Statistical tests for Hardy–Weinberg equilibrium have been an important tool for detecting genotyping errors in the past, and remain important in the quality control of next generation sequence data. In this paper, we analyze complete chromosomes of the 1000 genomes project by using exact test procedures for autosomal and X-chromosomal variants. We find that the rate of disequilibrium largely exceeds what might be expected by chance alone for all chromosomes. Observed disequilibrium is, in about 60% of the cases, due to heterozygote excess. We suggest that most excess disequilibrium can be explained by sequencing problems, and hypothesize mechanisms that can explain exceptional heterozygosities. We report higher rates of disequilibrium for the MHC region on chromosome 6, regions flanking centromeres and p-arms of acrocentric chromosomes. We also detected long-range haplotypes and areas with incidental high disequilibrium. We report disequilibrium to be related to read depth, with variants having extreme read depths being more likely to be out of equilibrium. Disequilibrium rates were found to be 11 times higher in segmental duplications and simple tandem repeat regions. The variants with significant disequilibrium are seen to be concentrated in these areas. For next generation sequence data, Hardy–Weinberg disequilibrium seems to be a major indicator for copy number variation.The online version of this article (doi:10.1007/s00439-017-1786-7) contains supplementary material, which is available to authorized users. AA, AB and BB of a bi-allelic genetic marker are expected to occur with relative frequencies pq and p and q being the A and B allele frequencies, respectively. The law rests on many assumptions including sexual reproduction with random mating, non-overlapping generations, negligible mutation and migration rates, equality of allele frequencies in the sexes, absence of natural selection and absence of genotyping errors, which are usually discussed at length in genetic textbooks  most genotyping error has been removed. We think it is important to understand and identify the causes of disequilibrium, rather than merely discarding the significant results. Hence, we evaluated variants by also taking into account their genomic context, position, pertinence to a region with repetitive DNA, read depth and other factors. Plotting HW test results against the chromosomal position helped us identify genomic regions where variants with significant disequilibrium were clustered and thus enabled us to systematically investigate the cause(s) of disequilibrium. The structure of this article is as follows. First, we give a brief description of the database used and summarize exact tests for HWP for the autosomes and the sex chromosomes. Second, the Results section reports on global results, and contains subsections that focus on particular areas with an exceptional rate of HWD. Finally, discussion and conclusions complete the paper.In modern large-scale genotyping studies, genetic markers are typically tested for Hardy–Weinberg proportions (HWP) by using exact test procedures ] of phase 3 of the 1000 Genomes project . We usedA  and false negatives (markers out of equilibrium that go unnoticed because the test does not reject) will arise.The power and Type I error rate of Chi-square and exact tests have been studied in detail by several scholars Emigh  and it iIn this section, we first report some overall results. Some areas with a high disequilibrium rate are discussed in separate subsections below. We first address the issue of missing values. Figure mentclass2pt{minimdespite the ever decreasing power.Note that the percentage of significant variants starts to drop for variants with more than 50% missing values. This should not be taken as evidence that markers with that many missing values are better, but is a consequence of a loss of power due to a decreasing sample size. Indeed, a salient feature of Fig. p values below the significance level and thus clearly shows that there is far more disequilibrium than would be expected by chance alone. The significant markers are typically polymorphic markers that do not have a low minor allele frequency . In order to avoid low-quality markers with many missing values and excessive genotyping error, RS variants with more than 5% missings were discarded for the following HW computations. As pointed out in earlier work , the X-chromosome and some regions with incidental spikes or horizontal stripes. We also address the relationship between HWD and read depth and copy number variation.The human major histocompatibility complex (MHC) region is well known for its role in the immune system. This extremely gene-dense region covers a region of 3.6 Mb on chromosome 6 (The MHC sequencing consortium https://www.ncbi.nlm.nih.gov/). This shows that strong spikes of heterozygote deficiency occur upstream of the HLA-A, downstream the HLA-B and downstream the HLA-C genes. The variants inside these genes themselves appear however, to have heterozygote excess , and reveals a similar picture, with three composite spikes in the MHC region with mainly heterozygote deficiency.Chromosome 21 has a p-arm of 11 Mb, and this whole arm is an area with a very high rate of disequilibrium .Two shorter adjacent stripes with heterozygote deficiency are observed between 145.7 and 146 Mb, with compositions  and . The variants of these stripes are not contiguous, as they are interrupted by markers that have a different genotypic composition. The two shorter stripes can be interpreted as pertaining to the same haplotype as the longest stripe, but having, respectively, 5 and 6 AA homozygotes recorded as heterozygotes. Inspection of the data shows that the shorter stripes also consist of only two haplotypes that again unambiguously explain the genotype data. Each stripe has therefore three genotypes. When the three stripes are combined, five different genotypes are found. Moreover, three long red stripes with non-significant Finally, many incidental spikes of strong HWD are observed outside the aforementioned areas in the preceding sections. A few salient spikes are shown in Fig. This figure shows that spikes are of a homogeneous nature in the sense that each individual spike is characterized by having all its variants with excess or deficiency of heterozygotes. The 17 Mb spike on chromosome 1 concerns variants inside segmental duplications, almost universally with heterozygote excess. The spikes on chromosome 3 have variants in duplications and in tandem repeats. Chromosome 4 and 8 show spikes of heterozygote excess that do not coincide with duplications or repeats. Chromosome 4 shows a duplication almost exclusively characterized by deficiency of heterozygotes.Read depth is recorded in phase 3 of the 1000 genomes project data files as the total read depth per variant, that is, summed over the 104 individuals. Figure We stratified the results of our exact tests for HWP according to whether variants occurred in areas with segmental duplications or not, using the annotation on segmental duplications of at least 1Kb in the UCSC genome browser  and chromosome 21 (with a lot of HWD in its p-arm) are outlying with many significant variants in segmental duplications. Likewise, exact test results were also stratified according to inclusion in simple tandem repeats, using the simple tandem repeat tracks of the UCSC genome browser Benson . Figure Because of the higher rate of HWD in repeats and duplications, we recalculated the descriptive statistics in Table The JPT genome studied in this paper has 3.8% of all of its variants in segmental duplications, and 3.0% in simple tandem repeat regions, totaling an overall of 6.8% variants in areas with copy number variation. If we focus on the variants with significant HWD, then over 60% of these is found in areas with segmental duplications or simple tandem repeats. Figure p value distribution. The top ten most significant variants of each chromosome almost invariably consist of variants with 100% heterozygosity that occur predominantly inside segmental duplications or simple tandem repeats.According to Fig. The analysis described in this section was repeated for 107 individuals of the Yoruba (YRI) sample of the 1000 Genomes project. This sample consists mainly of parent–offspring trios, and children were eliminated from the database prior to analysis. Of some existing sibling pairs, one individual was removed too, in order to best satisfy the assumption of a sample of unrelated individuals. All corresponding graphics and tables for the YRI sample are included as supplementary material in Appendix B. The YRI sample clearly shows less significant HWD than the JPT sample as shown in Figure S5. The rate of significant markers is about 50% lower for the YRI sample, and also increased as a function of the percentage of missings. The YRI sample has about 13% less monomorphic variants on the autosomes, and about 23% less monomorphic variants on the X-chromosome. The median inbreeding coefficient is the same for all chromosomes, which can be ascribed to the existence of many low MAF variants on all chromosomes. HWD spikes for the MHC region, p-arm of chromosome 21, centromeres, telomeres were also observed in the YRI sample. Reported incidental spikes for the JPT sample were also observed in the YRI sample. The horizontal band corresponding to a haplotype on chromosome 6 at 146 Mb was not observed in the YRI sample. Similar associations between HWD and read depth and HWD and segmental duplications and tandem repeat regions were also found for the YRI sample.Calculations in this paper show there is more significant HWD in the genome than is expected to occur by chance alone under the assumption of independent markers. Disequilibrium is found to be most often due to heterozygote excess. Disequilibrium rates are 11 times larger in simple tandem repeat regions and in segmental duplications, suggesting HWD to be due to, at least in part, sequencing problems that arise from the existence of multiple copies of a polymorphism. This would explain why increased HWD is more often observed in genomic regions where duplications (MHC region) and repetitive DNA sequences (centromeres) are known to occur. In the following we discuss causes that can generate higher rates of HWD. We suggest heterozygote lack to be due to null alleles, and heterozygote excess to be due to duplication.Null alleles can arise from substitutions and indel mutations  suggest there exists copy number variation for the interrogated locus in the sampled individuals, and such areas are indeed characterized by heterozygote excess see Fig. . This foThe other extreme of the spectrum, having no heterozygotes for polymorphic markers with an MAF above 0.05, was also found in the JPT data for 88 autosomal variants. Most of these were in the class II genes of the MHC region, and at a HWD spike on chromosome 4 between 69.38–69.49 Mb.Excess disequilibrium can, at least in part, also be due to LD. If many variants reside on a haplotype, and the haplotype locus is out of HWP, then many of its constituent variants can be expected to be out of HWP too. The horizontal band in Fig. The HW analysis of the YRI data included in the appendix shows that this sample has relatively fewer significant variants. We note that the rates of significant variants of the two samples cannot directly be compared, even though the two samples have approximately the same size. The YRI sample has more variants, and relatively many more rare variants with a low MAF. Statistical tests for HWP with rare variants have low power Emigh  and thusp value prior to association analysis, in order to avoid genotyping error. Copy number variation is known to play an important role in genetic disease Below is the link to the electronic supplementary material.


Plasmodium falciparum The life cycle of the parasite implies that the mixture of multiple strains within an infected individual is related to the out-crossing rate across populations, making methods for measuring this process in situ central to understanding the genetic epidemiology of the disease. The advent of whole-genome sequencing has generated increased interest in modelling the structure of strain mixture within clinical infections of P. falciparum clinical samples, which provides resources to assess within-sample mixture that connect to extensive literatures in population genetics and conservation ecology. Features of the P. falciparum genome mean that standard methods for inbreeding coefficients and related F-statistics cannot be used directly. After reviewing an initial effort to estimate the inbreeding coefficient within clinical isolates of P. falciparum, several generalizations using both frequentist and Bayesian approaches are provided. A simpler, more intuitive frequentist estimator is shown to have nearly identical properties to the initial estimator both in simulation and in real data sets. The Bayesian approach connects these estimates to the Balding–Nichols model, a mainstay within genetic epidemiology, and a possible framework for more complex modelling. A simulation study shows strong performance for all estimators with as few as ten variants. Application to samples from the PF3K data set indicate significant across-country variation in within-sample mixture. Finally, a comparison with results from a recent mixture model for within-sample strain mixture show that inbreeding coefficients provide a strong proxy for these more complex models. This paper derives a set of new estimators for inferring inbreeding coefficients using whole genome sequence read count data from P. falciparum samples from whole-genome sequence data, supported by simulation studies and empirical examples. It includes a substantially simple estimator with similar statistical properties to the estimator in current use. These methods will also be applicable to other species with similar life-cycles. Implementations of the methods described are available in an open-source R package pfmix. Estimates for the PF3K public data release are provide as part of this resource. This paper provides a set of methods for estimating inbreeding coefficients within  Plasmodium falciparum, many aspects of the genetic epidemiology of the parasite remain obscure [While genetic factors play a crucial role in the emergence of drug resistance within  obscure , 2. The  obscure . This stP. falciparum studies to other genetic contexts [P. falciparum 3000 genomes (PF3K), a publicly available data resource.Strain mixture has been traditionally assessed via multiplicity of infection (MOI) –6, usingcontexts , 10. Thiw) relative to the expected amount in a subpopulation (s). Since here estimates are only considered only relative to a single country (subpopulation), the use of paired subscripts, i.Inbreeding coefficients and the F-statistics from which they derive are measurements of the departure of allelic heterozygosity observed within a population from those expected at Hardy–Weinberg equilibrium (HWE) , 11. HWEP. falciparum and so cannot be used directly. The critical difference is that, within a human host, P. falciparum exists only in the haploid stage of its life-cycle [P. falciparum strains, no ’out-of-the-box’ use of standard F-statistics approaches with this new data appears possible.F-statistics have proven to be an effective and extremely popular means for investigating species’ population structure from both allelic and genomic data , 13, 14.fe-cycle . Since sP. falciparum within-sample mixture. These concepts—while not under the heading of inbreeding coefficients—undergird much of the seminal work on MOI estimation [msp-1 and msp-2 genes, showing strong correlation between these values in their sample sets.Several earlier works have applied the F-statistic framework to timation , 6. Moretimation  providedtimation  exploresP. falciparum, including analyses of populations in the Gambia, Ghana, and Guinea [Plasmodium vivax and Plasmodium knowlesi [F-statistics; an investigation of their properties through simulations; and several applications to relevant data sets.This estimator has been further utilized in a number of recent studies on d Guinea , 18. It knowlesi , 20. A rknowlesi . There hThis paper proceeds as follows. First, an overview of the data and the notation is provided. The initial estimator employed by Manske et al.  for estiP. falciparum samples collected from 14 countries. Starting from the publicly available vcf files, samples from Nigeria and Senegal were also excluded due to sample size and differing sequencing technology, respectively. First, only positions that exhibited minor allele frequencies greater than 0.01 were retained. Variants were furthered filtered at the country level by removing samples that exhibited fewer than 80 % of variant positions with at least 20× coverage. SNPs with less than 20× coverage were then removed from all remaining samples. This yielded variable number of SNPs within countries, from 1108 in Cambodia to 6596 in Laos. The number of samples within each country ranged from 35 for Laos to 344 in Ghana. Four additional samples—two replicates each of DD2 and 7G8—were taken from Release 5.0 of the PF3K resource for use as negative control. Each of these samples comprised a single, unmixed strain and was sequenced to high coverage (~65×). These were cleaned according to the steps above, yielding 23,109 viable positions. A subset of 1000 SNPs were randomly selected of those remaining for inference here.The data used comes largely from Release 3.0 of the PF3K resource. An overview of this project, collection protocols, and a full sequencing protocol can be found at the consortial website . For allj within sample i, we observe j in sample i and estimate it via the maximum-likelihood estimator (MLE) for proportions: Within a country, samples are indexed mentclass2pt{minimi, the estimator first partitions alleles into 10 equally-spaced bins based on their minor allele frequency: b, the average expected heterozygosity assuming country-level HWE is calculated byb. The average observed heterozygosity within each bin and each sample is calculated byIn Manske et al., the authors provide an initial approach to estimating F-statistics, can be found in the following way. For a single SNP j, suppose j. Averaging across all SNPs and taking the ratio of expectations to be the expectation of the ratios gives the estimatorThe binning procedure, while stabilizing the estimator against influence from an excess of low frequency alleles common within samples, may also introduce bias. This effect can be removed by discarding the binning procedure in favour of directly regressing observed heterozygosity for each SNP against the expected value, still constrained to pass through the origin. This provides a closed-form expression for the regressed estimator, For each of these estimators, a bootstrap approach is employed to estimate the variance in the estimates for confidence intervals , 24. TheP. falciparum [Inbreeding coefficients comparable to the above estimators can also be derived by the Balding—Nichols model, a widely used method for measuring inbreeding in other genetic contexts . This aplciparum , 6. In uP. falciparum WGS data [P. falciparum the relevant subpopulation is the collection of parasites within a clinical sample. For sample i and SNP j, conditional upon an inbreeding coefficient j and position j:The approach for the Bayesian estimator adapts the Balding–Nichols model of allele frequency within inbred subpopulations to the specific context of WGS data , 26. In x to new parameter state x and i and propose j and then draw the proposed parameter x and The autocorrelation of the log-posterior has minimal lag. As a secondary check, the chain was run for all of the chromosomes individually and compared values with the complete data set. Since SNPs are treated as independent, the performance of the model should be unaffected if the model performs similarly across chromosomes. Across all chromosomes performance is nearly identical, with greater than a posteriori (MAP) estimates.Since the posterior distribution is not known in closed form, standard random-walk Metropolis-Hastings Markov chain (MCMC) approach is used to numerically approximate it , 28. TheAll code was implemented in the R computational environment . The setTo compare the qualities of the four estimators, a simulation study was performed under a range of parameter values to capture how estimator performance may vary with the quality of data collected in the field. The number of SNPs, the number of read counts at each SNP, the degree of skew in the allele frequency distribution  estimate is reported. The Bayesian estimator is still highly correlated (>0.9) with the other estimators but is significiantly more variable in its estimates. Highly mixed and highly unmixed samples , above the standard threshold for clonality (0.95). The standard deviation of these estimates was less than 0.01. Within these samples, a moderate number of SNPs  evenly across the genome. However, inference from a small region of the genome will likely exhibit bias.The model underlying the inbreeding coefficient makes a number of assumptions about the structure of the read count data and the biological mixing process that may affect inference. For the read count data, read counts are assumed to be unbiased and the SNPs are unlinked. While short read data can be biased in several ways, previous research indicates that mixture proportions calculated by read count ratios are largely unbiased (for instance, see  supportiF-statistic. An F-statistic measures the departure of the observed number of heterozygotes relative to those expected under Hardy–Weinberg equilibrium. In the context of mixed P. falciparum infections, the equilibrium assumptions—random mating, no selection, large population size, genetic isolation—are likely each violated at some level. For example, the mixture within a sample may be the result of a small number of founding individuals or be strongly selected by the human immune system. Without a more general approach to understanding the mixing process, anticipating the robustness of these estimates to this sort of misspecification is difficult. However, we do find that the PF3K samples from Cambodia that possess quite significant population structure still exhibit strong correlation between A perhaps more troublesome assumption is embedded in the underlying structure of the P. falciparum genetic epidemiology, it will still be useful for field researchers to connect their work with population genetics and ecology through simple metrics. These issues are also relevant for researchers in a number of other Plasmodium species and protozoa with similar life-cycles. Inbreeding coefficients, which have a history going back to the beginnings of modern genetics, connect to a number of population genetic quantities such as effective population size and genetic drift [As genomic data enables more elaborate statistical models for mixed infections and a broader understanding of ic drift , 32, 33


P < 5 × 10−8, into four classes based on the direction of their effects on BMI and WHR. Our classes help differentiate adiposity genetics with respect to anthropometry, fat depots, and metabolic health. Class-specific Mendelian randomization reveals that variants associated with both WHR-decrease and BMI increase are linked to metabolically rather favorable adiposity through beneficial hip fat. Class-specific enrichment analyses implicate digestive systems as a pathway in adiposity genetics. Our results demonstrate that WHRadjBMI variants capture relevant effects of “unexpected fat distribution given the BMI” and that a joint view of the genetics underlying related phenotypes can inform on important biology.The problem of the genetics of related phenotypes is often addressed by analyzing adjusted-model traits, but such traits warrant cautious interpretation. Here, we adopt a joint view of adiposity traits in ~322,154 subjects (GIANT consortium). We classify 159 signals associated with body mass index (BMI), waist-to-hip ratio (WHR), or WHR adjusted for BMI (WHRadjBMI) at  In GWAS, waist-to-hip ratio (WHR) is often adjusted for body mass index (BMI) to account for their correlation (WHRadjBMI). Here, Winkler et al. classify 159 genetic variants for BMI, WHR, or WHRadjBMI based on their effect directions for BMI and WHR to differentiate subtypes of adiposity genetics. However, for our understanding of the underlying mechanisms, it is an important challenge to disentangle the genetics of related phenotypes. Frequently, this is approached by using an adjusted-model trait where the trait Y is adjusted for a covariate Z (YadjZ) in order to separate the genetics of YadjZ from the genetics of Z. However, these adjusted-model traits warrant cautious interpretation: as Aschard and colleagues pointed out, genome scans for traits adjusted for heritable covariates reveal not only genetic factors for the phenotype Y, but also those of the covariate Z to an extent that depends on their correlation1: BMI and WHR are correlated and capture two aspects of adiposity, overall fat mass, and fat distribution, respectively. Both are independently associated with type 2 diabetes (T2D), coronary artery disease (CAD), and mortality2. The phenotypic correlation between BMI and WHR and the biological mechanisms linking these two measures hamper the distinction of their genetic make-up4. Recently, meta-analyses by the GIANT consortium highlighted hundreds of associated loci for BMI, WHR, and WHRadjBMI6, whereas BMI and WHRadjBMI loci were shown to depict different biological processes , a direct comparison of the loci was lacking.We exemplify this issue on adiposity traits that were also utilized by Aschard and colleaguesbWHRadjBMI, is related to the estimate for WHR, bWHR, and the estimate for BMI, bBMI, by bWHRadjBMI = bWHR – r * bBMI, with r being the observational correlation between BMI and WHR in the analyzed study1. A genome-wide scan on WHRadjBMI will thus not only identify genetic factors for WHR, but will also tend to pick up variants with an additional opposite effect on BMI, or even an effect on BMI only when the sample size is large enough. Aschard and colleagues extended their point by cautioning against potentially false positive signals and biased genetic effect estimates. They propose to examine the potential of the bias by investigating the corrected effect bWHRadjBMI + r * bBMI to ensure that an established WHRadjBMI-association is not biased by the BMI-association.Aschard and colleagues pointed out that some of the WHRadjBMI lead variants were not completely independent of BMI and showed some effect on BMI in the unexpected direction . This is due to the fact that the genetic effect estimate for WHRadjBMI, 1 for its potential to yield biased estimates and spurious associations. As a consequence, it is a current concern whether adjusted-model trait loci like WHRadjBMI loci can reveal meaningful biological information or whether they represent uninterpretable artefacts. We thus investigated the genetic variants that are associated genome-wide with BMI, WHR, or WHRadjBMI in the GIANT data6 with regard to their co-association with BMI and WHR and the link of this co-association to metabolic health and pathways. We find that the joint view of the genetic variants across all three of the adiposity traits helps differentiate adiposity subtypes with distinct fat depots and distinct metabolic implications. Furthermore, the joint view helps resolve some of the issues that derive from conducting GWAS on adjusted-model traits.Therefore, the genome screening of adjusted-model traits in general, and WHRadjBMI in particular, has been criticizedP < 5 × 10−8) with any of the three adiposity traits, BMI, WHR, and WHRadjBMI from the GIANT consortium 6. This yielded 159 independent lead variants ( > 500kB or r2 < 0.1): 102, 38, or 53 variants genome-wide significant for BMI, WHR, or WHRadjBMI, respectively. We found a substantial overlap of WHR-derived variants  with BMI- or WHRadjBMI-derived variants , with four being exclusive to the WHR-scan, but no overlap between BMI- and WHRadjBMI-derived variants  or “an unexpected effect” can be determined by evaluating the variant’s co-association with BMI and WHR: the co-association of the 159 variants is visualized in a plane spanned by the genetic effects on WHR and BMI, i.e., bWHRvs. bBMI ; this is in line with a notion of “an expected change in fat distribution given the change in BMI”. Variants with a non-null effect on WHRadjBMI will be those distant from the WHRadjBMI null line. This includes variants with a WHR effect but no effect on BMI, variants with a WHR effect into the opposite direction as their BMI effect, variants with effects on WHR larger than expected from the BMI effect (“supra-expected”), or even BMI effects with no effect on WHR. All are in line with a notion that the observed WHR effect is unexpected given the variant’s BMI effect. We hypothesized important insights from a detailed view of these variants’ position on the bWHR−bBMI-plane and the link of this position to physiology and pathology.Whether or not a genetic variant has “an expected effect on WHR given the BMI effect” , corresponding to an uncertainty of beta-estimates given by a 95% confidence interval, and as a null effect otherwise. We defined the following four classes: (1) BMI and WHR effects in the same direction , (2) BMI only effects , (3) WHR only effects , (4) BMI and WHR effects into opposite directions . Of note, the WHR effects that were directionally consistent with the BMI effect, but larger than expected (“supra-expected”) were classified as BMI + WHR + . This classification resulted in 82, 25, 28, or 24 variants for each of the four classes, respectively . All 53 WHRadjBMI-derived variants were orthogonally distant from the WHRadjBMI null line and can be considered effects of “unexpected change in fat distribution given the effect on BMI”. (ii) The 102 BMI-derived variants were all in the BMI + WHR + or BMIonly + class  The 38 WHR-derived variants were spread across the classes BMI + WHR + , WHRonly−, or BMI + WHR−  of the 159 variants, the 53 WHRadjBMI-derived variants were all in the PWHR < 0.05, i.e., no spurious associations, weakest WHR association observed in GIANT PWHR = 7.5 × 10−3, Supplementary Data BMI ≥ 0.05), 24 had a nominally significant effect on BMI (PBMI < 0.05) into the opposite direction. Therefore, WHRadjBMI-derived variants cannot be considered as “independent of BMI”.We made further important observations regarding the WHRadjBMI-derived variants: (iv) All 53 WHRadjBMI-derived variants had nominally significant effects on WHR  resulted in 36 of the 53 WHRadjBMI-derived variants retaining the class, 11 variants changing from BMI + WHR− to WHRonly−, and six just missing the PWHR < 3 × 10−4 in the GIANT data . However, these six variants showed a significant association with WHR in the independent UK Biobank data . Among those, 11 variants showed significant sex-difference in the genetic effect on WHRadjBMI in our data (PSexdiff < 0.05/53). Among those, the 10 variants with women-specific effects retained class in the women-specific, but not in the men-specific classification; similarly, the one variant with men-specific effect retained class in the men-specific, but not in the women-specific classification. For all other variants there was no remarkable pattern by the re-classification for sex-specific effects , and splitting the WHRadjBMI-derived loci into two groups .Generally, with a few exceptions, our classification resulted in splitting the BMI-derived loci into two groups . We aimed to provide empirical data of how good this computation works by comparing the bWHR estimates computed as described above with the observed bWHR , we found perfect agreement between computed and observed bWHR (Spearman correlation coefficient= 0.98). When conducting this comparison in a meta-analysis setting where r could not be estimated directly , we found still a strong agreement (Spearman correlation coefficient = 0.88). We were able to improve this agreement even further by using sex-stratified correlation estimates  and sex-stratified effect estimates . Therefore, the formula bWHR = bWHRadjBMI + r*bBMI can very well be used to compute unadjusted estimates from adjusted estimates and BMI estimates; the corresponding standard errors are, however, slightly increased yielding lower power .We were interested in whether the four classes characterized meaningful phenotypes with regard to anthropometry, fat depots, and cardio-metabolic health. We thus derived genetic effects of our 159 variants for such measures from genetic consortia and UK Biobank . Effects were aligned for BMI-increasing alleles, where appropriate, and for WHR-decreasing alleles for N = 253,239), we found a clear separation of the four classes , which is a VAT-type fat stored in/around the heart13. We evaluated the 159 genetic variants’ association on measures derived by bioelectrical impedance  or imaging techniques , subcutaneous adipose tissue (SAT) that is ubiquitously stored with a preference at hip and thigh15, GLGC16, MAGIC17, CARDIoGRAMplusC4D18, up to N = 187,135, Supplementary Data BMI + WHR + or BMI + WHR−, respectively, but a rather neutral or inconclusive pattern for BMIonly + (except for the known extreme disease effect of TCF7L2 into the opposite direction as expected by the BMI effect) and WHRonly−. This was supported by enrichment and GRS analyses , metabolically neutral or inconclusive , and metabolically rather favorable adiposity (BMI + WHR−) with some exceptions.Third, we evaluated the effects of the 159 variants on eight cardio-metabolic traits . We applied Data-Driven Expression Prioritized Integration for Complex Traits (DEPICT) for different sets of variants: (i) by the scan that a variant was selected for or (ii) by class. Our scan-specific DEPICT analyses replicated previous findings6 . Previous work had not investigated the WHR-derived variants and we found here that they provided an inconclusive pattern without any significant pathway enrichment  for adipocyte-related cells and tissues as reported previously6, but also in physiological systems labeled ‘digestive’  and ‘urogenital’  , after excluding all five RSPO3 signals , or after using a wider locus definition treating the RSPO3 signals as a single region in the DEPICT analyses  Fig. . This WH20 to examine data from GTEx21 for tissue-specific enrichments of expression effects of genes overlapping our association results , again separating the variants by class. Consistent with the class-specific DEPICT analyses, genes harboring WHRonly− variants were significantly enriched (Bonferroni-adjusted P < 0.05) for expression effects in an adipocyte-related tissue (‘Adipose_Subcutaneous’) as well as in digestive tissues  that contributed to the significant digestive system results in both DEPICT and FUMA tissue-specificity analyses of WHRonly− class variants.To follow-up this finding, we used FUMAWHRonly− class variants.In summary, we identified the digestive system as a pathway for obesity genetics, which highlights an important biology underlying the BMI + WHR + alleles increased waist, hip, SAT, VAT as well as T2D and CAD risk consistent with the observed adverse lipids and insulin profile. This would be in line with a biological model of a CNS-triggered increase in fat mass and a metabolically unfavorable genetic pre-disposition to store fat subcutaneously and viscerally . (ii) BMIonly + alleles presented a similar pattern with increased hip, waist, and SAT, but without VAT storage consistent with an observed neutrality toward T2D or CAD . At least one WHRonly− variant pointed to a different mechanism of enhanced bone growth: the variant near GDF5-UQCC is a known height locus11 and got grasped by WHRonly− due to increased hip probably from bone growth rather than adiposity. For the genes within WHRonly− signals, we found enrichment of expression in digestive systems in DEPICT and FUMA analyses. (iv) BMI + WHR− alleles increased hip and SAT, but had no effect on waist or VAT, and a markedly favorable metabolic profile . Our Mendelian Randomization approach27 restricting the instruments to the BMI + WHR− variants showed that their BMI-increasing effect was causally linked to a favorable metabolic profile, particularly decreased risk of T2D and CAD. We also showed that the BMI increase of BMI + WHR− variants was causally linked to increased hip circumference and SAT, but had no effect on waist circumference or VAT. This would be in line with a direct beneficial effect of SAT stored on hip, possibly through adipokines12, for this subtype of adiposity effects.When summarizing the results of our data and analysis, we are able to characterize our four adiposity genetics classes with regard to anthropometry, fat depots, metabolic consequences, and implicated pathways (Supplementary Table BMI + WHR + ) and metabolically rather favorable adiposity(BMI + WHR−) at high precision that prompted the identification of 16 loci for favorable adiposity including 10 novel compared with previous work28. The focus on one of the four classes (WHRonly−) enabled us to reveal the digestive systems as a pathway for obesity genetics that extends upon previous work highlighting neural and adiposite/insulin pathways6. Our work has implications for adiposity research and GWAS methodology.Our investigation demonstrated that a classification of genetic adiposity variants based on their co-association with BMI and WHR characterized distinct anthropometry and different modes of fat deposition. Importantly, our four classes help distinguish metabolically unfavorable  and WHRadjBMI-derived variants into two groups . Our classification based on nominal significance of BMI and/or WHR association is straightforward and easy to apply. Certainly, there are other methodological approaches for clustering or evaluating multivariate effects worthwhile to be explored in the future30.With regard to adiposity research, our work links to previous work that separated between BMI-scan identified and WHRadjBMI-scan identified adiposity variants and highlighted differential pathways, neural (BMI) versus adipose/insulin (WHRadjBMI)BMI + WHR + and BMIonly + differentiate BMI-derived effects between those that involve VAT and increased disease risk from those that do not (with few exceptions). (ii) Our class BMI + WHR− distinguishes effects of pure hip increase (without any effect on waist) from those with altered waist . The BMI-increasing alleles of BMI + WHR− variants are all hip-increasing and jointly show a substantial reduction of disease risk . This finding contributes to the considerable debate on whether the acknowledged importance of SAT stored on the lower body31 stems from its role as a reservoir to avoid fat storage on more detrimental places like VAT32 or from a directly beneficial effect from lower body fat itself33, as the BMI + WHR− variants show no effect on waist circumference, their metabolically beneficial effect can only stem from a directly favorable effect from hip increase, but not from a less-detrimental storage compared with central body fat . This also emphasizes the role of WHRadjBMI as a relative measure of lower body fat that is missed when focusing on WHRadjBMI as a measure of central obesity. (iii) For the WHRonly− class, which focuses on a subset of WHRadjBMI variants, significant expression enrichment identifies digestive systems as a pathway for obesity genetics via two independent methods utilizing two independent data sets (FUMA and DEPICT). The fact that this enrichment emerged only when restricting to WHRonly− loci, but not when analysing all WHRadjBMI loci together, suggests that WHRonly− loci capture an adiposity subtype that is diluted in the larger set of WHRadjBMI loci. Still, further data and experiments will be necessary to determine the mechanisms through which these variants can be linked to transcriptional regulation in digestive systems. Altogether, we conclude that our four classes capture distinct anthropometry and fat depots and help distinguish important adiposity mechanisms  Our classes sms Fig.  that wer24 or 5328 loci previously identified for insulin resistance and put into context with favorable adiposity, 7 or 13 loci, respectively, capture favorable adiposity effects in the here utilized data following a definition where the BMI-increasing allele (PBMI < 0.05) shows decreased risk of T2D or CAD . To be comparable, we derived 1 Mb regions around our 159 lead variants resulting in 117 distinct regions. Of these 117 regions, excluding TCF7L2 owing to its extreme T2D risk , 16 regions contained one of our 159 signal variants with a favorable adiposity effect. Of these 16 regions, 10 were novel compared with previous work28, 10 were classified as BMI + WHR− including seven novel (Supplementary Data There have been different approaches to capture favorable adiposity. Among the 111: (1) WHRadjBMI effects differ from WHR effects by –r*bBMI (with r being the phenotypic correlation). We provided empirical data that WHR effects can be effectively computed from WHRadjBMI and BMI effects in a single study and in a meta-analysis setting . (3) WHRadjBMI GWAS enrich for WHR effects with simultaneous effects on BMI into the opposite direction, which are exactly the effects in the BMI + WHR− class.With regard to GWAS methodology, we confirm several points brought up by Aschard and colleaguesbWHR−bBMI plane  WHRadjBMI-derived variants were suspected to yield spurious association. When considering spurious association of WHRadjBMI-derived variants in the sense that such variants end up having no effect on WHR but only on BMI, we found no such alleged spurious association: all of our WHRadjBMI-derived variants showed an effect on WHR in GIANT data  and in UKBB data at Bonferroni-corrected significance  whereas WHRadjBMI effects are not “independent of BMI”, our WHRadjBMI-derived variants are distinct from BMI-derived variants with regard to their position on the ane Fig. . With inWe believe that, possibly due to the misinterpretation of the work of Aschard and colleagues, WHRadjBMI GWAS was perceived as treacherous and less useful than a WHR GWAS. However, we have shown that omitting the WHR-scan would have missed only four variants, whereas an omission of the WHRadjBMI-scan—out of a fear of bias and spurious association—would have missed 27 adiposity genetics signals. We have also shown that WHR-derived variants lack any distinct pathway pattern, whereas WHRadjBMI-derived variants are health-relevant  and pick up important biology (expression in adipose/insulin and digestive systems). We conclude that, in this example of adiposity genetics, the adjusted-model trait GWAS has advantages over the unadjusted trait GWAS. This does not mean that each adjusted-model trait GWAS is useful; this has to be evaluated on a case-by-case basis.bBMI−bWHR plane provides a clearer view on the underlying anthropometry than separating between BMI- and WHRadjBMI-derived genetics, and (D) a classification using the variants’ position on the bBMI−bWHR plane can serve to differentiate adiposity mechanisms and metabolic health. Particularly, BMI + WHR− variants can be used effectively to search for favorable adiposity effects. Our approach of joining WHRadjBMI and BMI-derived variants (with or without adding WHR-derived variants), rather than disentangling623, and linking the variants’ positions on the bBMI−bWHR plane to metabolic implications  if only two GWAS scans (rather than three) are feasible, stick with the BMI- and the WHRadjBMI GWAS, (B) WHR effects can be computed by WHRadjBMI and BMI effects, if not available otherwise, (C) a joint view of BMI- and WHRadjBMI-derived variants on the ons Fig.  helped rIn summary, our approach and results provide insights into adiposity subtypes and an example for a co-analysis of related phenotypes including adjusted-model traits to help reveal new biology.www.broadinstitute.org/collaboration/giant)6. We used sex-combined, European ancestry meta-analysis results including up to 322,154 persons (114 studies), 212,248 (101 studies), or 210,088 (101 studies) for BMI, WHR, or WHRadjBMI, respectively. In brief, in each study, inverse-normal transformed residuals were calculated from regressing BMI and WHR on age, age2, and other study covariates like principal components—and on BMI to derive WHRadjBMI. The genetic effect estimates on BMI, WHR, and WHRadjBMI were obtained from inverse-variance weighted meta-analyses of study-specific genetic effect estimates on BMI, WHR, or WHRadjBMI, respectively. Genome-wide association studies were either based on Hapmap-imputed SNPs (~2.8 M SNPs) or on genotyped Metabochip SNPs (~190 K SNPs). For further details, see the study descriptive in published literature for BMI5, WHR, and WHRadjBMI6. Informed consent was obtained from all study participants, and study protocols were approved by the local ethics committees.Our evaluation was based on genome-wide association meta-analysis results for BMI, WHR and WHRadjBMI (from 2015) that are publically available from the GIANT consortium website (P < 5 × 10−8). We then combined these three sets of SNPs derived across the three traits yielding a set of 2589 SNPs. We clumped these into 159 non-overlapping, independent regions using a combined distance- and linkage disequilibrium-based criterion . The lead SNP of a region was defined as the SNP with the smallest association P-value within the region (no matter from which trait the SNP was derived). The “traits of a region” were defined as the union of traits contributing with genome-wide significance across the SNPs of the respective region. We used PLINK36 and EasyStrata37 for the clumping and for the comparison across traits. Of note, our clumping strategy will yield a different number of independent signals compared with the two GIANT studies that applied a distance-only criterion  and used multiple ancestries as well as sex-specific results to select significantly associated variants.We excluded SNPs with < 10,000 individuals contributing to the respective meta-analysis and SNPs on sex chromosomes. For each of the three traits, we first selected all variants at genome-wide significance  to follow-up our WHRadjBMI-derived (PWHRadjBMI < 5 × 10−8 in GIANT) for their association with BMI and WHR. The results for WHR and BMI were downloaded from the GeneAtlas9 website (http://geneatlas.roslin.ed.ac.uk/downloads/?traits = 92) and from the Neale Lab website (https://sites.google.com/broadinstitute.org/ukbbgwasresults/), respectively.We used data from the final release of the UK Biobank . For this, we utilized linear regression to obtain residuals of BMI or WHR adjusted for age, age2 , five ancestry principal components, and batch indicators, and used these residuals to derive genetic association (as in GIANT). This was done by sex and results meta-analyzed.For the comparison of estimated and re-computed WHR effects, we used data from the UK biobank interim release (www.broadinstitute.org/collaboration/giant): From the 2015 round of meta-analyses, the sex-combined results for WC6 , HIP6  and HT38  as well as sex-specific results for BMI5 and WHR6; and from the 2013 round of sex-specific meta-analyses, the sex-specific results for WT8. In order to derive sex-combined meta-analysis results for WT , we conducted a fixed-effect inverse-variance weighted meta-analysis of the two sex-specific WT results. Again, the GIANT consortium results were based on inverse-normal transformed (or standardized for HT) phenotypes in the study-specific analyses.To investigate the identified loci for their effects on waist circumference, hip circumference (HIP), weight (WT), and height (HT), we utilized the publically available meta-analysis summary statistics from the GIANT consortium . We utilized the meta-analysis summary statistic for , visceral adipose tissue  and pericardial adipose tissue  as well as for the ratio of VAT and SAT . For comparison reasons, we computed beta-estimates (assuming a standardized outcome) from the publically available Z scores using beta = Z/sqrt(N * 2* eaf * (1-eaf)), where eaf is the allele frequency of the effect allele, N is the sample size of the meta-analysis and Z the corresponding (and provided) Z score.We evaluated the effects of the identified variants on computed tomography and magnetic resonance imaging derived measures of ectopic fat volumes as described previouslyN = 69,033) from DIAGRAM15 (www.diagram-consortium.org/downloads.html), fasting insulin  and fasting glucose  from MAGIC17 (www.magicinvestigators.org/downloads/), for triglycerides , HDL-C  and LDL-cholesterol  from Global Lipids Genetics Consortium16 (csg.sph.umich.edu/abecasis/public/lipids2013/), and for myocardial infarctions  and CAD  from the CARDIoGRAMplusC4D18 (www.cardiogramplusc4d.org/data-downloads/). Again, if not available from the downloaded data, we computed beta-estimates (assuming a standardized outcome) from the publically available z scores according to the formula provided before.To investigate the identified variants for their effects on other metabolic traits, we utilized publically available meta-analysis summary statistic from several genomic consortia: T2D .We applied binomial tests to evaluate whether the variants in each class were enriched for nominal significant effects on anthropometric, impedance, ectopic fat, or cardio-metabolic traits. We applied a conservative Bonferroni-corrected significance level to the binomial tests  separately by each of the adiposity subtypes. This way we estimated the causal effect of the BMI increase on all 20 traits by restricting the instruments  to one class. We excluded the WHRonly− variants, which are no effective instruments for BMI. We applied a conservative Bonferroni-corrected significance level to the meta-regression results .We also conducted class-specific inverse-variance weighted summary statistic based Mendelian randomization analysis19 to test whether genes harboring associated variants were enriched for genes with expression effects in different tissue, cell type and physiological system. More specifically, DEPICT tests whether the genes in associated regions are highly expressed in any of the 209 MeSH annotations for 37,427 microarrays on the Affymetrix U133 Plus 2.0 Array platform. DEPICT version 1 rel194 was downloaded from https://data.broadinstitute.org and updated using scripts from GitHub (https://github.com/perslab/depict). To prevent bias in the enrichment analysis results owing to the customized design of the Metabochip, we restricted the DEPICT analyses to variants with pronounced association in the GWAS-only meta-analyses  as done previously6. These data were available to us through our collaboration with GIANT. Our analysis was based on all variants reaching P < 1 × 10−5 in at least one of the GWAS-only meta-analyses for BMI, WHR, or WHRadjBMI. We clumped the GWAS-only variants based on linkage disequilibrium of r² > 0.1 from 1000 Genomes40 data and 500 kb flanking regions to obtain lists of independent SNPs. We conducted DEPICT analyses separately for the loci that were identified with the BMI-scan, the WHR-scan, or the WHRadjBMI-scan . We also conducted the enrichment analysis separately for the variants in each of the four classes BMI + WHR + , BMI only, WHR only, and BMI + WHR−  using all at P < 10−5 (GWAS-only) and then classifying variants based on the combined GWAS and Metabochip meta-analysis results. For each DEPICT analysis, the list of utilized SNPs were merged with overlapping genes utilizing precomputed gene regions based on 1000 Genomes project variants. SNPs within the major histocompatibility complex region on chromosome 6, base pairs 25,000,000–35,000,000, were excluded. DEPICT analyses were conducted using the following parameters: 100 repetitions to compute FDR and 1000 permutations based on 500 null GWAS to compute P values adjusted for bias due to gene length. A total of 10,968 reconstituted gene sets were used for the enrichment analysis. Tissue/cell type and Physiological system enrichment plots were generated using R-scripts on the basis of the R-script provided with DEPICT. Sensitivity analyses for the WHRonly- class were conducted that (1) excluded all known height-associated loci from , (2) excluded variants harboring the RSPO3 gene and (3) utilized a wider locus definition criterion that was based on distance-only ( ± 500 kb).In order to search for enriched pathways among the genes beneath association signals, there are several tools available, with little evidence, which one is superior. We utilized DEPICT20 web tool (http://fuma.ctglab.nl/) to investigate whether gene sets harboring our class-specific association signals were enriched for genes with expression signals in specific tissues of the GTEx v6 data21. FUMA applies a two-sided t-test to infer whether gene sets are differently expressed (up- or downregulated) in any tissue compared to all others tissues. Differentially expressed gene (DEG) sets are utilized by FUMA that were pre-calculated based on GTEx v6 expression values. DEGs in specific tissues compared with others reflect those gene sets with Bonferroni-corrected enrichment P values ≤ 0.05 and absolute log fold change ≥ 0.58. To prevent from bias through the customized design of the Metabochip, we selected associated variants  from GWAS-only meta-analyses (excluding Metabochip studies) of the GIANT consortium. We then utilized GWAS + Metabochip meta-analyses results for the classification, which comprise a larger sample size and thus higher accuracy for the classification. The applied input data sets of association results were identical to the ones that were applied to the DEPICT tissue-specificity analysis. For each class, genes harboring the respective associated regions were tested by FUMA against each of the DEG sets using the hypergeometric test, whereas background genes were those with an average Reads per kilo base per million mapped reads > 1 in at least one of the 53 GTEx tissues and exist in the list of background genes. We included all gene types in the prioritization that were available through FUMA and used default FUMA input values, e.g., r2 < 0.6 to define locus regions around the associated regions.We utilized the FUMAhttp://portals.broadinstitute.org/collaboration/giant/) for BMI (‘SNP_gwas_mc_merge_nogc.tbl.uniq.gz’)5, WHR (‘GIANT_2015_WHR_COMBINED_EUR.txt.gz’)6, and WHRadjBMI (‘GIANT_2015_WHRadjBMI_COMBINED_EUR.txt.gz’)6. All other data that support the findings of this study are available from the corresponding author upon reasonable request. Our integrative analysis of publically available GIANT data was performed with the open-source R package EasyStrata37. EasyStrata-scripts are available for download from www.genepi-regensburg.de/easystrata.Summary genetic association results that are used as basis for this study are available on the GIANT consortium website (Supplementary InformationDescription of Additional Supplementary FilesPeer Review FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14


Cottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement.A genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids , respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines.This study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy.The online version of this article (10.1186/s12864-018-4837-0) contains supplementary material, which is available to authorized users. Cotton, one of the most important crops, has been used extensively in many fields, such as textiles, food consumption and medical use. Cottonseed accounts for approximately two-thirds of the total cotton harvested while the remaining one-third is explained by fiber . Cottonsraimondii, G. arboreum and the G. hirsutum provides a crucial groundwork for identification, isolation and manipulation of important cotton functional genes controlling agronomic, yield, and quality traits [Using linkage mapping, several studies have been carried out for detecting QTLs associated with cottonseed traits in specifically designed mapping populations –10. Howey traits –20. Due y traits , 22 satuy traits .GhSus family genes and fiber- and seed-related traits in 277 upland cotton accessions based on an epistatic association mapping (EAM) model, which included main-effects, epistatic effects and one-dimensional gene × environment interaction effect. However, their study only focused on some specific genes, it’s still very valuable to explore the whole genetic architecture of the cottonseed traits across entire AD genomes [Generally, most GWAS approaches are based on simple additive or additive and dominance effects models , 25, ign genomes . In thisr2) of pairwise SNPs decreases to the half of its maximum value  [r2 ≥ 0.1, is less than 4 Mb in the landrace germplasm and larger than 12 Mb in the improved variety germplasm [According to the results of the population structure analysis, the total cotton accessions could be classified into two subgroups  , 31, whiermplasm ; whereasAccording to the result of SNP screening by GMDR method for each trait, total 4864 SNPs  were selected and used to perform the subsequent GWAS analysis. A total of 124 significant associated QTSs were identified for the protein content, the oil content, and the fatty acids. In general, all seven cottonseed traits exhibited significant additive and dominance effects; however, the epistatic effects and gene-environment effects, were largely diverse across traits  with total epistatic heritability of 65.18%, in which the heritability of dominance × dominance epistatic effect , and the dominance × dominance effect played a key role in genetic variation (de and ade) were also involved in the genetic architecture of the oil content. The oleic acid and the linoleic acid are two important and genetically related traits, and both were regulated mainly by dominance effect  through interaction with A3_115443958, wherein the additive-dominance epistasis explained 20.16% heritability, and other 3 kinds of epistasis  taked effects on the protein together with A7_1504479.15 QTSs, consisting of 5 QTSs with significant main-effects of additive or dominance, named individual QTS hereafter, and 6 pairs of QTSs involved in epistatic interaction, named epistatic QTSs hereafter, were detected to be significantly associated with the protein content . The total heritability of both A13_83121382 and A7_110987422, reached to 47.35%, accounting for over half of the total heritability of cottonseed oil content, indicating the importance of the two epistatic QTSs in regulating oil content. Both the phenotypic and the genotypic correlation analysis revealed significant negative correlation between the protein content and the oil content   for the oleic acid, accounting for 2.69% epistatic heritability and 3.33% additive heritability. In the case of the linoleic acid, 21 QTSs were identified in total, including 19 main-effect QTSs and 1 pair of epistatic QTSs. For the oleic acid, the dominance effects predominated the variation, and were mainly attributed to the QTSs exhibiting multiple different effect types, the number of additive QTSs with a p value below 1 × 10− 7 was more than that of dominance effect QTSs. D4_21291786 and D5_47644432 not only contributed single locus effects, but also strong digenic additive × dominance and dominance × dominance effects which explained heritability of 24.68% for the linoleic acid. The oleic acid was negatively correlated with the linoleic acid in both phenotypic and genotypic effects   on the linoleic acid, D3_1889546 also exhibited a strong positive dominance effect on the oleic acid and a negative dominance effect on the linoleic acid with the largest dominance heritability 20.28 and 10.76%, respectively. D3_29047260 affected the oleic acid by the additive and environment-specific additive effects, but the linoleic acid by the dominance and environment-specific dominance effects. D4_21291786 was associated with the oleic acid through the significant additive and dominance effects, as well as with the linoleic acid by the significant additive effect and epistatic effects with the D5_47644432.Based on the above analysis on heritability, it could be concluded that the dominance effect played a core role in regulating the oleic acid. There were total 21 QTSs associated with the oleic acid (Table 310p > 8.30) were detected for the palmitic acid ; A3_58421047 had no individual effect, whereas A12_117532407 contributed a positive additive effect, accounting for heritability of 1.47% . In addition, there seemed to exist an indirect genetic pathway connecting the linoleic acid and the myristic acid, in which A12_11753239 regulated the linoleic acid and the palmitic acid by opposite additive effects; while A7_642514 regulated the palmitic acid through individual negative additive effect and epistatic effect with D4_10313468, and also affected the myristic acid through negative additive and dominance effects.According to the Fig. Arabidopsis thaliana or rice [DK888 of maize, is also relevant to insect control or disease resistance [16 QTSs were identified in the regions of the annotated genes captured by 124 QTSs of cottonseed traits by using InterProScan , is located in the CDS of Gh_D01G0153, which encodes Pentatricopeptide repeat (IPR002885), Tetratricopeptide-like helical domain superfamily (IPR011990) and DYW domain (IPR032867). D5_66975738 that was significantly associated with the palmitic acid by negative additive effect is located in the CDS of Gh_A04G0196. Pleiotropic A7_642514, affecting both the palmitic acid and the myristic acid, is located in the CDS of Gh_A07G0057, which encodes IPR001806, IPR002041, IPR003577, IPR003578, IPR003579, IPR005225, IPR020849 and IPR027417. For the stearic acid, A4_17627308 is located in the intron of Gh_A03G0465 and its annotated functions are transferase (IPR003480) and Chloramphenicol acetyltransferase-like domain superfamily (IPR023213); A2_25310067 is located in the CDS of Gh_A02G0110, whose function is Calreticulin/calnexin (IPR001580), Calreticulin/calnexin, P domain superfamily (IPR009033), Calreticulin (IPR009169), Concanavalin A-like lectin/glucanase domain superfamily (IPR013320), Calreticulin/calnexin, conserved site (IPR018124); in addition, there are other three candidate genes  which are involved in the regulation on the stearic acid.QTS A7_2266630, which was associated with the oleic acid and had larger additive heritability than the dominance heritability, is located in the intron of Gh_D07G0133, encoding Armadillo-like helical (IPR011989), Uncharacterised protein domain (IPR012978) and Armadillo-type fold (IPR016024). Another QTS, D1_1087912 that exhibited positive dominance effect , the general superior hybrid line (GSH), the environment-specific superior homozygous line (SL) and the environment-specific superior hybrid line (SH), based on genetic effects of QTSs .On the genetic architecture of seven cottonseed traits, genotypic values were consistently observed across three environments for the protein and the palmitic acid, because of no significant gene × environment interaction, whereas the genotypic values of other 5 traits varied , GSH(+)) based on genetic main effects of QTSs would keep unchanged in every environment   cholesterol in serum, it is desired to reduce the components of myristic acid and palmitic acid in cottonseed for human health; thus, the optimal genotypes are expected to lower myristic acid and palmitic acid of cottonseed, which are provided in the Additional file 7http://ibi.zju.edu.cn/software/). Thus, our study employed this new method and software to explore intricate genetic architecture of cottonseed traits. Although relatively small compared with the main effects of an individual locus  for most of the cottonseed traits studied, significant epistatic effects and gene-environment interactions were detected, justifying the effectiveness of the method we employed.Mixed linear model approach has been successfully applied in linkage mapping of complex traits, including seed traits in crops , 40, 41.: Figure S2). Under the assumption of two subpopulations existent in our cotton population, chi-square test was applied to investigate significance of difference in genotypic frequency between subpopulations at the QTSs detected by our strategy. It was observed that the p-values of 120 of 124 QTSs were not significant after Bonferroni adjustment ; However, 4 QTSs  had significant difference in frequency between subpopulations.It should be noted that we first performed filtering on total ~ 390 K SNPs using the generalized multifactor dimensionality reduction (GMDR) , 48, and12 population, of which 6 QTLs interacted with environment [GhSus family genes with fiber- and seed-related traits in 277 upland cotton accessions by epistatic association mapping (EAM) model. For cottonseed-related traits, one main-effect quantitative trait nucleotides (QTNs) and one epistatic QTN were found to be associated with seed oil content and protein content respectively, but no significant one-dimensional gene by environment interaction was found [It has been well documented that epistasis and gene by environment interaction are mostly involved in the genetic architecture of most agronomic, yield and quality traits in crops. Liu et al. reported that 12 QTLs were detected for the protein content using IFironment . Zeng etas found . In our Bioinformatics analysis showed that the QTS A7_1504479, is located in the CDS of gene Gh_A07G0108, and associated with protein domains relevant to resistance mechanism in rice, maize, etc.; thus, further study on Gh_A07G0108 may reveal more information about resistance mechanism of cotton. Many studies have detected negative correlation between the protein content and the oil content in cottonseed and other oil crops, such as in soybean and sesame , 14, 50.Currently, some studies have reported that variation of complex traits result from non-coding regulatory variants more frequently than from coding sequence polymorphisms . SimilarIn summary, this study has detected significant epistasis and gene by environment interactions in the genetic architecture of seven cottonseed traits using association analysis based on a QTS full model and high density SNPs in cotton. The results on the genetic information of the distinguished variants and the superior genotype design provide new vision on the molecular mechanism and insight into marker-assisted selection (MAS) strategy on efficient molecular improvement of the traits in cotton.。E,36.10。N), E2 = Kuche of Xinjiang , E3 = Nanjing of Jiangsu . The seeds of each replicate were collected and the protein, the oil  and its components including oleic acid, linoleic acid, palmitic acid, myristic acid and stearic acid were tested three times each replicate. The names of all 316 accessions and the summary statistics of seven seed traits were presented in the supplement file . In 2009, all accessions were planted with three replicates in 3 regions of three China provinces . The seed kernel oil was measured by reference to animal and vegetable fats and oils--determination of moisture and volatile matter content (GB/T 9696–2008/ISO662:1998). The contents of seed kernel fatty acids including almitic, linoleic, oleic, myristic, stearic etc. in each accessions were assayed by gas chromatography (GC) using a 7890A chromatograph  according to the national standard for animal and vegetable fats and oils analysis by gas chromatography of methyl esters of fatty acids  and national standard for fatty acid test in the food (GB 5009.168–2016). This measurement was carried out by the Laboratory of Quality & Safty Risk Assessment for Oilseed Products (Wuhan), MOA, PR China. Since the absolute concentration is not the crucial factor for the fatty acids in 316 accession population, the internal normalization method was employed as a quantitative tool for relative amounts estimation of fatty acids analysis in this research.The peaks of different fatty acids were estimated by the reference for retention time and relative retention time of certain fatty acids. The reference for retention time (min) of the myristic acid, the palmitic acid, the stearic acid, the oleic acid and the linoleic acid, are 32.45, 38.81, 42.27, 44.38, and 47.73, respectively. The calculation for fatty acid components as follows, and the absolute difference of two experiment replications is less than 10% of arithmetic mean value,Xi is the content of a certain ingredient (%), Ai is the peak area of a certain ingredient, ∑A is the total area of all ingredients.where G. arboreum  and G. raimondii  by the software BWAv0.5.927. SNP calling procedure was carried out with realSFS  based on the Bayesian estimation. SNPs matching the following criteria were removed for quality control: (1) sequencing depth is greater than 6500× or less than 60×; (2) the distance between two adjacent SNP loci is shorter than 5 bp; (3) the call rate is less than 70% in the whole population; (4) the proportion of its heterozygous genotypes is more than 30%; and (5) minor allele frequency (MAF) is less than 0.01. Eventually, a total of 390,612 SNPs were obtained for association analysis.Total genomic DNA was extracted from the leaf tissues of 316 accessions using CTAB method with some modifications; the sequencing on all the accessions was conducted in 2013. The paired-end reads were mapped to the reference genome of r2), and selection of unlinked SNPs. The possible population structure was analyzed by the fastStructure software [r2 value larger than 0.1 with any other SNP within a 50-SNP sliding window, a total of 203,021 SNPs were retained. The hypothetical number of subpopulations (K) from 1 to 10 was performed at five independent run iterations using 203,021 unlinked SNPs by the fastStructure. The output of the fastStructure was used to estimate K according to the method described by Raj et al. [The software PLINK  was applsoftware . After ej et al. .s loci (QTSs), then, the phenotype ykh of the k-th genotype in the h-th environment can be expressed by following mixed linear model:Mixed linear approach was used in the GWAS on seven cottonseed traits of 316 accessions. The statistical model consists of the effects of environment, additive, dominance, additive × additive, additive × dominance, dominance × additive, and dominance × dominance epistatic effects, as well as their interactions with environments. The genetic effects were treated as fixed effects, and the environment effects and gene-environment interactions as random effects. Suppose one surveyed trait is controlled by μ is the population mean; ai and di are the additive and the dominance effects of the i-th QTS, with coefficients QQ, Qq and qq, respectively; aaij, adij, daij and ddij are the additive-additive, the additive-dominance, the dominance-additive, the dominance-dominance epistatic effects between the i-th QTS and the j-th QTS, with coefficients h-th environment; i-th QTS and the h-th environment, random, with coefficients i-th QTS and j-th QTS with h-th environment, random, with coefficients Where The software QTXNetwork, developed for the mixed linear model-based association analysis method by Zhang et al. , was empIn order to alleviate enormous computational burden and exponentially increased multiple testing for detecting epistasis, we used generalized multifactor dimensionality reduction method (GMDR)  and the Additional file 1:Figure S2. Population structure of 316 cotton accessions based on pruned unlinked SNPs. (DOC 69 kb)Additional file 2:Figure S3. Genome-wide average LD decay estimated based on the population of 316 cotton accessions. (DOC 58 kb)Additional file 3:Table S3. The other genome-wide significant QTSs associated with five fatty acids. (DOC 185 kb)Additional file 4:Table S2. Phenotypic and genotypic correlation between seven cottonseed traits. (DOC 58 kb)Additional file 5:Table S4. Predicted genetic values (G) for QQ, qq, Qq, superior homozygous line (SL), and superior hybrids (SH) of seven cottonseed traits. (DOC 114 kb)Additional file 6:Table S5. The SNP genotypes achieving the maximum genetic value of seed traits in designed superior homozygous lines and hybrids at the QTSs absent in the Table Additional file 7:Table S6. The SNP genotypes achieving the minimum genetic value of seed traits in designed lines for QTSs (DOC 210 kb)Additional file 8:Figure S1. The observed phenotype distribution of seven traits. (DOC 83 kb)Additional file 9:Table S1. The information of 316 accessions and the summary statistics of seven seed traits (DOC 321 kb)


The study was undertaken to find out the spatial dynamic occurrence and patterns of the global spread of bluetongue (BT) disease for the period from 1996 to 2016, as well as the assessment of the risk of occurrence and its spread in 2017-2018.Outbreaks  were confirmed and reported officially by veterinary departments which represent different geographical regions in the world to World Organization for Animal Health. These reports explained that ELISA and polymerase chain reaction were used to identify the BT disease, taking in the account number of infected, dead animals, and focus of BT infection in all susceptible animals from 1996 to 2016. Once conventional statistical population was defined , we had classified data as well as possible to answer to our aim, using descriptive statistics methods, including the test of the relationship between different epizootiological indicators.The spatial dynamic study of BT’s occurrence and its spread in the world over the two past decades was presented by different epizootic indicators. The given analysis includes assessment and measurement of risk factors. It was built too, regression models, and allowed to put different forecasts on the different epizootic indicators in the years 2017-2018 by the extrapolation method. We had also determined that, in 2017, BT continues to spread with the total expectancy of 3.4 focus of infection  and mortality of about 26 %; these rates tend to decrease in 2018. At abused points by BT, up to 78.4% of animals are mixed (more than one type) and in 21.6% - uniform. By this way, the relative risk of the incidence of appearance-abused points in mixed households has 3.64, which might be considered higher for the BT dissemination. Moreover, between the enzootic index and other epizootiological indicators had revealed an inverse correlation, i.e., to an increase in the level of enzootic index among the cattle population would be formed population less sensitive to BT. Cluster analysis was done, which had demonstrated the zoning of risk levels in the world and the occurrence of the disease intensity in the period 1996-2016 years. Then, assess connection degree of the dynamic of BT tension with geographical and socioeconomic conditions background 0.66 and 0.68, respectively.It is important to define a variety of BT risk factors and assess their influence on BT occurrence. However, the most important is to define the overlapping coinfluence between them that cause serious losses. To have an out of BT territory needs to make an emphasis of co-influence of risk factors on this zone. Was predicted a continue hits of disease in the next year with weight moderation through one year. Far from statists, to assess the given forecast may have a serious variety, taken in account problems of actual climate change in the world. Bluetongue (BT) recorded by the World Organization for Animal Health (OIE), included in the list A, which relates to the number of highly dangerous and widespread infectious diseases in the world, has also significant socioeconomic issues in international trade -5. AccorA mass BT outbreak annually is estimated in minimum at 3 billion of dollars . In Indith serotype in South Africa recently . By the time, BT gets spread in the entire world in a different incidence. In the last BT season had been reported more than 4700 outbreaks in 15 countries in the world (just in Europe 4560 outbreaks in 9 countries). BTV hurts more sheep and goats (in 92.9%) than cattle, although more than 64.28% of susceptible livestock in disadvantages points were cattle. In the past decades, dynamic of occurrence not only BT but also other transmissive diseases has shown an interesting evolution by time and spatial. It is why we focus our epizootical approach to determine BT factor of risk and make a schema to this situation and expected forecasts.It more than one and half century from scientific acquaintance with the first serotype of bluetongue virus (BTV), and until, this moment has been discovered 27 serotypes  and probEthical approval was not applicable for this study.Outbreaks  were confirmed and reported officially by veterinary departments which supervise conformed geographical regions in each country in the world to OIE. These reports explained that ELISA and polymerase chain reaction were used to identify the BT disease, taking into account number of susceptible, infected, dead animals, and focus of BT infection from 1996 to 2016 ,13.The data on the different indicators of BT and their growth dynamic were processed according to the guidelines on conducting epidemiological monitoring of exotic, very dangerous, and unknown diseases, taking into account the OIE Animal Health Code ,15 with Once conventional statistical population was defined , we had classified data as well as possible to answer to our aim, and it was used.s and Bravais-Pearson r. Used coefficients similarly show the dependence of the BT stationarity on incidence, focus of infection, and lethality as in cattle, as small cattle.Coefficient association test to define how connects animals holding manage with BT occurrence. Moreover, by this way, we could know not only the relative risk (RR) of the incidence but also we could have assessment and measurement of risk factors and more commonly used: Linear correlation coefficient: Rang rχ2, and calculation of the influence force of various systems of factors .To calculate the connection degree of the dynamic of BT tension with geographical and socioeconomic conditions was used methods of hypothesis testing, criterion Regression models were used to put different forecasts on the different epizootic indicators in the years 2017-2018 by the extrapolation method.The cartographical spatial dynamic study  was conducted with Microsoft Excel 2013 and QuickMAP program (geoinformation systems).During the period 1996 - (April) 2016, BT has been registered in 12 countries of the African continent (with 3987 outbreaks), 28 countries of Europe (156993 outbreaks), 9 countries in Asia (5684 outbreaks), and <0.001% of the remaining outbreaks spread in the rest of the world. European countries captured up 94% of all outbreaks, where it had been infected 1.5 million animals with more than 28% lethality .Using the available data, to study, the spatial dynamic of BT’s evolution process was formed a complex of cartograms and diagrams to describe the situation.Visual analysis of cartograms  leads toThe highest risk of registering cases of BT  is typicIn Visual analysis of clustering with data about wild animals  leads toThe tension of the epizootic situation was identified too, by indicators of the incidence of cases and outbreaks in disadvantaged countries.Any variation of this distribution  is so imFrom The coincidence of the periods of decline and rise between defeat (infected heads) and lethality indicates a certain relation between themselves. It was confirmed with correlation coefficient  - direct and strong link.Periods of remission accompanied with a closed widespread; in 1997, BT was present only in 6 countries around the world, and in 2004-2005, in 8 countries, and vice versa, but conversely, in 2009-2011, more than 20 countries announced his disadvantaged by BT, and 15 countries in 2016.Knowing the features of any disease is the key to its eradication and besides, if it is expected. Therefore, we tried to build regression models of different indicators to predict the expected future situation on 2017-2018 years.Construction of curves for different indicators shows large variations that complicated our task in their approximation (for trend line) and may not allow for reliable forecasts.2). In the order to obtain, a high R2 value was counted up more moderate functions (smoothing averages over three consecutive points [or smooth down 5 if necessary]), which more appropriate in such case, to give a more moderate form of regression models that improve the value of R2.One-off the test function is selected; the next step would be choosing the function parameters giving as possible a good approximation to the experimental points  had helped building different models, whose extrapolation for 2017 posted in All charts of the 3Seeing our previous analytics of the dynamics of BT prevalence and losses from until 2016, expected wave should be regenerate, and the results of this table have just been suit this dynamic. They can be compared with the packs of other waves. Hence, at the table, we may predict what will be expected from BT focus of infection with 3.37 and lethality rate around 25% in 2017. Moreover, all these indicators tend to decrease (not sharp) in 2018 globally.Since BT affects small cattle, as well as cattle, and other species of wild population ,14,19-28From t=0.0116χ3−69.688χ2+139618X−9E+07Obtained model: Yt- Value of models of the dynamics epizootic situation,Where YX - The serial number of the year.2=0.7 demonstrates a great approximation.RModel of theoretic values had comforted to 2014, where the value also increasing a bit and become 19.5% in 2016, and then, will reduce to 14.52% in 2018 .Interpretation of the curves of In our case, it had seen that lethality had strong connection with mortality (correlation coefficient r=0.94), therefore, every change of one index, other index will automatically change. Especially, can see this link at comparison of these indices  forms, and their transitions one form to another.Although BT sources and transmission paths are well known, in some cases, they could be so diverse, depending on the purpose and intensity of cultivation of animals, their description is only relevant to a concrete case (outbreak). Using available data from epizootic outbreak, investigations in different farms made an attempt to quantify the skidding BT ways. Information was gathered on the main routes of transmission of BTV, established at the epizootic outbreaks between 1996 and 2016 years .From Another important factor is considered too important in our opinion; this is the juxtaposition of BT infected points, which causes an increase in biological speed of transmission by vectors. It is the most speared factor in different countries in the world.The role of trade and wildlife in epizootiology of BT is not significant , for the first one due to the strict international legislative movement of live animals and their products, and for the second due to a two-fold reason: The difficulty of controlling and identifying incidents among them and their distribution.It remains to be noted that 22.77% of cases had unknown cause for us, we had explained this by the no established (studied) factors of BT episodic process during the analyzed period (1996-2016 years). This fact indicates less level of system control in disadvantaged regions. Mainly, this is due to depopulation (active or passive), including wild animals, procurement and systems of rearing animals, especially in private farms .Another factor, we tried to know how stationarity effect on BT tension  in a particular area to the total observation period). s and Bravais-Pearson (r). Used coefficients similarly show the dependence of the BT stationarity on the studied indices.The closeness of statistical relations may be determined by various factors , and more commonly used: Linear correlation coefficient: Rang rs=0.91; it is a very strong and direct connection. This dependence is due to the direct influence of enzootic factor on the occurrence of the focus of infection in small cattle and vice versa, reducing the recurrence of BT in the local population contributes to the reduction of livestock small cattle with a complex epizootic situation.The most significant direct correlation was found between the occurrence of the focus of infection among small cattle and the stationarity index rose rs=−0.6) takes place between the emergence of focus among the cattle and the stationarity index, i.e. to an increase in the level of the stationarity index among cattle formed population less susceptible to the disease (it serves as a vaccine).An average degree of dependence (rs=−0.7 (considered strong) was noted between mortality among cattle  and the stationarity index, which is due to the natural immunization of diseased animals during BT epizootic outbreaks was found too, a considerable correlation between the morbidity of cattle and stationarity r=−0.66.A strong degree of indirect rang correlation rIt was revealed a weak positive correlation between the degree (r=0.3) of the dynamic of morbidity among small cattle and stationarity, due to the emergence of different serotypes of BTV. Although there is less cross-immune protection, apparently, it seems that there is an influence of other factors, no studied here.Thus, it could be concluded that the stationarity index carries somewhere, character of natural vaccination, but with more severe losses.0 null hypothesis testing procedure had been verified for data: (Chi-square test) χ2obs=54.83 and χ2crit=3.84. χ2obs value is falling into the critical region: χ2>χ2crit, so the H0 hypothesis is rejected with an error probability of α 5%, and thus, both indicators are considered dependent. Mathematical calculations have confirmed that the connection to the BT susceptibility of small cattle is strong and direct (association rate=0.72).H1-R0=0.56) during the breeding of each species of animals separately.RRs frequency of BT infected points in the same period under the influence of factors - type of breeding (mixed or no) were greater than one and grew up to 3.64, which can be considered a high level of danger for spreading BT, with an excess risk  in 2017 years, and these values tend to decrease (not sharp) in 2018 years. The marked correlation between all risk factors and its coinfluence defines degree in the really expected situation. The occurrence of BT disease on new territory may generate as new factors as a new serotype.It should be emphasized here that such investigation study has to be checked in each geographical region to define local risk factors. Moreover, special attention should be taken to avoid or minimize vector spread reported by geographical proximity above as a main risk factor.VAA and OMP designed the work. FB conducted the research work. Data analysis and manuscript were written by FB and OMP under the guidance of VAA. All authors read and approved the final manuscript.