Selection of genetic markers for association analyses,using linkage disequilibrium and haplotypes

The genotyping of closely spaced single-nucleotide polymorphism (SNP) markers frequently yields highly correlated data, owing to extensive linkage disequilibrium (LD) between markers. The extent of LD varies widely across the genome and drives the number of frequent haplotypes observed in small regions. Several studies have illustrated the possibility that LD or haplotype data could be used to select a subset of SNPs that optimize the information retained in a genomic region while reducing the genotyping effort and simplifying the analysis. We propose a method based on the spectral decomposition of the matrices of pairwise LD between markers, and we select markers on the basis of their contributions to the total genetic variation. We also modify Clayton's "haplotype tagging SNP" selection method, which utilizes haplotype information. For both methods, we propose sliding window-based algorithms that allow the methods to be applied to large chromosomal regions. Our procedures require genotype information about a small number of individuals for an initial set of SNPs and selection of an optimum subset of SNPs that could be efficiently genotyped on larger numbers of samples while retaining most of the genetic variation in samples. We identify suitable parameter combinations for the procedures, and we show that a sample size of 50-100 individuals achieves consistent results in studies of simulated data sets in linkage equilibrium and LD. When applied to experimental data sets, both procedures were similarly effective at reducing the genotyping requirement while maintaining the genetic information content throughout the regions. We also show that haplotype-association results that Hosking et al. obtained near CYP2D6 were almost identical before and after marker selection.     

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.     

Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses

Although tocopherols play an important role in plants and animals, the genetic architecture of tocopherol content in maize kernels has remained largely unknown. In this study, linkage and association analyses were conducted to examine the genetic architecture of tocopherol content in maize kernels. Forty‐one unique quantitative trait loci (QTLs) were identified by linkage mapping in six populations of recombinant inbred lines (RILs). In addition, 32 significant loci were detected via genome‐wide association study (GWAS), 18 of which colocalized with the QTLs identified by linkage mapping. Fine mapping of a major QTL validated the accuracy of GWAS and QTL mapping results and suggested a role for nontocopherol pathway genes in the modulation of natural tocopherol variation. We provided genome‐wide evidence that genes involved in fatty acid metabolism, chlorophyll metabolism and chloroplast function may affect natural variation in tocopherols. These findings were confirmed through mutant analysis of a particular gene from the fatty acid pathway. In addition, the favourable alleles for many of the significant SNPs/QTLs represented rare alleles in natural populations. Together, our results revealed many novel genes that are potentially involved in the variation of tocopherol content in maize kernels. Pyramiding of the favourable alleles of the newly elucidated genes and the well‐known tocopherol pathway genes would greatly improve tocopherol content in maize.     

On averaging power for genetic association and linkage studies

A power calculation is crucial in planning genetic studies. In genetic association studies, the power is often calculated using the expected number of individuals with each genotype calculated from an assumed allele frequency under Hardy-Weinberg equilibrium. Since the allele frequency is often unknown, the number of individuals with each genotype is random and so a power calculation assuming a known allele frequency may be incorrect. Ambrosius et al. recently showed that the power ignoring this randomness may lead to studies with insufficient power and proposed averaging the power due to the randomness. We extend the method of averaging power in two directions. First, for testing association in case-control studies, we use the Cochran-Armitage trend test and find that the time needed for calculating the averaged power is much reduced compared to the chi-square test with two degrees of freedom studied by Ambrosius et al. A real study is used for illustration of the method. Second, we extend the method to linkage analysis, where the number of identical-by-descent alleles shared by siblings is random. The distribution of identical-by-descent numbers depends on the underlying genetic model rather than the allele frequency. The robust test for linkage analysis is also examined using the averaged powers. We also recommend a sensitivity analysis when the true allele frequency or the number of identical-by-descent alleles is unknown.     

Genetic linkage and association analyses for trait mapping in Plasmodium falciparum

Genetic studies of Plasmodium falciparum laboratory crosses and field isolates have produced valuable insights into determinants of drug responses, antigenic variation, disease virulence, cellular development and population structures of these virulent human malaria parasites. Full-genome sequences and high-resolution haplotype maps of SNPs and microsatellites are now available for all 14 parasite chromosomes. Rapidly increasing genetic and genomic information on Plasmodium parasites, mosquitoes and humans will combine as a rich resource for new advances in our understanding of malaria, its transmission and its manifestations of disease.     

SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci

The authors want to changed the web link of the software platform in this Briefing.Page 97,section "INTRODUCTION",the web link of SHEsis is changed from http://www.nhgg.org/analysis tohttp://analysis.bio-x.cn     

SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci

In multiloci-based genetic association studies of complex diseases, a powerful and high efficient tool for analyses of linkage disequilibrium (LD) between markers, haplotype distributions and many chi-square/p values with a large number of samples has been sought for long. In order to achieve the goal of obtaining meaningful results directly from raw data,we developed a robust and user-friendly software platform with a series of tools for analysis in association study with high efficiency. The platform has been well evaluated by several sets of real data.     

Genetic polymorphism of inter-alpha-trypsin-inhibitor (ITI): Formal genetic and linkage analyses

Summary The polymorphism of inter-alpha-trypsin-inhibitor, ITI, was demonstrated by isoelectric focusing in agarose gels (pH 5–8) followed by protein blotting and immunoassay. Segregation in 239 families with 677 children is consistent with the formal hypothesis that there are two common codominant alleles, ITI^*1, ITI^*2, and one rare codominant allele, ITI^*3, at an autosomal locus ITI. Allele frequencies were calculated as ITI^*1=0.600, ITI^*2=0.393, ITI^*3=0.007. Linkage analysis with 36 markers is presented. Slightly positive lod scores were obtained for PGM3 (z=1,35, =0.10) and AK1 (z=1.34, =0.10).     

Doubled haploids for estimating genetic variances in presence of linkage and gene association

Summary The effect of gene association (or dispersion) and linkage on the estimation of genetic variances in a diallel experiment involving doubled haploid lines is evaluated. It is shown that the estimates of the additive and the additive X additive genetic variances, as obtained by Choo et al. (1979), are biased if genes are linked or are not independently distributed in the parents. However, this bias only occurs in the presence of interaction between homozygous loci. Gene association (or dispersion) and linkage, if present, can be detected by comparing the parental vs the crosses mean, the parental vs the doubled haploid lines variance, and the among vs the within crosses variance.     

Genetic loci for blood lipid levels identified by linkage and association analyses in Caribbean Hispanics

To identify genetic loci influencing blood lipid levels in Caribbean Hispanics, we first conducted a genome-wide linkage scan in 1,211 subjects from 100 Dominican families on five lipid quantitative traits: total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglycerides (TG), and LDL-C/HDL-C ratio. We then investigated the association between blood lipid levels and 21,361 single nucleotide polymorphisms (SNP) under the 1-logarithm of odds (LOD) unit down regions of linkage peaks in an independent community-based subcohort (N = 814, 42% Dominican) from the Northern Manhattan Study (NOMAS). We found significant linkage evidence for LDL-C/HDL-C on 7p12 (multipoint LOD = 3.91) and for TC on 16q23 (LOD = 3.35). In addition, we identified suggestive linkage evidence of LOD > 2.0 on 15q23 for TG, 16q23 for LDL-C, 19q12 for TC and LDL-C, and 20p12 for LDL-C. In the association analysis of the linkage peaks, we found that seven SNPs near FLJ45974 were associated with LDL-C/HDL-C with a nominal P < 3.5 × 10(-5), in addition to associations (P < 0.0001) for other lipid traits with SNPs in or near CDH13, SUMF2, TLE3, FAH, ARNT2, TSHZ3, ZNF343, RPL7AL2, and TMC3. Further studies are warranted to perform in-depth investigations of functional genetic variants in these regions.     

Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium

Common genetic polymorphisms may explain a portion of the heritable risk for common diseases. Within candidate genes, the number of common polymorphisms is finite, but direct assay of all existing common polymorphism is inefficient, because genotypes at many of these sites are strongly correlated. Thus, it is not necessary to assay all common variants if the patterns of allelic association between common variants can be described. We have developed an algorithm to select the maximally informative set of common single-nucleotide polymorphisms (tagSNPs) to assay in candidate-gene association studies, such that all known common polymorphisms either are directly assayed or exceed a threshold level of association with a tagSNP. The algorithm is based on the r(2) linkage disequilibrium (LD) statistic, because r(2) is directly related to statistical power to detect disease associations with unassayed sites. We show that, at a relatively stringent r(2) threshold (r2>0.8), the LD-selected tagSNPs resolve >80% of all haplotypes across a set of 100 candidate genes, regardless of recombination, and tag specific haplotypes and clades of related haplotypes in nonrecombinant regions. Thus, if the patterns of common variation are described for a candidate gene, analysis of the tagSNP set can comprehensively interrogate for main effects from common functional variation. We demonstrate that, although common variation tends to be shared between populations, tagSNPs should be selected separately for populations with different ancestries.     

Refinement of the 6p21.3 quantitative trait locus influencing dyslexia: linkage and association analyses

Reading disability (RD), or dyslexia, is the most common learning disability with a prevalence rate of ~5%–10% in school-age children. RD is highly heritable with evidence of a neurobiological origin. Linkage studies have identified several quantitative trait loci (QTLs) for RD. The QTL on chromosome 6p21.3 has been independently replicated by several groups and spans a 16.4-Mb (13.8 cM) interval from D6S109 to D6S291. In this study, we performed sib-pair linkage analyses with Haseman–Elston and DeFries–Fulker methods to define more accurately the QTL interval. Linkage was assessed by using five quantitative phenotypes, including a composite measure of reading performance and four component phenotypes. When probands were selected for severe scores, single- and multi-point analyses showed significant linkage with all five phenotypes, converging over an interval of ~3.24 Mb spanning D6S1597 to D6S1571. Maximal linkage converged at marker D6S1554 across phenotypes. Out of 12 genes in the linkage interval, ten clustered within ~680 kb and were selected for association analysis based on central nervous system expression and putative function. Marker-trait associations were assessed by using QTDT (a general test of association for quantitative traits) and the family-based association test (FBAT), and haplotype analysis was performed by using FBAT and the GeneHunter Transmission/Disequilibrium Test TDT. Marker associations were detected in five of the ten genes, results that were corroborated by our haplotype TDT analysis. The results of the association study have thereby allowed us to significantly reduce the number of possible candidate genes and to prioritize genes for further mutation screening.     

Optimized group sequential study designs for tests of genetic linkage and association in complex diseases

The study of genetic linkage or association in complex traits requires large sample sizes, as the expected effect sizes are small and extremely low significance levels need to be adopted. One possible way to reduce the numbers of phenotypings and genotypings is the use of a sequential study design. Here, average sample sizes are decreased by conducting interim analyses with the possibility to stop the investigation early if the result is significant. We applied optimized group sequential study designs to the analysis of genetic linkage (one-sided mean test) and association (two-sided transmission/disequilibrium test). For designs with two and three stages at overall significance levels of.05 and.0001 and a power of.8, we calculated necessary sample sizes, time points, and critical boundaries for interim and final analyses. Monte Carlo simulation analyses were performed to confirm the validity of the asymptotic approximation. Furthermore, we calculated average sample sizes required under the null and alternative hypotheses in the different study designs. It was shown that the application of a group sequential design led to a maximal increase in sample size of 8% under the null hypothesis, compared with the fixed-sample design. This was contrasted by savings of up to 20% in average sample sizes under the alternative hypothesis, depending on the applied design. These savings affect the amounts of genotyping and phenotyping required for a study and therefore lead to a significant decrease in cost and time.     

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.     

Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits

SUMMARY: A website for performing power calculations for the design of linkage and association genetic mapping studies of complex traits. AVAILABILITY: The package is made available athttp://statgen.iop.kcl.ac.uk/gpc/.     

Designing new microsatellite markers for linkage and population genetic analyses in rhesus macaques and other nonhuman primates

Identification of polymorphic microsatellite loci in nonhuman primates is useful for various biomedical and evolutionary studies of these species. Prior methods for identifying microsatellites in nonhuman primates are inefficient. We describe a new strategy for marker development that uses the available whole genome sequence for rhesus macaques. Fifty-four novel rhesus-derived microsatellites were genotyped in large pedigrees of rhesus monkeys. Linkage analysis was used to place 51 of these loci into the existing rhesus linkage map. In addition, we find that microsatellites identified this way are polymorphic in other Old World monkeys such as baboons. This approach to marker development is more efficient than previous methods and produces polymorphisms with known locations in the rhesus genome assembly. Finally, we propose a nomenclature system that can be used for rhesus-derived microsatellites genotyped in any species or for novel loci derived from the genome sequence of any nonhuman primate.     

Genome-wide association and linkage analyses localize a progressive retinal atrophy locus in Persian cats

Hereditary eye diseases of animals serve as excellent models of human ocular disorders and assist in the development of gene and drug therapies for inherited forms of blindness. Several primary hereditary eye conditions affecting various ocular tissues and having different rates of progression have been documented in domestic cats. Gene therapy for canine retinopathies has been successful, thus the cat could be a gene therapy candidate for other forms of retinal degenerations. The current study investigates a hereditary, autosomal recessive, retinal degeneration specific to Persian cats. A multi-generational pedigree segregating for this progressive retinal atrophy was genotyped using a 63 K SNP array and analyzed via genome-wide linkage and association methods. A multi-point parametric linkage analysis localized the blindness phenotype to a ~1.75 Mb region with significant LOD scores (Z ≈ 14, θ = 0.00) on cat chromosome E1. Genome-wide TDT, sib-TDT, and case–control analyses also consistently supported significant association within the same region on chromosome E1, which is homologous to human chromosome 17. Using haplotype analysis, a ~1.3 Mb region was identified as highly associated for progressive retinal atrophy in Persian cats. Several candidate genes within the region are reasonable candidates as a potential causative gene and should be considered for molecular analyses.