-5q34, 17q24.3-17q25.3, and 22q12.3-22q13.3) were identified. Seven bins on chromosomes 4p, 5q, 7q, 15q, 22p, and 22q were common between both types of diabetes and in all subgroup analyses, in addition 5q14.3-5q23.2 was significant across all analyses. Conditional analyses: meta-analysis identified nine different cytogenetic locations, among which 7p22.3-7p15.3 was significant only in type 2 diabetes mellitus conditional analysis. Ethnicity subgroup analyses identified 11 different cytogenetic locations, 5 out of which are novel findings. However, none of the chromosomal regions reached genome-wide statistical significance (Prank < 0.00042). This meta-analysis provides evidence for linkage for nine novel cytogenetic regions that should be further investigated for genes that confer susceptibility to DN.

Inferring disease risk genes from sequencing data in multiplex pedigrees through sharing of rare variants.

We previously demonstrated how sharing of rare variants (RVs) in distant affected relatives can be used to identify variants causing a complex and heterogeneous disease. This approach tested whether single RVs were shared by all sequenced affected family members. However, as with other study designs, joint analysis of several RVs (e.g., within genes) is sometimes required to obtain sufficient statistical power. Further, phenocopies can lead to false negatives for some causal RVs if complete sharing among affected is required. Here, we extend our methodology (Rare Variant Sharing, RVS) to address these issues. Specifically, we introduce gene-based analyses, a partial sharing test based on RV sharing probabilities for subsets of affected relatives and a haplotype-based RV definition. RVS also has the desirable feature of not requiring external estimates of variant frequency or control samples, provides functionality to assess and address violations of key assumptions, and is available as open source software for genome-wide analysis. Simulations including phenocopies, based on the families of an oral cleft study, revealed the partial and complete sharing versions of RVS achieved similar statistical power compared with alternative methods (RareIBD and the Gene-Based Segregation Test), and had superior power compared with the pedigree Variant Annotation, Analysis, and Search Tool (pVAAST) linkage statistic. In studies of multiplex cleft families, analysis of rare single nucleotide variants in the exome of 151 affected relatives from 54 families revealed no significant excess sharing in any one gene, but highlighted different patterns of sharing revealed by the complete and partial sharing tests.

A genome-wide linkage study of autism spectrum disorder and the broad autism phenotype in extended pedigrees

BackgroundAlthough several genetic variants for autism spectrum disorder (ASD) have now been identified, these largely occur sporadically or are de novo. Much less progress has been made in identifying inherited variants, even though the disorder itself is familial in the majority of cases. The objective of this study was to identify chromosomal regions that harbor inherited variants increasing the risk for ASD using an approach that examined both ASD and the broad autism phenotype (BAP) among a unique sample of extended pedigrees.MethodsASD and BAP were assessed using standardized tools in 28 pedigrees from Canada and the USA, each with at least three ASD-diagnosed individuals from two nuclear families. Genome-wide linkage analysis was performed using the posterior probability of linkage (PPL) statistic, a quasi-Bayesian method that provides strength of evidence for or against linkage in an essentially model-free manner, with outcomes on the probability scale.ResultsThe results confirm appreciable interfamilial heterogeneity as well as a high level of intrafamilial heterogeneity. Both ASD and combined ASD/BAP specific loci are apparent.ConclusionsInclusion of subclinical phenotypes such as BAP should be more widely employed in genetic studies of ASD as a way of identifying inherited genetic variants for the disorder. Moreover, the results underscore the need for approaches to identifying genetic risk factors in extended pedigrees that are robust to high levels of inter/intrafamilial locus and allelic heterogeneity.

Displaying Linkage Success Statistics to Identify Systemic Errors

ABSTRACT&#x0D; ObjectiveThe primary objective is to create a method for displaying linkage statistics to researchers, data stewards, and linkage specialists in an informative and meaningful way. The method must visually display the linkage summary data and highlight drops in the linkage success rate.&#x0D; ApproachWe created a web interface which shows linkage statistics by age and geography in calendar/service years. Each cell contains both the percentage of linked values along with the percentage of successfully linked data. The interface is filterable by gender, data-type, and whether to display the number of successful or unsuccessful linkages. Due to the high volume of data which will appear on the screen at one time, we use a heat map to highlight cells which have unusually high or low values. Totals are displayed with their own heat maps to compare easily years across ages group or age groups across years. We mask small cell sizes to preserve privacy.&#x0D; ResultsThis approach allows people to easily spot drops in linkage success. If a particular year’s data or age group has a lower linkage rate than the rest of the dataset, the heat map can clearly highlight that discrepancy. Displaying the number of linkages along with the rate helps us determine if the sample size is playing a role in a low linkage success rate.&#x0D; ConclusionData quality issues can silently cause linkage success rates to drop in certain years, geographies, age groups, or genders. Displaying linkage statistics on a single page with a heat map allows people to quickly spot inconsistencies in linkages.

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.

Linking of the American Academy of Orthopaedic Surgeons Distal Radius Fracture Clinical Practice Guidelines to the International Classification of Functioning, Disability, and Health; International Classification of Diseases; and ICF Core Sets for Hand Conditions

Background: American Academy of Orthopaedic Surgeons (AAOS) distal radius fracture (DRF) clinical practice guidelines (CPG) are readily available to clinicians, patients, and policymakers. International Classification of Functioning, Disability, and Health (ICF) provides a framework for describing the impact of health conditions. The International Classification of Diseases-10th Revision (ICD-10) is a classification system to classify health conditions as specific disease or disorders. The aim of this study is to analyze and describe the scope and focus of the AAOS DRF CPG using the ICF and ICD-10 as a basis for content analysis, and to compare the content of the CPG with the ICF hand core sets as the reference standard. Methods: Established linking rules were used by 2 independent raters to analyze the 29 recommendations of the AAOS DRF CPG. ICD-10 codes were assigned in the same process. Summary linkage statistics were used to describe the results for ICF and the hand core sets. Results: Among the 29 recommendations of the AAOS DRF CPG, 5 meaningful concepts were linked to the ICF codes. Of these, 5 codes appeared on the comprehensive ICF core set and only 3 codes appeared in the brief ICF core set, and 7 conditions were covered in ICD-10 codes. Conclusions: The AAOS DRF CPG focuses on surgical interventions and has minimal linkage to the constructs of the ICD-10 and ICF. It does not address activity or participation (disability), and is not well linked to key concepts relevant to hand conditions.

MP73-14 WHO WATCHES THE WATCHMEN?: SYSTEMATIC UNDERESTIMATION OF THE INCIDENCE OF KIDNEY CANCER IN CANCER REGISTRY DATA

INTRODUCTION AND OBJECTIVES: Increasing numbers of small renal cell carcinomas (RCC) are being incidentally detected and followed longitudinally without surgical intervention. As additional cases of RCC’s are identified, we need to ensure that methods of detecting these individuals in regional cancer registries (CR) are timely and reflect clinical practice. The objective of this study was to identify if a gap exists in kidney cancer surveillance infrastructure which leads to a systematic underestimation of the true incidence of kidney cancer as reported by CR’s. METHODS: In Phase I of our study, data from 638 patients with a physician billing code for kidney cancer in 2008 (ICD-9: 189.0) was linked to the provincial CR. Individual patient chart review was performed to confirm diagnosis on discrepant cases. In Phase II of our study, a prospectively maintained database of 95 patients on active surveillance with presumed RCC in 2012 was linked with the CR. These years were chosen to allow for lag time of CR data capture. Data linkage statistics between these two sources and CR data are presented. RESULTS: Phase I revealed 184 patients (29%) with administratively coded kidney cancer were not captured in the CR. Ninety-five of these patients (52%) were not found for two reasons: no final cancer diagnosis was determined as per the current CR inclusion criteria or patients resided out of province. The remaining 89 patients (48%) were previously registered but with a nonkidney cancer. Phase II showed that 19 patients (20%) were identified in the CR with RCC. Of the remaining 76, 48 patients (50%) were eligible to be entered into the registry data as determined by CR rules. The remaining 28 patients (30%) did not meet current CR inclusion criteria. Thus we confirmed our hypothesized gap between CR reported and the true incidence of kidney cancer in both phases of our study. CONCLUSIONS: When utilizing administrative data, we identified a large proportion of patients with presumed kidney cancer who were not captured in the CR. When the system is challenged further by comparing it with a prospectively collected surveillance series with a lower rate of histological diagnosis, the gap is much greater. Traditionally, CR’s have relied heavily on histological confirmation for identification of patients as gold-standard cases of kidney cancer. This work demonstrates the need for CR’s to identify appropriate alternative sources of diagnostic data for non-histologically confirmed cancers. This will allow for the accurate quantification of the true incidence and burden of kidney cancer.

Meta-Analysis of Repository Data: Impact of Data Regularization on NIMH Schizophrenia Linkage Results

Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.

Metamoodics: meta-analysis and bioinformatics resource for mood disorders

Mood disorders, including Major Depression (MD) and Bipolar Disorder (BP), are among the most common psychiatric disorders1. Although genetic factors play an important role in their etiology2, 3, the heritability of these disorders remains largely unexplained. Due to advances in high-throughput genomic technologies, it is becoming increasingly feasible to investigate the genetic architecture of these disorders at an unprecedented resolution. The challenge is how to make sense of the enormous flood of data generated by the diverse genomic studies that use these new technologies. It is essential to integrate the findings from such studies in order to develop a more comprehensive understanding of the genetic contribution to these disorders that can be readily translated into new and more rational therapeutic interventions. Towards this end, we have developed a bioinformatics resource called Metamoodics (www.metamoodics.org), which integrate results from various genomic studies of mood disorders and enhance their interpretation by placing them within an enriched genomic context. Metamoodics was created as a resource for the research community to explore what is currently known about the genetic contribution to mood disorders. Metamoodics synthesizes data from existing genome-wide linkage, expression and association studies for both MD and BP. It brings together results from up-to-date meta- or mega-analyses of these studies, and presents them in both tabular and graphical modes. The genome-wide linkage data comes from among the largest and most comprehensive of such studies with MD and BP. Data for BP comes from a mega-analysis carried out with 972 informative pedigrees of European ancestry pooled from eight research groups in North America and the United Kingdom3. Linkage statistics for twelve different phenotype-by-genotype models were calculated for a genome-wide panel of 6090 SNPs. For MD, data from the Genetics of Recurrent Early-Onset Major Depression (GenRED) collaborative are provided4. This included data on 418 microsatellite markers genotyped in 656 families consisting of 1,494 all possible informative affective relative pairs. Data for genome-wide association studies (GWAS) were obtained from the psychiatric GWAS consortium (PGC). For BP, the sample included 7,481 cases and 9,250 controls, all of European ancestry, with data on ~2.5M genotyped and/or imputed SNPs5. Data on MD consisted of more than 1.2 million SNPs in 9,240 cases and 9,519 controls of recent European ancestry6. Gene expression data was obtained through a systematic review of studies on BP (n=13) and MD (n=7) using post-mortem brain tissue. Mega-analyses of the most comprehensive and rigorous of these studies were carried out using samples from all regions of the brain, the pre-frontal cortex, and the hippocampus, where sufficient data was available7. Finally, Metamoodics includes a comprehensive meta-analysis of published candidate gene association studies of BP. Data on 33 polymorphisms in 18 genes reported on by three or more case-control studies were included in the meta-analysis8. A similar meta-analysis for MD is forthcoming. Metamoodics includes a variety of online applications and tools for visualization and further analysis of the genomics data. Data visualization makes use of NCBI SeqView API and a local mirror of the UCSC genome browser. The results can be queried by gene or chromosomal position to obtain either a gene level view via chromosomal or regional plots, or a more global view via the genome browser to investigate the genomic context of the findings. User-defined data can be uploaded to the genome browser using the custom track option to visualize alongside the Metamoodics tracks. Analysis tools include SVAw9 for surrogate variable analysis, METAw for meta-analysis and a gene expression package for individual and mega analysis of expression data. Metamoodics presents the latest results from genomic studies of mood disorders in a user-friendly web interface that enables users to rapidly compare findings from different experimental platforms across the genome, as well as drill down to specific findings to visualize how these relate to underlying genomic structure and function. Metamoodics provides the entire set of results of a given study rather than just significant ones, thus providing a complete and unbiased report of the findings. We will continue to update Metamoodics with findings from the latest genomic experiments of mood disorders, such as whole exome or genome sequencing and epigenetic studies. Metamoodics is a vital bioinformatics tool for investigating the genetic etiology of mood disorders that will only grow more powerful as new data are added.

A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases

BackgroundIn the last years GWA studies have successfully identified common SNPs associated with complex diseases. However, most of the variants found this way account for only a small portion of the trait variance. This fact leads researchers to focus on rare-variant mapping with large scale sequencing, which can be facilitated by using linkage information. The question arises why linkage analysis often fails to identify genes when analyzing complex diseases. Using simulations we have investigated the power of parametric and nonparametric linkage statistics (KC-LOD, NPL, LOD and MOD scores), to detect the effect of genes responsible for complex diseases using different pedigree structures.ResultsAs expected, a small number of pedigrees with less than three affected individuals has low power to map disease genes with modest effect. Interestingly, the power decreases when unaffected individuals are included in the analysis, irrespective of the true mode of inheritance. Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance.ConclusionsWhen applied in a sensible way linkage is an appropriate and robust technique to map genes for complex disease. Unlike association analysis, linkage analysis is not hampered by allelic heterogeneity. So, why does linkage analysis often fail with complex diseases? Evidently, when using an insufficient number of small pedigrees, one might miss a true genetic linkage when actually a real effect exists. Furthermore, we show that the test statistic has an important effect on the power to detect linkage as well. Therefore, a linkage analysis might fail if an inadequate test statistic is employed. We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.

Analysis of Xq27-28 linkage in the international consortium for prostate cancer genetics (ICPCG) families

BackgroundGenetic variants are likely to contribute to a portion of prostate cancer risk. Full elucidation of the genetic etiology of prostate cancer is difficult because of incomplete penetrance and genetic and phenotypic heterogeneity. Current evidence suggests that genetic linkage to prostate cancer has been found on several chromosomes including the X; however, identification of causative genes has been elusive.MethodsParametric and non-parametric linkage analyses were performed using 26 microsatellite markers in each of 11 groups of multiple-case prostate cancer families from the International Consortium for Prostate Cancer Genetics (ICPCG). Meta-analyses of the resultant family-specific linkage statistics across the entire 1,323 families and in several predefined subsets were then performed.ResultsMeta-analyses of linkage statistics resulted in a maximum parametric heterogeneity lod score (HLOD) of 1.28, and an allele-sharing lod score (LOD) of 2.0 in favor of linkage to Xq27-q28 at 138 cM. In subset analyses, families with average age at onset less than 65 years exhibited a maximum HLOD of 1.8 (at 138 cM) versus a maximum regional HLOD of only 0.32 in families with average age at onset of 65 years or older. Surprisingly, the subset of families with only 2–3 affected men and some evidence of male-to-male transmission of prostate cancer gave the strongest evidence of linkage to the region (HLOD = 3.24, 134 cM). For this subset, the HLOD was slightly increased (HLOD = 3.47 at 134 cM) when families used in the original published report of linkage to Xq27-28 were excluded.ConclusionsAlthough there was not strong support for linkage to the Xq27-28 region in the complete set of families, the subset of families with earlier age at onset exhibited more evidence of linkage than families with later onset of disease. A subset of families with 2–3 affected individuals and with some evidence of male to male disease transmission showed stronger linkage signals. Our results suggest that the genetic basis for prostate cancer in our families is much more complex than a single susceptibility locus on the X chromosome, and that future explorations of the Xq27-28 region should focus on the subset of families identified here with the strongest evidence of linkage to this region.

Haplo2Ped: a tool using haplotypes as markers for linkage analysis

BackgroundGenerally, SNPs are abundant in the genome; however, they display low power in linkage analysis because of their limited heterozygosity. Haplotype markers, on the other hand, which are composed of many SNPs, greatly increase heterozygosity and have superiority in linkage statistics.ResultsHere we developed Haplo2Ped to automatically transform SNP data into haplotype markers and then to compute the logarithm (base 10) of odds (LOD) scores of regional haplotypes that are homozygous within the disease co-segregation haploid group. The results are reported as a hypertext file and a 3D figure to help users to obtain the candidate linkage regions. The hypertext file contains parameters of the disease linked regions, candidate genes, and their links to public databases. The 3D figure clearly displays the linkage signals in each chromosome. We tested Haplo2Ped in a simulated SNP dataset and also applied it to data from a real study. It successfully and accurately located the causative genomic regions. Comparison of Haplo2Ped with other existing software for linkage analysis further indicated the high effectiveness of this software.ConclusionsHaplo2Ped uses haplotype fragments as mapping markers in whole genome linkage analysis. The advantages of Haplo2Ped over other existing software include straightforward output files, increased accuracy and superior ability to deal with pedigrees showing incomplete penetrance. Haplo2Ped is freely available at: http://bighapmap.big.ac.cn/software.html.

Genome-wide linkage scan and association study of PARL to the expression of LHON families in Thailand

Leber hereditary optic neuropathy (LHON) is the most common mitochondrially inherited disease causing blindness, preferentially in young adult males. Most of the patients carry the G11778A mitochondrial DNA (mtDNA) mutation. However, the marked incomplete penetrance and the gender bias indicate some additional genetic and/or environmental factors to disease expression. Herein, we first conducted a genome-wide linkage scan with 400 microsatellite markers in 9 large Thai LHON G11778A pedigrees. Using an affecteds-only nonparametric linkage analysis, 4 regions on chromosomes 3, 12, 13 and 18 showed Zlr scores greater than 2 (P < 0.025), which is consistently significant across several linkage statistics. The most suggestive marker D3S1565 (Zlr > 2 in 10 of 16 allele sharing models tested) was then expanded to include the region 3q26.2-3q28 covering SLC7A14 (3q26.2), MFN1 (3q26.32), MRPL47 (3q26.33), MCCC1 (3q27.1), PARL (3q27.1) and OPA1 (3q28-q29). All of these candidate genes were selected from the Maestro database and had known to be localized in mitochondria. Sixty tag SNPs were genotyped in 86 cases, 211 of their relatives and 32 unrelated Thai controls, by multiplex-PCR-based Invader assay. Analyses using a powerful association testing tool that adjusts for relatedness (the M(QLS) statistic) showed the most evidence of association between two SNPs, rs3749446 and rs1402000 (located in PARL presenilins-associated rhomboid-like) and LHON expression (both P = 8.8 x 10(-5)). The mitochondrial PARL protease has been recently known to play a role with a dynamin-related OPA1 protein in preventing apoptotic events by slowing down the release of cytochrome c out of mitochondrial cristae junctions. Moreover, PARL is required to activate the intramembranous proteolyses resulting in the degradation of an accumulated pro-apoptotic protein in the outer mitochondrial membrane. Under these circumstances, variants of PARL are suggested to influence cell death by apoptosis which has long been believed to intrigue the neurodegeneration of LHON.

An Application of the Latent p Value Method to Assess Linkage in Asthma Pedigrees

Objective: The latent p value is a recently proposed empirical method for assessing evidence against a null hypothesis in a stochastic system involving latent, unobservable variables. It is particularly applicable to genome-wide genetic linkage analysis for test statistics with poorly defined analytical distributions. Methods: We describe an implementation of the latent p value method and its application to a linkage analysis of asthma in 81 extended pedigrees containing 1,858 people genotyped at 533 microsatellite markers. We compare the performance of the latent p value method to a more conventional p value calculation. We also compare the performance of various linkage statistics within this pedigree resource. Results: Using a novel linkage score referred to as the C-link statistic, our analysis provides strong evidence for a recessive gene influencing asthma on chromosome 5q13 (median latent p value = 0.03). We also demonstrate remarkable improvement in computational requirements compared to a more conventional empirical p value calculation. Conclusions: The latent p value method is indeed feasible and provides a computationally efficient means to evaluate evidence for linkage regardless of the choice of linkage statistic.

Evaluation of Approaches to Identify Associated SNPs That Explain the Linkage Evidence in Nuclear Families with Affected Siblings

Linkage analysis is often followed by association mapping to localize disease variants. In this paper, we evaluate approaches to determine how much of the observed linkage evidence, namely the identity-by-descent (IBD) sharing at the linkage peak, is explained by associated SNPs. We study several methods: Homozygote Sharing Tests (HST), Genotype Identity-by-Descent Sharing Test (GIST), and a permutation approach. We also propose a new approach, HSTMLB, combining HST and the Maximum Likelihood Binomial (MLB) linkage statistic. These methods can identify SNPs partially explaining the linkage peak, but only HST and HSTMLB can identify SNPs that do not fully explain the linkage evidence and be applied to multiple-SNPs. We contrast these methods with the association tests implemented in the software LAMP. In our simulations, GIST is more powerful at finding SNPs that partially explain the linkage peak, while HST and HSTMLB are equally powerful at identifying SNPs that do not fully explain the linkage peak. When applied to the North American Rheumatoid Arthritis Consortium data, HST and HSTMLB identify marker pairs that may fully explain the linkage peak on chromosome 6. In conclusion, HST and HSTMLB provide simple and flexible tools to identify SNPs that explain the IBD sharing at the linkage peak.

Robust Age at Onset Linkage Analysis in Nuclear Families

Objective: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset. Methods:Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation). Results:In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis.

New score tests for age-at-onset linkage analysis in general pedigrees

Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a log-normal frailty model with approximated likelihood around the null random effect. The score statistics are weighted nonparametric linkage statistics, with weights depending on the age at onset. These tests are correct under the null hypothesis irrespective of the weight used. They are simple, robust, computationally fast, and can be applied to large, complex pedigrees. We apply these methods to simulated data and to the Genetic Analysis Workshop 16 Framingham Heart Study data set. We investigate the time to the first of three events: hard coronary heart disease, diabetes, or death from any cause. We use a two-step procedure. In the first step, we estimate the population parameters under the null hypothesis of no linkage. In the second step, we apply the score tests, using the population parameters estimated in the first step.

Handling linkage disequilibrium in qualitative trait linkage analysis using dense SNPs: a two-step strategy

BackgroundIn affected sibling pair linkage analysis, the presence of linkage disequilibrium (LD) has been shown to lead to overestimation of the number of alleles shared identity-by-descent (IBD) among sibling pairs when parents are ungenotyped. This inflation results in spurious evidence for linkage even when the markers and the disease locus are not linked. In our study, we first theoretically evaluate how inflation in IBD probabilities leads to overestimation of a nonparametric linkage (NPL) statistic under the assumption of linkage equilibrium. Next, we propose a two-step processing strategy in order to systematically evaluate approaches to handle LD. Based on the observed inflation of expected logarithm of the odds ratio (LOD) from our theoretical exploration, we implemented our proposed two-step processing strategy. Step 1 involves three techniques to filter a dense set of markers. In step 2, we use the selected subset of markers from step 1 and apply four different methods of handling LD among dense markers: 1) marker thinning (MT); 2) recursive elimination; 3) SNPLINK; and 4) LD modeling approach in MERLIN. We evaluate relative performance of each method through simulation.ResultsWe observed LOD score inflation only when the parents were ungenotyped. For a given number of markers, all approaches evaluated for each type of LD threshold performed similarly; however, RE approach was the only one that eliminated the LOD score bias. Our simulation results indicate a reduction of approximately 75% to complete elimination of the LOD score inflation while maintaining the information content (IC) when setting a tolerable squared correlation coefficient LD threshold (r2) above 0.3 for or 2 SNPs per cM using MT.ConclusionWe have established a theoretical basis of how inflated IBD information among dense markers overestimates a NPL statistic. The two-step processing strategy serves as a useful framework to systematically evaluate relative performance of different methods to handle LD.

Score test for age at onset genetic linkage analysis in selected sibling pairs

A new score statistic is derived, which uses information from registries (age-specific incidences) and family studies (sib-sib marginal correlation) to weight affected sibling pairs according to their age at onset. Age at onset of sibling pairs is modelled by a gamma frailty model. From this model we derive a bivariate survival function, which depends on the marginal survival and on the marginal correlation. The score statistic for linkage is a classical nonparametric linkage (NPL) statistic where the identical by descent sharing is weighted by a particular function of the age at onset data. Since the statistic is based on survival models, it can also be applied to discordant and healthy sibling pairs. Simulation studies show that the proposed method is robust and more powerful than standard NPL methods. As illustration we apply the new score statistic to data from a breast cancer study.

A novel locus for adolescent idiopathic scoliosis on chromosome 12p.

Adolescent idiopathic scoliosis (AIS) is a common disorder with strong evidence for genetic predisposition. Quantitative trait loci (QTLs) for AIS susceptibility have been identified on chromosomes. We performed a genome-wide genetic linkage scan in seven multiplex families using 400 marker loci with a mean spacing of 8.6 cM. We used Genehunter Plus to generate linkage statistics, expressed as homogeneity (HLOD) scores, under dominant and recessive genetic models. We found a significant linkage signal on chromosome 12p, whose support interval extends from near 12 pter, spanning approximately 10 million bases or 31 cM. Fine mapping within the region using 20 additional markers reveals maximum HLOD = 3.7 at 5 cM under a dominant inheritance model, and a split peak maximum HLOD = 3.2 at 8 and 18 cM under a recessive inheritance model. The linkage support interval contains 95 known genes. We found evidence suggestive of linkage on chromosomes 1, 6, 7, 8, and 14. This study is the first to find evidence of an AIS susceptibility locus on chromosome 12. Detection of AIS susceptibility QTLs on multiple chromosomes in this and other studies demonstrate that the condition is genetically heterogeneous.