Affected sibpair linkage tests for multiple linked susceptibility genes

Linkage analysis in the next-generation sequencing era

Linkage analysis was developed to detect excess co-segregation of the putative alleles underlying a phenotype with the alleles at a marker locus in family data. Many different variations of this analysis and corresponding study design have been developed to detect this co-segregation. Linkage studies have been shown to have high power to detect loci that have alleles (or variants) with a large effect size, i.e. alleles that make large contributions to the risk of a disease or to the variation of a quantitative trait. However, alleles with a large effect size tend to be rare in the population. In contrast, association studies are designed to have high power to detect common alleles which tend to have a small effect size for most diseases or traits. Although genome-wide association studies have been successful in detecting many new loci with common alleles of small effect for many complex traits, these common variants often do not explain a large proportion of disease risk or variation of the trait. In the past, linkage studies were successful in detecting regions of the genome that were likely to harbor rare variants with large effect for many simple Mendelian diseases and for many complex traits. However, identifying the actual sequence variant(s) responsible for these linkage signals was challenging because of difficulties in sequencing the large regions implicated by each linkage peak. Current 'next-generation' DNA sequencing techniques have made it economically feasible to sequence all exons or the whole genomes of a reasonably large number of individuals. Studies have shown that rare variants are quite common in the general population, and it is now possible to combine these new DNA sequencing methods with linkage studies to identify rare causal variants with a large effect size. A brief review of linkage methods is presented here with examples of their relevance and usefulness for the interpretation of whole-exome and whole-genome sequence data.

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 polygenic model for integration of linkage and pathway information

We introduce an approximate model for linkage curves which accommodates the polygenic structure of complex diseases and accounts for the simultaneous action of closely located genes. The model is extended so that information on biological pathways can be integrated. Using data on rheumatoid arthritis, we describe some of the many applications which the model allows: it can be used to test for residual linkage in the presence of already established loci, to derive a global test for linkage, to test for the relevance of a gene list in terms of linkage and to help in candidate gene prioritization by integration of gene-pathway annotation data.

Conditional tests for localizing trait genes

BACKGROUND/AIMS

With pedigree data, genetic linkage can be detected using inheritance vector tests, which explore the discrepancy between the posterior distribution of the inheritance vectors given observed trait values and the prior distribution of the inheritance vectors. In this paper, we propose conditional inheritance vector tests for linkage localization. These conditional tests can also be used to detect additional linkage signals in the presence of previously detected causal genes.

METHODS

For linkage localization, we propose to perform inheritance vector tests conditioning on the inheritance vectors at two positions bounding a test region. We can detect additional linkage signals by conducting a further conditional test in a region with no previously detected genes. We use randomized p values to extend the marginal and conditional tests when the inheritance vectors cannot be completely determined from genetic marker data.

RESULTS

We conduct simulation studies to compare and contrast the marginal and the conditional tests and to demonstrate that randomized p values can capture both the significance and the uncertainty in the test results.

CONCLUSIONS

The simulation results demonstrate that the proposed conditional tests provide useful localization information, and with informative marker data, the uncertainty in randomized marginal and conditional test results is small.

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases.

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.

Detecting gene-gene interaction in linkage analysis

Linkage analysis has been very successful in identifying genes for many Mendelian diseases, but has not enjoyed the same level of success for complex diseases. A major reason is that complex diseases are multifactorial, involving multiple genes and environmental factors. Linkage analysis is powerful for localizing disease genes with moderate marginal effects with most realistic sample sizes. Traditionally it has been used to search for a single disease locus at one time, and most implementations lack the power to detect genes with small marginal effect but moderate to strong interaction effect with other genes. Thus, methods for detecting gene-gene interaction in linkage analysis are needed. A brief background on gene-gene interaction in linkage analysis is given, with a review of two major approaches: two-locus linkage analysis and interaction analysis. Three methods of interaction analysis are also reviewed: conditional linkage analysis, ordered subset analysis, and generalized estimating equations.

Strategies for conditional two-locus nonparametric linkage analysis

In this article we deal with two-locus nonparametric linkage (NPL) analysis, mainly in the context of conditional analysis. This means that one incorporates single-locus analysis information through conditioning when performing a two-locus analysis. Here we describe different strategies for using this approach. Cox et al. [Nat Genet 1999;21:213-215] implemented this as follows: (i) Calculate the one-locus NPL process over the included genome region(s). (ii) Weight the individual pedigree NPL scores using a weighting function depending on the NPL scores for the corresponding pedigrees at speci fi c conditioning loci. We generalize this by conditioning with respect to the inheritance vector rather than the NPL score and by separating between the case of known (prede fi ned) and unknown (estimated) conditioning loci. In the latter case we choose conditioning locus, or loci, according to prede fi ned criteria. The most general approach results in a random number of selected loci, depending on the results from the previous one-locus analysis. Major topics in this article include discussions on optimal score functions with respect to the noncentrality parameter (NCP), and how to calculate adequate p values and perform power calculations. We also discuss issues related to multiple tests which arise from the two-step procedure with several conditioning loci as well as from the genome-wide tests.

Searching for epistasis and linkage heterogeneity by correlations of pedigree-specific linkage scores

Recognizing that multiple genes are likely responsible for common complex traits, statistical methods are needed to rapidly screen for either interacting genes or locus heterogeneity in genetic linkage data. To achieve this, some investigators have proposed examining the correlation of pedigree linkage scores between pairs of chromosomal regions, because large positive correlations suggest interacting loci and large negative correlations suggest locus heterogeneity (Cox et al. [1999]; Maclean et al. [1993]). However, the statistical significance of these extreme correlations has been difficult to determine due to the autocorrelation of linkage scores along chromosomes. In this study, we provide novel solutions to this problem by using results from random field theory, combined with simulations to determine the null correlation for syntenic loci. Simulations illustrate that our new methods control the Type-I error rates, so that one can avoid the extremely conservative Bonferroni correction, as well as the extremely time-consuming permutational method to compute P-values for non-syntenic loci. Application of these methods to prostate cancer linkage studies illustrates interpretation of results and provides insights into the impact of marker information content on the resulting statistical correlations, and ultimately the asymptotic P-values.

A two-dimensional genome scan for rheumatoid arthritis susceptibility loci

We performed a genome-wide search for pairs of susceptibility loci that jointly contribute to rheumatoid arthritis in families recruited by the North American Rheumatoid Arthritis Consortium. A complete two-dimensional (2D) non-parametric linkage scan was carried out using 380 autosomal microsatellite markers in 511 families. At each 2D peak we obtained the most likely underlying genetic model explaining the two-locus effects, defining epistasis as a departure from an additive or a multiplicative two-locus penetrance function. The highest peak in the surface identified an epistatic interaction between loci 6p21 and 16p12 (two-locus lod score = 18.02, epistasis P < 0.012). Significant and suggestive two-locus effects were also obtained for region 6p21 in combination with loci 18q21, 8p23, 1q41, and 6p22, while the highest 2D peaks excluding region 6p21 were observed at locus pairs 8p23-18q21 and 1p21-18q21. The 2D peaks were further examined using combined microsatellite and single-nucleotide polymorphism (SNP) marker genotypes in 744 families. The two-locus evidence for linkage increased for region pairs 6p21-18q12, 6p21-16p12, 6p21-8p23, 1q41-6p21, and 6p21-6p22, but decreased for pairs of regions that did not include locus 6p21. In conclusion, we obtained evidence for multi-locus interactions in rheumatoid arthritis that are mediated by the major susceptibility locus at 6p21.

Affected relative pairs and simultaneous search for two-locus linkage in the presence of epistasis

It is commonly believed that multiple interacting genes increase the susceptibility of genetically complex diseases, yet few linkage analyses of human diseases scan for more than one locus at a time. To overcome some of the statistical and computational limitations of a simultaneous search for two disease susceptibility loci in the presence of epistasis, we developed new score statistics to simultaneously scan for two disease susceptibility loci in pedigree data. These model-free score statistics are based on developments for model-free maximum lod scores, which in turn are based on variance components for indicators of disease status. To overcome reduced power caused by many parameters in the general two-locus model, we impose constraints on ratios of variance components, much like those used for robust single-locus linkage statistics (e.g., minimax constraints). The resulting three-degree of freedom score statistic, constrained as a one-sided multivariate test, can be computed rapidly, making simultaneous search feasible for human genetic linkage studies. Furthermore, using recent developments to rapidly compute simulation P-values for score statistics, it is feasible to empirically evaluate the statistical significance of the proposed score statistics. Application of these methods to two large studies of the genetic linkage of prostate cancer illuminates their strengths and limitations. The results provide weak suggestions for linkage of several pairs of chromosomal regions (chromosome pairs 1-21, 3-13, 5-9, and 14-19), all of which showed stronger linkage signals when the score statistics accounted for epistasis. These novel score statistics should prove useful for linkage studies of other complex human diseases.

Explorative two-locus linkage analysis suggests a multiplicative interaction between the 7q32 and 16p13 myoclonic seizures-related photosensitivity loci

In traits suspected to be governed by at least two loci, linkage analysis incorporating the joint action of both loci may improve the power to detect linkage, increase the precision of estimating locus positions and provide insight into the underlying etiological mechanism. Recently, we mapped two susceptibility loci for epilepsy-related photosensitivity (or photoparoxysmal response, PPR) at regions 7q32 (PPR1) and 16p13 (PPR2) in PPR families with prominent myoclonic seizures background (MS-related PPR). To follow-up these results and evaluate interaction effects between these regions, we conducted two-locus (2L) linkage analyses using parametric and non-parametric methods. The 2L linkage was calculated under a multiplicative (MULT) epistasis model, encompassing models where each locus is necessary but not sufficient for MS-related PPR and a heterogeneity (HET) model, encompassing models in which each locus is by itself sufficient but not necessary for MS-related PPR expression. We found maximal 2L linkage under the (MULT) model, which was significantly better than the 2L linkage under the (HET) model (P = 0.001). The 2L analyses gave no increase in power to detect linkage over the single-locus analyses nor did they improve location estimates at PPR1 and PPR2, as expected under a best-fit 2L (MULT) model in an affecteds-only analysis. Our findings suggest that the genes underlying the PPR1 and PPR2 susceptibility loci may have similar functions or act in the same biochemical pathway.

Epistasis between type 2 diabetes susceptibility Loci on chromosomes 1q21-25 and 10q23-26 in northern Europeans

Characterisation of the interactions between susceptibility loci (epistasis) is central to a full understanding of the genetic aetiology and the molecular pathology of complex diseases. We have examined, in British and French pedigrees, evidence for epistasis between the type 2 diabetes susceptibility loci on chromosomes 1q21-25 and 10q23-26 using two complementary linkage-based approaches. Joint two-locus linkage analysis of 1q and 10q in British pedigrees provided significant evidence for interaction (P < or = 0.003) when comparing a general epistasis model with multiplicative or additive-effects-only models. Conditional linkage analysis (which models epistasis as a deviation from multiplicativity only) confirmed these findings, with significant LOD score increases at the 1q (P = 0.0002) and 10q (P = 0.0023) loci. These analyses provided sizeable reductions in the 1-LOD support intervals for both loci. Analyses of the British and French pedigrees together yielded comparable, but not enhanced, findings, with significant (P < or = 0.003) evidence for epistasis in joint two-locus linkage analysis, and during conditional linkage analysis significant increases in linkage evidence at the 1q (P = 0.0002) and 10q (P = 0.0036) loci. Our findings of epistasis nevertheless substantiate the evidence for genuine genetic effects at both loci, facilitate endeavours to fine-map these loci in population samples, and support further examination of this interaction at the nucleotide level by providing a robust prior hypothesis.

A multimarker regression-based test of linkage for affected sib-pairs at two linked loci

We address the analytical problem of evaluating the evidence for linkage at a test locus while taking into account the effect of a known linked disease locus. The method we propose is a multimarker regression approach that models the identity-by-descent states for affected sib-pairs at a series of linked markers in terms of the identity-by-descent state at the known disease locus. Our method allows analysis to be performed at a test location (or a series of locations) without the requirement that identity-by-descent be directly observed at either the test or the known conditioning locus. An advantage of our method is that identity-by-descent states from multiple markers are included simultaneously in the test of linkage, without recourse to multipoint imputation. The properties and power of the method are examined under various null and alternative hypotheses. The method is applied to data from a study of 1,056 type 1 diabetes families to examine the evidence for an additional putative locus (IDDM15) on chromosome 6q, linked to IDDM1 in the HLA region on chromosome 6p. After accounting for the strong effect of IDDM1 and the differing rates of male and female recombination in the region, we find only marginal evidence for IDDM15 (P = 0.03 to 0.002, using different methods) approximately 15 cM centromeric of the original localisation.

Analysis of IMGSAC autism susceptibility loci: evidence for sex limited and parent of origin specific effects

BACKGROUND AND METHODS

Autism is a severe neurodevelopmental disorder, which has a complex genetic predisposition. The ratio of males to females affected by autism is approximately 4:1, suggesting that sex specific factors are involved in its development. We reported previously the results of a genomewide screen for autism susceptibility loci in 83 affected sibling pairs (ASP), and follow up analysis in 152 ASP. Here, we report analysis of an expanded sample of 219 ASP, using sex and parent of origin linkage modelling at loci on chromosomes 2, 7, 9, 15, and 16.

RESULTS

The results suggest that linkage to chromosomes 7q and 16p is contributed largely by the male-male ASP (MLS = 2.55 v 0.12, and MLS = 2.48 v 0.00, for the 145 male-male and 74 male-female/female-female ASP on chromosomes 7 and 16 respectively). Conversely linkage to chromosome 15q appears to be attributable to the male-female/female-female ASP (MLS = 2.62 v 0.00, for non-male and male-male ASP respectively). On chromosomes 2 and 9, all ASP contribute to linkage. These data, supported by permutation, suggest a possible sex limited effect of susceptibility loci on chromosomes 7, 15, and 16. Parent of origin linkage modelling indicates two distinct regions of paternal and maternal identity by descent sharing on chromosome 7 (paternal MLS = 1.46 at approximately 112 cM, and maternal MLS = 1.83 at approximately 135 cM; corresponding maternal and paternal MLS = 0.53 and 0.28 respectively), and maternal specific sharing on chromosome 9 (maternal MLS = 1.99 at approximately 30 cM; paternal MLS = 0.02).

CONCLUSION

These data support the possibility of two discrete loci underlying linkage of autism to chromosome 7, and implicate possible parent of origin specific effects in the aetiology of autism.

Two-dimensional genome-scan identifies novel epistatic loci for essential hypertension

It is well established that gene interactions influence common human diseases, but to date linkage studies have been constrained to searching for single genes across the genome. We applied a novel approach to uncover significant gene-gene interactions in a systematic two-dimensional (2D) genome-scan of essential hypertension. The study cohort comprised 2076 affected sib-pairs and 66 affected half-sib-pairs of the British Genetics of HyperTension study. Extensive simulations were used to establish significance thresholds in the context of 2D genome-scans. Our analyses found significant and suggestive evidence for loci on chromosomes 5, 9, 11, 15, 16 and 19, which influence hypertension when gene-gene interactions are taken into account (5q13.1 and 11q22.1, two-locus lod score=5.72; 5q13.1 and 19q12, two-locus lod score=5.35; 9q22.3 and 15q12, two-locus lod score=4.80; 16p12.3 and 16q23.1, two-locus lod score=4.50). For each significant and suggestive pairwise interaction, the two-locus genetic model that best fitted the data was determined. Regions that were not detected using single-locus linkage analysis were identified in the 2D scan as contributing significant epistatic effects. This approach has discovered novel loci for hypertension and offers a unique potential to use existing data to uncover novel regions involved in complex human diseases.

Multilocus linkage analysis of affected sib pairs

OBJECTIVE

The conventional affected sib pair methods evaluate the linkage information at a locus by considering only marginal information. We describe a multilocus linkage method that uses both the marginal information and information derived from the possible interactions among several disease loci, thereby increasing the significance of loci with modest effects.

METHODS

Our method is based on a statistic that quantifies the linkage information contained in a set of markers. By a marker selection-reduction process, we screen a set of polymorphisms and select a few that seem linked to disease.

RESULTS

We test our approach on genome scan data for inflammatory bowel disease (InfBD) and on simulated data. On real data we detect 6 of the 8 known InfBD loci; on simulated data we obtain improvements in power of up to 40% compared to a conventional single-locus method.

CONCLUSION

Our extensive simulations and the results on real data show that our method is in general more powerful than single-locus methods in detecting disease loci responsible for complex traits. A further advantage of our approach is that it can be extended to make use of both the linkage and the linkage disequilibrium between disease loci and nearby markers.

Tests for the presence of two linked disease susceptibility genes

For diseases with complex genetic etiology, more than one susceptibility gene may exist in a single chromosomal region. Under explicit assumptions about the number of disease genes in a region, generalized estimating equations (GEE) can be used to estimate the putative disease gene location(s) and expected identical-by-descent allele sharing in affected sib pairs at these gene(s). Extending the work of Liang et al. developed a method for simultaneous localization of two susceptibility genes in one region using marker identical-by-descent (IBD) sharing in affected sib pairs. Here we propose methods to evaluate the evidence for two versus one disease loci in a region in a quasi-likelihood/GEE framework. We describe tests based on approximate quasi-likelihood ratio and generalized score test statistics. Because of difficulties in determining the asymptotic null distributions of these statistics and the small sample sizes that can be available in genetic studies, we recommend that significance be evaluated empirically. Application of the described methods to data from a genome scan for type 1 diabetes yielded some evidence for two linked disease genes on chromosome 6, approximately 20 cM apart (p value for an approximate quasi-likelihood ratio test=0.049). In simulation studies, we found that both tests performed quite well for a range of scenarios. Power to detect the presence of two linked disease genes increased with the number of affected sib pairs, greater IBD sharing at the two loci, and larger distance between the two loci.

Novel analytical methods applied to type 1 diabetes genome-scan data

Complex traits like type 1 diabetes mellitus (T1DM) are generally taken to be under the influence of multiple genes interacting with each other to confer disease susceptibility and/or protection. Although novel methods are being developed, analyses of whole-genome scans are most often performed with multipoint methods that work under the assumption that multiple trait loci are unrelated to each other; that is, most models specify the effect of only one locus at a time. We have applied a novel approach, which includes decision-tree construction and artificial neural networks, to the analysis of T1DM genome-scan data. We demonstrate that this approach (1) allows identification of all major susceptibility loci identified by nonparametric linkage analysis, (2) identifies a number of novel regions as well as combinations of markers with predictive value for T1DM, and (3) may be useful in characterizing markers in linkage disequilibrium with protective-gene variants. Furthermore, the approach outlined here permits combined analyses of genetic-marker data and information on environmental and clinical covariates.

Conditional multipoint linkage analysis using affected sib pairs: an alternative approach

Recently, Liang et al. ([2001b] Genet. Epidemiol. 21:105-122) proposed a conditional approach to assess linkage evidence on the target region by incorporating linkage information from an unlinked (reference) region using allele shared IBD (identity-by-decent) from affected sib pairs. This is carried out by conditioning on the IBD sharing value at the estimated trait locus of the reference region. Since markers considered are typically non-fully informative, the IBD sharing at each marker needs to be estimated (or imputed). In this report, we propose an alternative approach to deal with the IBD sharing in the reference region. This new approach makes full use of the observed data without having to categorize the imputed IBD sharing as needed in Liang et al. ([2001b] Genet. Epidemiol. 21:105-122). We compare these two approaches by simulating data from a variety of two-locus models including heterogeneity, additive and multiplicative with either fully informative markers or non-fully informative markers. The performance of both approaches is quite comparable showing consistent estimates of the trait locus and key genetic parameters.

Simultaneous localization of two linked disease susceptibility genes

For diseases with complex genetic etiology, more than one susceptibility gene may exist in a single chromosomal region. Extending the work of Liang et al. ([2001] Hum. Hered. 51:64-78), we developed a method for simultaneous localization of two susceptibility genes in one region. We derived an expression for expected allele sharing of an affected sib pair (ASP) at each point across a chromosomal segment containing two susceptibility genes. Using generalized estimating equations (GEE), we developed an algorithm that uses marker identical-by-descent (IBD) sharing in affected sib pairs to simultaneously estimate the locations of the two genes and the mean IBD sharing in ASPs at these two disease loci. Confidence intervals for gene locations can be constructed based on large sample approximations. Application of the described methods to data from a genome scan for type 1 diabetes (Mein et al. [1998] Nat. Genet. 19:297-300) yielded estimates of two putative disease gene locations on chromosome 6, approximately 20 cM apart. Properties of the estimators, including bias, precision, and confidence interval coverage, were studied by simulation for a range of genetic models. The simulations demonstrated that the proposed method can improve disease gene localization and aid in resolving large peaks when two disease genes are present in one chromosomal region. Joint localization of two disease genes improves with increased excess allele sharing at the disease gene loci, increased distance between the disease genes, and increased number of affected sib pairs in the sample.

Genetics of type 1 diabetes mellitus

At least 20 different chromosomal regions have been linked to type 1 diabetes (T1D) susceptibility in humans, using genome screening, candidate gene testing, and studies of human homologues of mouse susceptibility genes. The largest contribution from a single locus (IDDM1) comes from several genes located in the MHC complex on chromosome 6p21.3, accounting for at least 40% of the familial aggregation of this disease. Approximately 30% of T1D patients are heterozygous for HLA-DQA1*0501-DQB1*0201/DQA1*0301-DQB1*0302 alleles (formerly referred to as HLA-DR3/4 and for simplification usually shortened to HLA-DQ2/DQ8), and a particular HLA-DQ6 molecule (HLA-DQA1*0102-DQB1*0602) is associated with dominant protection from the disease. There is evidence that certain residues important for structure and function of both HLA-DQ and DR peptide-binding pockets determine disease susceptibility and resistance. Independent confirmation of the IDDM2 locus on chromosome 11p15.5 has been achieved in both case-control and family-based studies, whereas associations with the other potential IDDM loci have not always been replicated. Several possibilities to explain these variable results from different studies are discussed, and a key factor affecting both linkage and association studies is that the genetic basis of T1D susceptibility may differ between ethnic groups. Some future strategies to address these problems are proposed. These include increasing the sample size in homogenous ethnic groups, high throughput genotyping and genomewide linkage disequilibrium (LD) mapping to establish disease associated ancestral haplotypes. Elucidation of the function of particular genes ('functional genomics') in the pathogenesis of T1D will be a most important element in future studies in this field, in addition to more sophisticated methods of statistical analyses.

Multipoint analysis using affected sib pairs: incorporating linkage evidence from unlinked regions

In this paper, we proposed a multipoint method to assess evidence of linkage to one region by incorporating linkage evidence from another region. This approach uses affected sib pairs in which the number of alleles shared identical by descent (IBD) is the primary statistic. This generalized estimating equation (GEE) approach is robust in that no assumption about the mode of inheritance is required, other than assuming the two regions being considered are unlinked and that there is no more than one susceptibility gene in each region. The method proposed here uses data from all available families to simultaneously test the hypothesis of statistical interaction between regions and to estimate the location of the susceptibility gene in the target region. As an illustration, we have applied this GEE method to an asthma sib pair study (Wjst et al. [1999] Genomics 58:1-8), which earlier reported evidence of linkage to chromosome 6 but showed no evidence for chromosome 20. Our results yield strong evidence to chromosome 20 (P value = 0.0001) after incorporating linkage information from chromosome 6. Furthermore, it estimates with 95% certainty that the map location of the susceptibility gene is flanked by markers D20S186 and D20S101, which are approximately 16.3 cM apart.

Genomewide screen and identification of gene-gene interactions for asthma-susceptibility loci in three U.S. populations: collaborative study on the genetics of asthma

The genomewide screen to search for asthma-susceptibility loci, in the Collaborative Study on the Genetics of Asthma (CSGA), has been conducted in two stages and includes 266 families (199 nuclear and 67 extended pedigrees) from three U.S. populations: African American, European American, and Hispanic. Evidence for linkage with the asthma phenotype was observed for multiple chromosomal regions, through use of several analytical approaches that facilitated the identification of multiple disease loci. Ethnicity-specific analyses, which allowed for different frequencies of asthma-susceptibility genes in each ethnic population, provided the strongest evidence for linkage at 6p21 in the European American population, at 11q21 in the African American population, and at 1p32 in the Hispanic population. Both the conditional analysis and the affected-sib-pair two-locus analysis provided further evidence for linkage, at 5q31, 8p23, 12q22, and 15q13. Several of these regions have been observed in other genomewide screens and linkage or association studies, for asthma and related phenotypes. These results were used to develop a conceptual model to delineate asthma-susceptibility loci and their genetic interactions, which provides a promising basis for initiation of fine-mapping studies and, ultimately, for gene identification.

Overview of model-free methods for linkage analysis

Methods of model-free linkage analysis do not require a detailed specification for the mode of inheritance of the trait locus being linked. Beginning with methods proposed by Penrose in the 1930s, which allowed detection of linkage only, these methods now allow one to use multipoint analysis both to locate trait genes and to estimate variance components that give information on the genetic mechanism underlying the trait. The newer methods can utilize data on multiple types of pairs of relatives other than just sibpairs, and they can detect multiple trait loci. In combination with special sampling schemes, these methods give hope that they may play a crucial role in unraveling the genetic etiology of multifactorial traits, regardless of whether epistatic interactions are present. The results of such analyses can guide the use of more powerful model-based linkage analyses.

Discovery of cancer susceptibility genes: study designs, analytic approaches, and trends in technology

Determining the genetic causes of cancers has immense public health benefits, ranging from prevention to earlier detection and treatment of disease. Although a number of cancer susceptibility genes have been successfully identified, design and analytic issues remain that challenge the current paradigm of gene discovery. Some examples are the definition and measurement of cancer phenotype, the use of intermediate end points, the choice of sample (e.g., affected relative pairs versus large extended pedigrees), the choice of analytic method [e.g., parametric logarithm of the odds (LOD) score method versus model-free methods], and the influence of gene-environment interaction on linkage analysis. Furthermore, association methods, based on either the traditional case-control study design or family-based controls, are popular choices to evaluate candidate genes or screen for linkage disequilibrium. Finally, the study design and analytic methods for gene discovery are determined to some extent by what genomic technology is feasible within the laboratory. Many of the main issues related to gene discovery, as well as trends in genomic technology that will impact on gene discovery, are discussed from the perspective of their strengths and weaknesses, pointing to areas in need of further work.

Parametric and nonparametric multipoint linkage analysis with imprinting and two-locus-trait models: application to mite sensitization

We present two extensions to linkage analysis for genetically complex traits. The first extension allows investigators to perform parametric (LOD-score) analysis of traits caused by imprinted genes-that is, of traits showing a parent-of-origin effect. By specification of two heterozygote penetrance parameters, paternal and maternal origin of the mutation can be treated differently in terms of probability of expression of the trait. Therefore, a single-disease-locus-imprinting model includes four penetrances instead of only three. In the second extension, parametric and nonparametric linkage analysis with two trait loci is formulated for a multimarker setting, optionally taking imprinting into account. We have implemented both methods into the program GENEHUNTER. The new tools, GENEHUNTER-IMPRINTING and GENEHUNTER-TWOLOCUS, were applied to human family data for sensitization to mite allergens. The data set comprises pedigrees from England, Germany, Italy, and Portugal. With single-disease-locus-imprinting MOD-score analysis, we find several regions that show at least suggestive evidence for linkage. Most prominently, a maximum LOD score of 4.76 is obtained near D8S511, for the English population, when a model that implies complete maternal imprinting is used. Parametric two-trait-locus analysis yields a maximum LOD score of 6.09 for the German population, occurring exactly at D4S430 and D18S452. The heterogeneity model specified for analysis alludes to complete maternal imprinting at both disease loci. Altogether, our results suggest that the two novel formulations of linkage analysis provide valuable tools for genetic mapping of multifactorial traits.

Multilocus linkage tests based on affected relative pairs

For complex diseases, recent interest has focused on methods that take into account joint effects at interacting loci. Conditioning on effects of disease loci at known locations can lead to increased power to detect effects at other loci. Moreover, use of joint models allows investigation of the etiologic mechanisms that may be involved in the disease. Here we present a method for simultaneous analysis of the joint genetic effects at several loci that uses affected relative pairs. The method is a generalization of the two-locus LOD-score analysis for affected sib pairs proposed by Cordell et al. We derive expressions for the relative risk, lambdaR, to a relative of an affected individual, in terms of the additive and epistatic components of variance at an arbitrary number of disease loci, and we show how these can be used to fit a likelihood model to the identity-by-descent sharing among pairs of affected relatives in extended pedigrees. We implement the method by use of a stepwise strategy in which, given evidence of linkage to disease at m-1 locations on the genome, we calculate the conditional likelihood curve across the genome for an mth disease locus, using multipoint methods similar to those proposed by Kruglyak et al. We evaluate the properties of our method by use of simulated data and present an application to real data from families with insulin-dependent diabetes mellitus.

Improving the power for disease locus detection in affected-sib-pair studies by using two-locus analysis and multiple regression methods

In this paper we present a summary of an analysis of the simulated data (Problem 2) for GAW11. We used sib-pair and affected-sib-pair (ASP) methods to evaluate linkage to the mild form of disease at markers across the genome, in data sets of realistic moderate size (containing between 100 and 300 families selected from the simulated replicates). The true 'answers' were known in advance. Although in most cases we were successful in detecting linkage to disease in the correct regions, it was often difficult to distinguish these results from false positives elsewhere in the genome. We used two-locus methods to see whether the significance was improved by simultaneously modeling linkage to two disease loci, and found a modest increase in significance using two-locus methods in several cases.

A general conditional-logistic model for affected-relative-pair linkage studies

Model-free LOD-score methods are often employed to detect linkage between marker loci and common diseases, with samples of affected sib pairs. Although extensions of the basic one-disease-locus model have been proposed that allow separate inclusion of other types of affected relative pairs, discordant relative pairs, covariates, or additional disease loci, a unified framework that can handle all of these features has been lacking. In this report, I propose a conditional-logistic parameterization that generalizes easily to include all of these features. Two data examples, one using simulated data and one using type 1 diabetes, illustrate applications of the models.

Linkage analysis of candidate genes and gene-gene interactions in chinese hypertensive sib pairs

Previous studies of hypertension in humans and experimental animal models have identified a number of candidate genes that have since been implicated as possibly contributing to essential hypertension. Among them are the genes encoding angiotensinogen, renin, the beta- and gamma-subunits of the epithelial sodium channel (beta/gamma-ENaC), alpha-adducin, and kallikrein (KLK). To examine the role of possible contribution of these genes in ethnic Chinese, as well as the epistatic interaction among them, we studied a large cohort of hypertensive sib pairs from China. DNA samples from 310 concordant affected sibling pairs with hypertension were tested for linkage with the use of excess allele-sharing algorithms based on genotyping with highly informative GT-repeat microsatellite markers localized in the immediate vicinity of the genes encoding angiotensinogen, renin, beta- and gamma-ENaC, alpha-adducin, and KLK. Affected sib pair analysis conducted according to 3 different methods (Statistical Analysis for Genetic Epidemiology [S.A.G.E. ]/SIBPAL, MAPMAKER/SIBS, and affected pedigree member [APM] methods) revealed no evidence for linkage of any of these genes to primary hypertension in the population studied. Moreover, 2-locus sib pair linkage analyses to test for gene-gene interactions among each possible pair of candidate genes failed to yield any statistically significant results. Our findings provide no support for a significant contribution of the angiotensinogen, renin, beta/gamma-ENaC, alpha-adducin, or KLK genes, alone or in concert, to the pathogenesis of essential hypertension among Chinese. Our results emphasize the possible role of ethnic differences for complex disease genetics, as well as the need for large, well-characterized investigations.