Mapping genes for polygenic disorders: considerations for study design in the complex trait of inflammatory bowel disease

While the methodology for the mapping of Mendelian disorders is well established, the practical and theoretical steps required for successful gene identification in a complex trait are still difficult to predict. A number of analytical models and simulations based on repetitive drawings from predefined statistical distributions are available. To supplement these analytical models, we developed an integrated simulation approach by directly simulating entire populations under a disease model based on epidemiological data. Random mating, nonoverlapping populations and the absence of differential fitness were assumed. Samples were drawn from these homogeneous and heterogeneous populations and analyzed with established analysis tools. We investigated the properties of linkage and association studies in inflammatory bowel disease - modeled as a six-locus polygenic disorder - as an example of this approach. In nonparametric linkage studies, lod scores varied widely, with the median required sample size depending on the locus-specific relative sibling risk. A fine mapping resolution <4 cM was found to require nonparametric lod scores >10. Family-based association studies (TDT test) and case-control studies showed a similar sensitivity and can identify risk loci in populations with moderate levels of linkage disequilibrium in sample sizes of 500-800 triplets. Case-control association studies were prone to false-positive results if applied in heterogeneous populations, with the false-positive rate increasing with sample size because population heterogeneity is detected with increasing power.     

Probabilistic graphical models for genetic association studies

Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.     

STRP Screening Sets for the human genome at 5 cM density

Background  

Short tandem repeat polymorphisms (STRPs) are powerful tools for gene mapping and other applications. A STRP genome scan of 10 cM is usually adequate for mapping single gene disorders. However mapping studies involving genetically complex disorders and especially association (linkage disequilibrium) often require higher STRP density.     

Score tests of genetic association in the presence of linkage based on the additive genetic gamma frailty model

Nuclear families with multiple affected sibs are often collected for genetic linkage analysis of complex diseases. Once linkage evidence is established, dense markers are often typed in the linked region for genetic association analysis based on linkage disequilibrium (LD). Detection of association in the presence of linkage localizes disease genes more accurately than the methods that rely on linkage alone. However, test of association due to LD in the linked region needs to account for dependency of the allele transmissions to different sibs within a family. In this paper, we define a joint model for genetic linkage and association and derive the corresponding joint survival function of age of onset for the sibs within a sibship. The joint survival function is a function of both the inheritance vector and the genotypes at the candidate marker locus. Based on this joint survival function, we derive score tests for genetic association. The proposed methods utilize the phenotype data of all the sibs and have the advantages of family-based designs which can avoid the potential spurious association caused by population admixture. In addition, the methods can account for variable age of onset or age at censoring and possible covariate effects, and therefore provide important tools for modelling disease heterogeneity. Simulation studies and application to the data sets from the 12th Genetic Analysis Workshop indicate that the proposed methods have correct type 1 error rates and increased power over other existing methods for testing allelic association.     

FamPipe: An Automatic Analysis Pipeline for Analyzing Sequencing Data in Families for Disease Studies

In disease studies, family-based designs have become an attractive approach to analyzing next-generation sequencing (NGS) data for the identification of rare mutations enriched in families. Substantial research effort has been devoted to developing pipelines for automating sequence alignment, variant calling, and annotation. However, fewer pipelines have been designed specifically for disease studies. Most of the current analysis pipelines for family-based disease studies using NGS data focus on a specific function, such as identifying variants with Mendelian inheritance or identifying shared chromosomal regions among affected family members. Consequently, some other useful family-based analysis tools, such as imputation, linkage, and association tools, have yet to be integrated and automated. We developed FamPipe, a comprehensive analysis pipeline, which includes several family-specific analysis modules, including the identification of shared chromosomal regions among affected family members, prioritizing variants assuming a disease model, imputation of untyped variants, and linkage and association tests. We used simulation studies to compare properties of some modules implemented in FamPipe, and based on the results, we provided suggestions for the selection of modules to achieve an optimal analysis strategy. The pipeline is under the GNU GPL License and can be downloaded for free at http://fampipe.sourceforge.net.

This is a PLOS Computational Biology Software article.

     

PLINK: a tool set for whole-genome association and population-based linkage analyses

Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.     

Genetic analysis of the maximum drinks phenotype

Using data provided by the Collaborative Study on the Genetics of Alcoholism we studied the genetics of a quantitative trait: the maximum number of drinks consumed in a 24-hour period. A two-stage method was used. First, linkage analysis was performed, followed by association analysis in regions where linkage was detected. Additionally, the extent of linkage disequilibrium among single-nucleotide polymorphisms (SNP) associated with the phenotype was assessed. Linkage to chromosomes 2 and 7 was detected, and follow-up association analysis found multiple trait-associated SNPs in the chromosome 7 linkage region. Chromosome 4, which has been implicated in previous studies of the maximum drinks phenotype, did not pass our threshold for linkage evidence in stage 1, but secondary analyses of this chromosome indicated modest evidence for both linkage and association. The evidence suggests that chromosome 7 may harbor an additional locus influencing the maximum drinks consumption phenotype.     

Genomics to tree breeding and forest health

Genomic discovery in forest trees follows paradigms from both agricultural crop and livestock improvement and human medicine. Forest trees in a domesticated state can be improved using genomic-based breeding technologies, whereas the health of trees in a natural and undomesticated state might be managed using those same technologies. These applications begin by first dissecting complex traits in trees to their individual gene components and for that the association genetics approach is quite powerful in trees. This is true for several reasons including large, random mating, and unstructured populations and the rapid decay of linkage disequilibrium in many tree species. Once marker by trait associations are discovered, they can be used in genomic-based breeding and forest health diagnostics. Initial studies in trees have found ample nucleotide diversity in candidate genes to perform association studies and single nucleotide polymorphisms have been associated with economic and adaptive traits. Population genetic neutrality tests have been applied to identify genes probably under natural selection and thus make good candidates for developing forest health diagnostic tools.     

Editor's choice: High-Resolution Linkage and Quantitative Trait Locus Mapping Aided by Genome Survey Sequencing: Building Up An Integrative Genomic Framework for a Bivalve Mollusc

Genetic linkage maps are indispensable tools in genetic and genomic studies. Recent development of genotyping-by-sequencing (GBS) methods holds great promise for constructing high-resolution linkage maps in organisms lacking extensive genomic resources. In the present study, linkage mapping was conducted for a bivalve mollusc (Chlamys farreri) using a newly developed GBS method—2b-restriction site-associated DNA (2b-RAD). Genome survey sequencing was performed to generate a preliminary reference genome that was utilized to facilitate linkage and quantitative trait locus (QTL) mapping in C. farreri. A high-resolution linkage map was constructed with a marker density (3806) that has, to our knowledge, never been achieved in any other molluscs. The linkage map covered nearly the whole genome (99.5%) with a resolution of 0.41 cM. QTL mapping and association analysis congruously revealed two growth-related QTLs and one potential sex-determination region. An important candidate QTL gene named PROP1, which functions in the regulation of growth hormone production in vertebrates, was identified from the growth-related QTL region detected on the linkage group LG3. We demonstrate that this linkage map can serve as an important platform for improving genome assembly and unifying multiple genomic resources. Our study, therefore, exemplifies how to build up an integrative genomic framework in a non-model organism.     

A dense linkage map of hybrid cottonwood (Populus fremontii x P. angustifolia) contributes to long-term ecological research and comparison mapping in a model forest tree

Cottonwoods are foundation riparian species, and hybridization among species is known to produce ecological effects at levels higher than the population, including effects on dependent species, communities and ecosystems. Because these patterns result from increased genetic variation in key cottonwood traits, novel applications of genetic tools (for example, QTL mapping) could be used to place broad-scale ecological research into a genomic perspective. In addition, linkage maps have been produced for numerous species within the genus, and, coupled with the recent publication of the Populus genome sequence, these maps present a unique opportunity for genome comparisons in a model system. Here, we conducted linkage analyses in order to (1) create a platform for QTL and candidate gene studies of ecologically important traits, (2) create a framework for chromosomal-scale perspectives of introgression in a natural population, and (3) enhance genome-wide comparisons using two previously unmapped species. We produced 246 backcross mapping (BC(1)) progeny by crossing a naturally occurring F(1) hybrid (Populus fremontii x P. angustifolia) to a pure P. angustifolia from the same population. Linkage analysis resulted in a dense linkage map of 541 AFLP and 111 SSR markers distributed across 19 linkage groups. These results compared favorably with other Populus linkage studies, and addition of SSR loci from the poplar genome project provided coarse alignment with the genome sequence. Preliminary applications of the data suggest that our map represents a useful framework for applying genomic research to ecological questions in a well-studied system, and has enhanced genome-wide comparisons in a model tree.     

Polymorphic GGC repeats in the androgen receptor gene are associated with hereditary and sporadic prostate cancer risk

Androgen receptor (AR) has long been hypothesized to play an important role in prostate cancer etiology. Two trinucleotide repeat polymorphisms (CAG and GGC repeats in exon 1 of the AR gene) have been investigated as risk factors for prostate cancer in several studies. However, the results are inconclusive, probably because of the variations of study designs, characteristics of study samples, and choices of analytical methods. In this study, we evaluated evidence for linkage and association between the two AR repeats and prostate cancer by using the following comprehensive approaches: (1) a combination of linkage and association studies, (2) a test for linkage by parametric analysis and the male-limited X-linked transmission/disequilibrium test (XLRC-TDT), (3) a test for association by using both population-based and family-based tests, and (4) a study of both hereditary and sporadic cases. A positive but weak linkage score (HLOD=0.49, P=0.12) was identified in the AR region by parametric analysis; however, stronger evidence for linkage in the region, especially at the GGC locus, was observed in the subset of families whose proband had < or = 16 GGC repeats (HLOD=0.70, P=0.07) or by using XLRC-TDT ( z'=2.65, P=0.008). Significantly increased frequencies of the < or = 16 GGC repeat alleles in 159 independent hereditary cases (71%) and 245 sporadic cases (68%) cases compared with 211 controls (59%) suggested that GGC repeats were associated with prostate cancer ( P=0.02). Evidence for the association between the < or = 16 GGC repeats and prostate cancer risk was stronger with XLRC-TDT ( z'=2.66, P=0.007). No evidence for association between the CAG repeats and prostate cancer risk was observed. The consistent results from both linkage and association studies strongly implicate the GGC repeats in the AR as a prostate cancer susceptibility gene. Further studies on this polymorphism in other independent data sets and functional analysis of the GGC repeat length on AR activity are warranted.     

Disease association mapping in Drosophila can be replicated in the wild

Association and linkage mapping have become important tools in understanding the genetics of complex traits, including diseases in humans. As the success of association mapping is reduced by small effect sizes and limited power, linkage studies in laboratory-based model systems are still heavily used. But whether the results of these studies can be replicated in natural populations has been questioned. Here, we show that a polymorphism in the gene ref(2)P, which had previously been linked to sigma virus resistance in Drosophila melanogaster under laboratory conditions, also provides resistance against the virus in female flies in a wild population in the field. This genetic association is thus upheld in spite of a known genotype-by-genotype interaction and environmental variation.     

关联分析及其在植物遗传学研究中的应用

植物的很多重要经济性状均属于复杂性状。基于连锁分析的QTL作图是研究复杂性状的有效手段, 但其尚存在一定的局限性。随着现代生物学的发展, 一种基于连锁不平衡的新剖分复杂性状方法--关联分析法, 开始应用于植物遗传学研究。与QTL作图法相比, 应用关联分析法具有不需要构建特殊的群体, 可同时对多个等位基因进行分析, 定位QTL精度可达到单基因水平等优势。该文介绍了关联分析方法学的基础和特性, 简述了其在植物遗传学研究中的进展情况, 并对其未来发展和在植物遗传学研究中的应用进行了展望。     

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.     

Linkage disequilibrium clustering‐based approach for association mapping with tightly linked genomewide data

Genomewide association studies (GWAS) aim to identify genetic markers strongly associated with quantitative traits by utilizing linkage disequilibrium (LD) between candidate genes and markers. However, because of LD between nearby genetic markers, the standard GWAS approaches typically detect a number of correlated SNPs covering long genomic regions, making corrections for multiple testing overly conservative. Additionally, the high dimensionality of modern GWAS data poses considerable challenges for GWAS procedures such as permutation tests, which are computationally intensive. We propose a cluster‐based GWAS approach that first divides the genome into many large nonoverlapping windows and uses linkage disequilibrium network analysis in combination with principal component (PC) analysis as dimensional reduction tools to summarize the SNP data to independent PCs within clusters of loci connected by high LD. We then introduce single‐ and multilocus models that can efficiently conduct the association tests on such high‐dimensional data. The methods can be adapted to different model structures and used to analyse samples collected from the wild or from biparental F₂ populations, which are commonly used in ecological genetics mapping studies. We demonstrate the performance of our approaches with two publicly available data sets from a plant (Arabidopsis thaliana) and a fish (Pungitius pungitius), as well as with simulated data.     

Testing for Linkage and Association Across the Dihydrolipoyl Dehydrogenase Gene Region with Alzheimer’s Disease in Three Sample Populations

Prior case-control studies from our laboratory of a population enriched with individuals of Ashkenazi Jewish descent suggested that association exists between Alzheimer's disease (AD) and the chromosomal region near the DLD gene, which encodes the mitochondrial dihydrolipoamide dehydrogenase enzyme. In support of this finding, we found that linkage analysis restricted to autopsy-proven patients in the National Institute of Mental Health-National Cell Repository for Alzheimer's Disease (NIMH-NCRAD) Genetics Initiative pedigree data resulted in point-wise significant evidence for linkage (minimum p-value = 0.024) for a marker position close to the DLD locus. We now report case-control replication studies in two independent Caucasian series from the US and Italy, as well as a linkage analysis from the NIMH-NCRAD Genetics Initiative Database. Pair-wise analysis of the SNPs in the case-control series indicated there was strong linkage disequilibrium across the DLD locus in these populations, as previously reported. These findings suggest that testing for association of complex diseases with DLD locus should have considerable statistical power. Analysis of multi-locus genotypes or haplotypes based upon three SNP loci combined with results from our previous report provided trends toward significant evidence of association of DLD with AD, although neither of the present studies' association showed significance at the 0.05 level. Combining linkage and association findings for all AD patients (males and females) results in a p-value that is more significant than any of the individual findings' p-values. Finally, minimum sample size calculations using parameters from the DLD locus suggest that sample sizes on the order of 1,000 total cases and controls are needed to detect association for a wide range of genetic model parameters.     

Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).

A population association has consistently been observed between insulin-dependent diabetes mellitus (IDDM) and the "class 1" alleles of the region of tandem-repeat DNA (5'' flanking polymorphism [5''FP]) adjacent to the insulin gene on chromosome 11p. This finding suggests that the insulin gene region contains a gene or genes contributing to IDDM susceptibility. However, several studies that have sought to show linkage with IDDM by testing for cosegregation in affected sib pairs have failed to find evidence for linkage. As means for identifying genes for complex diseases, both the association and the affected-sib-pairs approaches have limitations. It is well known that population association between a disease and a genetic marker can arise as an artifact of population structure, even in the absence of linkage. On the other hand, linkage studies with modest numbers of affected sib pairs may fail to detect linkage, especially if there is linkage heterogeneity. We consider an alternative method to test for linkage with a genetic marker when population association has been found. Using data from families with at least one affected child, we evaluate the transmission of the associated marker allele from a heterozygous parent to an affected offspring. This approach has been used by several investigators, but the statistical properties of the method as a test for linkage have not been investigated. In the present paper we describe the statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test [TDT]). We then show the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs. The TDT provides strong evidence for linkage between the 5''FP and susceptibility to IDDM. The conclusions from this analysis apply in general to the study of disease associations, where genetic markers are usually closely linked to candidate genes. When a disease is found to be associated with such a marker, the TDT may detect linkage even when haplotype-sharing tests do not.     

Individual specific DNA fingerprints from a hypervariable region probe: alpha-globin 3′HVR

Summary A probe detecting a hypervariable region (HVR) 3 to the alpha globin locus on chromosome 16 has been used to produce DNA fingerprints. Segregation analysis has revealed multiple, randomly dispersed DNA fragments inherited in a Mendelian fashion with minimal allelism and linkage. The fingerprints are highly polymorphic (probability of chance association between random individuals «10^-14). The probe is, therefore, a powerful discriminating tool: it is envisaged that this probe will have forensic applications, including paternity cases, and will be informative in linkage analysis.     

不宁腿综合征遗传学研究进展

不宁腿综合征(Restless legs syndrome, RLS)遗传学研究近年来获得了许多重要的进展, 极大地丰富了对于这种疾病分子机制的认识。RLS是一种常见的复杂疾病, 几个遗传流行病学和双生子研究对RLS遗传组分进行了剖析, 说明RLS是一个遗传性很强的性状, 其遗传力约为50%。采用基于模型的连锁分析方法或者是不依赖于模型的连锁分析方法目前已定位了5个重要的RLS疾病连锁位点: 12q13-23, 14q13-21, 9p24-22, 2q33和20p13, 为定位克隆RLS致病基因或者易感基因提供了连锁图谱。最新基于高通量的SNPs分型平台开展的全基因组分析确立3个与RLS显著关联的区域: 6p21.2, 2p14和15q23。文章结合作者近年来从事不宁腿综合征遗传学的研究工作, 对该领域的重要成果进行了汇总和评述。