Ordered subset analysis in genetic linkage mapping of complex traits

Evidence for gene-smoking interactions for hearing loss and deafness in Japanese American families

BACKGROUND

This study investigated the relationship between smoking and hearing loss and deafness (HLD) and whether the relationship is modified by genetic variation. Data for these analyses was from the subset of Japanese American families collected as part of the American Diabetes Association Genetics of Non-insulin Dependent Diabetes Mellitus study. Logistic regression with generalized estimating equations assessed the relationship between HLD and smoking. Nonparametric linkage analysis identified genetic regions harboring HLD susceptibility genes and ordered subset analysis was used to identify regions showing evidence for gene-smoking interactions. Genetic variants within these candidate regions were then each tested for interaction with smoking using logistic regression models.

RESULTS

After adjusting for age, sex, diabetes status and smoking duration, for each pack of cigarettes smoked per day, risk of HLD increased 4.58 times (odds ratio (OR) = 4.58; 95% Confidence Interval (CI): (1.40,15.03)), and ever smokers were over 5 times more likely than nonsmokers to report HLD (OR = 5.22; 95% CI: (1.24, 22.03)). Suggestive evidence for linkage for HLD was observed in multiple genomic regions (Chromosomes 5p15, 8p23 and 17q21), and additional suggestive regions were identified when considering interactions with smoking status (Chromosomes 7p21, 11q23, 12q32, 15q26, and 20q13) and packs-per-day (Chromosome 8q21).

CONCLUSIONS

To our knowledge this was the first report of possible gene-by-smoking interactions in HLD using family data. Additional work, including independent replication, is needed to understand the basis of these findings. HLD are important public health issues and understanding the contributions of genetic and environmental factors may inform public health messages and policies.

Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method

The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they are often individually rare or even private to certain families. Family-based studies are powerful for detecting rare variants enriched in families, which is an important feature for sequencing studies due to the heterogeneous nature of rare variants. In addition, family designs can provide robust protection against population stratification. Nevertheless, statistical methods for analyzing family-based sequencing data are underdeveloped, especially those accounting for heterogeneous etiology of complex diseases. In this article, we introduce a random field framework for detecting gene-phenotype associations in family-based sequencing studies, referred to as family-based genetic random field (FGRF). Similar to existing family-based association tests, FGRF could utilize within-family and between-family information separately or jointly to test an association. We demonstrate that FGRF has comparable statistical power with existing methods when there is no genetic heterogeneity, but can improve statistical power when there is genetic heterogeneity across families. The proposed method also shares the same advantages with the conventional family-based association tests (e.g., being robust to population stratification). Finally, we applied the proposed method to a sequencing data from the Minnesota Twin Family Study, and revealed several genes, including SAMD14, potentially associated with alcohol dependence.

Detecting latent exposure in genome-wide association studies using a breakpoint model for logistic regression

Detecting gene-environment (G × E) interactions in the context of genome-wide association studies (GWAS) is a challenging problem since standard methods generally present a lack of power. An additional difficulty arises from the fact that the causal exposure is seldom observed and only a proxy of this exposure is observed. This leads to an additional drop in terms of power and it explains the failure of standard methods in detecting interactions, even very strong ones. In this article, we consider the latent exposure as a source of heterogeneity and we propose a new powerful method, named "Breakpoint Model for Logistic Regression" (BMLR), based on a breakpoint model, in order to detect G × E interactions when causal exposure is unobserved. First, the BMLR method is compared to the ordered-subset analysis for case-control method, which has been developed for the same purpose, through simulations. This highlights the ability of BMLR to detect the heterogeneity, and therefore, to detect interaction with latent exposure. Finally, the BMLR method is compared to standard methods, such as Plink, to perform a GWAS on a published realistic benchmark.

Design Considerations for Genetic Linkage and Association Studies

This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees, or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, the affected sibling pair design that is of more relevance for common, complex diseases. Power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree and genotyping errors and the effect of the type and density of genetic markers. For association studies, we consider the popular case-control design for dichotomous phenotypes and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritization of genetic variants, and for genome-wide association studies (GWAS) the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined.

DNM3 and genetic modifiers of age of onset in LRRK2 Gly2019Ser parkinsonism: a genome-wide linkage and association study

BACKGROUND

Leucine-rich repeat kinase 2 (LRRK2) mutation 6055G→A (Gly2019Ser) accounts for roughly 1% of patients with Parkinson's disease in white populations, 13-30% in Ashkenazi Jewish populations, and 30-40% in North African Arab-Berber populations, although age of onset is variable. Some carriers have early-onset parkinsonism, whereas others remain asymptomatic despite advanced age. We aimed to use a genome-wide approach to identify genetic variability that directly affects LRRK2 Gly2019Ser penetrance.

METHODS

Between 2006 and 2012, we recruited Arab-Berber patients with Parkinson's disease and their family members (aged 18 years or older) at the Mongi Ben Hamida National Institute of Neurology (Tunis, Tunisia). Patients with Parkinson's disease were diagnosed by movement disorder specialists in accordance with the UK Parkinson's Disease Society Brain Bank criteria, without exclusion of familial parkinsonism. LRRK2 carrier status was confirmed by Sanger sequencing or TaqMan SNP assays-on-demand. We did genome-wide linkage analysis using data from multi-incident Arab-Berber families with Parkinson's disease and LRRK2 Gly2019Ser (with both affected and unaffected family members). We assessed Parkinson's disease age of onset both as a categorical variable (dichotomised by median onset) and as a quantitative trait. We used data from another cohort of unrelated Tunisian LRRK2 Gly2019Ser carriers for subsequent locus-specific genotyping and association analyses. Whole-genome sequencing in a subset of 14 unrelated Arab-Berber individuals who were LRRK2 Gly2019Ser carriers (seven with early-onset disease and seven elderly unaffected individuals) subsequently informed imputation and haplotype analyses. We replicated the findings in separate series of LRRK2 Gly2019Ser carriers originating from Algeria, France, Norway, and North America. We also investigated associations between genotype, gene, and protein expression in human striatal tissues and murine LRRK2 Gly2019Ser cortical neurons.

FINDINGS

Using data from 41 multi-incident Arab-Berber families with Parkinson's disease and LRRK2 Gly2019Ser (150 patients and 103 unaffected family members), we identified significant linkage on chromosome 1q23.3 to 1q24.3 (non-parametric logarithm of odds score 2·9, model-based logarithm of odds score 4·99, θ=0 at D1S2768). In a cohort of unrelated Arab-Berber LRRK2 Gly2019Ser carriers, subsequent association mapping within the linkage region suggested genetic variability within DNM3 as an age-of-onset modifier of disease (n=232; rs2421947; haplotype p=1·07 × 10^-7). We found that DNM3 rs2421947 was a haplotype tag for which the median onset of LRRK2 parkinsonism in GG carriers was 12·5 years younger than that of CC carriers (Arab-Berber cohort, hazard ratio [HR] 1·89, 95% CI 1·20-2·98). Replication analyses in separate series from Algeria, France, Norway, and North America (n=263) supported this finding (meta-analysis HR 1·61, 95% CI 1·15-2·27, p=0·02). In human striatum, DNM3 expression varied as a function of rs2421947 genotype, and dynamin-3 localisation was perturbed in murine LRRK2 Gly2019Ser cortical neurons.

INTERPRETATION

Genetic variability in DNM3 modifies age of onset for LRRK2 Gly2019Ser parkinsonism and informs disease-relevant translational neuroscience. Our results could be useful in genetic counselling for carriers of this mutation and in clinical trial design.

FUNDING

The Canada Excellence Research Chairs (CERC), Leading Edge Endowment Fund (LEEF), Don Rix BC Leadership Chair in Genetic Medicine, National Institute on Aging, National Institute of Neurological Disorders and Stroke, the Michael J Fox Foundation, Mayo Foundation, the Roger de Spoelberch Foundation, and GlaxoSmithKline.

Family-based association test using both common and rare variants and accounting for directions of effects for sequencing data

Current family-based association tests for sequencing data were mainly developed for identifying rare variants associated with a complex disease. As the disease can be influenced by the joint effects of common and rare variants, common variants with modest effects may not be identified by the methods focusing on rare variants. Moreover, variants can have risk, neutral, or protective effects. Association tests that can effectively select groups of common and rare variants that are likely to be causal and consider the directions of effects have become important. We developed the Ordered Subset - Variable Threshold - Pedigree Disequilibrium Test (OVPDT), a combination of three algorithms, for association analysis in family sequencing data. The ordered subset algorithm is used to select a subset of common variants based on their relative risks, calculated using only parental mating types. The variable threshold algorithm is used to search for an optimal allele frequency threshold such that rare variants below the threshold are more likely to be causal. The PDT statistics from both rare and common variants selected by the two algorithms are combined as the OVPDT statistic. A permutation procedure is used in OVPDT to calculate the p-value. We used simulations to demonstrate that OVPDT has the correct type I error rates under different scenarios and compared the power of OVPDT with two other family-based association tests. The results suggested that OVPDT can have more power than the other tests if both common and rare variants have effects on the disease in a region.

An estimating function approach to linkage heterogeneity

Testing linkage heterogeneity between two loci is an important issue in genetics. Currently, there are four methods (K-test, A-test, B-test and D-test) for testing linkage heterogeneity in linkage analysis, which are based on the likelihood-ratio test. Among them, the commonly used methods are the K-test and A-test. In this paper, we present a novel test method which is different from the above four tests, called G-test. The new test statistic is based on estimating function, possessing a theoretic asymptotic distribution, and therefore demonstrates its own advantages. The proposed test is applied to analyse a real pedigree dataset. Our simulation results also indicate that the G-test performs well in terms of power of testing linkage heterogeneity and outperforms the current methods to some degree.

Ordered subset analysis of copy number variation association with age at onset of Alzheimer's disease

Genetic heterogeneity is a common problem for genome-wide association studies of complex human diseases. Ordered-subset analysis (OSA) reduces genetic heterogeneity and optimizes the use of phenotypic information, thus improving power under some disease models. We hypothesized that in a genetically heterogeneous disorder such as Alzheimer's disease (AD), utilizing OSA by age at onset (AAO) of AD may increase the power to detect relevant loci. Using this approach, 8 loci were detected, including the chr15 : 30,44 region harboring CHRFAM7A. The association was replicated in the NIA-LOAD Familial Study dataset. CHRFAM7A is a dominant negative regulator of CHRNA7 function, the receptor that facilitates amyloid-β1-42 internalization through endocytosis and has been implicated in AD. OSA, using AAO as a quantitative trait, optimized power and detected replicable signals suggesting that AD is genetically heterogeneous between AAO subsets.

Genetic evaluation and application of posterior cranial fossa traits as endophenotypes for Chiari type I malformation

Chiari Type I Malformation (CMI) is characterized by herniation of the cerebellar tonsils through the base of the skull. Although cerebellar tonsillar herniation (CTH) is hypothesized to result from an underdeveloped posterior cranial fossa (PF), patients are frequently diagnosed by the extent of CTH without cranial morphometric assessment. We recently completed the largest CMI whole genome qualitative linkage screen to date. Despite an initial lack of statistical evidence, stratified analyses using clinical criteria to reduce heterogeneity resulted in a striking increase in evidence for linkage. The present study focused on the use of cranial base morphometrics to further dissect this heterogeneity and increase power to identify disease genes. We characterized the genetic contribution for a series of PF traits and evaluated the use of heritable, disease-relevant PF traits in ordered subset analysis (OSA). Consistent with a genetic hypothesis for CMI, much of the PF morphology was found to be heritable and multiple genomic regions were strongly implicated from OSA, including regions on Chromosomes 1 (LOD = 3.07, p = 3 × 10(-3) ) and 22 (LOD = 3.45, p = 6 × 10(-5) ) containing several candidates warranting further investigation. This study underscores the genetic heterogeneity of CMI and the utility of PF traits in CMI genetic studies.

Taiwan Schizophrenia Linkage Study: lessons learned from endophenotype-based genome-wide linkage scans and perspective

Taiwan Schizophrenia Linkage Study (TSLS) was initiated with a linkage strategy for locating multiple genes, each of small to moderate effect, and aimed to recruit a large enough sample of pairs of affected siblings and their families ascertained from a multisite study. With a sample of 607 families successfully recruited, a total of 2,242 individuals (1,207 affected and 1,035 unaffected) from 557 families were genotyped using 386 microsatellite markers spaced at an average of 9-cM intervals. Here the author reviews the establishment of TSLS and initial signal derived from linkage scan using the diagnosis of schizophrenia. Based on the limited success of the initial linkage analysis, a sufficient-component causal model is proposed to incorporate endophenotypes and genes for schizophrenia. Four types of candidate endophenotype measured in TSLS, including schizotypal personality, Continuous Performance Test, Wisconsin Card Sorting Test, and niacin skin flush test, are briefly described. The author discusses different strategies of linkage analysis incorporating these endophenotypes, including quantitative trait loci (QTL) linkage analysis, clustering-derived subgroups, ordered subset analysis (OSA), and latent classes for linkage scan. Then the author summarizes the linkage signals generated from seven studies of endophenotype-based linkage analysis using TSLS, including QTL scan of neurocognitive performance, QTL scan of niacin skin flush, the family cluster of attention deficit and execution deficit, OSA by schizophrenia-schizotypy factors, nested OSA by age at onset and neurocognitive performance, and the latent class of deficit schizophrenia for linkage analysis. The perspective of combining next-generation sequencing with linkage analysis of families is also discussed.

Ordered subset linkage analysis based on admixture proportion identifies new linkage evidence for alcohol dependence in African-Americans

Genetic heterogeneity could reduce the power of linkage analysis to detect risk loci for complex traits such as alcohol dependence (AD). Previously, we performed a genomewide linkage analysis for AD in African-Americans (AAs) (Biol Psychiatry 65:111-115, 2009). The power of that linkage analysis could have been reduced by the presence of genetic heterogeneity owing to differences in admixture among AA families. We hypothesized that by examining a study sample whose genetic ancestry was more homogeneous, we could increase the power to detect linkage. To test this hypothesis, we performed ordered subset linkage analysis in 384 AA families using admixture proportion as a covariate to identify a more homogeneous subset of families and determine whether there is increased evidence for linkage with AD. Statistically significant increases in lod scores in subsets relative to the overall sample were identified on chromosomes 4 (P = 0.0001), 12 (P = 0.021), 15 (P = 0.026) and 22 (P = 0.0069). In a subset of 44 families with African ancestry proportions ranging from 0.858 to 0.996, we observed a genomewide significant linkage at 180 cM on chromosome 4 (lod = 4.24, pointwise P < 0.00001, empirical genomewide P = 0.008). A promising candidate gene located there, GLRA3, which encodes a subunit of the glycine neurotransmitter receptor. Our results demonstrate that admixture proportion can be used as a covariate to reduce genetic heterogeneity and enhance the detection of linkage for AD in an admixed population such as AAs. This approach could be applied to any linkage analysis for complex traits conducted in an admixed population.

Genetic heterogeneity in Finnish hereditary prostate cancer using ordered subset analysis

Prostate cancer (PrCa) is the most common male cancer in developed countries and the second most common cause of cancer death after lung cancer. We recently reported a genome-wide linkage scan in 69 Finnish hereditary PrCa (HPC) families, which replicated the HPC9 locus on 17q21-q22 and identified a locus on 2q37. The aim of this study was to identify and to detect other loci linked to HPC. Here we used ordered subset analysis (OSA), conditioned on nonparametric linkage to these loci to detect other loci linked to HPC in subsets of families, but not the overall sample. We analyzed the families based on their evidence for linkage to chromosome 2, chromosome 17 and a maximum score using the strongest evidence of linkage from either of the two loci. Significant linkage to a 5-cM linkage interval with a peak OSA nonparametric allele-sharing LOD score of 4.876 on Xq26.3-q27 (ΔLOD=3.193, empirical P=0.009) was observed in a subset of 41 families weakly linked to 2q37, overlapping the HPCX1 locus. Two peaks that were novel to the analysis combining linkage evidence from both primary loci were identified; 18q12.1-q12.2 (OSA LOD=2.541, ΔLOD=1.651, P=0.03) and 22q11.1-q11.21 (OSA LOD=2.395, ΔLOD=2.36, P=0.006), which is close to HPC6. Using OSA allows us to find additional loci linked to HPC in subsets of families, and underlines the complex genetic heterogeneity of HPC even in highly aggregated families.

Genome-wide linkage analysis of 972 bipolar pedigrees using single-nucleotide polymorphisms

Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing.

Genetic overlap of schizophrenia and bipolar disorder in a high-density linkage survey in the Portuguese Island population

Recent family and genome-wide association studies strongly suggest shared genetic risk factors for schizophrenia (SZ) and bipolar disorder (BP). However, linkage studies have not been used to test for statistically significant genome-wide overlap between them. Forty-seven Portuguese families with sibpairs concordant for SZ, BP, or psychosis (PSY, which includes either SZ or psychotic BP) were genotyped for over 57,000 markers using the Affymetrix 50K Xba SNP array. NPL and Kong and Cox LOD scores were calculated in Merlin for all three phenotypes. Empirical significance was determined using 1,000 gene-dropping simulations. Significance of genome-wide genetic overlap between SZ and BP was determined by the number of simulated BP scans having the same number of loci jointly linked with the real SZ scan, and vice versa. For all three phenotypes, a number of regions previously linked in this sample remained so. For BP, chromosome 1p36 achieved significance (11.54-15.71 MB, LOD = 3.51), whereas it was not even suggestively linked at lower marker densities, as did chromosome 11q14.1 (89.32-90.15 MB, NPL = 4.15). Four chromosomes had loci at which both SZ and BP had NPL ≥ 1.98, which was more than would be expected by chance (empirical P = 0.01 using simulated SZ scans; 0.07 using simulated BP scans), although they did not necessarily meet criteria for suggestive linkage individually. These results suggest that high-density marker maps may provide greater power and precision in linkage studies than lower density maps. They also further support the hypothesis that SZ and BP share at least some risk alleles.

A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits

Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.

Design considerations for genetic linkage and association studies

This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare, highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, affected sibling pairs, of more relevance for common, complex diseases. Theoretical and more practical power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree, and genotyping errors, as well as the effect of the type and density of genetic markers. Linkage studies should be as large as possible to have sufficient power in relation to the expected genetic effect size. Segregation analysis, a formal statistical technique to describe the underlying genetic susceptibility, may assist in the estimation of the relevant parameters to apply, for instance. However, segregation analyses estimate the total genetic component rather than a single-locus effect. Locus heterogeneity should be considered when power is estimated and at the analysis stage, i.e. assuming smaller locus effect than the total the genetic component from segregation studies. Disease heterogeneity should be minimised by considering subtypes if they are well defined or by otherwise collecting known sources of heterogeneity and adjusting for them as covariates; the power will depend upon the relationship between the disease subtype and the underlying genotypes. Ultimately, identifying susceptibility alleles of modest effects (e.g. RR≤1.5) requires a number of families that seem unfeasible in a single study. Meta-analysis and data pooling between different research groups can provide a sizeable study, but both approaches require even a higher level of vigilance about locus and disease heterogeneity when data come from different populations. All necessary steps should be taken to minimise pedigree and genotyping errors at the study design stage as they are, for the most part, due to human factors. A two-stage design is more cost-effective than one stage when using short tandem repeats (STRs). However, dense single-nucleotide polymorphism (SNP) arrays offer a more robust alternative, and due to their lower cost per unit, the total cost of studies using SNPs may in the future become comparable to that of studies using STRs in one or two stages. For association studies, we consider the popular case-control design for dichotomous phenotypes, and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritisation of genetic variants, and for genome-wide association studies (GWAS), the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined. GWAS have a high power to detect common variants of high or moderate effect. For weaker effects (e.g. relative risk<1.2), the power is greatly reduced, particularly for recessive loci. While sample sizes of 10,000 or 20,000 cases are not beyond reach for most common diseases, only meta-analyses and data pooling can allow attaining a study size of this magnitude for many other diseases. It is acknowledged that detecting the effects from rare alleles (i.e. frequency<5%) is not feasible in GWAS, and it is expected that novel methods and technology, such as next-generation resequencing, will fill this gap. At the current stage, the choice of which GWAS SNP array to use does not influence the power in populations of European ancestry. A multistage design reduces the study cost but has less power than the standard one-stage design. If one opts for a multistage design, the power can be improved by jointly analysing the data from different stages for the SNPs they share. The estimates of locus contribution to disease risk from genome-wide scans are often biased, and relying on them might result in an underpowered replication study. Population structure has so far caused less spurious associations than initially feared, thanks to systematic ethnicity matching and application of standard quality control measures. Differential bias could be a more serious threat and must be minimised by strictly controlling all the aspects of DNA acquisition, storage, and processing.

Single-marker family-based association analysis conditional on parental information

Family-based designs have been commonly used in association studies. Different family structures such as extended pedigrees and nuclear families, including parent-offspring triads and families with multiple affected siblings (multiplex families), can be ascertained for family-based association analysis. Flexible association tests that can accommodate different family structures have been proposed. The pedigree disequilibrium test (PDT) (Am J Hum Genet 67:146-154, 2000) can use full genotype information from general (possibly extended) pedigrees with one or multiple affected siblings but requires parental genotypes or genotypes of unaffected siblings. On the other hand, the association in the presence of linkage (APL) test (Am J Hum Genet 73:1016-1026, 2003) is restricted to nuclear families with one or more affected siblings but can infer missing parental genotypes properly by accounting for identity-by-descent (IBD) parameters. Both the PDT and APL are powerful association tests in the presence of linkage and can be used as complementary tools for association analysis. This chapter introduces these two tests and compares their properties. Recommendations and notes for performing the tests in practice are provided.

Genome-wide high-density SNP linkage search for glioma susceptibility loci: results from the Gliogene Consortium

Gliomas, which generally have a poor prognosis, are the most common primary malignant brain tumors in adults. Recent genome-wide association studies have shown that inherited susceptibility plays a role in the development of glioma. Although first-degree relatives of patients exhibit a two-fold increased risk of glioma, the search for susceptibility loci in familial forms of the disease has been challenging because the disease is relatively rare, fatal, and heterogeneous, making it difficult to collect sufficient biosamples from families for statistical power. To address this challenge, the Genetic Epidemiology of Glioma International Consortium (Gliogene) was formed to collect DNA samples from families with two or more cases of histologically confirmed glioma. In this study, we present results obtained from 46 U.S. families in which multipoint linkage analyses were undertaken using nonparametric (model-free) methods. After removal of high linkage disequilibrium single-nucleotide polymorphism, we obtained a maximum nonparametric linkage score (NPL) of 3.39 (P = 0.0005) at 17q12-21.32 and the Z-score of 4.20 (P = 0.000007). To replicate our findings, we genotyped 29 independent U.S. families and obtained a maximum NPL score of 1.26 (P = 0.008) and the Z-score of 1.47 (P = 0.035). Accounting for the genetic heterogeneity using the ordered subset analysis approach, the combined analyses of 75 families resulted in a maximum NPL score of 3.81 (P = 0.00001). The genomic regions we have implicated in this study may offer novel insights into glioma susceptibility, focusing future work to identify genes that cause familial glioma.

Shared genetic architecture in the relationship between adult stature and subclinical coronary artery atherosclerosis

BACKGROUND

Short stature is associated with increased risk of coronary heart disease (CHD); although the mechanisms for this relationship are unknown, shared genetic factors have been proposed. Subclinical atherosclerosis, measured by coronary artery calcification (CAC), is associated with CHD events and represents part of the biological continuum to overt CHD. Many molecular mechanisms of CAC development are shared with bone growth. Thus, we examined whether there was evidence of shared genes (pleiotropy) between adult stature and CAC.

METHODS

877 Asymptomatic white adults (46% men) from 625 families in a community-based sample had computed tomography measures of CAC. Pleiotropy between height and CAC was determined using maximum-likelihood estimation implemented in SOLAR.

RESULTS

Adult height was significantly and inversely associated with CAC score (P = 0.01). After adjusting for age, sex and CHD risk factors, the estimated genetic correlation between height and CAC score was -0.37 and was significantly different than 0 (P = 0.001) and -1 (P < 0.001). The environmental correlation between height and CAC score was 0.60 and was significantly different than 0 (P = 0.024).

CONCLUSIONS

Further studies of shared genetic factors between height and CAC may provide important insight into the complex genetic architecture of CHD, in part through increased understanding of the molecular pathways underlying the process of both normal growth and disease development. Bivariate genetic linkage analysis may provide a powerful mechanism for identifying specific genomic regions associated with both height and CAC.

A genome-wide linkage scan for distinct subsets of schizophrenia characterized by age at onset and neurocognitive deficits

BACKGROUND

As schizophrenia is genetically and phenotypically heterogeneous, targeting genetically informative phenotypes may help identify greater linkage signals. The aim of the study is to evaluate the genetic linkage evidence for schizophrenia in subsets of families with earlier age at onset or greater neurocognitive deficits.

METHODS

Patients with schizophrenia (n  =  1,207) and their first-degree relatives (n  =  1,035) from 557 families with schizophrenia were recruited from six data collection field research centers throughout Taiwan. Subjects completed a face-to-face semi-structured interview, the Continuous Performance Test (CPT), the Wisconsin Card Sorting Test, and were genotyped with 386 microsatellite markers across the genome.

RESULTS

A maximum nonparametric logarithm of odds (LOD) score of 4.17 at 2q22.1 was found in 295 families ranked by increasing age at onset, which had significant increases in the maximum LOD score compared with those obtained in initial linkage analyses using all available families. Based on this subset, a further subsetting by false alarm rate on the undegraded and degraded CPT obtained further increase in the nested subset-based LOD on 2q22.1, with a score of 7.36 in 228 families and 7.71 in 243 families, respectively.

CONCLUSION

We found possible evidence of linkage on chromosome 2q22.1 in families of schizophrenia patients with more CPT false alarm rates nested within the families with younger age at onset. These results highlight the importance of incorporating genetically informative phenotypes in unraveling the complex genetics of schizophrenia.

Genome-wide linkage scan for primary open angle glaucoma: influences of ancestry and age at diagnosis

Primary open-angle glaucoma (POAG) is the most common form of glaucoma and one of the leading causes of vision loss worldwide. The genetic etiology of POAG is complex and poorly understood. The purpose of this work is to identify genomic regions of interest linked to POAG. This study is the largest genetic linkage study of POAG performed to date: genomic DNA samples from 786 subjects (538 Caucasian ancestry, 248 African ancestry) were genotyped using either the Illumina GoldenGate Linkage 4 Panel or the Illumina Infinium Human Linkage-12 Panel. A total of 5233 SNPs was analyzed in 134 multiplex POAG families (89 Caucasian ancestry, 45 African ancestry). Parametric and non-parametric linkage analyses were performed on the overall dataset and within race-specific datasets (Caucasian ancestry and African ancestry). Ordered subset analysis was used to stratify the data on the basis of age of glaucoma diagnosis. Novel linkage regions were identified on chromosomes 1 and 20, and two previously described loci-GLC1D on chromosome 8 and GLC1I on chromosome 15--were replicated. These data will prove valuable in the context of interpreting results from genome-wide association studies for POAG.

High-density fine-mapping of a chromosome 10q26 linkage peak suggests association between endometriosis and variants close to CYP2C19

OBJECTIVE

To refine a previously reported linkage peak for endometriosis on chromosome 10q26, and conduct follow-up analyses and a fine-mapping association study across the region to identify new candidate genes for endometriosis.

DESIGN

Case-control study.

SETTING

Academic research.

PATIENT(S)

Cases=3,223 women with surgically confirmed endometriosis; controls=1,190 women without endometriosis and 7,060 population samples.

INTERVENTION(S)

Analysis of 11,984 single nucleotide polymorphisms on chromosome 10.

MAIN OUTCOME MEASURE(S)

Allele frequency differences between cases and controls.

RESULT(S)

Linkage analyses on families grouped by endometriosis symptoms (primarily subfertility) provided increased evidence for linkage (logarithm of odds score=3.62) near a previously reported linkage peak. Three independent association signals were found at 96.59 Mb (rs11592737), 105.63 Mb (rs1253130), and 124.25 Mb (rs2250804). Analyses including only samples from linkage families supported the association at all three regions. However, only rs11592737 in the cytochrome P450 subfamily C (CYP2C19) gene was replicated in an independent sample of 2,079 cases and 7,060 population controls.

CONCLUSION(S)

The role of the CYP2C19 gene in conferring risk for endometriosis warrants further investigation.

Genetic analysis of diabetic nephropathy on chromosome 18 in African Americans: linkage analysis and dense SNP mapping

Genetic studies in Turkish, Native American, European American, and African American (AA) families have linked chromosome 18q21.1-23 to susceptibility for diabetes-associated nephropathy. In this study, we have carried out fine linkage mapping in the 18q region previously linked to diabetic nephropathy in AAs by genotyping both microsatellite and single nucleotide polymorphisms (SNPs) for linkage analysis in an expanded set of 223 AA families multiplexed for type 2 diabetes associated ESRD (T2DM-ESRD). Several approaches were used to evaluate evidence of linkage with the strongest evidence for linkage in ordered subset analysis with an earlier age of T2DM diagnosis compared to the remaining pedigrees (LOD 3.9 at 90.1 cM, ΔP = 0.0161, NPL P value = 0.00002). Overall, the maximum LODs and LOD-1 intervals vary in magnitude and location depending upon analysis. The linkage mapping was followed up by performing a dense SNP map, genotyping 2,814 SNPs in the refined LOD-1 region in 1,029 AA T2DM-ESRD cases and 1,027 AA controls. Of the top 25 most associated SNPs, 10 resided within genic regions. Two candidate genes stood out: NEDD4L and SERPINB7. SNP rs512099, located in intron 1 of NEDD4L, was associated under a dominant model of inheritance [P value = 0.0006; Odds ratio (95% Confidence Interval) OR (95% CI) = 0.70 (0.57-0.86)]. SNP rs1720843, located in intron 2 of SERPINB7, was associated under a recessive model of inheritance [P value = 0.0017; OR (95% CI) = 0.65 (0.50-0.85)]. Collectively, these results suggest that multiple genes in this region may influence diabetic nephropathy susceptibility in AAs.

Dissection of chromosome 16p12 linkage peak suggests a possible role for CACNG3 variants in age-related macular degeneration susceptibility

PURPOSE

Age-related macular degeneration (AMD) is a complex disorder of the retina, characterized by drusen, geographic atrophy, and choroidal neovascularization. Cigarette smoking and the genetic variants CFH Y402H, ARMS2 A69S, CFB R32Q, and C3 R102G have been strongly and consistently associated with AMD. Multiple linkage studies have found evidence suggestive of another AMD locus on chromosome 16p12 but the gene responsible has yet to be identified.

METHODS

In the initial phase of the study, single-nucleotide polymorphisms (SNPs) across chromosome 16 were examined for linkage and/or association in 575 Caucasian individuals from 148 multiplex and 77 singleton families. Additional variants were tested in an independent dataset of unrelated cases and controls. According to these results, in combination with gene expression data and biological knowledge, five genes were selected for further study: CACNG3, HS3ST4, IL4R, Q7Z6F8, and ITGAM.

RESULTS

After genotyping additional tagging SNPs across each gene, the strongest evidence for linkage and association was found within CACNG3 (rs757200 nonparametric LOD* = 3.3, APL (association in the presence of linkage) P = 0.06, and rs2238498 MQLS (modified quasi-likelihood score) P = 0.006 in the families; rs2283550 P = 1.3 × 10(-6), and rs4787924 P = 0.002 in the case-control dataset). After adjusting for known AMD risk factors, rs2283550 remained strongly associated (P = 2.4 × 10(-4)). Furthermore, the association signal at rs4787924 was replicated in an independent dataset (P = 0.035) and in a joint analysis of all the data (P = 0.001).

CONCLUSIONS

These results suggest that CACNG3 is the best candidate for an AMD risk gene within the 16p12 linkage peak. More studies are needed to confirm this association and clarify the role of the gene in AMD pathogenesis.

Ordered subset analysis identifies loci influencing lung cancer risk on chromosomes 6q and 12q

BACKGROUND

Genetic susceptibility for cancer can differ substantially among families. We use trait-related covariates to identify a genetically homogeneous subset of families with the best evidence for linkage in the presence of heterogeneity.

METHODS

We performed a genome-wide linkage screen in 93 families. Samples and data were collected by the familial lung cancer recruitment sites of the Genetic Epidemiology of Lung Cancer Consortium. We estimated linkage scores for each family by the Markov chain Monte Carlo procedure using SimWalk2 software. We used ordered subset analysis (OSA) to identify genetically homogenous families by ordering families based on a disease-associated covariate. We performed permutation tests to determine the relationship between the trait-related covariate and the evidence for linkage.

RESULTS

A genome-wide screen for lung cancer loci identified strong evidence for linkage to 6q23-25 and suggestive evidence for linkage to 12q24 using OSA, with peak logarithm of odds (LOD) scores of 4.19 and 2.79, respectively. We found other chromosomes also suggestive for linkages, including 5q31-q33, 14q11, and 16q24.

CONCLUSIONS

Our OSA results support 6q as a lung cancer susceptibility locus and provide evidence for disease linkage on 12q24. This study further increased our understanding of the inheritability for lung cancer. Validation studies using larger sample size are needed to verify the presence of several other chromosomal regions suggestive of an increased risk for lung cancer and/or other cancers.

IMPACT

OSA can reduce genetic heterogeneity in linkage study and may assist in revealing novel susceptibility loci.

Ordered subset analysis for case-control studies

Genetic heterogeneity, which may manifest on a population level as different frequencies of a specific disease susceptibility allele in different subsets of patients, is a common problem for candidate gene and genome-wide association studies of complex human diseases. The ordered subset analysis (OSA) was originally developed as a method to reduce genetic heterogeneity in the context of family-based linkage studies. Here, we have extended a previously proposed method (OSACC) for applying the OSA methodology to case-control datasets. We have evaluated the type I error and power of different OSACC permutation tests with an extensive simulation study. Case-control datasets were generated under two different models by which continuous clinical or environmental covariates may influence the relationship between susceptibility genotypes and disease risk. Our results demonstrate that OSACC is more powerful under some disease models than the commonly used trend test and a previously proposed joint test of main genetic and gene-environment interaction effects. An additional unique benefit of OSACC is its ability to identify a more informative subset of cases that may be subjected to more detailed molecular analysis, such as DNA sequencing of selected genomic regions to detect functional variants in linkage disequilibrium with the associated polymorphism. The OSACC-identified covariate threshold may also improve the power of an additional dataset to replicate previously reported associations that may only be detectable in a fraction of the original and replication datasets. In summary, we have demonstrated that OSACC is a useful method for improving SNP association signals in genetically heterogeneous datasets.

Incorporation of covariates in simultaneous localization of two linked loci using affected relative pairs

Background

Many dichotomous traits for complex diseases are often involved more than one locus and/or associated with quantitative biomarkers or environmental factors. Incorporating these quantitative variables into linkage analysis as well as localizing two linked disease loci simultaneously could therefore improve the efficiency in mapping genes. We extended the robust multipoint Identity-by-Descent (IBD) approach with incorporation of covariates developed previously to simultaneously estimate two linked loci using different types of affected relative pairs (ARPs).

Results

We showed that the efficiency was enhanced by incorporating a quantitative covariate parametrically or non-parametrically while localizing two disease loci using ARPs. In addition to its help in identifying factors associated with the disease and in improving the efficiency in estimating disease loci, this extension also allows investigators to account for heterogeneity in risk-ratios for different ARPs. Data released from the collaborative study on the genetics of alcoholism (COGA) for Genetic Analysis Workshop 14 (GAW 14) were used to illustrate the application of this extended method.

Conclusions

The simulation studies and example illustrated that the efficiency in estimating disease loci was demonstratively enhanced by incorporating a quantitative covariate and by using all relative pairs while mapping two linked loci simultaneously.

A comprehensive genetic study on left atrium size in Caribbean Hispanics identifies potential candidate genes in 17p10

BACKGROUND

Left atrial (LA) enlargement is associated with cardiovascular disease and stroke. Genetic factors contributing to the LA dimension are poorly understood. We sought to map susceptibility genes for LA size in a large Dominican family data set and an independent population-based sample from the Northern Manhattan Study.

METHODS AND RESULTS

One hundred Dominican families comprising 1350 individuals were studied to estimate heritability and map quantitative trait loci for LA size using variance components analysis. LA dimension was measured by transthoracic echocardiography. A polygenic covariate screening was used to identify significant covariates. LA size had a moderate estimate of heritability (h(2)=0.42) after adjusting for significant covariates. Linkage analysis revealed suggestive evidence on chromosome 10p19 (D10S1423, MLOD=2.00) and 17p10 (D17S974, MLOD=2.05). Ordered subset analysis found significantly enhanced (P<0.05 for increase of LOD score) evidence for linkage at 17p10 (MLOD=2.9) in families with lower LDL level. Single nucleotide polymophisms (n=2233)were used to perform a peak-wide association mapping across 17p10 in 825 NOMAS individuals. Evidence for association were found in NTN1, MYH10, COX10, and MYOCD genes (P=0.00005 to 0.005).

CONCLUSIONS

Using nonbiased genome-wide linkage followed by peak-wide association analysis, we identified several possible susceptibility genes affecting LA size. Among them, MYOCD has been shown to serve as a key transducer of hypertrophic signals in cardiomyocytes. Our data support that polymorphisms in MYOCD modify LA size.

A latent class model for testing for linkage and classifying families when the sample may contain segregating and non-segregating families

In a quantitative trait locus (QTL) study, it is usually not feasible to select families with offspring that simultaneously display variability in more than one phenotype. When multiple phenotypes are of interest, the sample will, with high probability, contain 'non-segregating' families, i.e. families with both parents homozygous at the QTL. These families potentially reduce the power of regression-based methods to detect linkage. Moreover, follow-up studies in individual families will be inefficient, and potentially even misleading, if non-segregating families are selected for the study. Our work extends Haseman-Elston regression using a latent class model to account for the mixture of segregating and non-segregating families. We provide theoretical motivation for the method using an additive genetic model with two distinct functions of the phenotypic outcome, squared difference (SqD) and mean-corrected product (MCP). A permutation procedure is developed to test for linkage; simulation shows that the test is valid for both phenotypic functions. For rare alleles, the method provides increased power compared to a 'marginal' approach that ignores the two types of families; for more common alleles, the marginal approach has better power. These results appear to reflect the ability of the algorithm to accurately assign families to the two classes and the relative weights of segregating and non-segregating families to the test of linkage. An application of Bayes rule is used to estimate the family-specific probability of segregating. High predictive value positive values for segregating families, particularly for MCP, suggest that the method has considerable value for identifying segregating families. The method is illustrated for gene expression phenotypes measured on 27 candidate genes previously demonstrated to show linkage in a sample of 14 families.

Genetic linkage of serum homocysteine in Dominican families: the Family Study of Stroke Risk and Carotid Atherosclerosis

BACKGROUND AND PURPOSE

Homocysteine levels are determined by genetic and environmental factors. Several studies have linked high plasma levels of total homocysteine to the increased risk of cardiovascular disease, stroke, and many other conditions. However, the exact mechanism of documented and novel total homocysteine quantitative trait loci to that risk is unknown.

METHODS

We have performed linkage analysis in 100 high-risk Dominican families with 1362 members. Probands were selected from the population-based Northern Manhattan Study. A set of 405 microsatellite markers was used to screen the whole genome. Variance components analysis was used to detect evidence for linkage after adjusting for stroke risk factors. Ordered-subset analysis based on Dominican Republic enrollment was conducted.

RESULTS

Total homocysteine levels had a heritability of 0.44 (P<0.0001). The most significant evidence for linkage was found at chromosome 17q24 (maximum logarithm of odds [MLOD]=2.66, P=0.0005) with a peak at D17S2193 and was significantly increased in a subset of families with a high proportion of Dominican Republic enrollment (MLOD=3.92, P=0.0022). Additionally, modest evidence for linkage was found at chromosome 2p21 (MLOD=1.77, P=0.0033) with a peak at D2S1356 and was significantly increased in a subset of families with a low proportion of Dominican Republic enrollment (MLOD=2.82, P=0.0097).

CONCLUSIONS

We found a strong evidence for novel quantitative trait loci on chromosomes 2 and 17 for total homocysteine plasma levels in Dominican families. Our family study provides essential data for a better understanding of the genetic mechanisms associated with elevated total homocysteine levels leading to cardiovascular disease after accounting for environmental risk factors.

A genome wide linkage scan for dizygotic twinning in 525 families of mothers of dizygotic twins

BACKGROUND

The tendency to conceive dizygotic (DZ) twins is a complex trait influenced by genetic and environmental factors. To search for new candidate loci for twinning, we conducted a genome-wide linkage scan in 525 families using microsatellite and single nucleotide polymorphism marker panels.

METHODS AND RESULTS

Non-parametric linkage analyses, including 523 families containing a total of 1115 mothers of DZ twins (MODZT) from Australia and New Zealand (ANZ) and The Netherlands (NL), produced four linkage peaks above the threshold for suggestive linkage, including a highly suggestive peak at the extreme telomeric end of chromosome 6 with an exponential logarithm of odds [(exp)LOD] score of 2.813 (P = 0.0002). Since the DZ twinning rate increases steeply with maternal age independent of genetic effects, we also investigated linkage including only families where at least one MODZT gave birth to her first set of twins before the age of 30. These analyses produced a maximum expLOD score of 2.718 (P = 0.0002), largely due to linkage signal from the ANZ cohort, however, ordered subset analyses indicated this result is most likely a chance finding in the combined dataset. Linkage analyses were also performed for two large DZ twinning families from the USA, one of which produced a peak on chromosome 2 in the region of two potential candidate genes. Sequencing of FSHR and FIGLA, along with INHBB in MODZTs from two large NL families with family specific linkage peaks directly over this gene, revealed a potentially functional variant in the 5' untranslated region of FSHR that segregated with the DZ twinning phenotype in the Utah family.

CONCLUSION

Our data provide further evidence for complex inheritance of familial DZ twinning.

Chromosome 7p linkage and association study for diabetes related traits and type 2 diabetes in an African-American population enriched for nephropathy

BACKGROUND

Previously we performed a linkage scan of 638 African American affected sibling pairs (ASP) with type 2 diabetes (T2D) enriched for end-stage renal disease (ESRD). Ordered subset linkage analysis (OSA) revealed a linkage peak on chromosome 7p in the subset of families with earlier age of T2D diagnosis.

METHODS

We fine mapped this region by genotyping 11 additional polymorphic markers in the same ASP and investigated a total of 68 single nucleotide polymorphisms (SNPs) in functional candidate genes (GCK1, IL6, IGFBP1 and IGFBP3) for association with age of T2D diagnosis, age of ESRD diagnosis, duration of T2D to onset of ESRD, body mass index (BMI) in African American cases and T2D-ESRD in an African American case-control cohort. OSA of fine mapping markers supported linkage at 28 cM on 7p (near D7S3051) in early-onset T2D families (max. LOD = 3.61, P = 0.002). SNPs in candidate genes and 70 ancestry-informative markers (AIMs) were evaluated in 577 African American T2D-ESRD cases and 596 African American controls.

RESULTS

The most significant association was observed between ESRD age of diagnosis and SNP rs730497, located in intron 1 of the GCK1 gene (recessive T2D age-adjusted P = 0.0006). Nominal associations were observed with GCK1 SNPs and T2D age of diagnosis (BMI-adjusted P = 0.014 to 0.032). Also, one IGFBP1 and four IGFBP3 SNPs showed nominal genotypic association with T2D-ESRD (P = 0.002-0.049). After correcting for multiple tests, only rs730497 remanined significant.

CONCLUSION

Variant rs730947 in the GCK1 gene appears to play a role in early ESRD onset in African Americans.

The multidimensionality of schizotypy in nonpsychotic relatives of patients with schizophrenia and its applications in ordered subsets linkage analysis of schizophrenia

This study aimed to examine the multidimensionality of schizotypy and validate the structure using ordered subset linkage analyses on information from both relatives' schizotypy and probands' schizophrenia symptoms. A total of 203 and 1,310 nonpsychotic first-degree relatives from simplex and multiplex schizophrenia families, respectively, were interviewed with the Diagnostic Interview for Genetic Studies, which contains a modified Structured Interview for Schizotypy. Using Mplus program with categorical factor indicators, a four-factor model (Negative Schizotypy, Positive Schizotypy, Interpersonal Sensitivity, and Social Isolation/Introversion) was extracted by exploratory factor analysis from relatives of simplex families and was confirmed in relatives of multiplex families. The validity of each factor was supported by distinct linkage findings resulting from ordered subset analysis based on different combinations of schizophrenia-schizotypy factors. Six chromosomal regions with significant increase in nonparametric linkage z score (NPL-Z) were found as follows: 15q21.1 (NPL-Z = 3.60) for Negative Schizophrenia-Negative Schizotypy, 10q22.3 (NPL-Z = 3.83) and 15q21.3 (NPL-Z = 3.36) for Negative Schizophrenia-Social Isolation/Introversion, 5q14.2 (NPL-Z = 3.20) and 11q23.3 (NPL-Z = 3.31) for Positive Schizophrenia-Positive Schizotypy, and 4q32.1 (NPL-Z = 3.31) for Positive Schizophrenia-Interpersonal Sensitivity. The greatest NPL-Z of 3.83 on 10q22.3 in the subset was significantly higher than the greatest one of 2.88 in the whole sample (empirical P-value = 0.04). We concluded that a consistent four-factor model of schizotypy could be derived in nonpsychotic relatives across families of patients with different genetic loadings in schizophrenia. Their differential relations to linkage signals have etiological implications and provide further evidence for their validity.

Overview of linkage analysis in complex traits

Linkage analysis is a well-established and powerful method for mapping disease genes. While linkage analysis has been most successful when applied to disorders with clear patterns of Mendelian inheritance, it can also be a useful technique for mapping susceptibility genes for common complex diseases. In this unit, we outline the key concepts of complex disease, and how linkage analysis for complex traits differs from simple Mendelian traits. Optimal genetic studies require careful study design, ascertainment strategy, and analysis methods. We describe how disease parameters such as prevalence, heritability estimates, and mode of inheritance should be considered before data is collected. Furthermore, we outline a general strategic approach for conducting linkage analysis of a complex disease, along with several design considerations that can optimize statistical power to detect disease loci and generally improve the quality of a study. Finally, we discuss the benefits and weaknesses of linkage analysis in contrast to genome-wide association studies.

Adaptively weighted association statistics

We investigate methods for testing gene-disease outcome associations in situations where the genetic relationship potentially varies among subjects with differing environmental or clinical attributes. We propose a strategy which modestly increases multiple testing by evaluating weighted test statistics which focus (or enrich) association tests within subgroups and use a Monte-Carlo method, based on simulating from the approximate large sample distribution of the statistics, to control type 1 error. We also introduce a stage-wise calculated test statistic which allows more complex weighting on multiple environmental variables. Results from simulation studies confirm improved power of the proposed approaches compared to marginal testing in many situations.

Novel quantitative trait locus is mapped to chromosome 12p11 for left ventricular mass in Dominican families: the Family Study of Stroke Risk and Carotid Atherosclerosis

Background

Left ventricular mass (LVM) is an important risk factor for stroke and vascular disease. The genetic basis of LVM is unclear although a high heritability has been suggested. We sought to map quantitative trait loci (QTL) for LVM using large Dominican families.

Methods

Probands were selected from Dominican subjects of the population-based Northern Manhattan Study (NOMAS). LVM was measured by transthoracic echocardiography. A set of 405 microsatellite markers was used to screen the whole genome among 1360 subjects from 100 Dominican families who had complete phenotype data and DNA available. A polygenic covariate screening was run to identify the significant covariates. Variance components analysis was used to estimate heritability and to detect evidence for linkage, after adjusting for significant risk factors. Ordered-subset Analysis (OSA) was conducted to identify a more homogeneous subset for stratification analysis.

Results

LVM had a heritability of 0.58 in the studied population (p < 0.0001). The most significant evidence for linkage was found at chromosome 12p11 (MLOD = 3.11, empirical p = 0.0003) with peak marker at D12S1042. This linkage was significantly increased in a subset of families with the high average waist circumference (MLOD = 4.45, p = 0.0045 for increase in evidence for linkage).

Conclusion

We mapped a novel QTL near D12S1042 for LVM in Dominicans. Enhanced linkage evidence in families with larger waist circumference suggests that gene(s) residing within the QTL interact(s) with abdominal obesity to contribute to phenotypic variation of LVM. Suggestive evidence for linkage (LOD = 1.99) has been reported at the same peak marker for left ventricular geometry in a White population from the HyperGEN study, underscoring the importance of this QTL for left ventricular phenotype. Further fine mapping and validation studies are warranted to identify the underpinning genes.

The ordered transmission disequilibrium test: detection of modifier genes

We consider the problem of detection of modifier genes that lead to variations in a disease-related continuous variable (DRCV), such as the age of onset or a measure of disease severity, in a strategy of candidate genes. We propose a novel method, the ordered transmission disequilibrium test (OTDT), to test for a relation between the clinical heterogeneity expressed by a DRCV and marker genotypes of a candidate gene. The OTDT applies to trio families with one patients and his parents, all three genotyped at a bi-allelic marker M. The OTDT aims to find a critical value of the DRCV which separates the sample of families in two subsamples in which the transmission rates are significantly different. We investigate the power of the method by simulations under various genetic models and covariate distributions and compare it with a linear regression analysis.

Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm

BACKGROUND

Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).

METHODOLOGY/PRINCIPAL FINDINGS

This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.

CONCLUSIONS

The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease.

Examination of seven candidate regions for multiple sclerosis: strong evidence of linkage to chromosome 1q44

Multiple sclerosis (MS) is a debilitating neuroimmunological and neurodegenerative disease with a strong genetic component. Numerous studies have failed to consistently identify genes that confer disease susceptibility except for association with HLA-DR. Seven non-HLA regions (1q, 2q, 9q, 13q, 16q, 18p and 19q) identified in a recent genomic screen were investigated by genotyping approximately 20 single-nucleotide polymorphisms (SNPs) at approximately 1 Mb intervals. Non-parametric multipoint analyses identified a peak LOD* score of 2.99 for the 1q44 region and substantially narrowed the linkage peak to approximately 7 Mb. Ordered subset analyses (OSA) identified significant LOD score increases for 2q35 and 18p11 when ranking families by HLA-DR status and identified a significant LOD score increase in region 2q35 when ranking families by linkage to chromosome 1q44. 1q44 is particularly interesting because of linkage evidence for this region in studies of both rheumatoid arthritis and systemic lupus erythematosus.

Ordered-subset analysis (OSA) for family-based association mapping of complex traits

Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.

Genome screen in familial intracranial aneurysm

Background

Individuals with 1st degree relatives harboring an intracranial aneurysm (IA) are at an increased risk of IA, suggesting genetic variation is an important risk factor.

Methods

Families 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.

Results

Model-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.

Conclusion

These 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.

Neuropeptide Y gene polymorphisms confer risk of early-onset atherosclerosis

Neuropeptide Y (NPY) is a strong candidate gene for coronary artery disease (CAD). We have previously identified genetic linkage to familial CAD in the genomic region of NPY. We performed follow-up genetic, biostatistical, and functional analysis of NPY in early-onset CAD. In familial CAD (GENECARD, N = 420 families), we found increased microsatellite linkage to chromosome 7p14 (OSA LOD = 4.2, p = 0.004) in 97 earliest age-of-onset families. Tagged NPY SNPs demonstrated linkage to CAD of a 6-SNP block (LOD = 1.58-2.72), family-based association of this block with CAD (p = 0.02), and stronger linkage to CAD in the earliest age-of-onset families. Association of this 6-SNP block with CAD was validated in: (a) 556 non-familial early-onset CAD cases and 256 controls (OR 1.46-1.65, p = 0.01-0.05), showing stronger association in youngest cases (OR 1.84-2.20, p = 0.0004-0.09); and (b) GENECARD probands versus non-familial controls (OR 1.79-2.06, p = 0.003-0.02). A promoter SNP (rs16147) within this 6-SNP block was associated with higher plasma NPY levels (p = 0.04). To assess a causal role of NPY in atherosclerosis, we applied the NPY1-receptor-antagonist BIBP-3226 adventitially to endothelium-denuded carotid arteries of apolipoprotein E-deficient mice; treatment reduced atherosclerotic neointimal area by 50% (p = 0.03). Thus, NPY variants associate with atherosclerosis in two independent datasets (with strong age-of-onset effects) and show allele-specific expression with NPY levels, while NPY receptor antagonism reduces atherosclerosis in mice. We conclude that NPY contributes to atherosclerosis pathogenesis.

Genome-wide linkage scan in Gullah-speaking African American families with type 2 diabetes: the Sea Islands Genetic African American Registry (Project SuGAR)

OBJECTIVE

The Gullah-speaking African American population from the Sea Islands of South Carolina is characterized by a low degree of European admixture and high rates of type 2 diabetes and diabetic complications. Affected relative pairs with type 2 diabetes were recruited through the Sea Islands Genetic African American Registry (Project SuGAR).

RESEARCH DESIGN AND METHODS

We conducted a genome-wide linkage scan, genotyping 5,974 single nucleotide polymorphisms in 471 affected subjects and 50 unaffected relatives from 197 pedigrees. Data were analyzed using a multipoint engine for rapid likelihood inference and ordered subsets analyses (OSAs) for age at type 2 diabetes diagnosis, waist circumference, waist-to-hip ratio, and BMI. We searched for heterogeneity and interactions using a conditional logistic regression likelihood approach.

RESULTS

Linkage peaks on chromosome 14 at 123-124 cM were detected for type 2 diabetes (logarithm of odds [LOD] 2.10) and for the subset with later age at type 2 diabetes diagnosis (maximum LOD 4.05). Two linkage peaks on chromosome 7 were detected at 44-45 cM for type 2 diabetes (LOD 1.18) and at 78 cM for type 2 diabetes (LOD 1.64) and the subset with earlier age at type 2 diabetes diagnosis (maximum LOD 3.93). The chromosome 14 locus and a peak on 7p at 29.5 cM were identified as important in the multilocus model. Other regions that provided modest evidence for linkage included chromosome 1 at 167.5 cM (LOD 1.51) and chromosome 3 at 121.0 cM (LOD 1.61).

CONCLUSIONS

This study revealed a novel type 2 diabetes locus in an African American population on 14q that appears to reduce age of disease onset and confirmed two loci on chromosome 7.

ATOM: a powerful gene-based association test by combining optimally weighted markers

BACKGROUND

Large-scale candidate-gene and genome-wide association studies genotype multiple SNPs within or surrounding a gene, including both tag and functional SNPs. The immense amount of data generated in these studies poses new challenges to analysis. One particularly challenging yet important question is how to best use all genetic information to test whether a gene or a region is associated with the trait of interest.

METHODS

Here we propose a powerful gene-based Association Test by combining Optimally Weighted Markers (ATOM) within a genomic region. Due to variation in linkage disequilibrium, different markers often associate with the trait of interest at different levels. To appropriately apportion their contributions, we assign a weight to each marker that is proportional to the amount of information it captures about the trait locus. We analytically derive the optimal weights for both quantitative and binary traits, and describe a procedure for estimating the weights from a reference database such as the HapMap. Compared with existing approaches, our method has several distinct advantages, including (i) the ability to borrow information from an external database to increase power, (ii) the theoretical derivation of optimal marker weights and (iii) the scalability to simultaneous analysis of all SNPs in candidate genes and pathways.

RESULTS

Through extensive simulations and analysis of the FTO gene in our ongoing genome-wide association study on childhood obesity, we demonstrate that ATOM increases the power to detect genetic association as compared with several commonly used multi-marker association tests.

Ordered subsets linkage analysis of antisocial behavior in substance use disorder among participants in the Collaborative Study on the Genetics of Alcoholism

Heterogeneity in complex diseases such as Substance Use Disorder (SUD) reduces the power to detect linkage and makes replication of findings in other populations unlikely. It is therefore critical to refine the phenotype and use methods that account for genetic heterogeneity between families. SUD was operationalized as diagnosis of abuse or dependence to alcohol and/or any one of five illicit substances. Whole-genome linkage analysis of 241 extended pedigree families from the Collaborative Study on the Genetics of Alcoholism was performed in Merlin using an affected sibship design. An Ordered Subsets Analysis (OSA) using FLOSS sought to increase the homogeneity of the sample by ranking families by their density of childhood and adult antisocial behaviors, producing new maximum Nonparametric Lod (NPL) scores on each chromosome for each subset of families. Prior to OSA, modest evidence for linkage was found on chromosomes 8 and 17. Although changes in NPL scores were not statistically significant, OSA revealed possible evidence of linkages on chromosome 7, near markers D7S1795 and D7S821. NPL scores >3.0 were also observed on chromosomes 2, 3, 5, 9, and 14. However, the number of families used in these latter subsets for linkage may be too small to be meaningful. Results provide some evidence for the ability of OSA to reduce genetic heterogeneity, and add further support to chromosome 7 as a possible location to search for genes related to various SUD related processes. Nonetheless, replication of these results in other samples is essential.

Ordered stratification to reduce heterogeneity in linkage to diabetes-related quantitative traits

Phenotypic heterogeneity complicates detection of genomic loci predisposing to type 2 diabetes, potentially obscuring or unmasking specific loci. We conducted ordered-subsets linkage analyses (OSAs) for diabetes-related quantitative traits (fasting insulin and glucose, hemoglobin A1c (HbA1c), and 28-year-time-averaged fasting plasma glucose (FPG)) from 330 families of the Framingham Offspring Study. We calculated mean BMI, waist circumference (WC), and a diabetes "age-of-onset score" for each family. We constructed subsets by adding one family at a time in increasing (lean family to obese) or decreasing (obese to lean) adiposity order, or increasing or decreasing propensity to develop diabetes at a younger age, with the OSA LOD reported as the maximum LOD observed in any subset. Permutation P values tested the hypothesis that phenotypic ordering showed stronger linkage than random ordering. On chromosome 1, ordering by increasing family mean WC increased linkage to time-averaged FPG at 256 cM from LOD = 2.4 to 3.5 (permuted P = 0.02) and to HbA1c at 180 cM from LOD = 2.0 to 3.3 (P = 0.01). On chromosome 19, ordering by decreasing WC increased linkage to fasting insulin at 68 cM from LOD = 2.7 to 4.6 (P = 0.002), and ordering by decreasing propensity to develop diabetes at a young age increased linkage to fasting insulin at 73 cM from LOD = 2.7 to 4.0 (P = 0.046). We conclude that chromosomes 1 and 19 could harbor adiposity-interacting diabetes susceptibility genes. Such interactions might also influence trait-locus associations and may be useful to consider in diabetes genome-wide association studies.

Genome-wide linkage analyses of quantitative and categorical autism subphenotypes

BACKGROUND

The search for susceptibility genes in autism and autism spectrum disorders (ASD) has been hindered by the possible small effects of individual genes and by genetic (locus) heterogeneity. To overcome these obstacles, one method is to use autism-related subphenotypes instead of the categorical diagnosis of autism since they may be more directly related to the underlying susceptibility loci. Another strategy is to analyze subsets of families that meet certain clinical criteria to reduce genetic heterogeneity.

METHODS

In this study, using 976 multiplex families from the Autism Genome Project consortium, we performed genome-wide linkage analyses on two quantitative subphenotypes, the total scores of the reciprocal social interaction domain and the restricted, repetitive, and stereotyped patterns of behavior domain from the Autism Diagnostic Interview-Revised. We also selected subsets of ASD families based on four binary subphenotypes, delayed onset of first words, delayed onset of first phrases, verbal status, and IQ > or = 70.

RESULTS

When the ASD families with IQ > or = 70 were used, a logarithm of odds (LOD) score of 4.01 was obtained on chromosome 15q13.3-q14, which was previously linked to schizophrenia. We also obtained a LOD score of 3.40 on chromosome 11p15.4-p15.3 using the ASD families with delayed onset of first phrases. No significant evidence for linkage was obtained for the two quantitative traits.

CONCLUSIONS

This study demonstrates that selection of informative subphenotypes to define a homogeneous set of ASD families could be very important in detecting the susceptibility loci in autism.

Genetic studies on the APOA1-C3-A5 gene cluster in Asian Indians with premature coronary artery disease

BACKGROUND

The APOA1-C3-A5 gene cluster plays an important role in the regulation of lipids. Asian Indians have an increased tendency for abnormal lipid levels and high risk of Coronary Artery Disease (CAD). Therefore, the present study aimed to elucidate the relationship of four single nucleotide polymorphisms (SNPs) in the Apo11q cluster, namely the -75G>A, +83C>T SNPs in the APOA1 gene, the Sac1 SNP in the APOC3 gene and the S19W variant in the APOA5 gene to plasma lipids and CAD in 190 affected sibling pairs (ASPs) belonging to Asian Indian families with a strong CAD history.

METHODS & RESULTS

Genotyping and lipid assays were carried out using standard protocols. Plasma lipids showed a strong heritability (h2 48% - 70%; P < 0.0001). A subset of 77 ASPs with positive sign of Logarithm of Odds (LOD) score showed significant linkage to CAD trait by multi-point analysis (LOD score 7.42, P < 0.001) and to Sac1 (LOD score 4.49) and -75G>A (LOD score 2.77) SNPs by single-point analysis (P < 0.001). There was significant proportion of mean allele sharing (pi) for the Sac1 (pi 0.59), -75G>A (pi 0.56) and +83C>T (pi 0.52) (P < 0.001) SNPs, respectively. QTL analysis showed suggestive evidence of linkage of the Sac1 SNP to Total Cholesterol (TC), High Density Lipoprotein-cholesterol (HDL-C) and Apolipoprotein B (ApoB) with LOD scores of 1.42, 1.72 and 1.19, respectively (P < 0.01). The Sac1 and -75G>A SNPs along with hypertension showed maximized correlations with TC, TG and Apo B by association analysis.

CONCLUSION

The APOC3-Sac1 SNP is an important genetic variant that is associated with CAD through its interaction with plasma lipids and other standard risk factors among Asian Indians.