Multipoint Quantitative-Trait Linkage Analysis in General Pedigrees

Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245-0549, USA.
The American Journal of Human Genetics (Impact Factor: 10.93). 05/1998; 62(5):1198-211. DOI: 10.1086/301844
Source: PubMed

ABSTRACT Multipoint linkage analysis of quantitative-trait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variance-component linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint identity-by-descent (IBD) probability calculations. We extend the sib-pair multipoint mapping approach of Fulker et al. to general relative pairs. This multipoint IBD method uses the proportion of alleles shared identical by descent at genotyped loci to estimate IBD sharing at arbitrary points along a chromosome for each relative pair. We have derived correlations in IBD sharing as a function of chromosomal distance for relative pairs in general pedigrees and provide a simple framework whereby these correlations can be easily obtained for any relative pair related by a single line of descent or by multiple independent lines of descent. Once calculated, the multipoint relative-pair IBDs can be utilized in variance-component linkage analysis, which considers the likelihood of the entire pedigree jointly. Examples are given that use simulated data, demonstrating both the accuracy of QTL localization and the increase in power provided by multipoint analysis with 5-, 10-, and 20-cM marker maps. The general pedigree variance component and IBD estimation methods have been implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package.

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Available from: John Blangero, Sep 29, 2015
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    • "In the absence of population substructure, dominance or any environmental effect shared by family members, the phenotypic covariances can be expressed as a function of the kinship coefficient between family members in family-based samples. Under this parameterization, the additive polygenic variance is obtained from the covariances between family members using variance component models [2] [3] [4] [5]. Alternatively, since the advent of large-scale genome data, which reveals similarity in genotypic background, the genetic relationships between individuals have become estimable from genome-wide data and this has also been used to identify population substructure. "
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    ABSTRACT: For a family-based sample, the phenotypic variance-covariance matrix can be parameterized to include the variance of a polygenic effect that has then been estimated using a variance component analysis. However, with the advent of large-scale genomic data, the genetic relationship matrix (GRM) can be estimated and can be utilized to parameterize the variance of a polygenic effect for population-based samples. Therefore narrow sense heritability, which is both population and trait specific, can be estimated with both population- and family-based samples. In this study we estimate heritability from both family-based and population-based samples, collected in Korea, and the heritability estimates from the pooled samples were, for height, 0.60; body mass index (BMI), 0.32; log-transformed triglycerides (log TG), 0.24; total cholesterol (TCHL), 0.30; high-density lipoprotein (HDL), 0.38; low-density lipoprotein (LDL), 0.29; systolic blood pressure (SBP), 0.23; and diastolic blood pressure (DBP), 0.24. Furthermore, we found differences in how heritability is estimated-in particular the amount of variance attributable to common environment in twins can be substantial-which indicates heritability estimates should be interpreted with caution.
    09/2015; 2015:671349. DOI:10.1155/2015/671349
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    • "Finite size of the 36 genome (i.e., there are no infinite unlinked loci) causes that true " realized " IBD relationships deviate 37 from expected IBD relationships (Hill and Weir, 2011). Thus, more accurate measures of relationships 38 can be obtained using identity by descent measured with markers (Fernando and Grossmann, 1989, 39 Almasy and Blangero, 1998, Visscher et al., 2006). Other estimators of relationships based on 40 markers that do not use pedigree are based on identity by state (IBS) at markers, sometimes 41 corrected to be on an IBD scale (Ritland, 1996; Toro et al., 2002; VanRaden, 2008). "
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    ABSTRACT: Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, “missing heritabilities”.
    Theoretical Population Biology 08/2015; accepted. DOI:10.1016/j.tpb.2015.08.005 · 1.70 Impact Factor
    • "The association between SNPs and T. suis infection traits (FECs, worm burdens) was analysed in SOLAR version 4.2.7 using a measured genotype analysis (Almasy and Blangero, 1998). Farm and farrowing group (discovery study), and farrowing group and study (VS1, VS2) were fitted as fixed effects whereas litter and paddock were fitted as random effects (to account for additive genetic effects and shared environmental effects, respectively). "
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    ABSTRACT: Whipworms (Trichuris spp.) infect a variety of hosts, including domestic animals and humans. Of considerable interest is the porcine whipworm, T. suis, which is particularly prevalent in outdoor production systems. High infection levels may cause growth retardation, anaemia and haemorrhagic diarrhoea. A significant proportion of the variation in Trichuris faecal egg count (FEC) has been attributed to the host's genetic make-up. The aim of the present study was to identify genetic loci associated with resistance to T. suis in pigs. We used single nucleotide polymorphism (SNP) markers to perform a whole-genome scan of an F1 resource population (n=195) trickle-infected with T. suis. A measured genotype analysis revealed a putative quantitative trait locus (QTL) for T. suis FEC on chromosome 13 covering ∼4.5Mbp, although none of the SNPs reached genome-wide significance. We tested the hypothesis that this region of SSC13 harboured genes with effects on T. suis burden by genotyping three SNPs within the putative QTL in unrelated pigs exposed to either experimental or natural T. suis infections and from which we had FEC (n=113) or worm counts (n=178). In these studies, two of the SNPs (rs55618716, ST) were associated with FEC (P<0.01), thus confirming our initial findings. However, we did not find any of the SNPs to be associated with T. suis worm burden. In conclusion, our study demonstrates that genetic markers for resistance to T. suis as indicated by low FEC can be identified in pigs.
    Veterinary Parasitology 06/2015; 210(3-4):264-269. DOI:10.1016/j.vetpar.2015.03.014 · 2.46 Impact Factor
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