Evidence for linkage and association with reading disability on 6p21.3-22

Reading disability (RD), or dyslexia, is a common heterogeneous syndrome with a large genetic component. Several studies have consistently found evidence for a quantitative-trait locus (QTL) within the 17 Mb (14.9 cM) that span D6S109 and D6S291 on chromosome 6p21.3-22. To characterize further linkage to the QTL, to define more accurately the location and the effect size, and to identify a peak of association, we performed Haseman-Elston and DeFries-Fulker linkage analyses, as well as transmission/disequilibrium, total-association, and variance-components analyses, on 11 quantitative reading and language phenotypes. One hundred four families with RD were genotyped with a new panel of 29 markers that spans 9 Mb of this region. Linkage results varied widely in degree of statistical significance for the different linkage tests, but multipoint analysis suggested a peak near D6S461. The average 6p QTL heritability for the 11 reading and language phenotypes was 0.27, with a maximum of 0.66 for orthographic choice. Consistent with the region of linkage described by these studies and others, there was a peak of transmission disequilibrium with a QTL centered at JA04 (chi2=9.48; empirical P=.0033; orthographic choice), and there was strong evidence for total association at this same marker (chi2=11.49; P=.0007; orthographic choice). Although the boundaries of the peak could not be precisely defined, the most likely location of the QTL is within a 4-Mb region surrounding JA04.     

Testing for genetic linkage in families by a variance-components approach in the presence of genomic imprinting

Some genes that affect development and behavior in mammals are known to be imprinted; and > or = 1% of all mammalian genes are imprinted. Hence, incorporating an imprinting parameter into linkage analysis may increase the power to detect linkage for these traits. Here we propose theoretical justifications for a recently developed model for testing of linkage, in the presence of genetic imprinting, between a quantitative-trait locus and a polymorphic marker; this is achieved in the variance-components framework. We also incorporate sex-specific recombination fractions into this model. We discuss the effects that imprinting and nonimprinting have on the power of the usual variance-components method and on the variance-components method that incorporates an imprinting parameter. We provide noncentrality parameters that can be used to determine the sample size necessary to attain a specified power for a given significance level, which is useful in the planning of a linkage study. Optimal strategies for a genome scan of potentially imprinted traits are discussed.     

基于选择基因型对数量性状进行关联分析

数量性状的遗传分析可以通过＂选择基因型＂的方式完成。本文提出了一个利用极端样本来对数量性状位点（QTL）进行关联分析的统计量T。统计量T比较上极端群体样本中具有纯合子标记的性状值差异。通过计算机模拟考察了无关联情形时T的分布和Ⅰ型错误率,结果表明,在各种样本选择策略下,T的分布近似于χ^2-分布,Ⅰ型错误率接近设定的显著性水平。同时,考察了各种遗传模型下不同遗传率,不同样本大小,及不同样本选择阈值对T的统计功效的影响,结果表明,T的功效随着标记和QTL间连锁不平衡程度的增强及遗传率和样本大小的增大而增大,当样本选择阈值更严格时,功效也越大。     

Combined linkage and association mapping of quantitative trait loci by multiple markers

Using multiple diallelic markers, variance component models are proposed for high-resolution combined linkage and association mapping of quantitative trait loci (QTL) based on nuclear families. The objective is to build a model that may fully use marker information for fine association mapping of QTL in the presence of prior linkage. The measures of linkage disequilibrium and the genetic effects are incorporated in the mean coefficients and are decomposed into orthogonal additive and dominance effects. The linkage information is modeled in variance-covariance matrices. Hence, the proposed methods model both association and linkage in a unified model. On the basis of marker information, a multipoint interval mapping method is provided to estimate the proportion of allele sharing identical by descent (IBD) and the probability of sharing two alleles IBD at a putative QTL for a sib-pair. To test the association between the trait locus and the markers, both likelihood-ratio tests and F-tests can be constructed on the basis of the proposed models. In addition, analytical formulas of noncentrality parameter approximations of the F-test statistics are provided. Type I error rates of the proposed test statistics are calculated to show their robustness. After comparing with the association between-family and association within-family (AbAw) approach by Abecasis and Fulker et al., it is found that the method proposed in this article is more powerful and advantageous based on simulation study and power calculation. By power and sample size comparison, it is shown that models that use more markers may have higher power than models that use fewer markers. The multiple-marker analysis can be more advantageous and has higher power in fine mapping QTL. As an application, the Genetic Analysis Workshop 12 German asthma data are analyzed using the proposed methods.     

Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations

In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.     

Genome-wide association mapping of Fusarium head blight resistance in contemporary barley breeding germplasm

Utilization of quantitative trait loci (QTL) identified in bi-parental mapping populations has had limited success for improving complex quantitative traits with low to moderate heritability. Association mapping in contemporary breeding germplasm may lead to more effective marker strategies for crop improvement. To test this approach, we conducted association mapping of two complex traits with moderate heritability; Fusarium head blight (FHB) severity and the grain concentration of mycotoxin associated with disease, deoxynivalenol (DON). To map FHB resistance in barley, 768 breeding lines were evaluated in 2006 and 2007 in four locations. All lines were genotyped with 1,536 SNP markers and QTL were mapped using a mixed model that accounts for relatedness among lines. Average linkage disequilibrium within the breeding germplasm extended beyond 4 cM. Four QTL were identified for FHB severity and eight QTL were identified for the DON concentration in two independent sets of breeding lines. The QTL effects were small, explaining 1–3% of the phenotypic variation, as might be expected for complex polygenic traits. We show that using breeding germplasm to map QTL can complement bi-parental mapping studies by providing independent validation, mapping QTL with more precision, resolving questions of linkage and pleiotropy, and identifying genetic markers that can be applied immediately in crop improvement.     

Relevance of genetic relationship in GWAS and genomic prediction

The objective of this study was to analyze the relevance of relationship information on the identification of low heritability quantitative trait loci (QTLs) from a genome-wide association study (GWAS) and on the genomic prediction of complex traits in human, animal and cross-pollinating populations. The simulation-based data sets included 50 samples of 1000 individuals of seven populations derived from a common population with linkage disequilibrium. The populations had non-inbred and inbred progeny structure (50 to 200) with varying number of members (5 to 20). The individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for a quantitative trait controlled by 10 QTLs and 90 minor genes showing dominance. The SNP density was 0.1 cM and the narrow sense heritability was 25%. The QTL heritabilities ranged from 1.1 to 2.9%. We applied mixed model approaches for both GWAS and genomic prediction using pedigree-based and genomic relationship matrices. For GWAS, the observed false discovery rate was kept below the significance level of 5%, the power of detection for the low heritability QTLs ranged from 14 to 50%, and the average bias between significant SNPs and a QTL ranged from less than 0.01 to 0.23 cM. The QTL detection power was consistently higher using genomic relationship matrix. Regardless of population and training set size, genomic prediction provided higher prediction accuracy of complex trait when compared to pedigree-based prediction. The accuracy of genomic prediction when there is relatedness between individuals in the training set and the reference population is much higher than the value for unrelated individuals.     

Powerful regression-based quantitative-trait linkage analysis of general pedigrees

We present a new method of quantitative-trait linkage analysis that combines the simplicity and robustness of regression-based methods and the generality and greater power of variance-components models. The new method is based on a regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method and have performed simulation studies to assess, under a range of conditions, estimation accuracy, type I error rate, and power. For normally distributed traits and in large samples, the method is found to give the correct type I error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus-although, in cases where asymptotic theory is doubtful, significance levels should be checked by simulations. In large sibships, the new method is slightly more powerful than variance-components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.     

Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet

Association mapping has become a widely applied genomic approach to dissect the genetic architecture of complex traits. A major issue for association mapping is the need to control for the confounding effects of population structure, which is commonly done by mixed models incorporating kinship information. In this case study, we employed experimental data from a large sugar beet population to evaluate multi-locus models for association mapping. As in linkage mapping, markers are selected as cofactors to control for population structure and genetic background variation. We compared different biometric models with regard to important quantitative trait locus (QTL) mapping parameters like the false-positive rate, the QTL detection power and the predictive power for the proportion of explained genotypic variance. Employing different approaches we show that the multi-locus model, that is, incorporating cofactors, outperforms the other models, including the mixed model used as a reference model. Thus, multi-locus models are an attractive alternative for association mapping to efficiently detect QTL for knowledge-based breeding.     

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.     

Contribution of an additive locus to genetic variance when inheritance is multi-factorial with implications on interpretation of GWAS

Although the effects of linkage disequilibrium (LD) on partition of genetic variance have received attention in quantitative genetics, there has been little discussion on how this phenomenon affects attribution of variance to a given locus. This paper reinforces the point that standard metrics used for assessing the contribution of a locus to variance can be misleading when there is linkage LD and that factors such as distribution of effects and of allelic frequencies over loci, or existence of frequency-dependent effects, play a role as well. An apparently new metric is proposed for measuring how much of the variability is contributed by a locus when LD exists. Effects of intervening factors, such as type and extent of LD, number of loci, distribution of effects, and of allelic frequencies over loci, as well as a model for generating frequency-dependent effects, are illustrated via hypothetical simulation scenarios. Implications on the interpretation of genome-wide association studies (GWAS), as typically carried out in human genetics, where single marker regression and the assumption of a sole quantitative trait locus (QTL) are common, are discussed. It is concluded that the standard attributions to variance contributed by a single QTL from a GWAS analysis may be misleading, conceptually and statistically, when a trait is complex and affected by sets of many genes in linkage disequilibrium. Yet another factor to consider in the “missing heritability” saga?.     

Comparison of biometrical approaches for QTL detection in multiple segregating families

Detection of QTL in multiple segregating families possesses many advantages over the classical QTL mapping in biparental populations. It has thus become increasingly popular, and different biometrical approaches are available to analyze such data sets. We empirically compared an approach based on linkage mapping methodology with an association mapping approach. To this end, we used a large population of 788 elite maize lines derived from six biparental families genotyped with 857 SNP markers. In addition, we constructed genetic maps with reduced marker densities to assess the dependency of the performance of both mapping approaches on the marker density. We used cross-validation and resample model averaging and found that while association mapping performed better under high marker densities, this was reversed under low marker densities. In addition to main effect QTL, we also detected epistatic interactions. Our results suggest that both approaches will profit from a further increase in marker density and that a cross-validation should be applied irrespective of the biometrical approach.     

Power of QTL mapping experiments in commercial Atlantic salmon populations, exploiting linkage and linkage disequilibrium and effect of limited recombination in males

Whereas detection and positioning of genes that affect quantitative traits (quantitative trait loci (QTL)) using linkage mapping uses only information from recombinants in the genotyped generations, linkage disequilibrium (LD) mapping uses historical recombinants. Thus, whereas linkage mapping requires large family sizes to detect and accurately position QTL, LD mapping is more dependent on the number of families sampled from the population. In commercial Atlantic salmon breeding programmes, only a small number of individuals per family are routinely phenotyped for traits such as disease resistance and meat colour. In this paper, we assess the power and accuracy of combined linkage disequilibrium linkage analysis (LDLA) to detect QTL in the commercial population using simulation. When 15 half-sib sire families (each sire mated to 30 dams, each dam with 10 progeny) were sampled from the population for genotyping, we were able to detect a QTL explaining 10% of the phenotypic variance in 85% of replicates and position this QTL within 3 cM of the true position in 70% of replicates. When recombination was absent in males, a feature of the salmon genome, power to detect QTL increased; however, the accuracy of positioning the QTL was decreased. By increasing the number of sire families sampled from the population to be genotyped to 30, we were able to increase both the proportion of QTL detected and correctly positioned (even with no recombination in males). QTL with much smaller effect could also be detected. The results suggest that even with the existing recording structure in commercial salmon breeding programmes, there is considerable power to detect and accurately position QTL using LDLA.     

Genetic and nongenetic bases for the L-shaped distribution of quantitative trait loci effects

The L-shaped distribution of estimated QTL effects (R(2)) has long been reported. We recently showed that a metabolic mechanism could account for this phenomenon. But other nonexclusive genetic or nongenetic causes may contribute to generate such a distribution. Using analysis and simulations of an additive genetic model, we show that linkage disequilibrium between QTL, low heritability, and small population size may also be involved, regardless of the gene effect distribution. In addition, a comparison of the additive and metabolic genetic models revealed that estimates of the QTL effects for traits proportional to metabolic flux are far less robust than for additive traits. However, in both models the highest R(2)'s repeatedly correspond to the same set of QTL.     

Bayesian mapping of quantitative trait loci for multiple complex traits with the use of variance components

Complex traits important for humans are often correlated phenotypically and genetically. Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits. Compared with single-trait analysis, joint mapping addresses more questions and has advantages for power of QTL detection and precision of parameter estimation. Some statistical methods have been developed to map QTLs underlying multiple traits, most of which are based on maximum-likelihood methods. We develop here a multivariate version of the Bayes methodology for joint mapping of QTLs, using the Markov chain-Monte Carlo (MCMC) algorithm. We adopt a variance-components method to model complex traits in outbred populations (e.g., humans). The method is robust, can deal with an arbitrary number of alleles with arbitrary patterns of gene actions (such as additive and dominant), and allows for multiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mixtures of continuous traits and discrete traits). Under a Bayesian framework, parameters--including the number of QTLs--are estimated on the basis of their marginal posterior samples, which are generated through two samplers, the Gibbs sampler and the reversible-jump MCMC. In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy. The performance of our method is evaluated in simulations with full-sib families. The results show that our proposed Bayesian joint-mapping method performs well for mapping multiple QTLs in situations of either bivariate continuous traits or mixed data types. Compared with the analysis for each trait separately, Bayesian joint mapping improves statistical power, provides stronger evidence of QTL detection, and increases precision in estimation of parameter and QTL position. We also applied the proposed method to a set of real data and detected a coincident linkage responsible for determining bone mineral density and areal bone size of wrist in humans.     

Combined linkage and association sib-pair analysis for quantitative traits.

An extension to current maximum-likelihood variance-components procedures for mapping quantitative-trait loci in sib pairs that allows a simultaneous test of allelic association is proposed. The method involves modeling of the allelic means for a test of association, with simultaneous modeling of the sib-pair covariance structure for a test of linkage. By partitioning of the mean effect of a locus into between- and within-sibship components, the method controls for spurious associations due to population stratification and admixture. The power and efficacy of the method are illustrated through simulation of various models of both real and spurious association.     

Power of mixed-model QTL mapping from phenotypic, pedigree and marker data in self-pollinated crops

The power of QTL mapping by a mixed-model approach has been studied for hybrid crops but remains unknown in self-pollinated crops. Our objective was to evaluate the usefulness of mixed-model QTL mapping in the context of a breeding program for a self-pollinated crop. Specifically, we simulated a soybean (Glycine max L. Merr.) breeding program and applied a mixed-model approach that comprised three steps: variance component estimation, single-marker analyses, and multiple-marker analysis. Average power to detect QTL ranged from <1 to 47% depending on the significance level (0.01 or 0.0001), number of QTL (20 or 80), heritability of the trait (0.40 or 0.70), population size (600 or 1,200 inbreds), and number of markers (300 or 600). The corresponding false discovery rate ranged from 2 to 43%. Larger populations, higher heritability, and fewer QTL controlling the trait led to a substantial increase in power and to a reduction in the false discovery rate and bias. A stringent significance level reduced both the power and false discovery rate. There was greater power to detect major QTL than minor QTL. Power was higher and the false discovery rate was lower in hybrid crops than in self-pollinated crops. We conclude that mixed-model QTL mapping is useful for gene discovery in plant breeding programs of self-pollinated crops.     

Association studies of QTL for multi-allele markers by mixed models

In this paper, we extend association study methods of both Fan et al. [Hum Hered 2002;53:130-145], in which a quantitative trait locus (QTL) and a multi-allele marker are considered for trio families, and Fan and Xiong [Biostatistics 2003, in press], in which a QTL and a bi-allelic marker are considered for nuclear families. The objective is to build mixed models for association study between a QTL and a multi-allelic marker for nuclear families with any number of offspring. Two types of nuclear family data are considered: the first is genetic data of offspring from at least one heterozygous parents, and the second is genetic data of offspring of nuclear family. (1) For the data of offspring from at least one heterozygous parents, we assume that at least one parent is heterozygous at the marker locus, and we may infer clearly the transmission of parental marker alleles to the offspring. We show that it can be used in association study in the presence of linkage. The theoretical basis is the difference between the conditional mean of trait value given an allele is transmitted and the conditional mean of trait value given the allele is not transmitted from a heterozygous parent. To build valid models, we calculate the variance covariance structure of trait values of offspring. Besides, the reduction of the number of parameters is discussed under an assumption of tight linkage between the trait locus and the marker. (2) For the data of offspring of nuclear family, we show that it can be used in general association study. In this case, the theoretical basis is the difference between the conditional mean of trait values given an allele is transmitted from a parent and the population mean. Then, we calculate variance-covariance structure of trait values of offspring. (3) Based on the theoretical analysis, mixed models are built for each type of the data, and related test statistics are proposed for association study. By power calculation and comparison, we show that, in some instances, the proposed test statistics have higher power than that by collapsing alleles to be new ones. The proposed models are used to analyze chromosomes 4 and chromosome 16 data of the Oxford asthma data, Genetic Analysis Workshop 12.     

Equivalence between Haseman-Elston and variance-components linkage analyses for sib pairs

The Haseman-Elston regression method offers a simpler alternative to variance-components (VC) models, for the linkage analysis of quantitative traits. However, even the "revisited" method, which uses the cross-product--rather than the squared difference--in sib trait values, is, in general, less powerful than VC models. In this report, we clarify the relative efficiencies of existing Haseman-Elston methods and show how a new Haseman-Elston method can be constructed to have power equivalent to that of VC models. This method uses as the dependent variable a linear combination of squared sums and squared differences, in which the weights are determined by the overall trait correlation between sibs in a population. We show how this method can be used for both the selection of maximally informative sib pairs for genotyping and the subsequent analysis of such selected samples.     

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand.