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Original Research ARTICLE

Front. Public Health, 23 November 2016 | https://doi.org/10.3389/fpubh.2016.00259

The Generalized Relative Pairs IBD Distribution: Its Use in the Detection of Linkage

  • Department of Statistics, The George Washington University, Washington, DC, USA

I introduce a novel approach to derive the distribution of disease affectional status given alleles identical by descent (IBD) sharing through ITO method. My approach tremendously simplifies the calculation of the affectional status distribution compared to the conventional method, which requires the parental mating information, and could be applied to disease with both dichotomous trait and quantitative trait locus (QTL). This distribution is shown to be independent of relative relationship and be employed to develop the marker IBD distributions for relative relationship. In addition, three linkage tests: the proportion, the mean test, and the LOD score test are proposed for different relative pairs based on their marker IBD distributions. Among all three tests, the mean test for sib pair requires the least sample size, thus, has the highest power. Finally, I evaluate the significance of different relative relationships by a Monte-Carlo simulation approach.

1. Introduction

Upon the completion of human genome sequences, genetic markers have enabled mapping of human disease genes through linkage analysis. Sib pairs are the most common design among all possible family configurations. A variety of linkage analyses have been developed for testing identical by descent (IBD) sharing of affected sib pairs. Penrose first considered the covariance of the quantitative sib pair trait phenotype and genetic marker in the linkage analysis (1). Haseman and Elston logistically regressed the squared quantitative trait difference on the shared alleles IBD in sib pairs (2). Suarez illustrated the perturbations in the marker IBD for sib pair to detect linked dichotomous trait locus (3). Risch applied recurrence risk ratio method to investigate the IBD sharing of affected sib pairs with dichotomous traits and has also extended this method to other relative pairs (4, 5). Amos showed that a variance components procedure could assess the genetic linkage (6, 7). The model also accommodates gene–environment interactions and the effects of covariates and epistasis.

The basic principle of linkage analysis is the similarity between disease trait and marker genotype, which are measured by (disease) affectional status and (marker) alleles IBD of the relative pairs, respectively. If the trait and marker loci are linked, relative pair, that is likely to share disease alleles, is also likely to inherit the same marker allele or vice versa. Thus, doubly affected sib pair should show greater than expected chance of sharing two linked maker alleles IBD. Using the similarity measure of geno- and phenotype, several statistical tests for linkage can be constructed by deriving the expected degree of similarity under certain linkage assumption. The simplest approach is chi-square “goodness of fit” test to compare the observed and expected marker alleles IBD under the hypothesis of no linkage (8). The proportion test based on the counts of doubly affected sib pairs with two marker alleles IBD, was proposed by Day and Simons and Suarez et al. (3, 9). The mean test, suggested by de Vries et al. and Green and Woodrow, is based on the average number of marker alleles IBD weighted by their probabilities (10, 11). The mean test is generally more powerful than the proportion and the goodness-of-fit tests (12). Another type of method is likelihood ratio test, which utilizes LOD score of the proportion of marker alleles IBD (4). The power of likelihood ratio test can be increased by restricting IBD proportions to certain genetic models (13, 14).

The ITO method refers to the stochastic matrices developed by Li and Sacks, where I, T, and O denote the probabilities sharing 2, 1, and 0 alleles IBD given relative pairs’ genotypes, respectively (15). These ITO matrices provide a simple relationship between relative genotypes and their IBD status and have been wildly used in genetic analysis (16, 17). For example, the conditional genotype probabilities of sib pairs could be calculated from ITO matrices (18). The general formulation of genotype distributions of other relative pairs are also suggested by using the ITO method (19). The ordered ITO transition matrices were extended to calculate the genetic covariance (20).

In order to examine the IBD sharing within affected family, Risch has shown that the IBD probabilities of affected relative pair depend on the recurrence risk ratio, known as λ (4). Under the assumption of incompletely penetrant model, the probabilities of the sibling’s affectional status given alleles IBD sharing could also be recovered from Table II in Haseman and Elston, by conditioning on parental mating types (2, 3). However, this approach will require the information of second-degree parental mating when being applied to relative relationships other than sib pair. In the present paper, I partition the relative pairs’ affectional status on their genotype information with respect to alleles IBD sharing, i.e., the ITO matrices. The ITO method greatly simplifies the derivation of the conditional distribution of affectional status for both the quantitative and the dichotomous traits. Furthermore, it is shown that these probabilities are independent of relative relationships.

In this research, I adopt a novel ITO method and develop the allelic identical by descent (IBD) distributions at marker locus given disease affectional status for siblings, uncle–nephew, grandparent–grandchild, half sibs, and first cousin pairs. By taking advantage of the ITO matrices, I first demonstrate that the probabilities of dichotomous disease status given trait IBD score are independent of relative relationships. Then, I fully derive the marker IBD distributions given dichotomous disease affectional status for various relative relationships by utilizing the relative pairs’ joint probabilities of IBD scores at both trait and marker loci. I also calculate the marker IBD distributions given extreme discordant relative pairs at a quantitative trait locus (QTL) for different relative relationships by my novel ITO method. Next, I examine the power to detect the presence of a significant disease susceptibility locus through linkage analysis by perturbing the conditional marker IBD distribution. Specifically, three tests, the proportion test, the mean test, and the logarithm of odds (LOD) score test, were applied to obtain the sample size required to achieve significance level p with different power. Finally, the Monte-Carlo simulation studies have been conducted in order to evaluate the performance of my methods. I assume Hardy–Weinberg equilibrium, random mating and the marker locus to be completely polymorphic such that all matings are informative.

2. Materials and Methods

Let us consider the situation where alleles (T/t) at the trait locus are linked to alleles (M/m) at a marker locus through recombination fraction θ and assume that the marker locus is completely polymorphic. Additionally, the diallelic frequencies are p and q for alleles T and t, where p + q = 1. I denote penetrance frequencies, i.e., the probability of the affected relative given genotypes TT, Tt, or tt by f1, f2, or f3, respectively. The prevalence of the trait in the population is defined as KP = p2f1 + 2pq f2 + q2f3, in addition to the additive variance (VA = 2pq[p(f2f1) + q(f3f2)]2) and dominance variance (VD = p2q2(f1 − 2f2 + f3)2). I assume no major gene by residual interaction and no epistasis, i.e., the non-allelic interaction of different genes.

2.1. The Conditional Marker IBD Given the Affected Status Distributions

Let X denotes the number of affected individuals in a relative pair. In order to calculate the conditional probabilities of X = k (k = 0, 1, 2) given IBD score at trait locus for generalized relative pairs, I reckon the genotype information of relative pairs derived from the ITO matrices, as shown in Table 1 (15).

TABLE 1
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Table 1. The conditional distributions of relative pair with genotypes (G1G2) given the trait IBD values.

The conditional affected status given IBDT (t = 0, 1, 2) probabilities has been partitioned on all possible genotypes of relative pairs, GTi, i = 1, 2, …, 9: TT − TT, TT − Tt, TT − tt, Tt − TT, Tt − Tt, Tt − tt, tt − TT, tt − Tt, and tt − tt, as shown in equation (1):

Pr(X=k|IBDT=t)=i=19Pr(X=k,GTi|IBDT=t)=i=19Pr(X=k|GTi)Pr(GTi|IBDT=t).

Note that I have utilized the fact that the affected status of relative pair is conditionally independent of trait IBD score, given their genotype. Clearly, knowledge of the trait IBD score provides no extra information on the likelihood of affected status given their genotype. For example, given IBDT = 2, there are only 3 genotypes of the relative pair involved: TT − TT, Tt − Tt, and tt − tt, which implies that

Pr(X=2|IBDT=2)=i=19Pr(X=2|GTi)Pr(GTi|IBDT=2)=f12p2+2f22pq+f32q2=KP2+VA+VD.

The resulting Pr(X = k | IBDT = t) as in Table 1 of Suarez was reproduced here in Table 2 by Li’s ITO method (3, 15). Throughout the calculation, I merely depend on the ITO matrices and trait genotype penetrance frequencies f1, f2, and f3. It is easy to see that conditional distribution of affected status on IBD score at trait locus is independent of relative relationships. Indeed, the affected number of relative pair should only depend on the numbers of trait alleles shared between the relative pairs.

TABLE 2
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Table 2. The conditional distributions of the affected status given the trait IBD values.

The probabilities of IBD at trait locus, Pr(IBDT = t) (t = 0, 1, 2), for sib pair, grandparent–grandchild, uncle–nephew, half sib, and first cousin are given in Table 3. By Bayes’ theorem, Pr(X) = ΣtPr(X | IBDT = t)⋅Pr(IBDT = t), I give the marginal affected status probabilities for different relative relationships from a randomly mating population in Table 4.

TABLE 3
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Table 3. The IBD probabilities at the trait locus.

TABLE 4
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Table 4. The marginal distributions of the affected status.

Let the IBD scores at the marker and trait loci be denoted by IBDM and IBDT, respectively. The joint probabilities for a relative pair to have IBD scores at the marker locus M and the number of affected relative pair is calculated as equation (3).

Pr(IBDM=m,X=k,r)=tPr(X=k|IBDT=t)Pr(IBDM=m,IBDT=t,r).

where relationship subscript r (relationship) refers s (sib), g (grandparent–grandchild), u (uncle–nephew), h (half sib), and f (first cousin). One notices that conditional probabilities Pr(X = x|IBDT = t) are independent of the relative relationships. Hence, the differences among relative relationships of the joint probabilities Pr(IBDM = m, X = k, r) are due to the contribution of Pr(IBDM = m, IBDT = t,r). Combining Pr(IBDM = m, IBDT = t) [see Table 1 in Risch (5)] and Table 4 according to equation (3), I obtain Table 5, in which θ is the recombination fraction between the trait and marker loci, parameter ψ defines θ2 + (1 − θ)2.

TABLE 5
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Table 5. The conditional marker IBD given the affected status distribution.

2.2. Extreme Discordant Relative Pair for Quantitative Trait Locus (QTL)

Risch and Zhang have shown that sib pairs from opposite tails of the phenotypic distribution have substantial power to detect linkage for a quantitative trait locus (QTL) (21, 22). Assuming the Haseman and Elston model, x denotes the individual observed phenotypic value: x = μ + g + e, where μ is the general mean, g and e are the genetic and environmental effects, respectively (2). Following Risch and Zhang, define biallelic locus (T/t) with gene frequencies p and q, respectively (21). Let a be the mean value of genetic effect being TT, d the mean being Tt, and −a being tt. Without loss of generality, I assume a = 1, d = 0, residual variance within each genotype σe2=1 and no residual correlation between relative pairs, i.e., ρ = 0. Therefore, the cumulative distribution function F(x) for the population distribution of the trait is a mixture of three normal distributions:

F(x)=x[p2ϕ(s1)+2pqϕ(s)+q2ϕ(s+1)]ds.

where ϕ(s) is the standard normal density function. Next, the probability of one relative’s phenotype falls in the top decile and the other relative’s in the bottom decile given their trait genotypes, Pr(T1B1|GTi) (i = 1, 2, …, 9), is given as

Pr(T1B1|GTi)=F1(0.1)F1(0.9)ϕ(s,t:G1,G2)dsdt,

where ϕ(s, t) is the bivariate normal density function, G1, G2 take 1, 0, or −1 as their genotypes are TT, Tt, or tt, respectively. Thus, the probabilities of the general extreme discordant relative pair given allele IBD sharing at trait locus is obtained through ITO method:

Pr(T1B1|IBDT=t)=i=19Pr(T1B1,GTi|IBDT=t)=i=19Pr(T1B1|GTi)Pr(GTi|IBDT=t),

where Pr(T1B1|GTi) is integrated according to equation (5), and Pr(GTi|IBDT = t) are the ITO matrices given in Table 1. Again, the probabilities of extreme discordant relative pair with QTL given IBDT are partitioned over their genotypes through the ITO approach. Similar to the discrete case, Pr(T1B1|IBDT = t) is also independent to the relative relationships. If one regards the extreme discordant relative pair with QTL as the continuous case for X = 1, then the probabilities of Pr(T1B1) and Pr(IBDM|T1B1) could be derived in a similar fashion as in the discrete case.

3. Results

The power to detect linkage will naturally decrease as the distance between the trait (T/t) and marker (M/m) loci decreases. Here, I refer the perturbation as the absolute deviation of the conditional probabilities in Table 5 from those under the null hypothesis, i.e., |Pr(IBDM|X)θPr(IBDM|X)θ0|. In general, the less perturbation is, the harder the linkage is detected. I fix a reasonable KP value as 10% and focus on doubly affected relative pairs. In order to compare the test power between the full sib pair and other relative relationships, I let VD = 0.01 such that the perturbation of sib pairs is increasing as VA increases (3). For extreme discordant relative pairs with QTL, I use an additive model with p = 0.8, a = 1, d = 0, σe2=1, and ρ = 0. High recessive frequency allele with correlated residual will yields the maximal perturbation in the conditional marker IBD probabilities, i.e., the perturbation increases, as allele frequency p or phenotype value of heterozygote d decreases, or residual correlation ρ increases (21, 22). In this section, I derive both common Wald- and score-type tests with either binary or continuous trait. Further, I consider the Monte-Carlo simulation to validate the power of the previous tests.

3.1. Proportion Test

I define Nj (j = 2 for sib pair and j = 1 for other relative pairs) as the counts of doubly affected relative pairs with the dichotomous trait or extreme discordant relative pairs with QTL, which share j marker allele(s) IBD among total N relative pairs sampled. The Wald test statistic is

Wr=NjE(Nj)Var(Nj).

Under the alternative hypothesis that θ<12, Nj is approximately normally distributed with

NNr(4ϵr1)3,16ϵr(1ϵr)3,forr=s,f;NNr(2ϵr1),4ϵr(1ϵr),forr=g,u,h.

by Central Limit Theorem, where ϵr refers to conditional marker IBD probabilities of relative relationship found in Table 5.

Since all the IBD perturbations are monotonic based on the parameters chosen, the proportion tests are one-sided: Wr > Zα for doubly affected relative pairs and Wr < −Zα for relative pairs with QTL. The required sample size Nr for this test to have the power of 1 − β is (14):

Nr=±3Zα4Z1βϵr(1ϵr)4ϵr12,forr=s,f;Nr=±Zα2Z1βϵr(1ϵr)2ϵr12,forr=g,u,h.

As previously noted, I take the parameters of KP = 0.1 and VA = 0.01 for doubly affected relative pair with the dichotomous trait, and consider the level α = 0.05 proportion test with 90% power to detect the linkage for various relative types. Figure 1A shows that the required sample size N plotted as a function of recombination fraction θ. The power is calculated for a sample of N = 300 relative pairs (Figure 1B). The power of test for sib pair (solid line) is uniformly larger than that of first cousin (dotted dash), which is explained by larger marker IBD perturbation of sib pairs. However, grandparent–grandchild has the best power among all five relative relationships, when θ > 0.217 (Figure 1B). The increasing test power of grandparent–grandchild relative pair is due to the less decrease in perturbation when θ is large. The grandparent–grandchild relative pair dominates the test power among other relative relationships whenever θ14, which is consistent with the results of Risch (4). For extreme discordant relative pairs with QTL, the results are similar to the case of doubly affected relative pairs with the dichotomous trait (see Figures 1C,D).

FIGURE 1
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Figure 1. The proportion test power for doubly affected relative pairs with the dichotomous trait and extreme discordant relative pairs with QTL. Required sample size N of level α = 0.05 proportion test with 90% power to detect linkage θ for doubly affected relative pairs (A) and extreme discordant relative pairs with QTL (C). Power to detect linkage θ of level α = 0.05 proportion test by using N = 300 doubly affected relative pairs (B) and N = 300 extreme discordant relative pairs with QTL (D).

3.2. LOD Score Test

Following previous notation, the kernel of the likelihood of Nj (j = 2 for sib pair, j = 1 for other relatives) is the following:

NjNNjNNjNNNj.

Note that the parameter of interest is not the recombination fraction θ any more, but Nj, the count of relative pairs sharing j allele(s) IBD. With ϵ^=NjN denoting the ML estimates for ϵ as it varies in the parameter space, then the LOD score T for the likelihood ratio test based on equation (10) is given by

T=2lgϵ^Nϵ^(1ϵ^)N(1ϵ^)ϵ0Nϵ^(1ϵ0)N(1ϵ^),

where ϵ0 is the conditional marker IBD probabilities under null hypothesis. Thus, the likelihood ratio test statistic T asymptotically distributed as χ2 with 1 d.f. Defining equation (11) as T(Nj, N), and assuming level-α test with 1 −β power, I obtain {Nj, N} for each relative relationship as the critical size of relative pairs sharing allele IBD and total required sample size, respectively. One can check easily that T is an increasing function of Nj when Ns are fixed. In other words, for an each N, I reject the null hypothesis if the counts of allele IBD are greater than Nj. Usually, the LOD score test use more strict criterion than the proportion test does. Here, the total required sample size N of the 90% power, level α = 0.001 LOD score test power is plotted as a function of the recombination fraction θ for both doubly affected relative pairs with the dichotomous trait and extreme discordant relative pairs with QTL (Figures 2A,C). In many respects, they behave similarly such that sib pairs have larger power for low θ, while grandparent–grandchild pairs have the best power for high θ (Figures 2B,D). In general, both critical allele IBD sharing size Nj and total relative pair size N are increasing as θ gets closer to 0.5 or as the power of the test increases.

FIGURE 2
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Figure 2. The LOD test power for doubly affected relative pairs with the dichotomous trait and extreme discordant relative pairs with QTL. Required sample size N of level α = 0.001 LOD score test with 90% power to detect linkage θ for doubly affected relative pairs (A) and extreme discordant relative pairs with QTL (C). Power to detect linkage θ of level α = 0.001 LOD score test by using N = 300 doubly affected relative pairs (B) and N = 300 extreme discordant relative pairs with QTL (D).

3.3. Mean Test

Since N interested sib pair can share either two or one allele(s) IBD, I weight N1 with 12, and define Tsm=N2+12N1, the Wald test statistics is:

Wsm=2N(2TsmN).

Under the alternative hypothesis of θ<12, Wsm is approximately normally distributed with

WsmNNsm(2ϵ2+ϵ11),8ϵ2(1ϵ2)+2ϵ1(1ϵ1)8ϵ2ϵ1.

by Central Limit Theorem, where ϵ2 and ϵ1 are conditional marker IBD probabilities for sib pair sharing two or one allele(s) IBD, respectively. For sib pair, one expects the increased allele sharing under the alternative hypothesis, the level-α one-sided mean test is: Ws–m > Zα for doubly affected sib pairs with the dichotomous trait and Ws–m < −Zα for extreme discordant sib pairs with QTL. Following similar procedure as the proportion test, I obtain the required sib pair sample size Ns–m for level-α mean test with power 1−β (14):

Nsm=±Zα2Z1β4ϵ2(1ϵ2)+ϵ1(1ϵ1)4ϵ2ϵ12(2ϵ2+ϵ11)2.

Figure 3A compares the required total sample size N of doubly affected sib pair with the dichotomous trait for all three test at α level of 0.05 with 90% power: the proportion test (solid line), the mean test (dotted line), and the LOD score test (medium dash). The mean test for sib pair requires the least sample size than other two. For example, the required sample sizes are {157, 128, 176} for the proportion, mean and LOD score tests, respectively, when θ = 0.1. Here, the mean test demonstrates the largest test power among all three tests (Figure 3B). The results are similar for extreme discordant sib pairs (figures not shown).

FIGURE 3
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Figure 3. The power comparison of the proportion test, the LOD test and the mean test for doubly affected Sib Pairs with the dichotomous trait. (A) Required sample size N of 3 different test statistics at level α = 0.05 with 90% power to detect linkage θ. For doubly affected sib pairs. (B) Power to detect linkage θ of 3 test statistics at level α = 0.05 by using N = 300 doubly affected sib pairs.

3.4. Simulation Study

In this section, I perform the Monte-Carlo simulation procedures to evaluate the power of three statistical tests. The pedigree data consists of 300 replicates of 5 nuclear families. Within each nuclear family, there are two affected individuals with the dichotomous trait representing relative relationship of sibs, grandparent–grandchild, uncle–nephew, half sibs, and first cousin. Since the simulation programs use the parameters set {p, f1, f2, f3}, I take only one reasonable solution set for {KP, VA, VD} = {0.1, 0.01, 0.01}, where p = 0.7887 is the gene frequency of the normal allele, f1 = 0.05359, f2 = 0.1 are the first two penetrance frequencies of homozygous individual of normal alleles, and heterozygous individual, f3 = 0.7464 is the penetrance frequency of homozygous individual carrying recessive disease alleles. Total 100,000 data set were generated under different hypothesis of θ. The test power was then evaluated at putative α level of 0.05 for the proportion and mean test statistics, and α level of 0.001 for the LOD score test statistic. The simulated empirical powers are consistent with the theoretical calculations for all relative relationships, which serve as a validation of the test statistics, and result of sib pair is shown in Figure 4.

FIGURE 4
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Figure 4. The comparison between the simulated and theoretical test powers of Sib Pairs. The simulated/calculated powers for proportion test (solid line/circle), the mean test (dotted line/triangle), and the LOD score test (medium dash/square) are plotted as a function of recombination fraction θ for sib pairs.

4. Discussion

I have demonstrated the Pr(IBDM|X) perturbation is closely related to the power to linkage tests. When VA = 0, the non-zero perturbation of full sib pair is due to the VD term. However, there are no VD term in the perturbations of other relative relationships, i.e., the perturbations are always zero, whenever VA hits zero for relative relationship, grandparent–grandchild, uncle–nephew, half sibs, and first cousin. Among all the relative relationships, only the perturbation of grandparent–grandchild shows linear dependence upon the recombination fraction, θ, while the remaining perturbations are higher order polynomial functions of θ. One also notices that the condition of θ = 0 and VA=27128 yields the maximal perturbations for all relative relationships: 0.2394 for full sibs, 0.5438 for first cousin, and equal maximal perturbation of 0.4203 for grandparent–grandchild, uncle–nephew and half sibs. Thus, for the relative relationships, grandparent–grandchild, uncle–nephew, and half sibs, the tests start with equal sample size, N = 211 at θ = 0 (in Figure 1A) and N = 298 at θ = 0 (in Figure 2A). This conclusion also holds for extreme discordant relative pair with QTL.

There exist programs that could evaluate the type I error rate of the three statistical tests under the null hypothesis of no linkage. The marker genotypes of each relative pair are independently generated by either SLINK or SIMULATE programs. The SLINK program randomly predicts the marker genotypes by calculating their conditional probabilities given the disease phenotypes (23, 24), while the SIMULATE program simulates pedigree data by using a crossover formation (CF) process to generate the counts of crossovers and their locations along a chromosome (25). Once the pedigree files have been created by either SLINK or SIMULATE program, test statistics are calculated through exact counts of relative pairs sharing allele(s) IBD. The empirical type I error rates generated by both programs are consistent with the nominal α levels (results not shown). However, neither SLINK nor SIMULATE could track allele segregation unambiguously under the alternative hypothesis. Therefore, I constructed Monte-Carlo simulation directly from Table 5, so that the tests’ power could be evaluated under both null and alternative hypotheses.

Because the counting statistic relies on the number of alleles shared IBD in affected relative pairs to detect linkage, informativeness of the marker IBD determines the accuracy of linkage analysis. A marker is highly informative for linkage studies, if any individual chosen at random is likely to be heterozygous for that marker. Nonetheless, in almost all applications, the biallelic IBD value can not be determined unambiguously, but has to be estimated. Previous work has been shown that increased information of allele shared IBD of sib pair can be achieved by analyzing two or more linked marker loci simultaneously (26, 27). In order to recapture the lost information, Kruglyak et al. and Kong and Cox have performed weighting schemes to take account of all pedigree information (28, 29). Buckman and Li combined both alleles identical by descent (IBS) and IBD missing at random (MAR) into the test statistic which has equal power as those in Kong and Cox (30).

The allele-sharing methods, originally designed for application of affected sib pair, are also referred as model-free (no assumption of the distribution) linkage analysis and advantageous over traditional model based methods. Thus, this method does not require specification of the disease model and could be readily applied to either early- or late-onset disease. In practice, samples collected for affected relative pair will likely contain three or more affected relatives, such as siblings, grandparent–grandchild, uncle–nephew, half sib, or first cousin. However, most commonly used methods, restrict the linkage analysis to sib pair only. Thus, a large amount of information contained in the data is discarded. The simple way to achieve larger power is to include all available affected individuals from each relative type. Since the possible selected pairs are no longer independent, several weighting schemes were applied to sib pair (18, 31). The most powerful weighting scheme for various relative pairs are still need to be considered, perhaps their theoretical sample size and power could be calculated.

Author Contributions

QZ contributed to the research topic, derived model formulation, carried out the numerical simulation, and wrote the manuscript.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author would like to express his deep gratitude to Dr. Zhaohai Li (Professor of Statistics, the George Washington University) for sharing his wisdom, inspiration, and criticism during the course of this research. He is also grateful to Dr. Hong Zhang (Professor, Institute of Biostatistics, School of Life Science, Fudan University) for many helpful discussions.

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Keywords: allele identical by descent, ITO method, quantitative trait locus, relative pairs, linkage analysis

Citation: Zou Q (2016) The Generalized Relative Pairs IBD Distribution: Its Use in the Detection of Linkage. Front. Public Health 4:259. doi: 10.3389/fpubh.2016.00259

Received: 23 May 2016; Accepted: 02 November 2016;
Published: 23 November 2016

Edited by:

Yuanzhang Li, Walter Reed Army Institute of Research, USA

Reviewed by:

Jia Liu, Pfizer Inc., USA
Gaelle Marenne, Wellcome Trust Sanger Institute, UK
Lira Pi, Duke University School of Medicine, USA

Copyright: © 2016 Zou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Quan Zou, qzou@gwmail.gwu.edu