A statistical method for identification of polymorphisms that explain a linkage result

G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies

In a large-scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed-size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes related individuals, power for association can be gained by including partial information, such as phenotype data of ungenotyped relatives, in the analysis, and this should be taken into account when assessing whom to sequence. We propose G-STRATEGY, which uses simulated annealing to choose a subset of individuals for sequencing that maximizes the expected power for association. In simulations, G-STRATEGY performs extremely well for a range of complex disease models and outperforms other strategies with, in many cases, relative power increases of 20-40% over the next best strategy, while maintaining correct type 1 error. G-STRATEGY is computationally feasible even for large datasets and complex pedigrees. We apply G-STRATEGY to data on high-density lipoprotein and low-density lipoprotein from the AGES-Reykjavik and REFINE-Reykjavik studies, in which G-STRATEGY is able to closely approximate the power of sequencing the full sample by selecting for sequencing a only small subset of the individuals.

Combined linkage and family-based association analysis improves candidate gene detection in Genetic Analysis Workshop 18 simulation data

Because the genotype-phenotype correlation information is investigated differently by linkage and association analyses, various efforts have been made to model linkage and association jointly. However, joint modeling methods are usually computationally intensive; hence they cannot currently accommodate large pedigrees with dense markers. This article proposes a simple method to combine the linkage and association evidence with the aim of improving the detection power of disease susceptibility genes. Our detection power comparisons show that the combined linkage-association p values can improve remarkably the causal gene detection power in Genetic Analysis Workshop 18 simulation data.

Complex pedigrees in the sequencing era: to track transmissions or decorrelate?

Next-generation sequencing (NGS) studies are becoming commonplace, and the NGS field is continuing to develop rapidly. Analytic methods aimed at testing for the various roles that genetic susceptibility plays in disease are also rapidly being developed and optimized. Studies that incorporate large, complex pedigrees are of particular importance because they provide detailed information about inheritance patterns and can be analyzed in a variety of complementary ways. The nine contributions from our Genetic Analysis Workshop 18 working group on family-based tests of association for rare variants using simulated data examined analytic methods for testing genetic association using whole-genome sequencing data from 20 large pedigrees with 200 phenotype simulation replicates. What distinguishes the approaches explored is how the complexities of analyzing familial genetic data were handled. Here, we explore the methods that either harness inheritance patterns and transmission information or attempt to adjust for the correlation between family members in order to utilize computationally and conceptually simpler statistical testing procedures. Although directly comparing these two classes of approaches across contributions is difficult, we note that the two classes balance robustness to population stratification and computational complexity (the transmission-based approaches) with simplicity and increased power, assuming no population stratification or proper adjustment for it (decorrelation approaches).

Developmental dysplasia of the hip: linkage mapping and whole exome sequencing identify a shared variant in CX3CR1 in all affected members of a large multigeneration family

Developmental dysplasia of the hip (DDH) is a debilitating condition characterized by incomplete formation of the acetabulum leading to dislocation of the femur, suboptimal joint function, and accelerated wear of the articular cartilage resulting in arthritis. DDH affects 1 in 1000 newborns in the United States; there are well-defined "pockets" of high prevalence in Japan, and in Italy and other Mediterranean countries. Although reasonably accurate for detecting gross forms of hip dysplasia, existing techniques fail to find milder forms of dysplasia. Undetected hip dysplasia is the leading cause of osteoarthritis of the hip in young individuals, causing over 40% of cases in this age group. A sensitive and specific test for DDH has remained a desirable yet elusive goal in orthopedics for a long time. A 72-member, four-generation affected family has been recruited, and DNA from its members retrieved. Genomewide linkage analysis revealed a 2.61-Mb candidate region (38.7-41.31 Mb from the p term of chromosome 3) co-inherited by all affected members with a maximum logarithm (base 10) of odds (LOD) score of 3.31. Whole exome sequencing and analysis of this candidate region in four severely affected family members revealed one shared variant, rs3732378, that causes a threonine (polar) to methionine (non-polar) alteration at position 280 in the transmembrane domain of CX3CR1. This mutation is predicted to have a deleterious effect on its encoded protein, which functions as a receptor for the ligand fractalkine. By Sanger sequencing this variant was found to be present in the DNA of all affected individuals and obligate heterozygotes. CX3CR1 mediates cellular adhesive and migratory functions and is known to be expressed in mesenchymal stem cells destined to become chondrocytes. A genetic risk factor that might be among the etiologic factors for the family in this study has been identified, along with other possible aggravating mutations shared by four severely affected family members. These findings might illuminate the molecular pathways affecting chondrocyte maturation and bone formation.

Variants in CPT1A, FADS1, and FADS2 are Associated with Higher Levels of Estimated Plasma and Erythrocyte Delta-5 Desaturases in Alaskan Eskimos

The delta-5 and delta-6 desaturases (D5D and D6D), encoded by fatty acid desaturase 1 (FADS1) and 2 (FADS2) genes, respectively, are rate-limiting enzymes in the metabolism of ω-3 and ω-6 fatty acids. The objective of this study was to identify genes influencing variation in estimated D5D and D6D activities in plasma and erythrocytes in Alaskan Eskimos (n = 761) participating in the genetics of coronary artery disease in Alaska Natives (GOCADAN) study. Desaturase activity was estimated by product: precursor ratio of polyunsaturated fatty acids. We found evidence of linkage for estimated erythrocyte D5D (eD5D) on chromosome 11q12-q13 (logarithm of odds score = 3.5). The confidence interval contains candidate genes FADS1, FADS2, 7-dehydrocholesterol reductase (DHCR7), and carnitine palmitoyl transferase 1A, liver (CPT1A). Measured genotype analysis found association between CPT1A, FADS1, and FADS2 single-nucleotide polymorphisms (SNPs) and estimated eD5D activity (p-values between 10⁻²⁸ and 10⁻⁵). A Bayesian quantitative trait nucleotide analysis showed that rs3019594 in CPT1A, rs174541 in FADS1, and rs174568 in FADS2 had posterior probabilities > 0.8, thereby demonstrating significant statistical support for a functional effect on eD5D activity. Highly significant associations of FADS1, FADS2, and CPT1A transcripts with their respective SNPs (p-values between 10⁻⁷⁵ and 10⁻⁷) in Mexican Americans of the San Antonio Family Heart Study corroborated our results. These findings strongly suggest a functional role for FADS1, FADS2, and CPT1A SNPs in the variation in eD5D activity.

A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach.

Family-based designs for genome-wide association studies

Association mapping has successfully identified common SNPs associated with many diseases. However, the inability of this class of variation to account for most of the supposed heritability has led to a renewed interest in methods - primarily linkage analysis - to detect rare variants. Family designs allow for control of population stratification, investigations of questions such as parent-of-origin effects and other applications that are imperfectly or not readily addressed in case-control association studies. This article guides readers through the interface between linkage and association analysis, reviews the new methodologies and provides useful guidelines for applications. Just as effective SNP-genotyping tools helped to realize the potential of association studies, next-generation sequencing tools will benefit genetic studies by improving the power of family-based approaches.

Evidence that multiple genetic variants of MC4R play a functional role in the regulation of energy expenditure and appetite in Hispanic children

BACKGROUND

Melanocortin-4-receptor (MC4R) haploinsufficiency is the most common form of monogenic obesity; however, the frequency of MC4R variants and their functional effects in general populations remain uncertain.

OBJECTIVE

The aim was to identify and characterize the effects of MC4R variants in Hispanic children.

DESIGN

MC4R was resequenced in 376 parents, and the identified single nucleotide polymorphisms (SNPs) were genotyped in 613 parents and 1016 children from the Viva la Familia cohort. Measured genotype analysis (MGA) tested associations between SNPs and phenotypes. Bayesian quantitative trait nucleotide (BQTN) analysis was used to infer the most likely functional polymorphisms influencing obesity-related traits.

RESULTS

Seven rare SNPs in coding and 18 SNPs in flanking regions of MC4R were identified. MGA showed suggestive associations between MC4R variants and body size, adiposity, glucose, insulin, leptin, ghrelin, energy expenditure, physical activity, and food intake. BQTN analysis identified SNP 1704 in a predicted micro-RNA target sequence in the downstream flanking region of MC4R as a strong, probable functional variant influencing total, sedentary, and moderate activities with posterior probabilities of 1.0. SNP 2132 was identified as a variant with a high probability (1.0) of exerting a functional effect on total energy expenditure and sleeping metabolic rate. SNP rs34114122 was selected as having likely functional effects on the appetite hormone ghrelin, with a posterior probability of 0.81.

CONCLUSION

This comprehensive investigation provides strong evidence that MC4R genetic variants are likely to play a functional role in the regulation of weight, not only through energy intake but through energy expenditure.

Evaluation of approaches to identify associated SNPs that explain the linkage evidence in nuclear families with affected siblings

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

Common and rare variants of DAOA in bipolar disorder

The D-amino acid oxidase activator (DAOA, previously known as G72) gene, mapped on 13q33, has been reported to be genetically associated with bipolar disorder (BP) in several populations. The consistency of associated variants is unclear and rare variants in exons of the DAOA gene have not been investigated in psychiatric diseases. We employed a conditional linkage method-STatistical Explanation for Positional Cloning (STEPC) to evaluate whether any associated single nucleotide polymorphisms (SNPs) account for the evidence of linkage in a pedigree series that previously has been linked to marker D13S779 at 13q33. We also performed an association study in a sample of 376 Caucasian BP parent-proband trios by genotyping 38 common SNPs in the gene region. Besides, we resequenced coding regions and flanking intronic sequences of DAOA in 555 Caucasian unrelated BP patients and 564 mentally healthy controls, to identify putative functional rare variants that may contribute to disease. One SNP rs1935058 could "explain" the linkage signal in the family sample set (P = 0.055) using STEPC analysis. No significant allelic association was detected in an association study by genotyping 38 common SNPs in 376 Caucasian BP trios. Resequencing identified 53 SNPs, of which 46 were novel SNPs. There was no significant excess of rare variants in cases relative to controls. Our results suggest that DAOA does not have a major effect on BP susceptibility. However, DAOA may contribute to bipolar susceptibility in some specific families as evidenced by the STEPC analysis.

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.

Genome-wide association studies: implications for multiethnic samples

The current gene mapping for complex diseases is heavily weighted by studies of population samples from northern Europe. To capture the full range of genetic diversity and exploit the potential of genetic epidemiology to identify important variants, multiple additional populations will need to be examined. The conduct of genome-wide association studies will therefore confront many of the challenges identified in the first generation of candidate gene and linkage studies, with a substantial increase in complexity. Initial efforts to map causal effects will have to take account of varying patterns of linkage disequilibrium through careful attention to local haplotype structure. Refined statistical techniques that permit joint analyses of samples from multiple populations will also be required, as well as improved methods to account for on-going gene flow between populations with geographically distinct ancestral origins. This variation can either be an impediment, slowing the process of replication, or an opportunity, allowing finer dissection of the relevant variants. Clinical translation of these data will present major challenges. Large cosmopolitan populations, such as those found in large urban centers, are likely to exhibit both known and cryptic sub-structure across groups, as well as admixture within individuals. Great care will need to be devoted to generalizability of association findings to avoid their premature adoption as predictive tests in the face of this widespread heterogeneity.

A composite-likelihood approach for identifying polymorphisms that are potentially directly associated with disease

If a linkage signal can be fully accounted for by the association of a particular polymorphism with the disease, this polymorphism may be the sole causal variant in the region. On the other hand, if the linkage signal exceeds that explained by the association, different or additional directly associated loci must exist in the region. Several methods have been proposed for testing the hypothesis that association with a particular candidate single-nucleotide polymorphism (SNP) can explain an observed linkage signal. When several candidate SNPs exist, all of the existing methods test the hypothesis for each candidate SNP separately, by fitting the appropriate model for each individual candidate SNP. Here we propose a method that combines analyses of two or more candidate SNPs using a composite-likelihood approach. We use simulations to demonstrate that the proposed method can lead to substantial power increases over the earlier single SNP analyses.

Does APOE explain the linkage of Alzheimer's disease to chromosome 19q13?

We have studied the impact of the apolipoprotein E gene (APOE) on the chromosome 19 linkage peak from an analysis of sib-pairs affected by Alzheimer's disease. We genotyped 417 affected sib-pairs (ASPs) collected in Sweden and Norway (SWE), the UK and the USA for 10 microsatellite markers on chromosome 19. The highest Zlr (3.28, chromosome-wide P-value 0.036) from the multipoint linkage analysis was located approximately 1 Mb from APOE, at marker D19S178. The linkage to chromosome 19 was well explained by APOE in the whole sample as well as in the UK and USA subsamples, as identity by descent (IBD) increased with the number of epsilon4 alleles in ASPs. There was a suggestion from the SWE subsample that linkage was higher than would be expected from APOE alone, although the test for this did not reach formal statistical significance. There was also a significant age at onset (aao) effect on linkage to chromosome 19q13 in the whole sample, which manifested itself as increased IBD sharing in relative pairs with lower mean aao. This effect was partially, although not completely, explained by APOE. The aao effect varied considerably between the different subsamples, with most of the effect coming from the UK sample. The other samples showed smaller effects in the same direction, but these were not significant.

Using linkage and association to identify and model genetic effects: summary of GAW15 Group 4

Group 4 at Genetic Analysis Workshop 15 focused on methods that exploited both linkage and association information to map disease loci. All contributions considered the dichotomous trait of rheumatoid arthritis, using either affected sibpairs and/or unrelated controls. While one contribution investigated linkage and association approaches separately in genome-wide analyses, the remaining others focused on joint linkage and association methods in specific genomic regions. The latter contributions proposed new methods and/or examined existing methods that addressed whether one or more polymorphisms partially or fully explained a linkage signal, particularly the methods proposed by Li et al. that are implemented in the computer program Linkage and Association Modeling in Pedigrees (LAMP). Using simulated SNP data under linkage peaks, several contributions found that existing family-based association approaches such as those of Martin et al. and Lake et al. had power similar to LAMP and to several methods proposed by the contributors for testing that a single nucleotide polymorphism partially explains a linkage peak. In evaluating methods for identifying if a polymorphism or a set of polymorphisms fully accounted for a linkage signal, several contributions found that it was important to understand that these methods may be subject to low power in some situations and thus, a non-significant result was not necessarily indicative of the polymorphism(s) being fully responsible for the linkage signal. Finally, modeling the disease using association evidence conditional on linkage may improve understanding of the etiology of disease.

Exploring causality via identification of SNPs or haplotypes responsible for a linkage signal

In a small chromosomal region, a number of polymorphisms may be both linked to and associated with a disease. Distinguishing the potential causal sites from those indirectly associated due to linkage disequilibrium (LD) with a causal site is an important problem. This problem may be approached by determining which of the associations can explain the observed linkage signal. Recently, several methods have been proposed to aid in the identification of disease associated polymorphisms that may explain an observed linkage signal, using genotype data from affected sib pairs (ASPs) [Li et al. [2005] Am. J. Hum. Genet. 76:934–949; Sun et al. [2002] Am. J. Hum. Genet. 70:399–411]. These methods can be used to test the null hypothesis that a candidate single nucleotide polymorphism (SNP) is the sole causal variant in the region, or is in complete LD with the sole causal variant in the region. We extend variations of these methods to test for complete LD between a disease locus and haplotypes composed of two or more tightly linked candidate SNPs. We study properties of the proposed methods by simulation and apply them to type 1 diabetes data for ASPs and their parents at candidate SNP and microsatellite marker loci in the Insulin (INS) gene region. Genet. Epidemiol. 31:2727–740, 2007. © 2007 Wiley-Liss, Inc.

Searching for additional disease loci in a genomic region

Our aim is to review methods to optimize detection of all disease genes in a genetic region. As a starting point, we assume there is sufficient evidence from linkage and/or association studies, based on significance levels or replication studies, for the involvement in disease risk of the genetic region under study. For closely linked markers, there will often be multiple associations with disease, and linkage analyses identify a region rather than the specific disease-predisposing gene. Hence, the first task is to identify the primary (major) disease-predisposing gene or genes in a genetic region, and single nucleotide polymorphisms thereof, that is, how to distinguish true associations from those that are just due to linkage disequilibrium with the actual disease-predisposing variants. Then, how do we detect additional disease genes in this genetic region? These two issues are of course very closely interrelated. No existing programs, either individually or in aggregate, can handle the magnitude and complexity of the analyses needed using currently available methods. Further, even with modern computers, one cannot study every possible combination of genetic markers and their haplotypes across the genome, or even within a genetic region. Although we must rely heavily on computers, in the final analysis of multiple effects in a genetic region and/or interaction or independent effects between unlinked genes, manipulation of the data by the individual investigator will play a crucial role. We recommend a multistrategy approach using a variety of complementary methods described below.

Joint linkage and association analysis for identification of potentially causal polymorphisms in GAW15 data

In a small chromosomal region, a number of polymorphisms may be both linked to and associated with a disease. Potentially directly associated causal loci may be distinguished from indirectly associated loci by determining which associations can explain the observed linkage signal. We apply methods for testing whether association with a particular polymorphism or haplotype can explain an observed linkage signal to the Genetic Analysis Workshop 15 simulated (Problem 3) data, to try to identify potentially causal polymorphisms. We compare the power of several methods for testing the null hypothesis that association with a particular variant can explain the observed linkage signal, and discuss scenarios under which the various methods may be advantageous.

A new score statistic to test for association given linkage in affected sibling pair-control designs

To detect association of the DR1 allele with rheumatoid arthritis (RA) given linkage in the affected sibling pairs of the replicates of Problem 3 of Genetic Analysis Workshop 15 (GAW15), we propose a new score statistic that takes into account the linkage information. We knew the answers. Linkage studies are often followed by case-control association studies of candidate genes located under the peak to identify the causes of a linkage peak. One strategy is to type the affected sibling pairs from the original linkage study and a set of unrelated controls for single-nuclear polymorphisms describing the genetic variation of these genes. For this affected sibling pair-control design, we propose a relative-risk model for the relationship between the disease outcomes of sibling pairs and their genotypes and identity-by-descent status at the locus of interest. From this model, we derive a score statistic to analyze genetic association given linkage. We compare the performance of the new statistic to the method of Li et al. and to a standard association analysis that neglects the information on the identity-by-descent status of the sibling pair. We conclude that for the GAW15 data the new method performs well and that methods that use the linkage information may be more efficient than standard comparisons of genotypes in cases and controls.

No evidence for multiple loci affecting rheumatoid arthritis risk on chromosome 6p21

The influence of certain alleles of the HLA-DRB1 locus on risk for rheumatoid arthritis has been well established through linkage and association studies. In addition, other loci in the HLA region on 6p21 may also affect an individual's risk profile. Here, we used a method to detect excess identity-by-descent sharing between affected sib pairs conditional on the observed genotypes at the hypothesized causal locus to test for the presence of additional arthritis risk loci in the linked region. We used affected sib pairs from two different studies. Because the test depends heavily on specifying accurate allele frequency estimates at the proposed causal locus, we used HLA-DRB1 allele frequency estimates from a large, population-based sample. We also discuss an alternate form of the test in which we could condition on parental genotypes, thereby eliminating the need for actual allele frequencies. The test showed no evidence for the presence of additional arthritis risk loci in the region in the British or North American samples made available for Genetic Analysis Workshop 15. Given the prior knowledge that there likely are arthritis risk loci other than HLA-DRB1 in the region, it appears the tests may have inadequate power to detect the presence of these loci in certain cases.

Case-control association testing with related individuals: a more powerful quasi-likelihood score test

We consider the problem of genomewide association testing of a binary trait when some sampled individuals are related, with known relationships. This commonly arises when families sampled for a linkage study are included in an association study. Furthermore, power to detect association with complex traits can be increased when affected individuals with affected relatives are sampled, because they are more likely to carry disease alleles than are randomly sampled affected individuals. With related individuals, correlations among relatives must be taken into account, to ensure validity of the test, and consideration of these correlations can also improve power. We provide new insight into the use of pedigree-based weights to improve power, and we propose a novel test, the MQLS test, which, as we demonstrate, represents an overall, and in many cases, substantial, improvement in power over previous tests, while retaining a computational simplicity that makes it useful in genomewide association studies in arbitrary pedigrees. Other features of the MQLS are as follows: (1) it is applicable to completely general combinations of family and case-control designs, (2) it can incorporate both unaffected controls and controls of unknown phenotype into the same analysis, and (3) it can incorporate phenotype data about relatives with missing genotype data. The methods are applied to data from the Genetic Analysis Workshop 14 Collaborative Study of the Genetics of Alcoholism, where the MQLS detects genomewide significant association (after Bonferroni correction) with an alcoholism-related phenotype for four different single-nucleotide polymorphisms: tsc1177811 (P=5.9x10(-7)), tsc1750530 (P=4.0x10(-7)), tsc0046696 (P=4.7x10(-7)), and tsc0057290 (P=5.2x10(-7)) on chromosomes 1, 16, 18, and 18, respectively. Three of these four significant associations were not detected in previous studies analyzing these data.

Design and analysis of genetic association studies to finely map a locus identified by linkage analysis: assessment of the extent to which an association can account for the linkage

Association studies are often used to finely map quantitative trait loci identified by linkage analysis. Once a polymorphism associated with the trait has been identified, it may be useful to conduct linkage analyses which adjust for this polymorphism to determine the extent to which the association accounts for the linkage signal. However, methods for conducting statistical significance tests for an observed reduction in the linkage signal are not well developed. In the present study we develop methods for assessment of the statistical significance of an observed reduction in the variance due to the linked locus, with variance components or with Haseman-Elston linkage methods. Simulations indicate that these methods have appropriate levels of type I error and that, like other association statistics, their power depends on the magnitude of linkage disequilibrium between functional and marker alleles and on the extent of similarity between the frequency of the functional allele and the frequency of the associated marker allele. These methods can help determine which association results are likely due to strong linkage disequilibrium with functional alleles and, thus, can facilitate the selection of small chromosomal regions for more extensive study.

Calpains and their multiple roles in diabetes mellitus

Type 2 diabetes mellitus (T2DM) can lead to death without treatment and it has been predicted that the condition will affect 215 million people worldwide by 2010. T2DM is a multifactorial disorder whose precise genetic causes and biochemical defects have not been fully elucidated, but at both levels, calpains appear to play a role. Positional cloning studies mapped T2DM susceptibility to CAPN10, the gene encoding the intracellular cysteine protease, calpain 10. Further studies have shown a number of noncoding polymorphisms in CAPN10 to be functionally associated with T2DM while the identification of coding polymorphisms, suggested that mutant calpain 10 proteins may also contribute to the disease. Here we review recent studies, which in addition to the latter enzyme, have linked calpain 5, calpain 3, and its splice variants, calpain 2 and calpain 1 to T2DM-related metabolic pathways along with T2DM-associated phenotypes, such as obesity and impaired insulin secretion, and T2DM-related complications, such as epithelial dysfunction and diabetic cataract.

Residual linkage: why do linkage peaks not disappear after an association study?

Family-based candidate gene and genome-wide association studies are a logical progression from linkage studies for the identification of gene and polymorphisms underlying complex traits. An efficient way to analyse phenotypic and genotypic data is to model linkage and association simultaneously. An important result from such an analysis is whether any evidence for linkage remains after fitting polymorphisms at candidate genes (residual linkage), because this may indicate locus and allelic heterogeneity in the population and will influence subsequent molecular strategies. Here we report that substantial residual linkage is to be expected, even under genetic homogeneity and when the underlying causal polymorphisms are genotyped and fitted in the model. We simulated a powerful design to detect linkage to quantitative trait loci, with 5, 10 or 20 causal SNPs spread throughout the genome. These SNPs were responsible for all genetic variation, and hence for both linkage and association. Residual linkage at the largest linkage peak from a genome-wide scan was substantial, with mean LOD scores of 0.4, 0.7, and 1.4 for the case of 5, 10 and 20 underlying causal SNPs, respectively. For less powerful designs, the proportion of the original LOD scores that remains after association will be even larger. All cases of 'significant' residual linkage are false positives. The reason for the apparent paradox of detecting residual linkage after fitting causal polymorphisms is that the linkage signals at the largest peaks in a genome-scan are severely inflated, even if all peaks correspond to true linkage. Our findings are general and apply to linkage mapping of any phenotype and to any pedigree structure.

HLA-G: an asthma gene on chromosome 6p

We identified HLA-G as an asthma susceptibility gene in multiple populations and demonstrated that variation in this gene influences subsequent risk for asthma. Prenatal exposure to factors that are correlated with maternal BHR (or perhaps BHR itself) interacts with fetal genotype to determine risk, however. Among fetuses of unaffected mothers, the +1489TT genotype is a marker for increased risk, whereas among fetuses of affected mothers the +1489CC genotype is a marker for increased risk. Studies are underway to understand the mechanism for this interaction and the role of this gene in the pathogenesis of asthma.

Characterization of allelic variants at chromosome 15q14 in schizophrenia

Evidence of genetic linkage for schizophrenia at chromosome 15q14 has been reported in nine independent studies, but the molecular variants responsible for transmission of genetic risk are unknown. National Institute of Mental Health Schizophrenia Genetics Initiative families were genotyped for single nucleotide polymorphisms (SNPs) and dinucleotide repeat markers in the 15q14 linkage region and analyzed based on the presence of particular alleles of the dinucleotide repeat marker D15S165 in the 15q14 region. Two alleles showed both familial transmission disequilibrium and population-wide association with schizophrenia. The two groups identified by these two D15S165 alleles differ in age of onset, number of hospitalizations and intensity of nicotine abuse, as well as in predominant ethnicity. Variations in the frequency of SNPs in CHRNA7, the alpha-7-nicotinic acetylcholine receptor subunit gene at 15q14, were found in each group. Further sequencing in these two groups may yield more definitive identification of the molecular pathology.

A comparison of methods for intermediate fine mapping

The arrival of highly dense genetic maps at low cost has geared the focus of linkage analysis studies toward developing methods for placing putative trait loci in narrow regions with high confidence. This shift has led to a new analytic scheme that expands the traditional two-stage protocol of preliminary genome scan followed by fine mapping through inserting a new stage in between the two. The goal of this new "intermediate" fine mapping stage is to isolate disease loci to narrow intervals with high confidence so that association studies can be more focused, efficient, and cost-effective. In this paper, we compared and contrasted five methods that can be used for performing this intermediate step. These methods are: the lod support approach, the generalized estimating equations (GEE) method, the confidence set inference (CSI) procedure, and two bootstrap methods. We compared these methods in terms of the coverage probability and precision of localization of the resulting intervals. Results from a simulation study considering several two-locus models demonstrated that the two bootstrap methods yield intervals with approximately correct coverage. On the other hand, the 1-lod support intervals, and those produced by the GEE method, tend to significantly undercover the trait locus, while the regions obtained by the CSI incline to overcover the gene position. When the observed coverage of the confidence intervals produced by all the methods was held to be the same, those obtained through the CSI procedure displayed a higher ability to localize loci, especially when these loci have a minor contribution to the trait and when the amount of data available for the analysis is relatively small. However, with very large sample sizes, lod support intervals emerged as a winner. Application of the methods to the data from the Arthritis Research Campaign National Repository led to intervals containing the position of a known trait locus for all methods, with the greatest precision achieved by the CSI.

The linkage and association of the gene encoding upstream stimulatory factor 1 with type 2 diabetes and metabolic syndrome in the Chinese population

AIMS/HYPOTHESIS

The transcription factor upstream stimulatory factor 1 (USF1) regulates the expression of genes involved in glucose and lipid metabolism and has been associated with familial combined hyperlipidaemia. USF1 is located on chromosome 1q22-23, a region with evidence for linkage to type 2 diabetes and various traits of the metabolic syndrome in Chinese and other populations. The aim of this study was to investigate the linkage and association of USF1 with type 2 diabetes and the metabolic syndrome in Chinese individuals.

MATERIALS AND METHODS

We genotyped three haplotype-tagging single nucleotide polymorphisms (SNPs) (rs3737787, rs2516841 and rs2516839) at USF1 in three samples of the Hong Kong Chinese population, including members of 179 families from the Hong Kong Family Diabetes Study, 1,383 hospital cases with type 2 diabetes and/or the metabolic syndrome and 454 normal control subjects.

RESULTS

We found significant association of individual polymorphisms and haplotypes with type 2 diabetes and/or metabolic syndrome-related traits in the family samples using either family-based or unrelated normal control subjects. However, these variants could not explain much of the evidence for linkage in this region. Moreover, they were not associated with type 2 diabetes and/or the metabolic syndrome in the hospital cases.

CONCLUSIONS/INTERPRETATION

The results are consistent with the hypothesis that variation at USF1 contributes to the risk of type 2 diabetes and the metabolic syndrome in families with strong evidence for linkage in the chromosome 1q region. However, they provide little support for USF1 as the susceptibility locus that generates the observed evidence for linkage at 1q21-25 for type 2 diabetes and/or the metabolic syndrome, and USF1 does not appear to have a major contribution to these phenotypes in the general Chinese population.

Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal

Once genetic linkage has been identified for a complex disease, the next step is often association analysis, in which single-nucleotide polymorphisms (SNPs) within the linkage region are genotyped and tested for association with the disease. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained, in part or in full, by the candidate SNP. We propose a novel approach that quantifies the degree of linkage disequilibrium (LD) between the candidate SNP and the putative disease locus through joint modeling of linkage and association. We describe a simple likelihood of the marker data conditional on the trait data for a sample of affected sib pairs, with disease penetrances and disease-SNP haplotype frequencies as parameters. We estimate model parameters by maximum likelihood and propose two likelihood-ratio tests to characterize the relationship of the candidate SNP and the disease locus. The first test assesses whether the candidate SNP and the disease locus are in linkage equilibrium so that the SNP plays no causal role in the linkage signal. The second test assesses whether the candidate SNP and the disease locus are in complete LD so that the SNP or a marker in complete LD with it may account fully for the linkage signal. Our method also yields a genetic model that includes parameter estimates for disease-SNP haplotype frequencies and the degree of disease-SNP LD. Our method provides a new tool for detecting linkage and association and can be extended to study designs that include unaffected family members.

Fine mapping and positional candidate studies identify HLA-G as an asthma susceptibility gene on chromosome 6p21

Asthma affects nearly 14 million people worldwide and has been steadily increasing in frequency for the past 50 years. Although environmental factors clearly influence the onset, progression, and severity of this disease, family and twin studies indicate that genetic variation also influences susceptibility. Linkage of asthma and related phenotypes to chromosome 6p21 has been reported in seven genome screens, making it the most replicated region of the genome. However, because many genes with individually small effects are likely to contribute to risk, identification of asthma susceptibility loci has been challenging. In this study, we present evidence from four independent samples in support of HLA-G as a novel asthma and bronchial hyperresponsiveness susceptibility gene in the human leukocyte antigen region on chromosome 6p21, and we speculate that this gene might contribute to risk for other inflammatory diseases that show linkage to this region.

Testing association and linkage using affected-sib-parent study designs

We have developed a method for jointly testing linkage and association using data from affected sib pairs and their parents. We specify a conditional logistic regression model with two covariates, one that quantifies association (either direct association or indirect association via linkage disequilibrium), and a second that quantifies linkage. The latter covariate is computed based on expected identity-by-descend (ibd) sharing of marker alleles between siblings. In addition to a joint test of linkage and association, our general framework can be used to obtain a linkage test comparable to the mean test (Blackwelder and Elston [1985] Genet. Epidemiol. 2:85-97), and an association test comparable to the Family-Based Association Test (FBAT; Rabinowitz and Laird [2000] Hum. Hered. 50:211-223). We present simulation results demonstrating that our joint test can be more powerful than some standard tests of linkage or association. For example, with a relative risk of 2.7 per variant allele at a disease locus, the estimated power to detect a nearby marker with a modest level of LD was 58.1% by the mean test (linkage only), 69.8% by FBAT, and 82.5% by our joint test of linkage and association. Our model can also be used to obtain tests of linkage conditional on association and association conditional on linkage, which can be helpful in fine mapping.

Genome-wide association study identifies ITGB3 as a QTL for whole blood serotonin

Serotonin has been implicated in common disorders involving the central nervous, gastrointestinal, cardiovascular, and pulmonary systems. We describe the first genome-wide screen to identify quantitative trait loci (QTLs) influencing whole blood serotonin in 567 members of a single large pedigree, using a novel association-based mapping approach. We identified an association between the beta3 integrin (ITGB3) Leu33Pro polymorphism on 17q21 and whole blood serotonin levels (P-value = 9.8 x 10(-5)). This variant explained the evidence for linkage in this region when included as a covariate in the linkage analysis (change in LOD from 1.87 to 0.16), indicating that ITGB3 may be an important serotonin QTL.

Identifying the susceptibility gene(s) in a set of trait-linked genes using genotype data

There are generally three steps to isolate a disease linkage-susceptibility gene: genome-wide scan, fine mapping, and, last, positional cloning. The last step is time consuming and involves intensive laboratory work. In some cases, fine mapping cannot proceed further on a set of markers because they are tightly linked. For years, genetic statisticians have been trying different ways to narrow the fine-mapping results to provide some guidance for the next step of laboratory work. Although these methods are practical and efficient, most of them are based on IBD data, which usually can be inferred only from the genotype data with some uncertainty. The corresponding methods thus have no greater power than one using genotype data directly. Also, IBD-based methods apply only to relative pair data. Here, using genotype data, we have developed a statistical hypothesis-testing method to pinpoint a SNP, or SNPs, suspected of responsibility for a disease trait linkage among a set of SNPs tightly linked in a region. Our method uses genotype data of affected individuals or case-control studies, which are widely available in the laboratory. The testing statistic can be constructed using any genotype-based disease-marker disequilibrium measure and is asymptotically distributed as a chi-square mixture. This method can be used for singleton data, relative pair data, or general pedigree data. We have applied the method to simulated data as well as a real data set; it gives satisfactory results.

Assessing whether an allele can account in part for a linkage signal: the Genotype-IBD Sharing Test (GIST)

To fine map genes, investigators often test for disease-marker association in chromosomal regions with evidence for linkage. Given a marker allele tentatively associated with disease, one would ask if this allele, or one in linkage disequilibrium (LD) with it, could account in part for the observed linkage signal. This question can be addressed by determining if families selected on the basis of the presence of the tentatively associated allele show stronger evidence of linkage as measured by increased allele sharing identical by descent (IBD) by affected family members. However, common selection strategies can be biased for or against linkage in the marker region, even given no disease-marker association. We define unbiased selection schemes and extend the definition to allow weighted selection on the basis of all genotyped family members. For affected-sibship data, we describe three genotype-based weight variables, corresponding to dominant, recessive, and additive models. We then introduce a test for association of a family weight variable with excess IBD sharing. This test allows us to determine if the linkage signal in a region can be attributed in part to the presence of a marker allele, either because of direct involvement in disease etiology or because of LD with a predisposing genetic variant. For samples of 500 affected sib pairs, the tests are powerful in detection of genotype-IBD sharing association, even for disease models with sib relative risk as low as lambda S=1.1, or when evidence for linkage is absent because of sampling variation. This makes our method a new tool for detecting linkage as well as association, especially in regions harboring a candidate gene. We have implemented these methods in the software package GIST (Genotype-IBD Sharing Test).

Positional identification of hypertension susceptibility genes on chromosome 2

Chromosome 2 has been consistently identified as a genomic region with genetic linkage evidence suggesting that one or more loci contributes to blood pressure and hypertension status. As with all complex disease traits, following-up linkage evidence to identify the underlying susceptibility gene(s) is an arduous yet biologically and clinically important task. Using combined positional candidate gene methods, the Family Blood Pressure Program (FBPP) has concentrated efforts in narrowing a large region of chromosome 2, demonstrating evidence for linkage in several populations, and identifying underlying candidate hypertension susceptibility gene(s). Initial informatics efforts identified the boundaries of the region and the known genes within it. A total of 82 polymorphic sites in 8 genes were genotyped in a large hypothesis-generating sample consisting of 1640 African Americans, 1339 whites, and 1616 Mexican Americans. After resampling-based false discovery adjustment, SLC4A5, a sodium bicarbonate transporter, was identified as a primary candidate gene for hypertension. Polymorphisms in SLC4A5 were subsequently genotyped and analyzed for validation in two other subcomponents of the FBPP, each contributing African Americans (N=461; N=778) and whites (N=550; N=967). Again, single nucleotide polymorphisms within this gene were significantly associated with blood pressure levels and hypertension status. Although not identifying a single causal gene variant that is significantly associated with blood pressure levels and hypertension status across all samples, the results further implicate SLC4A5 as a candidate hypertension susceptibility gene. Moreover, the present study validates previous evidence for one or more genes on chromosome 2 that influence hypertension-related phenotypes in the population-at-large.

Findings in an independent sample support an association between bipolar affective disorder and the G72/G30 locus on chromosome 13q33

Markers near the nested genes G72 and G30 on chromosome 13q33 have been implicated in the etiology of schizophrenia and, recently, bipolar affective disorder (BPAD). Hattori et al (2003) reported that single-nucleotide polymorphisms (SNPs) near the G72/G30 locus were associated with BPAD in a sample of 22 pedigrees, and that SNP haplotypes were associated in a second, larger sample of triads. The present study attempts to replicate this finding in an independent case-control sample. Six SNPs near the G72/G30 locus, including the most strongly associated markers in the previous study, were tested in 139 cases and 113 ethnically matched controls. Significant association was detected between BPAD and two adjacent SNPs (smallest P=0.007; global P=0.024). Haplotype analysis produced additional support for association (smallest P=0.004; global P=0.004). Analysis of 31 unlinked microsatellite markers detected no population stratification in the cases or controls studied. Although the associated alleles and haplotypes differ from those previously reported, these new results provide further evidence, in an independent sample, for an association between BPAD and genetic variation in the vicinity of the genes G72 and G30.

GAD2 on chromosome 10p12 is a candidate gene for human obesity

The gene GAD2 encoding the glutamic acid decarboxylase enzyme (GAD65) is a positional candidate gene for obesity on Chromosome 10p11-12, a susceptibility locus for morbid obesity in four independent ethnic populations. GAD65 catalyzes the formation of gamma-aminobutyric acid (GABA), which interacts with neuropeptide Y in the paraventricular nucleus to contribute to stimulate food intake. A case-control study (575 morbidly obese and 646 control subjects) analyzing GAD2 variants identified both a protective haplotype, including the most frequent alleles of single nucleotide polymorphisms (SNPs) +61450 C>A and +83897 T>A (OR = 0.81, 95% CI [0.681-0.972], p = 0.0049) and an at-risk SNP (-243 A>G) for morbid obesity (OR = 1.3, 95% CI [1.053-1.585], p = 0.014). Furthermore, familial-based analyses confirmed the association with the obesity of SNP +61450 C>A and +83897 T>A haplotype (chi(2) = 7.637, p = 0.02). In the murine insulinoma cell line betaTC3, the G at-risk allele of SNP -243 A>G increased six times GAD2 promoter activity (p < 0.0001) and induced a 6-fold higher affinity for nuclear extracts. The -243 A>G SNP was associated with higher hunger scores (p = 0.007) and disinhibition scores (p = 0.028), as assessed by the Stunkard Three-Factor Eating Questionnaire. As GAD2 is highly expressed in pancreatic beta cells, we analyzed GAD65 antibody level as a marker of beta-cell activity and of insulin secretion. In the control group, -243 A>G, +61450 C>A, and +83897 T>A SNPs were associated with lower GAD65 autoantibody levels (p values of 0.003, 0.047, and 0.006, respectively). SNP +83897 T>A was associated with lower fasting insulin and insulin secretion, as assessed by the HOMA-B% homeostasis model of beta-cell function (p = 0.009 and 0.01, respectively). These data support the hypothesis of the orexigenic effect of GABA in humans and of a contribution of genes involved in GABA metabolism in the modulation of food intake and in the development of morbid obesity.

Association and haplotype analysis of the insulin-degrading enzyme (IDE) gene, a strong positional and biological candidate for type 2 diabetes susceptibility

The gene for insulin-degrading enzyme (IDE) represents a strong positional and biological candidate for type 2 diabetes susceptibility. IDE maps to chromosome 10q23.3, a region linked to diabetes in several populations; the rat homolog has been directly implicated in diabetes susceptibility; and known functions of IDE support an important role in glucose homeostasis. We sought evidence for association between IDE variation and diabetes by mutation screening, defining local haplotype structure, and genotyping variants delineating common haplotypic diversity. An initial case-control analysis (628 diabetic probands from multiplex sibships and 604 control subjects) found no haplotypic associations, although one variant (IDE2, -179T-->C) showed modest association with diabetes (odds ratio [OR]1.25, P = 0.03). Linkage partitioning analyses failed to support this association, but provided borderline evidence for a different variant (IDE10, IVS20-405A-->G) (P = 0.06). Neither variant was associated with diabetes when replication was sought in 377 early onset diabetic subjects and 825 control subjects, though combined analysis of all typed cohorts indicated a nominally significant effect at IDE2 (OR 1.21 [1.04-1.40], P = 0.013). In the absence of convincing support for this association from linkage partitioning or analyses of continuous measures of glycemia, we conclude that analysis of over 2,400 samples provides no compelling evidence that variation in IDE contributes to diabetes susceptibility in humans.

Liver pyruvate kinase polymorphisms are associated with type 2 diabetes in northern European Caucasians

Pyruvate kinase is a key glycolytic enzyme. Isoforms that are expressed in the red cell, liver, pancreatic beta-cells, small intestine, and proximal renal tubule are encoded by the 12 exons of the PKLR gene, which maps to chromosome 1q23. We hypothesized that common variants of the PKLR gene could account for the linkage of diabetes to this region. We screened the promoter regions, exons and surrounding introns, and the 3' untranslated region for mutations. We identified five single-nucleotide polymorphisms (SNPs), and only one (V506I, exon 11) altered the coding sequence. We tested the five SNPs, a poly-T insertion-deletion polymorphism, and an ATT triplet repeat in 131 unrelated diabetic patients and 118 nondiabetic control subjects. The V506I variant was rare and not associated with type 2 diabetes. The four SNPs and the insertion-deletion polymorphism were associated with diabetes, with a 10% difference between individuals with diabetes and nondiabetic individuals (P = 0.001-0.011, relative risk for minor allele 1.85). The same trend was found for the ATT repeat (P = 0.029). Common variants in the PKLR are associated with increased risk of type 2 diabetes, but because of strong linkage disequilibrium between variants, the actual susceptibility allele may be in a different gene.

Genetic approaches to the molecular understanding of type 2 diabetes

The appreciation that individual susceptibility to type 2 diabetes (T2D) and related components of the dysmetabolic syndrome has a strong inherited component provides a coherent framework within which to develop a molecular understanding of the pathogenesis of T2D. This review focuses on the main approaches currently adopted by researchers seeking to identify the inherited basis of T2D and the present state of our knowledge. One central theme that emerges is that progress in defining the genetic basis of the common, multifactorial forms of T2D is hindered by etiological heterogeneity: T2D is likely to represent the final common pathway of diverse interacting primary disturbances. Such heterogeneity equally compromises efforts to understand the basis for T2D by use of other approaches, such as cellular biochemistry and classical physiology. Analyses that seek to ally sophisticated physiological characterization with measures of genomic variation are likely to provide powerful tools for redressing the loss of power associated with such heterogeneity.