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. 2019 Jun 19;136(6):418–429. doi: 10.1111/jbg.12419

Significance testing and genomic inflation factor using high‐density genotypes or whole‐genome sequence data

Sanne van den Berg 1,2, Jérémie Vandenplas 1, Fred A van Eeuwijk 2, Marcos S Lopes 3, Roel F Veerkamp 1,✉
PMCID: PMC6900143  PMID: 31215703

Abstract

Significance testing for genome‐wide association study (GWAS) with increasing SNP density up to whole‐genome sequence data (WGS) is not straightforward, because of strong LD between SNP and population stratification. Therefore, the objective of this study was to investigate genomic control and different significance testing procedures using data from a commercial pig breeding scheme. A GWAS was performed in GCTA with data of 4,964 Large White pigs using medium density, high density or imputed whole‐genome sequence data, fitting a genomic relationship matrix based on a leave‐one–chromosome‐out approach to account for population structure. Subsequently, genomic inflation factors were assessed on whole‐genome level and the chromosome level. To establish a significance threshold, permutation testing, Bonferroni corrections using either the total number of SNPs or the number of independent chromosome fragments, and false discovery rates (FDR) using either the Benjamini–Hochberg procedure or the Benjamini and Yekutieli procedure were evaluated. We found that genomic inflation factors did not differ between different density genotypes but do differ between chromosomes. Also, the leave‐one‐chromosome‐out approach for GWAS or using the pedigree relationships did not account appropriately for population stratification and gave strong genomic inflation. Regarding different procedures for significance testing, when the aim is to find QTL regions that are associated with a trait of interest, we recommend applying the FDR following the Benjamini and Yekutieli approach to establish a significance threshold that is adjusted for multiple testing. When the aim is to pinpoint a specific mutation, the more conservative Bonferroni correction based on the total number of SNPs is more appropriate, till an appropriate method is established to adjust for the number of independent tests.

Keywords: DNA analysis, genome‐wide association studies, pig population, significance testing, whole‐genome sequence

1. INTRODUCTION

Genome‐wide association studies (GWAS) aim to associate single nucleotide polymorphism (SNP) with a trait of interest in order to get a better understanding of the genetic architecture and to improve the accuracy and persistency of genomic prediction (VanRaden, Tooker, O'Connell, Cole, & Bickhart, 2017). A SNP is classified as associated SNP when it exceeds a significance threshold usually expressed as the –log10 (p‐value). These associated SNPs could however also be a false positive, leading to wrong conclusions about the genetic architecture underlying a trait or to a suboptimal subset of SNPs for genomic prediction. With the increasing number of SNPs used for GWAS, especially up to imputed whole‐genome sequences (iWGS), the number of false‐positive associations is expected to increase and choosing an appropriate significance threshold becomes an issue. Although the whole concept of significance thresholds should not be misused to assume causality and reproducibility of SNP effects (Baker, 2016; Wasserstein, Schirm, & Lazar, 2019), it is a useful concept for animal breeding to preselect and differentially weight SNPs in across‐breed genomic prediction (Raymond et al., 2018).

One approach to reduce the number of false positives is genomic control based on the genomic inflation factor. The genomic inflation factor expresses the deviation of the distribution of the observed test statistic compared to the distribution of the expected test statistic. High genomic inflation factors are caused by population stratification, strong linkage disequilibrium (LD) between SNPs, strong association between SNPs and phenotypes, and systematic bias (Devlin & Roeder, 1999; Hinrichs, Larkin, & Suarez, 2009; Reich & Goldstein, 2001; Zheng, Freidlin, & Gastwirth, 2006). Within livestock populations, we deal with genotype data that are imputed to higher density SNP data or even to whole‐genome sequence data, and also with individuals that are closely related to each other. Henceforth, the question is how the genomic inflation factor is affected in a GWAS with such data. Furthermore, different LD patterns across chromosomes may exist (Veroneze et al., 2013), and therefore, we would also expect different genomic inflation factors across chromosomes. To our knowledge, this has not been investigated yet.

In addition to applying genomic control, choosing an appropriate significance threshold can also control the number of false positives. A significance threshold of −log10 (p‐value) >7.2 (Welter et al., 2013) is commonly accepted in human genetic studies, while there is no commonly agreed threshold for livestock studies, such as for a pig breeding population. Permutation testing is a method to derive a empirical significance threshold that accounts for multiple testing and allows for the statistical dependence between SNPs (Churchill & Doerge, 1994). However, permutation testing is computationally intensive, especially when a large number of SNPs are involved in a large population.

Less computationally intensive approaches to account for multiple testing, which could cause an higher number of false positives, involve adjusting the significance threshold for either the family‐wise type 1 error rate or the false discovery rate (FDR). The family‐wise type 1 error rate aims to minimize the probability of finding at least one false positive. The family‐wise type 1 error rate is usually controlled by applying a Bonferroni correction that adjusts the significance threshold by the number of independent tests. In livestock, the number of independent tests is usually defined as the total number of SNPs or as the number of independent chromosome fragments, defined as regions of the genome that explain unique genetic variation (Duggal, Gillanders, Holmes, & Bailey‐Wilson, 2008; Ricard et al., 2017). Using the total number of SNPs can result in too conservative thresholds because it violates the assumption of independency between tests (Duggal et al., 2008; Nicodemus, Liu, Chase, Tsai, & Fallin, 2005). In contrast to the Bonferroni correction, the FDR does not aim to control the probability of finding at least one false positive but allows a proportion of the positive results to be false. Two commonly used FDR adjustments, among others, are the Benjamini and Hochberg (Benjamini & Hochberg, 1995) approach and the Benjamini and Yekutieli approach (Benjamini & Yekutieli, 2001).

In published studies in pig populations, threshold values for −log10 (p‐value) ranged from 3.3 to 6, using either no multiple testing correction, a Bonferroni correction, the false discovery rate, or genomic control (Do et al., 2013; Hao et al., 2017; Le, Christensen, Nielsen, & Sahana, 2017; Ma et al., 2013; Sahana, Kadlecová, Hornshøj, Nielsen, & Christensen, 2013; Sanchez et al., 2014; van Son et al., 2017). Therefore, the objective of this study was to investigate genomic control and different significance testing procedures using data from a commercial pig breeding scheme to establish guidelines for significance testing in a GWAS using either medium‐density genotypes, high‐density genotypes or imputed WGS (iWGS).

2. MATERIALS AND METHODS

2.1. Data

The data set for a Large White (LW) line was provided by Topigs Norsvin. The data set included precorrected phenotypes for number of teats of 4,964 Large White (LW line) pigs (Lopes et al., 2017), medium‐density genotypes (34,588 SNPs) and high‐density genotypes (491,169 SNPs). High‐density genotypes were imputed to WGS with Beagle 4.0 (Browning & Browning, 2009), using a multi‐line reference population of 168 animals of which 32 individuals originate from the LW line (van den Berg et al., 2019). After imputation and quality control, 10,212,687 SNPs that had a Beagle imputation accuracy (R 2) > 0.6 were considered for iWGS and the average Beagle R 2 was 0.93.

2.2. Single‐SNP genome‐wide association study

A single‐SNP GWAS was performed for medium density, high density and imputed WGS (iWGS), applying a mixed linear association model with a leave‐one‐chromosome‐out (LOCO) approach as implemented in GCTA version 1.25.2 (Yang, Manolio, et al., 2011; Yang, Zaitlen, Goddard, Visscher, & Price, 2014). The model was as follows:

y=1μ+xb+u+e (1)

where y is a vector of the phenotypes, µ is the mean, b is the fixed effect of the SNP tested for association, x is a vector of the SNP genotypes coded as 0, 1 or 2, u is a vector of random polygenetic effect and e is a vector of residuals. The residuals were distributed following a normal distribution N(0,Iσe 2) with σe 2 being the residual variance. The random polygenetic effect followed a normal distribution u ~ N(0,Gσg 2), where G is the genomic relationship matrix (Yang, Manolio, et al., 2011) for which the chromosome of the SNP tested is ignored and σg 2 is the genetic variance.

2.3. Genomic control

The chi‐square test statistics, needed for the computation of the genomic inflation factors, were calculated from the p‐values assuming 1 degree of freedom. The genomic inflation factor was defined as the median of the observed chi‐squared test statistics divided by the expected median of the corresponding chi‐squared distribution and was computed for each chromosome separately and for the whole genome for the different densities.

2.4. Definition of the significance threshold

Three approaches to establish significance thresholds were evaluated: (a) permutation testing, (b) the Bonferroni correction and (c) the FDR. The definition of the significance threshold was expressed as the –log10 (p‐value). The established significance thresholds were compared between medium‐density, high‐density and iWGS genotypes.

The permutation test followed the procedure for the estimation of the experimental critical values that was proposed by Churchill and Doerge (1994). The phenotypes were randomly shuffled and subsequently used for a single‐SNP GWAS analysis. A total of 1,000 permutations were performed, and the maximum –log10 (p‐value) of each permutation was recorded. The –log10 (p‐value) significance threshold was defined as the 95th percentile of the ordered recorded values. Using either medium‐density genotypes, high‐density genotypes or iWGS, the permutation test was performed for only 3 chromosomes to limit the computational costs. Chromosomes 4, 7 and 10 were chosen to represent different levels of association.

The Bonferroni correction divides the probability of having at least one false‐positive result when the null hypothesis (H 0) is true (α) by the number of independent tests. In this study, α was set to 0.05. The number of independent tests was either the total number of SNPs or the number of independent chromosome fragments. The number of independent chromosome fragments (M e) was calculated as follows (Goddard, Hayes, & Meuwissen, 2011):

Me=1VarGij (2)

where G is the genomic relationship matrix computed following the first method of VanRaden (2008) with SNPs of all chromosomes. Goddard et al. (2011) showed that this estimation of M e is equal to the inverse of the average LD (measured as r 2) across the whole genome.

Thresholds adjusted for the FDR were established using two different procedures. The first procedure evaluated was a three‐step strategy proposed by Benjamini and Hochberg (BH) (Benjamini & Hochberg, 1995). First, the p‐values were ordered in ascending order. Second, k was determined as the rank of max p(i)<iαm, with α = 0.05, and m being the total number of SNPs. Third, the p‐value at rank k was defined as the threshold and all SNPs with a rank smaller than k are declared significant. The second procedure evaluated was proposed by Benjamini and Yekutieli (BY) (Benjamini & Yekutieli, 2001). This procedure followed the same steps as the BH one, except that k was defined as the rank of maxp(texti)<iαm∗∑i=im1i. Both false discovery rates were estimated using the R package “mutoss” (Blanchard et al., 2010).

3. RESULTS

3.1. GWAS

In comparison with lower densities, more peaks are observed with iWGS (Figure 1). These peaks become also higher and more pronounced. The strongest peak found with all densities was located at approximately 103.4 MB on chromosome 7 and had a maximum –log10 (p‐value) of 28.5 using iWGS. Furthermore, highly significant peaks were found with all densities at chromosomes 2, 6, 10 and 12.

Figure 1.

Figure 1

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Manhattan plots for the number of teats using either medium‐density, high‐density or iWGS genotypes

3.2. Genomic control

The genomic inflation factors at a whole‐genome level were all higher than 2 (average about 2.50) and were similar for the medium density, high densities, and iWGS. At the chromosome level, the genomic inflation factors also remained constant across the different densities (Figure 2), although genomic inflation factors varied between chromosomes, from 0.98 to 4.18 (Figure 2). Using a threshold of –log10 (p‐value) > 5 for iWGS, 67,784 SNPs were considered as significant without genomic control, 2,154 with genome‐wide control and 1,362 with chromosome‐specific genomic control. To investigate potential causes for these high genomic inflation factors, first the average genomic inflation factors over the 1,000 permutations were calculated for the chromosomes 4, 7 and 10. Values were around 1 and remained constant across chromosomes and densities (Table 1). Thus, when the association between genotype and phenotype was broken down with permutation testing, the genomic inflation factors decreased to 1.

Figure 2.

Figure 2

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Inflation factors per chromosome and the total genome found with medium density, high density and iWGS

Table 1.

Significance thresholds and genomic inflation factors from permutation testing of chromosomes 4, 7 and 10 for medium and high densities and iWGS

Chromosome Density Thresholda Genomic inflation factorb
4 Medium 4.178 0.997 (0.198)
High 4.927 1.004 (0.204)
iWGS 5.469 0.991 (0.196)
7 Medium 4.232 1.003 (0.207)
High 4.922 1.003 (0.201)
iWGS 5.449 1.005 (0.210)
10 Medium 4.100 1.003 (0.194)
High 4.743 0.988 (0.188)
iWGS 5.426 0.988 (0.185)
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a

p‐value thresholds are expressed as –log10 (p‐values).

b

Averages and standard deviation within brackets over 1,000 permutations.

Second, we also fitted three highly significant SNP (Table 2) on chromosome 2, 7 and 10 as fixed effect in the GWAS with medium‐density SNPs. By fitting the highly significant SNP as fixed effect, the peaks in the Manhattan plot disappeared (Figure 3), indicating that the association was removed. Although the genomic inflation factor dropped with at least 22% compared to the model without the most significant SNP as fixed effect (Figure 4), the genomic inflation factors were still not close to unity (that is, all above 1.5). Finally, the analysis was re‐run with the same phenotype and iWGS data sets but using a pedigree relationship matrix or a genomic relationship matrix based on all iWGS markers. Using the pedigree relationship matrix resulted in genomic inflation factors close to those obtained with the LOCO approach (Figure 5). However, using a genomic relationship matrix based on all iWGS markers resulted in all genomic inflation factors per chromosome close to 1 (Figure 5).

Table 2.

Details on the top 4 QTL used as a fixed effect in the GWAS model

Chromosome Positiona −log10(p‐value) SNP effect
2 125.63 9.1 −0.13
7 103.5 26.6 0.34
10 525.9 11.5 −0.15
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a

Position is given in mega base pairs (MB).

Figure 3.

Figure 3

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Manhattan plot of GWAS without (upper) or with 3 QTL as fixed effect (lower)

Figure 4.

Figure 4

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Genomic inflation factors of GWAS without (grey) or with 3 QTL as fixed effect (Blue)

Figure 5.

Figure 5

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Inflation factors found per chromosome and across the whole genome using a pedigree relationship matrix (A matrix), a genomic relationship matrix based on the leave‐one‐chromosome‐out approach (LOCO G) and a genomic relationship matrix based on all iWGS markers (Full G)

3.3. Significance thresholds

Permutation testing was used to set the baseline value for the significance threshold for –log10 (p‐value). The significance thresholds found with permutation testing for the medium‐density SNP chip were 4.1 and increased to 5.5 when the marker density increased to iWGS. In contrast to the genomic inflation factors, significance thresholds were constant across chromosomes (Table 1).

The level and behaviour of the significance threshold were dependent on the number of independent tests applied to the Bonferroni correction. When the total number of SNPs was used as the number of independent tests, the threshold increased from 5.8 to 8.3 when moving from medium density to iWGS (Table 3). However, the significance thresholds remained constant at about 3.6 when the total number of independent chromosome fragments was used as the number of independent tests (Table 3), because the total numbers of independent chromosome fragments were also constant at approximately 200 across densities.

Table 3.

Significance thresholds of a Bonferroni correction using the total number of SNPs or the number of independent chromosome fragments for medium and high densities and iWGS

  Bonferroni_totala Bonferroni_Me b FDR_BHc FDR_BYd
# SNP Thresholde Me f Thresholde Thresholde Thresholde
Medium density 34,588 5.84 198.9 3.60 2.48 4.19
High density 491,169 6.99 193.4 3.59 2.44 4.38
iWGS 10.2 M 8.31 223.4 3.65 2.57 4.54
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a

Bonferroni_total = 0.05/total number of SNPs.

b

Bonferroni_M e = 0.05/ M e.

c

The false discovery rate (FDR) computed following Benjamini and Hochberg (1995) (BH).

d

The false discovery rate (FDR) computed following Benjamini and Yekutieli (2001) (BY).

e

Significance thresholds are expressed as –log10 (p‐values).

f

M e is the number of independent chromosome fragment calculated with the formula proposed by Goddard et al. (2011).

Using the FDR, the significance threshold remained more constant, at about 2.50 across the different SNP densities when using the BH procedure, while a slight increase was observed, from 4.19 to 4.54, with increasing marker density when using the BY procedure.

The effect of significance threshold on the number of SNPs above a significance threshold is illustrated in Figure 6, and there was an obvious effect of genotype density on the number of identified significant SNPs. Figure 7 shows the number of QTL regions (a region includes all SNPs within a 0.5 Mb region to the left and right of the most significant SNP) is also highly dependent on genotype density and significance threshold. For example, for iWGS, the number of QTL regions was equal to 36 with a significance threshold of 8.3 (that is, with the Bonferroni correction based on the total number of SNPs), to 264 with a signification threshold of 5.4 (that is, a threshold obtained from the permutation test) and to 977 with a significance threshold of 3.6 (that is, with the Bonferroni correction based on the number of independent chromosome fragments).

Figure 6.

Figure 6

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Number of SNPs with a significance level above a range of significance thresholds (without correction) for medium density (grey), high density (blue), and iWGS (orange)

Figure 7.

Figure 7

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Number of QTL regions with a significance level above a range of significance thresholds (without correction) for medium density (grey), high density (blue) and iWGS (orange)

4. DISCUSSION

The objective of this study was to evaluate and compare different statistical testing procedures in order to minimize the chance of finding false positives for a GWAS using data from a commercial pig line.

Genomic inflation factors in this study were high (that is, all above 2), suggesting that we need to adjust for population stratification in the data. Accounting appropriately for population stratification is important to avoid strong genomic inflation and consequently avoid false positives (Cardon & Palmer, 2003; Hinrichs et al., 2009; Patterson, Price, & Reich, 2006; Price et al., 2006). In other GWAS using commercial pig lines, the genomic inflation factors were much lower (Diniz et al., 2014; Lopes, Bastiaansen, Harlizius, Knol, & Bovenhuis, 2014; Luo et al., 2012; Sanchez et al., 2014). For example, Lopes et al. (2014) found genomic inflation factors of 1.13 for the number of teats using 60K Large White genotypes. The difference between literature and this study was likely not due to a different density of SNPs since the genomic inflation factors did not vary between the densities used in this study. Instead, we showed that the LOCO approach in GCTA is a likely reason for the genomic inflation in our population. With the LOCO approach, the genomic relationship matrix used to account for population stratification only included the other chromosomes than the one where the tested SNP is located. It has been shown that the LOCO approach improves the power of GWAS in human studies (Lippert et al., 2011; Listgarten et al., 2012; Yang et al., 2014) because the tested SNP is not double fitted in the model. However, in human studies, most individuals are often unrelated, while in livestock breeding populations, many strong family relationships exist. For example, in this data set, each individual had on average 2.2 full sibs and 42 half‐sibs. This complicated family structure is also illustrated by the eigenvalues of the genomic relationship matrix computed using the medium‐density genotypes (Figure 8). It can be observed that a few eigenvalues explain a relatively large proportion of the variance (e.g., the 10 first largest eigenvalues explain >17% of the variance). Therefore, our results confirm that the LOCO approach does not account appropriately for the population stratification in a pig breeding population and probably also in many other livestock populations used for a GWAS. Furthermore, we showed that the pedigree relationship matrix does not account appropriately for the population stratification neither. Hence, fitting a genomic relationship matrix using all chromosomes might be more appropriate than the other options and might diminish the need to adjust for population structure.

Figure 8.

Figure 8

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Cumulative proportion of variance explained in the genomic relationship matrix by its eigenvalues

Although the genomic inflation factors did not differ a lot between the different SNP densities, genomic inflation factors varied largely across the chromosomes. A likely explanation might be that there was a different level of association between the SNP on the chromosome and the trait of interest. Strong association between SNP and the trait of interest can cause inflation of the test statistic in the neighbouring regions because of LD between these SNPs, and many more SNPs were significant when an average genomic control was performed across the chromosome. Therefore, we suggest that genomic control should be applied per chromosome.

4.1. Definition of the significance threshold

Permutation testing is a method to derive empirical thresholds that can be used to validate thresholds found with less computationally intensive approaches, such as the Bonferroni correction or the FDR. The procedure for permutation testing used in this study assumed that the observations are independent (Churchill & Doerge, 1994). Because the 900 principal components suggest that there are many families (Patterson et al., 2006), it is likely that with 1,000 permutations, the dependency between observations between family members is broken down and the assumption of the independence between observations is fulfilled.

The empirical thresholds established by permutation testing were used as a reference to validate the thresholds established by the less computationally intensive Bonferroni correction and FDR. Based on the empirical thresholds, it was expected that the significance threshold increased with increasing SNP density. However, the significance threshold based on the Bonferroni correction using the total number of SNPs was much higher than the empirical thresholds; that is, the threshold based on the Bonferroni correction increased from 5.84 to 8.31, whereas the empirical thresholds increased from 4.1 to 5.5. The assumption that the total number of SNPs is equal to the number of independent tests is too conservative, since there is LD between the SNPs.

The Bonferroni correction with the number of independent chromosome fragments did account for an average LD between SNPs across the genome. However, even though using the number of independent chromosome fragments to represent the LD structure of the data better, the significance thresholds did not increase with increasing SNP density and the thresholds were quite low compared to those ones established with permutation testing, for example, 5.4 with permutation testing and 3.4 with the Bonferroni correction for iWGS. Dudbridge and Gusnanto (Dudbridge & Gusnanto, 2008) also reported underestimated significance thresholds when the number of independent chromosome fragments was used with the Bonferroni correction. They argued that using principal component analysis to estimate the number of independent chromosome fragments might not be an appropriate method, and therefore, the thresholds were lower than expected. In this study, the number of independent chromosome fragments was defined as the inverse of the variance of the off‐diagonal elements of the genomic relationship matrix. However, it could be argued that this method might be not appropriate because it assumed uniformity of LD patterns across the genome (Goddard 2009). This assumption is not valid because LD patterns differ across chromosomes (Figure 9). Ignoring the variation in LD patterns could lead to an underestimation of the number of independent chromosome fragments and subsequently to underestimated thresholds, especially with iWGS.

Figure 9.

Figure 9

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LD decay on chromosome 2, 7 and 10 between medium‐density SNPs. LD was measured as r 2 between SNP in bins of 50 kilo‐base pair (KB)

4.2. Applications

The aim of this study was to investigate different approaches to test for significance in a GWAS with iWGS. The number of false positives can be minimized by a combination of applying genomic control and then choosing an appropriate threshold. We expected the combination of the genomic control and the FDR to result in an overestimation of the significance thresholds. Additional analysis confirmed that the combination of FDR and genomic control inflates the significance threshold, because the FDR threshold was 4.54 without considering genomic control (Table 3), and increased to 7.64 with considering first genomic control (result not shown). We hypothesize the high thresholds were caused by an entanglement of theory behind genomic control and FDR. Genomic control corrects p‐values for their deviations from the expected distribution. Also, the FDR establishes a new distribution by applying a cut‐off for p‐values that do not follow the expected distribution under the null hypothesis. So, both genomic control and FDR rescale the expected distribution and thus they are confounded in their underlying procedures.

Fitting the full genomic relationship matrix based on all chromosomes might alleviate the need for genomic control. When the aim is to find QTL regions that are associated with a trait of interest, we recommend to use the FDR following the BY approach to establish a significance threshold that is adjusted multiple testing. When the aim is to pinpoint a specific mutation, the more conservative Bonferroni correction based on the total number of SNPs appears more appropriate. However, it is still not satisfying that adjusting for the total number of SNPs is needed to account for increasing number of tests with iWGS, and a more appropriate method to determine the number of independent test is required.

5. CONCLUSION

The objective of this study was to compare different significance testing procedures and to establish guidelines for significance testing in a GWAS using either medium‐density genotypes, high‐density genotypes or iWGS for a commercial pig population. We found that genomic inflation factors did not differ between different densities but did differ between chromosomes. In addition, a genomic relationship matrix based on the leave‐one‐chromosome‐out approach does not account appropriately for population stratification and gave strong genomic inflation in this pig breeding population. Based on our results, we recommend to use either genomic control in combination with Bonferroni correction (using the total number of SNPs and depending on the aim of the study, relax the significance level) or the FDR without applying genomic control. Fitting the full GRM based on all chromosomes might alleviate the need for genomic control.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest regarding the publication of this article.

6.

ACKNOWLEDGEMENTS

This study was financially supported by NWO‐TTW, the Dutch Ministry of Economic Affairs (TKI Agri & Food project 16022, code BO‐22.04‐011‐001‐ASG‐LR) and the Breed4Food partners Cobb Europe, CRV, Hendrix Genetics and Topigs Norsvin. The use of the HPC cluster has been made possible by CAT‐AgroFood (Shared Research Facilities, Wageningen University and Research). The authors want to acknowledge Topigs Norsvin for providing the data.

van den Berg S, Vandenplas J, van Eeuwijk FA, Lopes MS, Veerkamp RF. Significance testing and genomic inflation factor using high‐density genotypes or whole‐genome sequence data. J Anim Breed Genet. 2019;136:418–429. 10.1111/jbg.12419

DATA AVAILABILITY

The data that support the findings of this study are from Topigs Norsvin. Restrictions apply to the availability of these data, which were used under licence for this study. Data are available from the authors with the permission of Topigs Norsvin.

REFERENCES

  1. Baker, M. (2016). Statisticians issue warning over misuse of P values. Nature, 531, 151 10.1038/nature.2016.19503 [DOI] [PubMed] [Google Scholar]
  2. Benjamini, Y. , & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300 [Google Scholar]
  3. Benjamini, Y. , & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165–1188. [Google Scholar]
  4. Blanchard, G. , Dickhaus, T. , Hack, N. , Konietschke, F. , Rohmeyer, K. , Rosenblatt, J. , … Werft, W. . μWerft TOSS‐Multiple hypothesis testing in an open software system. In: Proceedings of the First Workshop on Applications of Pattern Analysis. 2010. [Google Scholar]
  5. Browning, B. L. , & Browning, S. R. (2009). A unified approach to genotype imputation and haplotype‐phase inference for large data sets of trios and unrelated individuals. American Journal of Human Genetics, 84(2), 210–223. 10.1016/j.ajhg.2009.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cardon, L. R. , & Palmer, L. J. (2003). Population stratification and spurious allelic association. The Lancet, 361, 598–604. 10.1016/S0140-6736(03)12520-2 [DOI] [PubMed] [Google Scholar]
  7. Churchill, G. A. , & Doerge, R. W. (1994). Empirical threshold values for quantitative trait mapping. Genetics, 138, 963–971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Devlin, B. , & Roeder, K. (1999). Genomic control for association studies. Biometrics, 55, 997–1004. 10.1111/j.0006-341X.1999.00997.x [DOI] [PubMed] [Google Scholar]
  9. Diniz, D. , Lopes, M. , Broekhuijse, M. , Lopes, P. , Harlizius, B. , Guimarães, S. , … Silva, F. (2014). A genome‐wide association study reveals a novel candidate gene for sperm motility in pigs. Animal Reproduction Science, 151, 201–207. 10.1016/j.anireprosci.2014.10.014 [DOI] [PubMed] [Google Scholar]
  10. Do, D. N. , Strathe, A. B. , Ostersen, T. , Jensen, J. , Mark, T. , & Kadarmideen, H. N. (2013). Genome‐wide association study reveals genetic architecture of eating behavior in pigs and its implications for humans obesity by comparative mapping. PLoS ONE, 8, e71509 10.1371/journal.pone.0071509 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dudbridge, F. , & Gusnanto, A. (2008). Estimation of significance thresholds for genomewide association scans. Genetic Epidemiology, 32, 227–234. 10.1002/gepi.20297 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Duggal, P. , Gillanders, E. M. , Holmes, T. N. , & Bailey‐Wilson, J. E. (2008). Establishing an adjusted p‐value threshold to control the family‐wide type 1 error in genome wide association studies. BMC Genomics, 9, 516 10.1186/1471-2164-9-516 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Goddard, M.E (2009). Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica, 136, 245–257. [DOI] [PubMed] [Google Scholar]
  14. Goddard, M. E. , Hayes, B. J. , & Meuwissen, T. H. E. (2011). Using the genomic relationship matrix to predict the accuracy of genomic selection. Journal of Animal Breeding and Genetics, 128, 409–421. 10.1111/j.1439-0388.2011.00964.x [DOI] [PubMed] [Google Scholar]
  15. Hao, X. , Plastow, G. , Zhang, C. , Xu, S. , Hu, Z. , Yang, T. , … Zhang, S. (2017). Genome‐wide association study identifies candidate genes for piglet splay leg syndrome in different populations. BMC Genetics, 18, 64 10.1186/s12863-017-0532-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hinrichs, A. L. , Larkin, E. K. , & Suarez, B. K. (2009). Population stratification and patterns of linkage disequilibrium. Genetic Epidemiology, 33, S88–S92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Le, T. H. , Christensen, O. F. , Nielsen, B. , & Sahana, G. (2017). Genome‐wide association study for conformation traits in three Danish pig breeds. Genet. Select. Evol., 49, 12 10.1186/s12711-017-0289-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lippert, C. , Listgarten, J. , Liu, Y. , Kadie, C. M. , Davidson, R. I. , & Heckerman, D. (2011). FaST linear mixed models for genome‐wide association studies. Nature Methods, 8, 833 10.1038/nmeth.1681 [DOI] [PubMed] [Google Scholar]
  19. Listgarten, J. , Lippert, C. , Kadie, C. M. , Davidson, R. I. , Eskin, E. , & Heckerman, D. (2012). Improved linear mixed models for genome‐wide association studies. Nature Methods, 9, 525 10.1038/nmeth.2037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lopes, M. S. , Bastiaansen, J. W. , Harlizius, B. , Knol, E. F. , & Bovenhuis, H. (2014). A genome‐wide association study reveals dominance effects on number of teats in pigs. PLoS ONE, 9, e105867 10.1371/journal.pone.0105867 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Lopes, M. S. , Bovenhuis, H. , van Son, M. , Nordbø, Ø. , Grindflek, E. H. , Knol, E. F. , & Bastiaansen, J. W. M. (2017). Using markers with large effect in genetic and genomic predictions. Journal of Animal Science, 95, 59–71. 10.2527/jas2016.0754 [DOI] [PubMed] [Google Scholar]
  22. Luo, W. , Cheng, D. , Chen, S. , Wang, L. , Li, Y. , Ma, X. , … Zhang, L. (2012). Genome‐wide association analysis of meat quality traits in a porcine Large White× Minzhu intercross population. International Journal of Biological Sciences, 8, 580 10.7150/ijbs.3614 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ma, J. , Yang, J. , Zhou, L. , Zhang, Z. , Ma, H. , Xie, X. , … Huang, L. (2013). Genome‐wide association study of meat quality traits in a White Duroc× Erhualian F2 intercross and Chinese Sutai pigs. PLoS ONE, 8, e64047 10.1371/journal.pone.0064047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Nicodemus, K. K. , Liu, W. , Chase, G. A. , Tsai, Y.‐Y. , & Fallin, M. D. (2005). Comparison of type I error for multiple test corrections in large single‐nucleotide polymorphism studies using principal components versus haplotype blocking algorithms. BMC Genetics, 6(Suppl 1), S78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Patterson, N. , Price, A. L. , & Reich, D. (2006). Population structure and eigenanalysis. PLoS Genetics, 2(12), e190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Price, A. L. , Patterson, N. J. , Plenge, R. M. , Weinblatt, M. E. , Shadick, N. A. , & Reich, D. (2006). Principal components analysis corrects for stratification in genome‐wide association studies. Nature Genetics, 38, 904 10.1038/ng1847 [DOI] [PubMed] [Google Scholar]
  27. Raymond, B. , Bouwman, A. C. , Wientjes, Y. C. J. , Schrooten, C. , Houwing‐Duistermaat, J. , & Veerkamp, R. F. (2018). Genomic prediction for numerically small breeds, using models with pre‐selected and differentially weighted markers. Genetics Selection Evolution, 50, 14 10.1186/s12711-018-0419-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Reich, D. E. , & Goldstein, D. B. (2001). Detecting association in a case‐control study while correcting for population stratification. Genetic Epidemiology, 20, 4–16. [DOI] [PubMed] [Google Scholar]
  29. Ricard, A. , Robert, C. , Blouin, C. , Baste, F. , Torquet, G. , Morgenthaler, C. , … Barrey, E. (2017). Endurance exercise ability in the horse: A trait with complex polygenic determinism. Frontiers in Genetics, 8, 89 10.3389/fgene.2017.00089 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Sahana, G. , Kadlecová, V. , Hornshøj, H. , Nielsen, B. , & Christensen, O. F. (2013). A genome‐wide association scan in pig identifies novel regions associated with feed efficiency trait. Journal of Animal Science, 91, 1041–1050. [DOI] [PubMed] [Google Scholar]
  31. Sanchez, M.‐P. , Tribout, T. , Iannuccelli, N. , Bouffaud, M. , Servin, B. , Tenghe, A. , … Gilbert, H. (2014). A genome‐wide association study of production traits in a commercial population of Large White pigs: Evidence of haplotypes affecting meat quality. Genetics Selection Evolution, 46, 12 10.1186/1297-9686-46-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. van den Berg, S. , Vandenplas, J. , van Eeuwijk, F. A. , Bouwman, A. C. , Lopes, M. S. , & Veerkamp, R. F. (2019). Imputation to whole‐genome sequence using multiple pig populations and its use in genome‐wide association studies. Genetics Selection Evolution, 51, 13 10.1186/s12711-019-0445-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. van Son, M. , Enger, E. G. , Grove, H. , Ros‐Freixedes, R. , Kent, M. P. , Lien, S. , & Grindflek, E. H. (2017). Genome‐wide association study confirm major QTL for backfat fatty acid composition on SSC14 in Duroc pigs. BMC Genomics, 18, 369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91(11), 4414–4423. 10.3168/jds.2007-0980 [DOI] [PubMed] [Google Scholar]
  35. VanRaden, P. M. , Tooker, M. E. , O’Connell, J. R. , Cole, J. B. , & Bickhart, D. M. (2017). Selecting sequence variants to improve genomic predictions for dairy cattle. Genet Select Evol, 49, 32 10.1186/s12711-017-0307-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Veroneze, R. , Lopes, P. S. , Guimarães, S. E. F. , Silva, F. F. , Lopes, M. S. , Harlizius, B. , & Knol, E. F. (2013). Linkage disequilibrium and haplotype block structure in six commercial pig lines. Journal of Animal Science, 91, 3493–3501. 10.2527/jas.2012-6052 [DOI] [PubMed] [Google Scholar]
  37. Wasserstein, R. L. , Schirm, A. L. , & Lazar, N. A. (2019). Moving to a World Beyond “p < 0.05”. The American Statistician, 73, 1–19. [Google Scholar]
  38. Welter, D. , MacArthur, J. , Morales, J. , Burdett, T. , Hall, P. , Junkins, H. A. , … Hindorff, L. A. (2013). The NHGRI GWAS Catalog, a curated resource of SNP‐trait associations. Nucleic Acids Research, 42, 1001–1006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Yang, J. , Manolio, T. A. , Pasquale, L. R. , Boerwinkle, E. , Caporaso, N. , Cunningham, J. M. , … Visscher, P. M. (2011). Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genetics, 43, 519–525. 10.1038/ng.823 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Yang, J. , Zaitlen, N. A. , Goddard, M. E. , Visscher, P. M. , & Price, A. (2014). Advantages and pitfalls in the application of mixedmodel association methods. Nature Genet, 46, 100–106. 10.1038/ng.2876 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Zheng, G. , Freidlin, B. , & Gastwirth, J. L. (2006). Robust genomic control for association studies. American Journal of Human Genetics, 78, 350–356. 10.1086/500054 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are from Topigs Norsvin. Restrictions apply to the availability of these data, which were used under licence for this study. Data are available from the authors with the permission of Topigs Norsvin.

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