Nonparametric linkage regression. II: Identification of influential pedigrees in tests for linkage

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.

Coincident linkage of type 2 diabetes, metabolic syndrome, and measures of cardiovascular disease in a genome scan of the diabetes heart study

Cardiovascular disease (CVD) is a major contributor to morbidity and mortality in type 2 diabetes, but the relationship between CVD and type 2 diabetes is not well understood. The Diabetes Heart Study is a study of type 2 diabetes-enriched families extensively phenotyped for measures of CVD, type 2 diabetes, and metabolic syndrome. A total of 977 Caucasian subjects from 358 pedigrees (575 type 2 diabetic relative pairs) with at least two individuals with type 2 diabetes and, where possible, unaffected siblings were included in a genome scan. Qualitative traits evaluated in this analysis are with or without the presence of coronary calcified plaque (CCP) and with or without carotid calcified plaque (CarCP) measured by electrocardiogram-gated helical computed tomography. In addition, prevalent CVD was measured using two definitions: CVD1, based on self-reported history of clinical CVD (393 subjects), and CVD2, defined as CVD1 and/or CCP >400 (606 subjects). These discrete traits (type 2 diabetes, metabolic syndrome, CVD1, CVD2, CCP, and CarCP) frequently coincide in the same individuals with concordance ranging from 42.9 to 99%. Multipoint nonparametric linkage analysis revealed evidence for coincident mapping of each trait (type 2 diabetes, metabolic syndrome, CVD1, CVD2, CCP, and CarCP) to three different genomic regions: a broad region on chromosome 3 (70-160 cM; logarithm of odds [LOD] scores ranging between 1.15 and 2.71), chromosome 4q31 (peak LOD 146 cM; LOD scores ranging between 0.90 and 2.41), and on chromosome 14p (peak LOD 23 cM; LOD scores ranging between 1.43 and 2.31). Ordered subset analysis (OSA) suggests that the linked chromosome 3 region consists of at least two separate loci on 3p and 3q. In addition, OSA based on lipid measures and other traits identify family subsets with significantly stronger evidence of linkage (e.g., CVD2 on chromosome 3 at 87 cM subsetting on low HDL with an initial LOD of 2.19 is maximized to an LOD of 7.04 in a subset of 25% of the families and CVD2 on chromosome 14 at 22 cM subsetting on high triglycerides with an initial LOD of 1.99 maximized to an LOD of 4.90 in 44% of the families). When subjects are defined as affected by the presence of each trait (type 2 diabetes, metabolic syndrome, CVD1, and CCP), significant evidence for linkage to the 3p locus is observed with a peak LOD of 4.13 at 87 cM. While the correlated nature of the traits makes it unclear whether these loci represent distinct type 2 diabetes, metabolic syndrome, or CVD loci or single loci with pleiotropic effects, the coincident linkage suggests that identification of the underlying genes may help clarify the relationship of diabetes, metabolic syndrome, and CVD.

Fine-mapping chromosome 20 in 230 systemic lupus erythematosus sib pair and multiplex families: evidence for genetic epistasis with chromosome 16q12

The presence of systemic lupus erythematosus (SLE) susceptibility genes on chromosome 20 is suggested by the observation of genetic linkage in several independent SLE family collections. To further localize the genetic effects, we typed 59 microsatellites in the two best regions, as defined by genome screens. Genotypes were analyzed for statistical linkage and/or association with SLE, by use of a combination of nonparametric linkage methods, family-based tests of association (transmission/disequilibrium and pedigree disequilibrium tests), and haplotype-sharing statistics (haplotype runs test), in a set of 230 SLE pedigrees. Maximal evidence for linkage to SLE was to 20p12 (LOD = 2.84) and 20q13.1 (LOD = 1.64) in the white pedigrees. Subsetting families on the basis of evidence for linkage to 16q12 significantly improved the LOD scores at both chromosome 20 locations (20p12 LOD = 5.06 and 20q13 LOD = 3.65), consistent with epistasis. We then typed 162 single-nucleotide polymorphism markers across a 1.3-Mb candidate region on 20q13.1 and identified several SNPs that demonstrated significant evidence for association. These data provide additional support for linkage and association to 20p12 and 20q13.1 in SLE and further refine the intervals of interest. These data further suggest the possibility of epistatic relationships among loci within the 20q12, 20q13, and 16q12 regions in SLE families.

Linkage mapping methods applied to the COGA data set: presentation Group 4 of Genetic Analysis Workshop 14

Presentation Group 4 participants analyzed the Collaborative Study on the Genetics of Alcoholism data provided for Genetic Analysis Workshop 14. This group examined various aspects of linkage analysis and related issues. Seven papers included linkage analyses, while the eighth calculated identity-by-descent (IBD) probabilities. Six papers analyzed linkage to an alcoholism phenotype: ALDX1 (four papers), ALDX2 (one paper), or a combination both (one paper). Methods used included Bayesian variable selection coupled with Haseman-Elston regression, recursive partitioning to identify phenotype and covariate groupings that interact with evidence for linkage, nonparametric linkage regression modeling, affected sib-pair linkage analysis with discordant sib-pair controls, simulation-based homozygosity mapping in a single pedigree, and application of a propensity score to collapse covariates in a general conditional logistic model. Alcoholism linkage was found with > or =2 of these approaches on chromosomes 2, 4, 6, 7, 9, 14, and 21. The remaining linkage paper compared the utility of several single-nucleotide polymorphism (SNP) and microsatellite marker maps for Monte Carlo Markov chain combined oligogenic segregation and linkage analysis, and analyzed one of the electrophysiological endophenotypes, ttth1, on chromosome 7. Linkage was found with all marker sets. The last paper compared the multipoint IBD information content of several SNP sets and the microsatellite set, and found that while all SNP sets examined contained more information than the microsatellite set, most of the information contained in the SNP sets was captured by a subset of the SNP markers with approximately 1-cM marker spacing. From these papers, we highlight three points: a 1-cM SNP map seems to capture most of the linkage information, so denser maps do not appear necessary; careful and appropriate use of covariates can aid linkage analysis; and sources of increased gene-sharing between relatives should be accounted for in analyses.

A quantitative linkage score for an association study following a linkage analysis

BACKGROUND

Currently, a commonly used strategy for mapping complex quantitative traits is to use a genome-wide linkage analysis to narrow suspected genes to regions on a scale of centiMorgans (cM), followed by an association analysis to fine map the genetic variation in regions showing linkage. Two important questions arise in the design and the resulting inference at the association stage of this sequential procedure: (1) how should we design an efficient association study given the information provided by the previous linkage study? and (2) can an association in a linkage region explain, in part, the detected linkage signal?

RESULTS

We derive a quantitative linkage score (QLS) based on Haseman-Elston regression (Haseman and Elston 1972) and make use of this score to address both questions. In designing an association study, the selection of a subsample from the linkage study sample can be guided by the linkage information summarized in the QLS. When heterogeneity exists, we show that selection based on the QLS can increase the proportion of sample individuals from the subpopulation affected by a disease allele and therefore greatly improves the power of the association study. For the resulting inference, we frame as a hypothesis test the question of whether a linkage signal in a region can be in part explained by a marker allele. A simple one sided paired t-statistic is defined by comparing the two sets of QLSs obtained with/without modeling a marker association: a significant difference indicates that the marker can at least partly account for the detected linkage. We also show that this statistic can be used to detect a spurious association.

CONCLUSION

All our results suggest that a careful examination of QLSs should be helpful for understanding the results of both association and linkage studies.

Multilocus and interaction-based genome scan for alcoholism risk factors in Caucasian Americans: the COGA study

In this paper, we applied the nonparametric linkage regression approach to the Caucasian genome scan data from the Collaborative Study on the Genetics of Alcoholism to search for regions of the genome that exhibit evidence for linkage to putative alcoholism-predisposing genes. The multipoint single-locus model identified four regions of the genome with LOD scores greater than one. These regions were on 7p near D7S1790 (LOD = 1.31), two regions on 7q near D7S1870 (LOD = 1.15) and D7S1799 (LOD = 1.13) and 21q near D21S1440 and D21S1446 (LOD = 1.78). Jointly modeling these loci provided stronger evidence for linkage in each of these regions (LOD = 1.58 on 7q11, LOD = 1.61 on 11q23, and LOD = 1.95 on 21q22). The evidence for linkage tended to increase among pedigrees with earlier mean age of onset at 8q23 (p = 0.0016), 14q21 (p = 0.0079), and 18p12 (p = 0.0021) and with later mean age of onset at 4q35 (p = 0.0067) and 9p22 (p = 0.0008).

A genome scan for diabetic nephropathy in African Americans

BACKGROUND

There is substantial evidence for a genetic contribution to diabetic nephropathy susceptibility in the African American population, but little is known about location or identity of susceptibility genes.

METHODS

DNA samples were collected from 206 type 2 diabetes (T2DM) and end-stage renal disease (ESRD)/nephropathy-affected sib pairs from 166 African American families (355 affected individuals). A genome scan was performed and data analyzed using nonparametric linkage regression (NPLR) analysis and ordered subsets analysis (OSA) methods.

RESULTS

In initial NPLR analyses no logarithm of odds (LOD) scores >2.0 were observed. Four loci had LOD scores > or =1.0, with LOD = 1.43 at 29 cM on chromosome 7p the highest. NPLR analyses of multilocus interactions detected 6 loci (7p, 12p, 14q, 16p, 18q, and 21q) with LOD scores 1.15 to 1.63. NPLR analyses evaluating phenotypic interactions revealed multiple locations with evidence (P < 0.05) for interactions with age-at-onset of ESRD (9 loci), duration of diabetes before onset of ESRD (19 loci), and age-at-onset of diabetes (14 loci). Several loci identified by NPLR analyses were also identified using OSA. OSA revealed evidence for a nephropathy locus at 135 cM on chromosome 3 in an estimated 29% of the families (LOD = 4.55 in the optimal subset). Additional linkage evidence, LOD = 3.59, was observed on chromosome 7p (37% of the families, longer duration of diabetes prior to diagnosis of ESRD), and 18q (max. LOD = 3.72; 64% of the families, early diabetes diagnosis). The 7p linkage has been observed in a recent genome scan of African American type 2 diabetes.

CONCLUSION

This first genome scan of diabetic nephropathy in African Americans reveals evidence for susceptibility loci on chromosomes 3q, 7p, and 18q. The 7p locus may represent a type 2 diabetes susceptibility locus.

Interaction effect of PTEN and CDKN1B chromosomal regions on prostate cancer linkage

The tumor suppressor functions of PTEN and CDKN1B have been extensively characterized. Recent data from mouse models suggest that, for some organs, the combined action of both PTEN and CDKN1B has a stronger tumor suppressor function than each alone; for the prostate, heterozygous knockout of both genes leads to 100% penetrance for prostate cancer. To assess whether such an interaction contributes to an increased risk of prostate cancer in humans, we performed a series of epistatic PTEN and CDKN1B interaction analyses in a collection of 188 high-risk hereditary prostate cancer families. Two different analytical approaches were performed; a nonparametric linkage (NPL) regression analysis that simultaneously models allele sharing at these two regions in all families, and an ordered subset analysis (OSA) that assesses linkage evidence at a target region in a subset of families based on the magnitude of allele sharing at the reference region. The strongest evidence of interaction effect was observed at 10q23-24 and 12p11-13 from both the NPL regression analysis (P = 0.0002) in all families and the OSA analyses in subsets of families. A LOD-delta of 3.15 (P = 0.01) was observed at 10q23-24 among 54 families with the highest NPL scores at 12p11-13, and a LOD-delta of 2.63 (P = 0.02) was observed at 12p11-13 among 34 families with the highest NPL scores at 10q23-24. The evidence for the interaction was stronger when using additional fine-mapping markers in the PTEN (10q23) and CDKN1B (12p13) regions. Our data are consistent with epistatic interactions between the PTEN and CDKN1B genes affecting risk for prostate cancer and demonstrate the utility of modeling epistatic effects in linkage analysis to detect susceptibility genes of complex diseases.

Increasing the power and efficiency of disease-marker case-control association studies through use of allele-sharing information

Case-control disease-marker association studies are often used in the search for variants that predispose to complex diseases. One approach to increasing the power of these studies is to enrich the case sample for individuals likely to be affected because of genetic factors. In this article, we compare three case-selection strategies that use allele-sharing information with the standard strategy that selects a single individual from each family at random. In affected sibship samples, we show that, by carefully selecting sibships and/or individuals on the basis of allele sharing, we can increase the frequency of disease-associated alleles in the case sample. When these cases are compared with unrelated controls, the difference in the frequency of the disease-associated allele is therefore also increased. We find that, by choosing the affected sib who shows the most evidence for pairwise allele sharing with the other affected sibs in families, the test statistic is increased by >20%, on average, for additive models with modest genotype relative risks. In addition, we find that the per-genotype information associated with the allele sharing-based strategies is increased compared with that associated with random selection of a sib for genotyping. Even though we select sibs on the basis of a nonparametric statistic, the additional gain for selection based on the unknown underlying mode of inheritance is minimal. We show that these properties hold even when the power to detect linkage to a region in the entire sample is negligible. This approach can be extended to more-general pedigree structures and quantitative traits.

A genome-wide scan for type 2 diabetes in African-American families reveals evidence for a locus on chromosome 6q

African Americans are at increased risk of type 2 diabetes and many diabetes complications. We have carried out a genome-wide scan for African American type 2 diabetes using 638 affected sibling pairs (ASPs) from 247 families ascertained through impaired renal function to identify type 2 diabetes loci in this high-risk population. Of the 638 ASPs, 210 were concordant for diabetes with impaired renal function. A total of 390 markers, at an average spacing of 9 cM, were genotyped by the Center for Inherited Disease Research (CIDR) as part of the International Type 2 Diabetes Linkage Analysis Consortium. Nonparametric linkage (NPL) analyses conducted using the exponential model implemented in Genehunter Plus provided suggestive evidence for linkage at 6q24-q27 (163.5 cM, logarithm of odds [LOD] 2.26). Multilocus NPL regression analysis identified the 6q locus (D6S1035, LOD 2.67) and two additional regions: 7p (LOD 1.06) and 18q (LOD 0.87) as important in this model. NPL regression-based interaction analyses and ordered subset analyses (OSAs) supported the presence of a locus at chromosome 7p (29-34 cM) in the pedigrees with the earliest mean age of diagnosis of type 2 diabetes (P = 0.009 for interaction, DeltaP = 0.0034 for OSA) and lower mean BMI (P = 0.009 for interaction, DeltaP = 0.070 for OSA). These results provide evidence that genes predisposing African-American individuals to type 2 diabetes are located in the 6q and 7p regions of the genome.

Ordered subset analysis in genetic linkage mapping of complex traits

Etiologic heterogeneity is a fundamental feature of complex disease etiology; genetic linkage analysis methods to map genes for complex traits that acknowledge the presence of genetic heterogeneity are likely to have greater power to identify subtle changes in complex biologic systems. We investigate the use of trait-related covariates to examine evidence for linkage in the presence of heterogeneity. Ordered-subset analysis (OSA) identifies subsets of families defined by the level of a trait-related covariate that provide maximal evidence for linkage, without requiring a priori specification of the subset. We propose that examining evidence for linkage in the subset directly may result in a more etiologically homogeneous sample. In turn, the reduced impact of heterogeneity will result in increased overall evidence for linkage to a specific region and a more distinct lod score peak. In addition, identification of a subset defined by a specific trait-related covariate showing increased evidence for linkage may help refine the list of candidate genes in a given region and suggest a useful sample in which to begin searching for trait-associated polymorphisms. This method provides a means to begin to bridge the gap between initial identification of linkage and identification of the disease predisposing variant(s) within a region when mapping genes for complex diseases. We illustrate this method by analyzing data on breast cancer age of onset and chromosome 17q [Hall et al., 1990, Science 250:1684-1689]. We evaluate OSA using simulation studies under a variety of genetic models.

Nonparametric linkage regression. I: Combined Caucasian CSGA and German genome scans for asthma

In this paper we applied the nonparametric linkage (NPL) regression approach to the combined Caucasian genome scan data from the Collaborative Study on the Genetics of Asthma (CSGA) and German family studies to search for regions of the genome that exhibit evidence for linkage to putative asthma-predisposing genes. The multipoint single-locus model identified three regions of the genome with LOD scores of approximately two or greater. These regions were on 6p near D6S291 (LOD = 2.78), 2p near D2S2298 (LOD = 2.11), and 1p near D1S1597 (LOD = 1.92). Modeling multiple loci together and testing for interactions among loci yielded stronger evidence for linkage in these regions. We observed a potential epistatic interaction between 2p and 14p (p = 0.0003) and conditional LOD scores of 2.75 on 1p, 3.89 on 6p, 1.64 on 7p, and 1.64 on 15q.