Parentage and Sibship Inference From Multilocus Genotype Data Under Polygamy

Likelihood methods have been developed to partition individuals in a sample into sibling clusters using genetic marker data without parental information. Most of these methods assume either both sexes are monogamous to infer full sibships only or only one sex is polygamous to infer full sibships and paternal or maternal (but not both) half sibships. We extend our previous method to the more general case of both sexes being polygamous to infer full sibships, paternal half sibships, and maternal half sibships and to the case of a two-generation sample of individuals to infer parentage jointly with sibships. The extension not only expands enormously the scope of application of the method, but also increases its statistical power. The method is implemented for both diploid and haplodiploid species and for codominant and dominant markers, with mutations and genotyping errors accommodated. The performance and robustness of the method are evaluated by analyzing both simulated and empirical data sets. Our method is shown to be much more powerful than pairwise methods in both parentage and sibship assignments because of the more efficient use of marker information. It is little affected by inbreeding in parents and is moderately robust to nonrandom mating and linkage of markers. We also show that individually much less informative markers, such as SNPs or AFLPs, can reach the same power for parentage and sibship inferences as the highly informative marker simple sequence repeats (SSRs), as long as a sufficient number of loci are employed in the analysis.     

Population genetics of autopolyploids under a mixed mating model and the estimation of selfing rate

Nowadays, the population genetics analysis of autopolyploid species faces many difficulties due to (i) limited development of population genetics tools under polysomic inheritance, (ii) difficulties to assess allelic dosage when genotyping individuals and (iii) a form of inbreeding resulting from the mechanism of ‘double reduction’. Consequently, few data analysis computer programs are applicable to autopolyploids. To contribute bridging this gap, this article first derives theoretical expectations for the inbreeding and identity disequilibrium coefficients under polysomic inheritance in a mixed mating model. Moment estimators of these coefficients are proposed when exact genotypes or just markers phenotypes (i.e. allelic dosage unknown) are available. This led to the development of estimators of the selfing rate based on adult genotypes or phenotypes and applicable to any even‐ploidy level. Their statistical performances and robustness were assessed by numerical simulations. Contrary to inbreeding‐based estimators, the identity disequilibrium‐based estimator using phenotypes is robust (absolute bias generally < 0.05), even in the presence of double reduction, null alleles or biparental inbreeding due to isolation by distance. A fairly good precision of the selfing rate estimates (root mean squared error < 0.1) is already achievable using a sample of 30–50 individuals phenotyped at 10 loci bearing 5–10 alleles each, conditions reachable using microsatellite markers. Diallelic markers (e.g. SNP) can also perform satisfactorily in diploids and tetraploids but more polymorphic markers are necessary for higher ploidy levels. The method is implemented in the software SPAGeDi and should contribute to reduce the lack of population genetics tools applicable to autopolyploids.     

Identification and characterization of microsatellites for striped bass from repeat-enriched libraries

Striped bass (Morone saxatilis) is economically important in the US due to its value as an aquaculture species and in supporting commercial and recreational fisheries, especially those off the Atlantic coast and in the Gulf of Mexico. Modern strategies for managing fishery populations and aquaculture broodstocks employ the use of molecular genetic markers to identify individuals, assign parentage, and characterize population genetic structure and levels of inbreeding and migration. As part of a collaborative effort to utilize molecular genetic technologies in striped bass breeding programs we generated microsatellite markers for use in population genetic studies, broodstock selection and management strategies, and the construction of a genetic map. We developed 345 new microsatellite markers for striped bass, a subset (n=71) of which was characterized by genotyping samples from two striped bass broodstock populations to evaluate marker polymorphism, percent heterozygosity, Hardy–Weinberg equilibrium (HWE), linkage disequilibrium (LD) and utility for population genetic studies.     

Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between single nucleotide polymorphisms

Effectiveness of marker-assisted selection (MAS) and quantitative trait locus (QTL) mapping using population-wide linkage disequilibrium (LD) between markers and QTLs depends on the extent of LD and how it declines with distance between markers and QTLs in a population. Marker-QTL LD can be predicted from LD between markers. Our previous work evaluated LD measures between multi-allelic markers as predictors of usable LD of multi-allelic markers with QTLs. Since single nucleotide polymorphisms (SNPs) are the current marker of choice for high-density genotyping and LD-mapping of QTLs, the objective of this study was to use LD between multi-allelic markers to predict LD among biallelic SNPs or between SNPs and QTLs. Observable LD between multi-allelic markers was evaluated using nine measures. These included two pooled and standardized measures of LD between pairs of alleles at two markers based on Lewontin's LD measure, two pooled measures of squared correlations between alleles, one standardized measure using Hardy-Weinberg heterozygosities, and four measures based on the chi-square statistic for testing for association between alleles at two loci. The standardized chi-square measure that best predicted usable LD between multi-allelic markers and QTLs, based on our previous work, overestimated usable SNP-SNP or SNP-QTL LD. Instead, three other measures were found to be good predictors of usable SNP-SNP or SNP-QTL LD when LD is generated by drift. Therefore, the LD measure between multi-allelic markers that is best for predicting usable LD in a population depends on the type of markers (i.e. multi-allelic or biallelic) that will eventually be used for QTL mapping or MAS.     

Equilibrium processes cannot explain high levels of short- and medium-range linkage disequilibrium in the domesticated grass Sorghum bicolor

Patterns of linkage disequilibrium (LD) are of interest because they provide evidence of both equilibrium (e.g., mating system or long-term population structure) and nonequilibrium (e.g., demographic or selective) processes, as well as because of their importance in strategies for identifying the genetic basis of complex phenotypes. We report patterns of short and medium range (up to 100 kb) LD in six unlinked genomic regions in the partially selfing domesticated grass, Sorghum bicolor. The extent of allelic associations in S. bicolor, as assessed by pairwise measures of LD, is higher than in maize but lower than in Arabidopsis, in qualitative agreement with expectations based on mating system. Quantitative analyses of the population recombination parameter, rho, however, based on empirical estimates of rates of recombination, mutation, and self-pollination, show that LD is more extensive than expected under a neutral equilibrium model. The disparity between rho and the population mutation parameter, , is similar to that observed in other species whose population history appears to be complex. From a practical standpoint, these results suggest that S. bicolor is well suited for association studies using reasonable numbers of markers, since LD typically extends at least several kilobases but has largely decayed by 15 kb.     

A new method for estimating effective population sizes from a single sample of multilocus genotypes

Equations for the effective size ( N_e ) of a population were derived in terms of the frequencies of a pair of offspring taken at random from the population being sibs sharing the same one or two parents. Based on these equations, a novel method (called sibship assignment method) was proposed to infer N_e from the sibship frequencies estimated from a sibship assignment analysis, using the multilocus genotypes of a sample of offspring taken at random from a single cohort in a population. Comparative analyses of extensive simulated data and some empirical data clearly demonstrated that the sibship assignment method is much more accurate [measured by the root mean squared error, RMSE, of 1/(2 N_e )] than other methods such as the heterozygote excess method, the linkage disequilibrium method, and the temporal method. The RMSE of 1/(2 N_e ) from the sibship assignment method is typically a small fraction of that from other methods. The new method is also more general and flexible than other methods. It can be applied to populations with nonoverlapping generations of both diploid and haplodiploid species under random or nonrandom mating, using either codominant or dominant markers. It can also be applied to the estimation of N_e for a subpopulation with immigration. With some modification, it could be applied to monoecious diploid populations with self-fertilization, and to populations with overlapping generations.     

A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value

Genomic selection uses genome-wide dense SNP marker genotyping for the prediction of genetic values, and consists of two steps: (1) estimation of SNP effects, and (2) prediction of genetic value based on SNP genotypes and estimates of their effects. For the former step, BayesB type of estimators have been proposed, which assume a priori that many markers have no effects, and some have an effect coming from a gamma or exponential distribution, i.e. a fat-tailed distribution. Whilst such estimators have been developed using Monte Carlo Markov chain (MCMC), here we derive a much faster non-MCMC based estimator by analytically performing the required integrations. The accuracy of the genome-wide breeding value estimates was 0.011 (s.e. 0.005) lower than that of the MCMC based BayesB predictor, which may be because the integrations were performed one-by-one instead of for all SNPs simultaneously. The bias of the new method was opposite to that of the MCMC based BayesB, in that the new method underestimates the breeding values of the best selection candidates, whereas MCMC-BayesB overestimated their breeding values. The new method was computationally several orders of magnitude faster than MCMC based BayesB, which will mainly be advantageous in computer simulations of entire breeding schemes, in cross-validation testing, and practical schemes with frequent re-estimation of breeding values.     

Cross-trait prediction accuracy of summary statistics in genome-wide association studies

In the era of big data, univariate models have widely been used as a workhorse tool for quickly producing marginal estimators; and this is true even when in a high-dimensional dense setting, in which many features are “true,” but weak signals. Genome-wide association studies (GWAS) epitomize this type of setting. Although the GWAS marginal estimator is popular, it has long been criticized for ignoring the correlation structure of genetic variants (i.e., the linkage disequilibrium [LD] pattern). In this paper, we study the effects of LD pattern on the GWAS marginal estimator and investigate whether or not additionally accounting for the LD can improve the prediction accuracy of complex traits. We consider a general high-dimensional dense setting for GWAS and study a class of ridge-type estimators, including the popular marginal estimator and the best linear unbiased prediction (BLUP) estimator as two special cases. We show that the performance of GWAS marginal estimator depends on the LD pattern through the first three moments of its eigenvalue distribution. Furthermore, we uncover that the relative performance of GWAS marginal and BLUP estimators highly depends on the ratio of GWAS sample size over the number of genetic variants. Particularly, our finding reveals that the marginal estimator can easily become near-optimal within this class when the sample size is relatively small, even though it ignores the LD pattern. On the other hand, BLUP estimator has substantially better performance than the marginal estimator as the sample size increases toward the number of genetic variants, which is typically in millions. Therefore, adjusting for the LD (such as in the BLUP) is most needed when GWAS sample size is large. We illustrate the importance of our results by using the simulated data and real GWAS.     

An Association Analysis of Reelin Gene (<Emphasis Type="Italic">RELN</Emphasis>) Polymorphisms with Childhood Epilepsy in Eastern Indian Population from West Bengal

Epilepsy is a common neurological condition characterized by unprovoked seizure attacks. Early brain developmental abnormalities involving neuronal migration and lamination are implicated in childhood epilepsy. Reelin, a neuronal-signaling molecule plays a crucial role in these migratory processes. Therefore, reelin gene (RELN), which is located on human chromosome 7q22 is considered as a potential candidate gene for childhood epilepsy. In this study, we recruited 63 patients with childhood-onset epilepsy and 103 healthy controls from West Bengal in India. Genomic DNA isolated from leukocytes of cases and control individuals were used for genotyping analysis of 16 markers of RELN. Case–control analysis revealed significant over-representation of G/C and (G/C+C/C) genotypes, and C allele of exon 22 G/C marker (rs362691) in cases as compared to controls. Pair-wise linkage disequilibrium analysis demonstrated two separate LD blocks with moderately high D′ values in epileptic cases. Based on these data, we have carried out haplotype case–control analysis. Even though we found over-representation of A-C haplotype of intron 12 A/C/exon 22 G/C markers and haplotype combination involving G-allele of exon 22 marker in cases and controls, respectively, the overall test was not significant. LD in this region involving this marker was also more robust in epileptic cases. Taken together, the results provide possible evidences for association of exon 22 G/C marker or any marker in the vicinity, which is in LD with this marker with epilepsy in the West Bengal population. Further investigations involving higher sample sizes are warranted to validate the present finding.     

Population structure and linkage disequilibrium in oat (<Emphasis Type="Italic">Avena sativa</Emphasis> L.): implications for genome-wide association studies

The level of population structure and the extent of linkage disequilibrium (LD) can have large impacts on the power, resolution, and design of genome-wide association studies (GWAS) in plants. Until recently, the topics of LD and population structure have not been explored in oat due to the lack of a high-throughput, high-density marker system. The objectives of this research were to survey the level of population structure and the extent of LD in oat germplasm and determine their implications for GWAS. In total, 1,205 lines and 402 diversity array technology (DArT) markers were used to explore population structure. Principal component analysis and model-based cluster analysis of these data indicated that, for the lines used in this study, relatively weak population structure exists. To explore LD decay, map distances of 2,225 linked DArT marker pairs were compared with LD (estimated as r ²). Results showed that LD between linked markers decayed rapidly to r ² = 0.2 for marker pairs with a map distance of 1.0 centi-Morgan (cM). For GWAS, we suggest a minimum of one marker every cM, but higher densities of markers should increase marker-QTL association and therefore detection power. Additionally, it was found that LD was relatively consistent across the majority of germplasm clusters. These findings suggest that GWAS in oat can include germplasm with diverse origins and backgrounds. The results from this research demonstrate the feasibility of GWAS and related analyses in oat.     

Evaluation of linkage disequilibrium and its effect on non-parametric multipoint linkage analysis using two high density single-nucleotide polymorphism mapping panels

Genotype data from the Illumina Linkage III SNP panel (n = 4,720 SNPs) and the Affymetrix 10 k mapping array (n = 11,120 SNPs) were used to test the effects of linkage disequilibrium (LD) between SNPs in a linkage analysis in the Collaborative Study on the Genetics of Alcoholism pedigree collection (143 pedigrees; 1,614 individuals). The average r2 between adjacent markers across the genetic map was 0.099 +/- 0.003 in the Illumina III panel and 0.17 +/- 0.003 in the Affymetrix 10 k array. In order to determine the effect of LD between marker loci in a nonparametric multipoint linkage analysis, markers in strong LD with another marker (r2 > 0.40) were removed (n = 471 loci in the Illumina panel; n = 1,804 loci in the Affymetrix panel) and the linkage analysis results were compared to the results using the entire marker sets. In all analyses using the ALDX1 phenotype, 8 linkage regions on 5 chromosomes (2, 7, 10, 11, X) were detected (peak markers p < 0.01), and the Illumina panel detected an additional region on chromosome 6. Analysis of the same pedigree set and ALDX1 phenotype using short tandem repeat markers (STRs) resulted in 3 linkage regions on 3 chromosomes (peak markers p < 0.01). These results suggest that in this pedigree set, LD between loci with spacing similar to the SNP panels tested may not significantly affect the overall detection of linkage regions in a genome scan. Moreover, since the data quality and information content are greatly improved in the SNP panels over STR genotyping methods, new linkage regions may be identified due to higher information content and data quality in a dense SNP linkage panel.     

Optimal Step Length EM Algorithm (OSLEM) for the estimation of haplotype frequency and its application in lipoprotein lipase genotyping

Background  

Haplotype based linkage disequilibrium (LD) mapping has become a powerful and cost-effective method for performing genetic association studies, particularly in the search for genetic markers in linkage disequilibrium with complex disease loci. Various methods (e.g. Monte-Carlo (Gibbs sampling); EM (expectation maximization); and Clark's method) have been used to estimate haplotype frequencies from routine genotyping data.     

Genotype‐by‐sequencing of the plant‐pathogenic fungi Pyrenophora teres and Sphaerulina musiva utilizing Ion Torrent sequence technology

Genetic and genomics tools to characterize host–pathogen interactions are disproportionately directed to the host because of the focus on resistance. However, understanding the genetics of pathogen virulence is equally important and has been limited by the high cost of de novo genotyping of species with limited marker data. Non‐resource‐prohibitive methods that overcome the limitation of genotyping are now available through genotype‐by‐sequencing (GBS). The use of a two‐enzyme restriction‐associated DNA (RAD)‐GBS method adapted for Ion Torrent sequencing technology provided robust and reproducible high‐density genotyping of several fungal species. A total of 5783 and 2373 unique loci, ‘sequence tags’, containing 16 441 and 9992 single nucleotide polymorphisms (SNPs) were identified and characterized from natural populations of Pyrenophora teres f. maculata and Sphaerulina musiva, respectively. The data generated from the P. teres f. maculata natural population were used in association mapping analysis to map the mating‐type gene to high resolution. To further validate the methodology, a biparental population of P. teres f. teres, previously used to develop a genetic map utilizing simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers, was re‐analysed using the SNP markers generated from this protocol. A robust genetic map containing 1393 SNPs on 997 sequence tags spread across 15 linkage groups with anchored reference markers was generated from the P. teres f. teres biparental population. The robust high‐density markers generated using this protocol will allow positional cloning in biparental fungal populations, association mapping of natural fungal populations and population genetics studies.     

Population differences in levels of linkage disequilibrium in the wild

Information about the levels of linkage disequilibrium (LD) in wild animal populations is still limited, and this is true particularly with respect to possible interpopulation variation in the levels of LD. We compared the levels and extent of LD at the genome‐wide scale in three Siberian jay (Perisoreus infaustus) populations, two of which (Kuusamo and Ylläs) represented outbred populations within the main distribution area of the species, whereas the third (Suupohja) was a semi‐isolated, partially inbred population at the margin of the species’ distribution area. Although extensive long‐range LD (>20 cM) was observed in all three populations, LD generally decayed to background levels at a distance of 1–5 cM or c. 200–600 kb. The degree and extent of LD differed markedly between populations but aligned closely with both observed levels of within‐population genetic variation and expectations based on population history. The levels of LD were highest in the most inbred population with strong population substructure (Suupohja), compared with the two outbred populations. Furthermore, the decay of LD with increasing distance was slower in Suupohja, compared with the other two populations. By demonstrating that levels of LD can vary greatly over relatively short geographical distances within a species, these results suggest that prospects for association mapping differ from population to population. In this example, the prospects are best in the Suupohja population, given that minimized marker genotyping and a minimum marker spacing of 1–5 cM (c. 200–600 kb) would be sufficient for a whole genome scan for detecting QTL.     

Evolution of Zygotic Linkage Disequilibrium in a Finite Local Population

One crucial feature of zygotic linkage disequilibrium (LD) analysis is its direct use of diploid genotyping data, irrespective of the type of mating system. Previous theories from an evolutionary perspective mainly focus on gametic LD, but the equivalent development for zygotic LD is not available. Here I study the evolution of zygotic LD and the covariances between gametic and zygotic LDs or between distinct zygotic LDs in a finite local population under constant immigration from a continent population. I derive the analytical theory under genetic hitchhiking effects or in a neutral process. Results indicate that zygotic LDs (diploid level) are more informative than gametic LD (haploid level) in indicating the effects of different evolutionary forces. Zygotic LDs may be greater than or comparable to gametic LD under the epistatic selection process, but smaller than gametic LD under the non epistatic selection process. The covariances between gametic and zygotic LDs are strongly affected by the mating system, linkage distance, and genetic drift effects, but weakly affected by seed and pollen flow and natural selection. The covariances between different zygotic LDs are generally robust to the effects of gene flow, selection, and linkage distance, but sensitive to the effects of genetic drift and mating system. Consistent patterns exist for the covariances between the zygotic LDs for the two-locus genotypes with one common genotype at one locus or without any common genotype at each locus. The results highlight that zygotic LDs can be applied to detecting natural population history.     

Linkage disequilibrium in European elite maize germplasm investigated with SSRs

Information about the extent and genomic distribution of linkage disequilibrium (LD) is of fundamental importance for association mapping. The main objectives of this study were to (1) investigate genetic diversity within germplasm groups of elite European maize (Zea mays L.) inbred lines, (2) examine the population structure of elite European maize germplasm, and (3) determine the extent and genomic distribution of LD between pairs of simple sequence repeat (SSR) markers. We examined genetic diversity and LD in a cross section of European and US elite breeding material comprising 147 inbred lines genotyped with 100 SSR markers. For gene diversity within each group, significant (P<0.05) differences existed among the groups. The LD was significant (P<0.05) for 49% of the SSR marker pairs in the 80 flint lines and for 56% of the SSR marker pairs in the 57 dent lines. The ratio of linked to unlinked loci in LD was 1.1 for both germplasm groups. The high incidence of LD suggests that the extent of LD between SSR markers should allow the detection of marker-phenotype associations in a genome scan. However, our results also indicate that a high proportion of the observed LD is generated by forces, such as relatedness, population stratification, and genetic drift, which cause a high risk of detecting false positives in association mapping.     

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.     

Comparison of marker-based pairwise relatedness estimators on a pedigreed plant population

Several estimators have been proposed that use molecular marker data to infer the degree of relatedness for pairs of individuals. The objective of this study was to evaluate the performance of seven estimators when applied to marker data of a set of 33 key individuals from a large complex apple pedigree. The evaluation considered different scenarios of allele frequencies and different numbers of marker loci. The method of moments estimators were Similarity, Queller-Goodknight, Lynch-Ritland and Wang. The maximum likelihood estimators were Thompson, Anderson-Weir and Jacquard. The pedigree-based coancestry coefficients were taken as the point of reference in calculating correlations and root mean square error (RMSE). The marker data comprised 86 multi-allelic SSR markers on 17 linkage groups, covering 11 Morgans. Additionally, we simulated 10 datasets conditional on the real pedigree to support the results on the real dataset. None of the estimators outperformed the others. Knowledge of allele frequencies appeared to be the most influential, i.e., the highest correlations and lowest RMSE were found when frequencies from the founder population were available. When equal allele frequencies were used, all estimators resulted in very similar, but on average lower, correlations. The use of allele frequencies estimated from the set of 33 individuals gave, on average, the poorest results. The maximum likelihood estimators and the Lynch-Ritland estimator were the most sensitive to allele frequencies. The results from the simulation study fully supported the trends in results of the real dataset. This study indicated that high correlations (up to 0.90) and small RMSE (below 0.03), may be obtained when population allelic frequencies are available. In this scenario, the performances of the various estimators were similar, but seemed to favor the maximum likelihood estimators. In the absence of reliable allele frequencies the method of moments estimators were shown to be more robust. The number of marker loci influenced the average performance of the estimators; however, the ranking was not affected. Correlations up to 0.80 were obtained when two markers per chromosome and appropriate allele frequencies were available. Adding more markers to the current dataset may lead to marginal improvements.     

Genotyping‐by‐sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias

There has been remarkably little attention to using the high resolution provided by genotyping‐by‐sequencing (i.e., RADseq and similar methods) for assessing relatedness in wildlife populations. A major hurdle is the genotyping error, especially allelic dropout, often found in this type of data that could lead to downward‐biased, yet precise, estimates of relatedness. Here, we assess the applicability of genotyping‐by‐sequencing for relatedness inferences given its relatively high genotyping error rate. Individuals of known relatedness were simulated under genotyping error, allelic dropout and missing data scenarios based on an empirical ddRAD data set, and their true relatedness was compared to that estimated by seven relatedness estimators. We found that an estimator chosen through such analyses can circumvent the influence of genotyping error, with the estimator of Ritland (Genetics Research, 67, 175) shown to be unaffected by allelic dropout and to be the most accurate when there is genotyping error. We also found that the choice of estimator should not rely solely on the strength of correlation between estimated and true relatedness as a strong correlation does not necessarily mean estimates are close to true relatedness. We also demonstrated how even a large SNP data set with genotyping error (allelic dropout or otherwise) or missing data still performs better than a perfectly genotyped microsatellite data set of tens of markers. The simulation‐based approach used here can be easily implemented by others on their own genotyping‐by‐sequencing data sets to confirm the most appropriate and powerful estimator for their data.     

Linkage disequilibrium fine mapping of quantitative trait loci: A simulation study

Recently, the use of linkage disequilibrium (LD) to locate genes which affect quantitative traits (QTL) has received an increasing interest, but the plausibility of fine mapping using linkage disequilibrium techniques for QTL has not been well studied. The main objectives of this work were to (1) measure the extent and pattern of LD between a putative QTL and nearby markers in finite populations and (2) investigate the usefulness of LD in fine mapping QTL in simulated populations using a dense map of multiallelic or biallelic marker loci. The test of association between a marker and QTL and the power of the test were calculated based on single-marker regression analysis. The results show the presence of substantial linkage disequilibrium with closely linked marker loci after 100 to 200 generations of random mating. Although the power to test the association with a frequent QTL of large effect was satisfactory, the power was low for the QTL with a small effect and/or low frequency. More powerful, multi-locus methods may be required to map low frequent QTL with small genetic effects, as well as combining both linkage and linkage disequilibrium information. The results also showed that multiallelic markers are more useful than biallelic markers to detect linkage disequilibrium and association at an equal distance.