When analyzing the data that arises from exome or whole-genome sequencing studies, window-based tests, , are very popular. However, power is known to be quite low for finding associations with phenotypes using these tests, and therefore a variety of analytic strategies may be employed to potentially improve power. Using sequencing data of all of chromosome 3 from an interim release of data on 2432 individuals from the UK10K project, we simulated phenotypes associated with rare genetic variation, and used the results to explore the window-based test power. We asked two specific questions: firstly, whether there could be substantial benefits associated with incorporating information from external annotation on the genetic variants, and secondly whether the false discovery rate (FDRs) would be a useful metric for assessing significance. Although, as expected, there are benefits to using additional information (such as annotation) when it is associated with causality, we confirmed the general pattern of low sensitivity and power for window-based tests. For our chosen example, even when power is high to detect some of the associations, many of the regions containing causal variants are not detectable, despite using lax significance thresholds and optimal analytic methods. Furthermore, our estimated FDR values tended to be much smaller than the true FDRs. Long-range correlations between variants—due to linkage disequilibrium—likely explain some of this bias. A more sophisticated approach to using the annotation information may improve power, however, many causal variants of realistic effect sizes may simply be undetectable, at least with this sample size. Perhaps annotation information could assist in distinguishing windows containing causal variants from windows that are merely correlated with causal variants. Several studies have estimated approximately the same threshold, but derived it from different arguments: Risch and Merikangas used an early estimate of the potential number of genes in the genome  to control the rate of identification of single nucleotide polymorphisms (SNPs) that may be falsely associated with a disease or trait of interest  in a chosen window or region of the genome , whereas other statistics may be optimal for smaller proportions of causal variants   proportion of false positive associations among the total number of significant tests, it is usually possible to identify more true positive associations overall. This feature can be extremely beneficial to sensitivity and power when there is an apriori expectation of many true associations. Secondly, if the number of tests performed is increasing due to a range of choices for windows, test statistics, or variant selection in the same genomic regions, such tests can be expected to be positively correlated. An upper bound on the FDR has been demonstrated for situations with positive dependence , together with external information on amino acid alterations are the foundation for a simulation study comparing analytic strategies and their performance.To answer these questions, genetic sequencing data from a preliminary release of the UK10K project . For region-based analysis of rare variants in this consortium, an initial analysis plan defined regions so that they contain 50 rare variants, where “rare” is either MAF <0.01 or MAF <0.05. Adjacent regions were chosen to overlap by 25 rare variants. To study correlation patterns, we used a portion of this sequencing data, specifically chromosome 3 sequencing data from an interim release, including 2432 individuals and 2,577,674 genetic variants. For an MAF threshold of 0.01 we defined 74,156 analysis windows or regions, derived from 1,853,923 rare variants at this threshold.The UK10K project is undertaking whole genome sequencing and analysis of approximately 10,000 individuals from the UK with the goal of understanding the contribution of rare genetic variation to common traits and diseases  to influence the phenotype, and then using these causal variants, we simulated a continuous phenotype value for each individual in our data set. This step allowed us to calculate 74,156 region-based tests of association across chromosome 3, and thus obtain one estimate of FDR. Therefore the second level of the simulation design repeats this process 100 times, so that we can describe the variability in the FDR estimates and in the distribution of Within each simulation, we randomly selected 40 genes to be causally related to the phenotype from the 1116 genes on the chromosome. Rare variants in and near these genes were then randomly sampled to be the influential genetic variants. We defined, for the purposes of selecting causal variants associated with each gene, all variants in the adjacent intergenic intervals as potentially associated with the selected gene. We excluded singleton variants from consideration since they provide little additional benefit to power in tests of rare genetic variation  represents random variability in the ith person's phenotype. The genotype covariates, xij, represent the number of minor alleles for individual i at causal variant j, and the effects of the causal variants, βj, were randomly generated following the parameters in Table The risk of a variant being causal, and its effect on a continuous phenotype, were generated as a function of the PolyPhen-2 score values. The phenotypes For each value of σ, 100 sets of causal genes and variants were randomly selected, and correspondingly, 100 different phenotypes were generated for each person.p-values in order to estimate sensitivity and FDR. These two phases were each undertaken using several strategies to account for external annotation information about the genetic variants.In general, the analysis plan involved region-based tests of association for all defined windows on chromosome 3, followed by examination of the joint distribution of the resulting y is a vector of phenotype values, y under null hypothesis, and K is the SKAT kernel matrix, which depends on the genotype matrix and a choice of variant weights. Under the null hypothesis, the distribution of Q is asymptotically equal to a positive quadratic form of standard normal distributions, and the p-value can be calculated using Davies exact method or other approximation methods  contained no annotated variants and were not analyzed with strategy S. The default SKAT weighting of variants was also used here.r = 0.9 (H1-Corr0.90) or r ≥ 0.75 (H1-Corr0.75). All the nomenclature is presented in Table After analysis, region-based test results were summarized across all windows (All), windows that contained no variants with damaging PolyPhen-2 scores (Stratum 1), and windows that contained at least one variant with a PolyPhen-2 score of probably or possibly damaging (Stratum 2). Note, therefore, that analysis type “S” of Stratum 2 (S-Stratum2) describes windows containing only damaging variants (possibly or probably), S-Stratum1 describes windows containing non-annotated or benign variants, and that “S-All” are the windows with any kind of variants. Furthermore, we categorized windows into those truly containing at least one causal variant (H1), and windows where at least one variant was strongly correlated with a causal SNP at either p-value thresholds. Let pw represent the p-value for window w using a particular testing strategy, and let α be a p-value threshold defining whether the null hypothesis is rejected. With some abuse of notation, let H1w represent the logical event that the window w truly contains at least one causal variant. We can therefore define “true” sensitivity asw = 1, … W windows tested. Similarly the “true” FDR can be writtenI(° H1w) indicates that a window does not contain a causal variant. These sensitivities and FDRs were averaged across the 100 simulations for each threshold α.Within the set of tests arising from each analytic strategy, we calculated the true sensitivity and the true value of the FDR for several chosen p-values , p(2), …, pw) correction of d Morris  use the p-value threshold α is then the proportion of rejected tests estimated to follow the uniform distribution, and can be writtenHere, π represents the proportion of test statistics that follow the null hypothesis. The estimated FDR for p-values under the null hypothesis, the method of Strimmer  and F0(.) represent the distribution functions for the mixture model and the null distribution, respectively. A Grenander density estimator is used to obtain a nonparametric estimate of the distribution F and a truncated maximum likelihood is used to estimate the null density. Therefore, these three methods encompass very different approaches to the estimation of FDRs.Strimmer writes FDR at a given http://cran.r-project.org/web/packages/locfdr/index.html), but an empirical smoother of the histogram of test statistics was used instead of the Grenander function. We were unable to obtain reasonable results with this method and they are not shown.An approach conceptually similar to Strimmer's was taken by Efron  coding for the number of minor alleles at the genetic variant.For each simulation, the phenotypes were designed to depend on genotypes at a randomly selected set of 40 genes. Then within the chosen set of genes, a variable number of causal variants were sampled. Table p-values for 3 simulations. The 3 columns in Figure p-values for simulation #3 reach 10−80. It is also notable that the most significant test statistics can vary by almost an order of magnitude across different simulations. In contrast, when σ = 1.5, there is very little power to detect association with the genetic variation in the windows, at a sample size of 2432 individuals, for any method. Single SNP results can have more power than region-based tests, as in simulation #1, or less power, as in simulations 2 and 3.Figure p-values obtained for all region-based tests of association for three different simulations, for all tests, and also separately for Stratum 1  and Stratum 2 . In each histogram there is a visible peak of small p-values, indicating that a subset of the tests deviate markedly from the null hypothesis but there is also an apparent peak of p-values at or near the value of 1.0. This latter spike is more visible when the background random variability is at its smallest (σ = 0.5), and particularly dramatic for the “S” analytic strategy where only annotated variants were analyzed. Therefore, we believe that this spike is likely due to violations of the asymptotic convergence of the test statistics. For the S strategy, the data sets of analyzed variants could be very sparse—a much smaller number of windows were analyzed, and within those windows there may have been little genetic variability. Since there was a small number of damaging variants, a window may contain only one or two analyzable variants. Furthermore, only a few individuals may carry alternative alleles due to the rarity of the high risk alleles. When σ = 0.5, individuals carrying one of these causal variants would appear to have extreme phenotypes (large outliers) and this may compromise the convergence of the test statistics.Figure p-values than the other.The tests in Stratum 2 are a very small minority of all tests for the N and P analysis strategies, but a large proportion of all tests for the S strategy. This can be seen in Figure p-value threshold for significance, the sensitivity is the proportion, of the windows where the null hypothesis was rejected, that actually contain at least one true causal variant. For a p-value threshold of 1e-05, the performance of five different analytic strategies are presented in Table The capacity to identify windows containing causal variants is shown in Table p-value threshold becomes less stringent. This conclusion appears to contradict the highly deviant QQ-plot results seen in Figure p-value threshold of 1e-3 and for the S-Stratum 2 analytic strategy, reaches only 35%.Examining strategy H1, where the focus is only on windows containing causal variants, it can be seen that the sensitivities tend to be very low for any analytic strategy, and they do not increase much as the r ≥ 0.9 (H1-Corr0.90) or r ≥ 0.75 (H1-Corr0.75). Sensitivity then measures the proportion of this larger set of windows with p-values below the chosen threshold. Table p-value threshold of 1e-05, and either N-All or S-Stratum2 , Table r ≥ 0.75; By expanding the definition of a “true” association to include windows that are correlated with causal windows, there is some improvement. The proportion of null rejections drops from well over 90% to as low as 40–50% when using the BUM method, and in the Stratum 2 subset analyses, but these values are still far higher than the estimated FDR of 0.05. When using a slightly more liberal definition of a true association , then we counted this as a true identification. Sensitivity increased quite substantially with the relaxed definition, and in some models reached 50–60%. If we had used a lower level of correlation when defining a “true positive” region identification, we would undoubtedly have been able to improve our sensitivity further. In fact, the correlation, r, is not ideal as a measure of the strength of linkage disequilibrium between variants, especially for rare genetic variants. A more nuanced consideration of haplotypic structure could provide an interesting perspective and may lead to improved sensitivities. Our relaxed definition of success also raises questions about how to perform fine mapping, since “true positive” windows could be quite genetically distant from any causal variants, if there was long-range disequilibrium. Of course, many studies of real phenotypes have identified associations that are located far from any likely gene.Although the number of causal variants is small, the number of windows potentially showing association could be much larger due to patterns of linkage disequilibrium leading to extensive correlations. We therefore implemented a more relaxed definition of successfully identifying a true signal: if a window showing a significant result contained at least one genetic variant strongly correlated with a causal variant , and the correlations between windows. Table A that are too small. Furthermore, if C+D is a very large number (π is large) then it becomes easy for C to be much larger than A. Finally, the linkage disequilibrium structure leads to complex patterns of dependence; signals resulting from causal genetic variants may be detected in windows some distance away. Hence, the choice of definition for a “causal” window influences the number of tests placed into the two columns.One of our most striking findings was the discrepancy between estimated FDR values  and the true FDR based on the proportion of windows with small The lack of power for detection of many of the causally-associated regions may have been due to very small MAFs, to the presence of only a very small number of causal variants in each window, or even to the fact that with 41 causal variants on average Table , that thIf window-based tests perform poorly when there are only a few causal variants, perhaps single-SNP tests have more power. Therefore, in a few selected cases, we compared window-based results to SNP results Figure , and in p-value in turn, and provides an upper bound for the FDR under certain patterns of dependency  probability that the genetic variation in a chosen region is associated with the phenotype. Regions with annotations likely to contain causal genetic variation would have higher prior probabilities of association with phenotype, and hence also higher posterior probabilities of association. This avenue may be worth further consideration and exploration.Prior to undertaking this simulation study, our hypothesis was that use of stratified FDR methods, using genomic annotation information, could lead to improved power to detect associations with rare genetic variation. We did not find any improved performance using stratified FDR methods in this context. However in another context where the strata may more clearly delineate the probability of a true association, or if informative prior information could be constructed effectively, then perhaps a return to consideration of these issues would be warranted.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Expression levels for 47294 transcripts in lymphoblastoid cell lines from all 270 HapMap phase II individuals, and genotypes (both HapMap phase II and III) of 3.96 million single nucleotide polymorphisms (SNPs) in the same individuals are publicly available. We aimed to generate a user-friendly web based tool for visualization of the correlation between SNP genotypes within a specified genomic region and a gene of interest, which is also well-known as an expression quantitative trait locus (eQTL) analysis.http://tinyurl.com/snpexp. Correlation between genotype and transcript expression levels are calculated by performing linear regression and the Wald test as implemented in PLINK and visualized using the UCSC Genome Browser. Validation of SNPexp using previously published eQTLs yielded comparable results.SNPexp is implemented as a server-side script, and publicly available on this website: SNPexp provides a convenient and platform-independent way to calculate and visualize the correlation between HapMap genotypes within a specified genetic region anywhere in the genome and gene expression levels. This allows for investigation of both cis and trans effects. The web interface and utilization of publicly available and widely used software resources makes it an attractive supplement to more advanced bioinformatic tools. For the advanced user the program can be used on a local computer on custom datasets. According to dbSNP build 131 . This deGene expression levels may be considered a quantitative trait that is influenced by genetic variation and amenable to genetic mapping by means of SNP correlation statistics. Studying this correlation is called expression Quantitative Trait Locus (eQTL) mapping, and has proven to be a useful tool to detect regions and variants of importance to gene expression and thus also in raising hypotheses for the underlying mechanisms of genetic findings in GWAS ,7,9,10. While powerful for the computationally skilled user, most tools do not allow for fast and immediate assessment of a region or gene of interest. eQTL viewer  is a cusGenome-wide SNP genotypes and gene expression levels from lymphoblastoid cell-lines from the HapMap project are publicly available -16. We whttp://tinyurl.com/snpexp is implemented as a server-side script, written in Perl 5.10 [SNPexp erl 5.10  executinerl 5.10 . It takeerl 5.10  for calcerl 5.10  for visuThe HapMap phase II release 23 data set consists of 3.96 million SNP genotypes from 270 individuals from 4 populations (CEU: 90 (30 trios) Utah residents with ancestry from northern and western Europe; CHB: 45 unrelated Han Chinese in Beijing; JPT: 45 unrelated Japanese in Tokyo; YRI: 90 (30 trios) Yoruba in Ibadan, Nigeria) . The datExpression levels for 47294 transcripts from EBV-transformed lymphoblastoid cell lines from the same 270 Hapmap individuals are also available . Each geFigure Several downloadable files are generated. First, a file formatted for upload as a "custom track" on the UCSC Genome Browser  to visuaFigure extracted_snps_with_expression_valuesPROBE.ped, pvaluesPROBE.linear.assoc.txt, pvaluesPROBE.linear.assoc.adjusted.txt, PROBE.qassoc.means.txt and customtrack.txt where PROBE refers to the transcript targetID found on the expression array) are downloaded and evaluated along with the log-file. The locally saved customtrack-file can later be uploaded and viewed on the UCSC Genome Browser.We advise that the various results files . This plot is similar (referenced on the opposite strand) to the plot of the same region in [-12). Small differences might be caused by filtering of SNPs in [To assert the validity of SNPexp we specifically aimed to reproduce previously published eQTL results. In particular, the study by Veyrieras et al  is basedegion in  and show SNPs in , normaliORMDL3 locus against SNPs in the surrounding region on chromosome 17q21. In this assessment, only partial overlap was observed for the most strongly associated SNPs in the eQTL mapping and several exclusive associations was detected using either approach. These apparent discrepancies are not surprising given likely differences between the asthma population and HapMap in genetic constitution as well as with regard to linkage disequilibrium patterns.A genome-wide association study for asthma by Moffat et al  is also SNPexp is created with the intention of being a fast and user-friendly, readily available web tool to analyze and visualize the correlation between two high-quality and publicly available data sets. We decided to use source data as-is, with no additional quality filters applied to neither SNPs nor genes, thereby providing a complete and unbiased set of results which is left to the researcher to further inspect and interpret.Since the number of possible gene vs. SNP combinations is extremely high and the true model for an allelic effect on gene expression may differ from gene to gene, SNPexp supports the option of using either an additive, dominant, recessive or genotypic genetic model assumption. The pragmatic approach of using the built in Wald test for quantitative traits in PLINK was chosen. While this test is applicable for most purposes, we advice in-depth statistical validation of results from SNPexp that are taken onward to a publishable conclusion or further experiments. The advanced user with knowledge of Perl programming may want to download an "offline version" of the script from the tool's help page, set it up locally and do adaptions to support other data sources etc.By combining publicly available HapMap genotype and gene expression data we have developed an interactive web tool (SNPexp) where the user can visualize the correlation between SNP genotypes within a specified genetic region anywhere in the genome and expression levels for a gene of interest. The SNPs and the gene encoding the transcript may reside on separate chromosomes, thereby supporting searches for both cis- and trans-acting eQTLs. The quick and convenient user interface which require minimal computer knowledge and no preparation of source data makes SNPexp an attractive supplement to more advanced eQTL tools.Project nameSNPexpProject home pagehttp://tinyurl.com/snpexphttp://app3.titan.uio.no/biotools/tool.php?app=snpexp).(Alias for: Operating systemPlatform independentProgramming languagePerl 5.10LicensePublic DomainKH implemented the software, performed eQTL mapping and wrote the manuscript. EM participated in the design of the project and helped writing the paper. AF and THK designed and supervised the project and contributed to the manuscript. All authors approved the final manuscript.


Novel technologies allow sequencing of whole genomes and are considered as an emerging approach for the identification of rare disease-associated variants. Recent studies have shown that multiple rare variants can explain a particular proportion of the genetic basis for disease. Following this assumption, we compare five collapsing approaches to test for groupwise association with disease status, using simulated data provided by Genetic Analysis Workshop 17 (GAW17). Variants are collapsed in different scenarios per gene according to different minor allele frequency (MAF) thresholds and their functionality. For comparing the different approaches, we consider the family-wise error rate and the power. Most of the methods could maintain the nominal type I error levels well for small MAF thresholds, but the power was generally low. Although the methods considered in this report are common approaches for analyzing rare variants, they performed poorly with respect to the simulated disease phenotype in the GAW17 data set. New technologies allow the sequencing of genomes of a large number of individuals, thus identifying millions of rare variants in the genome. This allows researchers to investigate the common disease/rare variants (CDRV) hypothesis. Because these variants are rare or are even private mutations, standard statistical approaches fail. However, different variants within a gene may act similarly. Therefore one approach of making data accessible to statistical analysis is pooling rare variants in a specific genetic region of interest . AnotherIn this study we focus on the CDRV hypothesis by grouping multiple rare variants according to a gene. To test for groupwise association with the simulated disease status in unrelated individuals, we compare the following five collapsing approaches: cohort allelic sum test (CAST) , combineThe five different collapsing approaches have been described in detail elsewhere, for example, by Dering et al. . Here, wFor CAST we use the Fisher two-sided exact test for statistical analysis, as suggested in the original publication . For thep-values are obtained by permutation. The classical permutation approach estimates the proportion of permuted test statistics that exceeds the original test statistic. If p-values are supposed to be low, a large number of permutations is required, and this requires a lengthy computing time. Therefore Madsen and Browning proposed to estimate the first two moments of the permutation distribution under the null hypothesis of no association in the first step. In the second step, the estimated mean and standard deviation from the permutation distribution under the null hypothesis are used to standardize the original test statistic. This standardized test statistic is finally used to estimate the p-value from the standard normal distribution; for details, see Dering et al. [The WS statistic was proposed by Madsen and Browning . In the g et al.  and Madsg et al. . Here, wg et al. .The GAW17 sequence data are based on the 1000 Genomes Project , and pheTo compare the different collapsing approaches, we consider the binary disease phenotype without any covariates. We first convert the genotype files of the GAW17 data using PLINK, version 1.05 ,9, to geOnly genes with at least two variants are included. Furthermore, we exclude the 695 spuriously associated genes that were identified by Luedtke et al. .We investigate three groups of variants: (1) all variants, (2) synonymous variants only, and (3) nonsynonymous variants only. Because the effect size of variants may depend on their frequency, we consider different MAF limits for collapsing: 0.01, 0.02, 0.03, 0.05, 0.07, and 0.10. For example, for synonymous variants with MAF < 0.01, only 4,218 variants in 765 genes with at least 2 variants remained.For the CMC method in all scenarios we collapse variants with MAF < 0.01, and variants with larger MAFs were investigated separately for each gene. As a consequence, in scenarios with MAF < 0.01, the CMC method and CAST were identical.−5.For controlling the family-wise error rate (FWER), which is the probability of committing at least one type I error, we set the multiple significance level to 0.05. The local significance levels are obtained by Bonferroni correction, that is, by dividing the multiple level by the number of genes in the individual scenarios under the assumption of independent genes. In the previous example with synonymous variants and MAF < 0.01, the local significance level is therefore 0.05/765 ≈ 6.5 × 10We use two different definitions of power, namely, average and minimal power, to compare the performance of the different collapsing methods. The average power is defined as the expected proportion of identified true gene disease associations among all true associations, and the minimal power is defined as the probability to detect at least one of the true associations. The FWER and the power are averaged by the number of replicates.None of the considered collapsing methods satisfied the multiple significance level in all scenarios Figure . WithoutThe minimal power for all methods was low in all scenarios Table , and it In this study, we focused on the CDRV hypothesis by grouping multiple rare variants according to genes. We compared collapsing approaches proposed in the literature using the GAW17 data of unrelated individuals.Results differed substantially with respect to SNP functionality. Specifically, for nonsynonymous variants the minimal power was up to 28.5%. In contrast, the minimal power was close to 0 for all methods in almost every scenario when only synonymous variants were considered. However, this result is not surprising because no synonymous variants are directly associated with the simulated disease phenotype.Furthermore, the minimal power of all methods differed for several MAF thresholds Table . Price eConsidering only valid methods , the WS approach could not be applied. Furthermore, the remaining methods could be applied only in the scenarios with MAF thresholds smaller than 0.05 with respect to the FWER. If both type I error and power are considered simultaneously, we have to conclude that none of the methods perform sufficiently well on the simulated data.In conclusion, none of the investigated approaches can be recommended with respect to the simulated disease phenotype. An improvement in the performance of the methods could be achieved if the underlying disease model was known. Furthermore, the inclusion of prior knowledge, such as pathway information, or putative functional information of the variant might be beneficial for the analysis of real data sets.The authors declare that there are no competing interests.CD implemented the considered methods and performed the statistical analysis. IRK and AZ participated in the design of the study and in the selection of the methods. CH conceived of the study and participated in its design and coordination. All authors drafted, read and approved the final manuscript.


This study focused on the dynamics of genome-wide effects on five milk production and eight fertility traits as well as genetic correlations between the traits. For 2,405 Holstein Friesian bulls, estimated breeding values (EBVs) were used. The production traits were additionally assessed in 10-day intervals over the first 60 lactation days, as this stage is physiologically the most crucial time in milk production.DGAT1 region showed high density of significant markers, and thus, haplotype blocks. Further functional candidate genes are proposed for haplotype blocks of significant SNPs . Negative genetic correlations were found between yield and fertility traits, whilst content traits showed positive correlations with some fertility traits. Genetic correlations became stronger with progressing lactation. When correlations were estimated within genotype classes, correlations were on average 0.1 units weaker between production and fertility traits when the yield increasing allele was present in the genotype.SNPs significantly affecting the EBVs of the production traits could be separated into three groups according to the development of the size of allele effects over time: 1) increasing effects for all traits; 2) decreasing effects for all traits; and 3) increasing effects for all traits except fat yield. Most of the significant markers were found within 22 haplotypes spanning on average 135,338 bp. The This study provides insight into the expression of genetic effects during early lactation and suggests possible biological explanations for the presented time-dependent effects. Even though only three markers were found with effects on fertility, the direction of genetic correlations within genotype classes between production and fertility traits suggests that alleles increasing the milk production do not affect fertility in a more negative way compared to the decreasing allele. DGAT1 (diacylglycerol O-transferase 1) gene on milk yield and the protein production [Time dependency in milk production traits in dairy cattle has been known and analyzed over the last century. Just like the phenotype, underlying genetic effects follow a dynamic expression over time. To account for the dynamic effects of genotypes, functional mapping has been introduced for the detection of QTLs, but has been applied mainly in human and plant genetics -4. In lioduction -11 are loduction . Furtheroduction .The first weeks of lactation are a crucial time, especially in high yielding dairy cows. During that time, an energy deficiency manifests due to a drastic increase in milk production and a physiologically restricted energy intake. In those first weeks, the cow needs to draw energy from its adipose stores and in some cases even from its muscles, leading to a loss in body weight. The nutritional status of a cow after calving affects disease resistance and reproductive performance and regions affecting energy status were reported to overlap with regions significant for fertility traits . On one The objectives of this study were to analyze how the genetic influence of genomic regions changes during the most critical interval between early and peak lactation and how changes of genetic effects in milk production could affect fertility. For this purpose, estimated breeding values (EBVs) for the five main milk production traits of 2,405 German Holstein Friesian bulls assessed in 10-day intervals over the first 60 lactation days, cumulated 305-day records and eight fertility traits were used. Association analyses were run between the production and fertility traits and the genome-wide markers of a 50 k SNP chip array. Finally, genetic correlations over the first 60 lactation days and within genotype classes were studied.DGAT1 locus was deducted, an additional ten and seven markers for the 10-day intervals and 305-day records, respectively, were discovered on chromosomes 5 and 27  in three groups:DGAT1 resides  for fat and protein content with larger effects for the 305-day records for groups 1 and 3  were constructed; 38 out of 53 significant markers  were located in 22 haplotype blocks spanning on average 135,338 bp. Considering the markers that were significant when accounting for the DGAT1 gene region was the only one for milk production traits with a haplotype block harboring more than one significant marker. Most markers on chromosome 6 were located around the Casein-gene cluster , however, the four significant markers are not within a single haplotype block and only CSN1S1 is located within the boundaries of a haplotype block. An additional marker on chromosome 6 is located 0.9 Mb upstream of the ABCG2  gene. This marker had a very low MAF (<0.01) and was therefore not included in a haplotype block  gene which might be responsible for changes in protein content in early lactation. Apart from an additional seven significant markers with polymorphisms in gene regions, a direct functional relation between these genes to the traits under investigation was not obvious.The highest density of protein coding genes (25 genes) was found for the haplotype block on chromosome 14 that included the KLHL8 (Kelch-like-8) on chromosome 6 located 68.91 Kb down-stream of the next significant marker, CYP11B , which itself harbors a significant marker that does not contribute to a HTB on chromosome 14 at 1.29 Mb, AGPAT6 (1-acylglycerol-3-phosphate O-acyltransferase 6) on chromosome 27 located 79.38 Kb upstream of the next significant marker and NID1 (nidogen 1) on chromosome 28 located 303.97 Kb downstream of the next significant marker  and whilst milk yield was always negatively correlated with the content traits , fat and protein yield were positively correlated with protein content . The majority of fertility traits were highly genetically correlated with each other .Referring to the 305-day records, negative correlations were found between all fertility traits and the yield traits. Fat content, however, showed low but positive correlations (0.03 to 0.08) though not always significant, and protein content showed a mix of positive and negative correlations, but equally low and only significant for non-return rate in heifers, calving to first insemination and days open  and 6 , respectively, were exceptions to the above described pattern. For those markers, both copies of the major allele led to a stronger negative correlation between milk yield and fertility traits (on average −0.27 to −0.17) whilst the major allele was presumed to have an increasing effect on milk yield  and 27 (ARS-BFGL-NGS-57448).For the content traits, most correlations within genotype classes were significant for calving to first insemination and days open. Those two fertility traits showed a stronger positive correlation (+0.05) if both copies of that allele were present that was associated with an increase in EBVs for fat or protein content. Exceptions were again the same two markers on chromosome 6 as well as one marker on chromosome 5  in the response variable between consecutive intervals. However, the change in allele effects seems to be independent from the change in EBVs which is particularly obvious for milk yield, where the EBVs decreased over the first 60 lactation days but most of the allele effects increased. Furthermore, the variation in EBVs was fairly constant and only marginally increased for all traits over the first 60 lactation days, therefore presented comparisons of absolute allele effects over time is reasonable.DGAT1 region between 0.05 and 2.6 Mb on chromosome 14 that showed increasing effect sizes for all traits during the first 60 days of lactation. The rest of 29 markers on chromosome 14 were regarded as group 3 with increasing effect sizes, apart from fat yield. DGAT1 is a major gene for a QTL on chromosome 14 with opposing effects on milk yield and milk fat [. Even though it was shown that the enzyme activity of the DGAT1-protein can depend on the mutation variant [CYP11B gene, which is located 1 Mb upstream of the DGAT1 gene and on the edge of a haplotype block of three significant markers, showed an opposite effect on fat yield compared to DGAT1[DGAT1 region on fat yield, which could explain the herein reported decreasing effect size of markers associated with fat yield in group 3.Three groups of markers were identified that showed different directions in allele effect changes. Group 1 consisted of five markers in the milk fat ,27,28. E variant , it was  variant ,32. Ther to DGAT1. This opCYP11B was also a region containing eight genes encoding proteins for the lymphocyte complex. Markers in that region were significant for the first 60 lactation days as well as for the 305-day records and strongly affecting most milk production traits. This region could therefore play a role in the immune response of the mammary gland and prevents inflammation during lactation.Overlapping with a haplotype close to Casein-gene cluster were only significant during the first 60 lactation days with largest effects at the beginning of the lactation. This co-occurrence with changes in gene expression patterns of the casein genes during early lactation [SICLEC12 gene and linked to SICLEC genes 10 and 14 which are all involved in immunoglobulin production. The co-occurrence of changes in allele effects over time of markers in the Casein-gene cluster and the SICLEC genes can be explained by their biological functions. In the first days after calving, when the colostrum is produced, milk is rich in proteins, especially in immunoglobulins, which have bioactive functions and help to activate the calf’s immune system [SICLEC family have been reported to affect productive live and fertility in Holstein cattle and are linked to leptin signaling [Group 2 included markers on chromosomes 6, 18 and 27. The significant markers showed decreasing effects for all traits, which were significant for protein content for markers on chromosomes 6 and 18. The markers on chromosome 6 in the actation . The same system ,35. Furtignaling ,37. Leptignaling ,39.DGAT1 effect. The 1-acylglycerol-3-phosphate O-acyltransferase 6 gene (AGPAT6) is located 79.38 Kb upstream of the initially only significant marker on the edge of a haplotype block. AGPAT6 has a similar function as DGAT1. It is an enzyme in the phospholipid and triglyceride biosynthesis, and thus, contributes to the production of milk fat [AGPAT6 in the mammary gland was shown to increase drastically over the first 60 lactation days and decreases afterwards [AGPAT6 region were only significant at the beginning of lactation showed that the impact on milk fat must be diminishing in late lactation.The significant region on chromosome 27, with decreasing effects on fat yield and content during the first 60 days of lactation, was supported by only one marker in the initial model, but five more markers when accounting for the milk fat . The expterwards . The chaAdditionally, the GO terms chemokines and peptidases occurred significantly often for our marker set and especially in the region of markers on chromosome 6, 18 and 27 which were only significant in early lactation. Chemokines attract lymphocytes and macrophages and are thus related to immune response, either to help the offspring or prevent infections of the udder itself in a time of high activity and metabolic stress . SomethiDGAT1 effect. The markers had a tendency towards increasing allele effects for fat yield and content over the first 60 lactation days. This, and the fact that initially most of the markers on chromosome 5 were significant for the 305-day records concurs with a decrease in fat content when energy availability is low during early lactation [Not included in the three described effect groups were markers on chromosomes 5. Only one marker was initially significant for the last 10-day interval, and thus, no change in effect size could be determined. However, five more markers for the 10-day intervals and six markers for the 305-day records became significant when accounting for the actation .Only a few studies have focused on time-dependent genetic associations in livestock to date and, as this study shows, the investigation of association at certain lactation stages seem to be a promising approach to detect loci with only small overall effects ,46-48. Tkelch-like 8 (KLHL8), located on chromosome 6 within 68.91 Kb of the nearest significant marker. The KLHL8 gene was reported to be preferentially expressed in the female gonads of fish (zebrafish and carbiomedaka) where it may play a role in oogenesis [nidogen 1 (NID1) on chromosome 28 and within 303.97 Kb of the nearest significant marker. NID1 is increasingly expressed in the focimatrix  when follicles enlarge before ovulation [KLHL8 and NID1, could affect the non-return rate by regulating the ovulation process.Finally, we want to propose two candidate genes for markers detected for the non-return rate in heifers (NRH). The first gene is the ogenesis . Even thvulation . Thus, bAn antagonistic relation between production and fertility traits reflects the competition between these traits for the same body resources ,17. We cAdditionally to the time dependency of genetic correlation between production and fertility traits, we reported differences for genetic correlation within genotypes of the most significant markers from the GWAS. With only few exceptions, alleles that significantly increased the EBV for milk yield showed a weaker negative correlation with most of the fertility traits if this allele was present in the genotype. Though for different markers compared to this study, Pimentel et al. (2011) reported SNPs with favorable effects on production and fertility traits and came to the same conclusion that a selection for higher performance based on the genotypes of certain loci does not further influence the fertility negatively .The content traits showed mainly stronger positive correlations with most of the fertility traits if the yield decreasing allele was present in the genotype. Again, a spurious effect due to the negative correlation of content traits as well as fertility traits with milk yield cannot be ruled out.This study provided evidence for new loci and confirmed less known loci with effects on milk production traits. Furthermore, significant associations can sometimes only be found when the phenotype was recorded at the right time. Because the impact of loci changes over time, an investigation at certain developmental or lactation stages might be a contribution to detect parts of the otherwise missed genetic variance and may be better in detecting quantitative trait genes with overall small effect. By analyzing the first 60 lactation days more closely, we could show that changes in effect sizes mirror results from gene expression studies and might, therefore, be a less elaborate method in addition to determine the consequences of gene expression. Lactation-stage specific association studies provide information independently of knowing where the genes exert their activity in the body.Correlations between fertility and production traits, positive or negative, became stronger with progressing lactation. Depending on the genotype, the correlations between traits differed. The correlation was less negative if the yield increasing allele of a candidate marker was present in the genotype. If these results can be confirmed in further studies, there is a good chance that selection for higher performance based on the here presented marker genotypes would not increase the negative influence on fertility.Estimated breeding values for the five main production traits milk yield (MY), fat and protein yield , and fat and protein content  of 2,405 German Holstein Friesian bulls were averaged over the first three lactations. All records were provided by VIT  and the production EBVs are raw breeding values not normalized to the average breeding value of a birth cohort.On average, each bull had 752 daughters contributing to the breeding value estimation, with a median of 114, a maximum of 85,393 and a minimum of 50 . Data for the production traits were given as cumulated 305-day records , interval from first to successful insemination classified into cows and heifers (FLc and FLh), non-return rate to 56 days for cows and heifers (NRc and NRh), conception , interval from calving to first insemination for cows (CFc) and days open . The chip features 54,001 SNPs. The array information was remapped against the Btau 4.0 assembly according to and cleaned of duplicated and not yet allocated markers  . 2,339 aGenABEL package in R [DGAT1 region allowed the detection of only a few additional markers outside that QTL, a supplemental analysis was run using the marker ARS-BFGL-NGS-4939, which is closest to the gene, as a fixed effect and thus, to deduct its effects. This marker had no effects on the analysis of fertility traits. A Genome-wide Rapid Analysis using Mixed Models And Score test (GRAMMAS), as implemented in GenABEL , was used to account for population stratification based on the genomic kinship matrix [DGAT1 locus in the model:A genome wide association study was conducted with the age in R  where wep matrix ,58. The yijkl are the EBVs for 10-day intervals, 305-day records or fertility traits, respectively; μ is the mean of the population; Gi is the polygenic contribution of individual i; sj is the fixed effect of SNP j; DGAT1j is the fixed effect of the genotype j  of marker ARS-BFGL-NGS-4939 ; and eijkl is a vector of random residuals.where 2-distribution of the test statistic. Finally, Bonferroni correction was applied to account for multiple testing with α = 0.05, resulting in an adjusted P-value of 1.15-6 .Beside GRAMMAS, the lambda factor was used on the P-values to achieve a non-conservative test statistic. Lambda is defined as the ratio between observed and expected median of the chiBased on the significant markers in the GWAS, least square means differences between allele effects of each 10-day interval as well as between allele effects of the 10-day intervals and 305-day records were estimated with the following model in SAS :yijk being the allele effects of significant markers estimated from the GWAS ; mj is a random effect of the marker j to account for the different number of markers and possible relations between the markers; and eijk is a vector of random residuals. The test of LSM differences between the 10-day intervals was performed for each effect group that was found in the GWAS separately.with The genetic correlations between all traits were estimated as Pearson’s correlation coefficients between the EBVs. Additionally, genetic correlations within genotype classes of markers with most significant P-values within a haplotype block (HTB) were used.fastPhase with filtering criteria of <3% missing genotype calls, <5% missing SNP calls, MAF >5%, 10 random starts and 25 iterations [HAPLOVIEW v4.1 [Haplotype blocks over all markers featured on the chip array were derived using the software erations , and HAPIEW v4.1  using thGENCODIS was used for a gene enrichment analysis to suggest possible functional candidate genes within HTBs [GENCODIS integrates different sources of information to search for annotations that frequently co-occur in a set of genes and rank them by their statistical significance.Protein coding genes within the haplotype blocks were taken from the Btau 4.0 assembly. hin HTBs ,24. GENCEMS conceived of the study, carried out statistical analysis of the data and drafted the manuscript. RHB carried out the position correction of SNP data and provided the haplotype blocks. JT and GT provided the genotypes. GAB participated in conceiving of the study, its design and coordination and helped drafting the manuscript. All authors read and approved the final manuscript.Complete list of significant markers over the first 60 lactation days separated after 10-day intervals. DIM: days in milk; MY: milk yield; FY: fat yield; PY: protein yield; FC: fat content; PC: protein conten.Click here for fileComplete list of markers significant for the 305-day records. MY: milk yield; FY: fat yield; PY: protein yield; FC: fat content; PC: protein content.Click here for fileDGAT1 locus. DIM: days in milk; MY: milk yield; FY: fat yield; PY: protein yield; FC: fat content; PC: protein content. Effects, SD and P-values are given as the results from the analysis without DGAT1 in the model and in brackets with DGAT1 in the model.Additional markers detected when accounting for the Click here for fileSignificant markers and allele effects for fertility traits. RZR: fertility index, summarizing all fertility traits, NRh: non-return rate for heifers.Click here for fileLSM-differences between the first 60 lactation days separated after effect groups. P-values *** < 0.0001 ** < 0.001 * < 0.01 † < 0.05. LSM: least square means; DIM: days in milk; MY: milk yield; FY: fat yield; FC: fat content; PC: protein content.Click here for fileLSM- differences between the 10-day intervals over the first 60 lactation days and 305-day records after effect groups. P-values *** < 0.0001 ** < 0.001 * < 0.01 † < 0.05. LSM: least square means; DIM: days in milk; FC: fat content; PC: protein content.Click here for fileDGAT1 region was deducted; allele effects are given as original results due to the bias when a major locus is deducted.Haplotype blocks, marker and candidate gene information over all approaches. HTB: haplotype block; DIM: days in milk; MAF: minor allele frequency; MY: milk yield; FY: fat yield; PY: protein yield; FC: fat content; PC: protein content; RZR: fertility index, summarizing all fertility traits; NRh: non-return rate for heifers; ns: non-significant. * only significant when Click here for fileEBVs for 305-day production traits. EBV: estimated breeding value; MY: milk yield; FY: fat yield; PY: protein yield; FC: fat content; PC: protein content.Click here for filefilled triangle milk yield; filled circle fat yield; filled square protein yield; empty circle fat content; empty square protein content (axis for milk yield is on the right hand side).Average EBVs over first 60 lactation days. EBV: estimated breeding value; Click here for filest to successful insemination separated after cows and heifers, NRc and NRh: non-return rate for cows and heifers, CON: conception , CFc: calving to first insemination; DO: and days open.EBVs for fertility traits. EBV: estimated breeding value; RZR: fertility index, summarizing all following traits, FLc and FLh: 1Click here for file


Spirometric parameters are the mainstay for diagnosis of COPD, but cannot distinguish airway obstruction from emphysema. We aimed to develop a computer model that quantifies airway collapse on forced expiratory flow–volume loops. We then explored and validated the relationship of airway collapse with computed tomography (CT) diagnosed emphysema in two large independent cohorts.A computer model was developed in 513 Caucasian individuals with ≥15 pack-years who performed spirometry, diffusion capacity and CT scans to quantify emphysema presence. The model computed the two best fitting regression lines on the expiratory phase of the flow-volume loop and calculated the angle between them. The collapse was expressed as an Angle of collapse (AC) which was then correlated with the presence of emphysema. Findings were validated in an independent group of 340 individuals.2 = 0.505, p < 0.0001) with little significant contribution of KCO, %predicted and FEV1, %predicted to the total model . Similar associations were obtained when using CT-automated density scores for emphysema assessment. Receiver operating characteristic (ROC) curves pointed to 131° as the best cut-off for emphysema . Validation in a second group confirmed the significant difference in mean AC between emphysema and non-emphysema subjects. When applying the 131° cut-off, a positive predictive value of 95.6%, a specificity of 96% and a sensitivity of 59% were demonstrated.AC in emphysema subjects (N = 251) was significantly lower (131° ± 14°) compared to AC in subjects without emphysema (N = 223), (152° ± 10°) (p < 0.0001). Multivariate regression analysis revealed AC as best indicator of visually scored emphysema (RAirway collapse on forced expiration quantified by a computer model correlates with emphysema. An AC below 131° can be considered as a specific cut-off for predicting the presence of emphysema in heavy smokers. Chronic Obstructive Pulmonary Disease (COPD) is characterized by airflow limitation that is not fully reversible, usually progressive and associated with an abnormal inflammatory response of the lung to noxious particles or gases, most often from cigarette smoke . Being 4L,CO and KCO are often used as a surrogate marker of emphysema, for instance in the differential diagnosis with asthma, because they basically reflect impaired oxygen uptake by the loss of alveoli typically observed when the parenchyma is destroyed [Emphysema may be considered as one of the oldest phenotypes in COPD. It is characterized by the loss of lung tissue leading to breakdown of alveolar walls and subsequent airway collapse during forced expiration. It has been demonstrated that emphysema is associated with lower body mass index and bone mineral density, reduced exercise capacity, impaired quality of life and higher BODE index compared to COPD patients with similar airflow obstruction without emphysema -12. Emphestroyed ,17. Agaiestroyed -20. We tIn the present study our objective was first to develop a mathematical model for precise computerized quantification of airway collapse on forced expiratory flow-volume loops. Secondly, we related these measures of airway collapse to CT-diagnosed emphysema in two large independent cohorts of smoking individuals.http://www.clinicaltrials.gov (NCT00858520).To develop the computer model we included data of 513 individuals of the LEUVEN COPD cohort who performed complete pulmonary function testing at cohort entry (including post-bronchodilator spirometry and diffusing capacity) and of whom a computed tomography (CT) scan was available within 1 year of enrolment. All subjects were included between October 2007 and January 2009 at the University Hospital of Leuven (Belgium), as earlier described ,22. BrieL,CO) was measured by the single-breath carbon monoxide gas transfer method and corrected for alveolar ventilation but not hemoglobin concentration [All pulmonary function tests were performed with standardized equipment  by respiratory technicians, according to the ATS/ERS criteria . Spiromentration . Patientntration .All CT scans were examined by two independent radiologists and visually scored for the presence and extent of emphysema at 3 predefined levels . The preTo develop a computer model for the calculation of angle of collapse (AC) we used MATLAB . In all individuals the best expiratory flow-volume curve (highest sum of FEV1 and FVC) within one spirometry was exported from the Masterlab system at a sampling rate of 125 Hz. By extracting data points it was possible to reconstruct the best expiratory manoeuvre in MATLAB and to develop a unique algorithm for automatic quantification of airway collapse (see Results section).T-test was used to evaluate differences in AC between patients with and without emphysema within disease severity stages. Linear-regression models were applied for continuous variables analyses and logistic-regression models for binary variables. Stepwise selection was used to identify the subset of variables that had the strongest relation to emphysema scores, using default criteria of significance at the 0.15 level to enter or egress the model. Receiver operating characteristic (ROC) curve analysis was performed with GraphPad Prism version 5.01, .Statistical analysis was performed using Statistical Analysis System (SAS) version 9.3, . The Shapiro-Wilk test was used to control normality of the datasets, while a The algorithm for quantification of airway collapse computed the two best fitting regression lines on the expiratory phase of the flow-volume loop from peak flow to the end of expiration Figure . The int1 expressed as percent predicted as stated in former Global Initiative for Chronic Obstruction Lung Disease (GOLD) classification [Baseline characteristics of 474 subjects with good quality measures of AC and visually assessed emphysema are presented in Table fication .1/FVC ratio, DL,CO, %predicted) were found as between emphysema scores and AC. Associations with FVC, %predicted and FEV1, %predicted were less pronounced   Table . LogistiCO, %predicted and a 0.43 cut-off for the FEV1/FVC ratio with comparable results for specificity and sensitivity in predicting the presence of emphysema. When joining both cut-offs of either 131° for AC or 66%predicted for Kco, sensitivity rose to 67%, whilst positive predictive value and specificity remained within the same range .We used ROC curve analysis to compute the sensitivity and specificity of AC for predicting emphysema presence. An area under the curve of 0.863 (0.830-0.896) was found to be statistically significant (p < 0.0001). If using maximal sum of sensitivity and specificity to select best cut-off, ROC curve pointed to 143°, with specificity of 86% and sensitivity of 74%. However, if determining the cut-off based on the clinical need for high specificity and strong positive predictive value, ROC curve indicated 131° as most appropriate cut-off, with specificity of 97% and sensitivity of 51%  to quantify emphysema. An additional number of 8 patients had to be excluded due to lack of density scores. When defining emphysema based on attenuation of at least 1% of the total lung volume below the -950 HU threshold, 446 (96%) subjects were categorised with emphysema. When using an arbitrary but more strict approach to define emphysema (>10% below -950 HU), only 213 patients were labelled with emphysema, which had also a best agreement with the obtained visual scores, kappa 0.393 (95%CI: 0.310 -0.476). Subsequent stratification according to the 10% cut-off demonstrated worse pulmonary function and significantly lower AC (134° ± 16° ) in the emphysema group compared to the controls (148° ± 13°) (p < 0.0001)  of subjects. 6.5% N = 21) of subjects were excluded due to exceeded limit of 180° or failed quantification. Baseline characteristics are described in Additional file  of subjeOur study demonstrates that airway collapse observed at forced expiration can be automatically quantified by a computer model as an angle varying from 180° to 90°, correlating with the presence and severity of emphysema. Although our method was not sensitive to identify mild emphysema in the early stages of smoke-induced lung disease, an angle ≤ 131° proved to be a reliable cut-off for the positive prediction of emphysema in smoking individuals.Emphysema is characterized by the disruption of alveolar attachments leading to loss of alveolar-airway interdependence and reduced airway tethering during breathing . Upon thAn important strength of our approach is the use of a computer model to automatically quantify collapse. Previous studies have used a beta-angle for quantification . With thper se. It is obvious that the severity of emphysema is closely related to COPD severity, but the fact that within each former GOLD category patients with emphysema have significantly lower angles compared to their non-emphysematous counterparts, indicates that the angle quantifies flow limitation beyond FEV1. From a physiological point of view, airway obstruction must present with a curvilinear decrease of flow versus volume, whereas sudden drops in flow for little volume changes are representative of collapse [As mild emphysema may also present without airflow limitation and collapse, one may hypothesize that the angle is rather representative of airway obstruction than of emphysema collapse . Althougcollapse .2 = 0.1948, p < 0.0001) [Despite the fact that we confirmed our findings in a second independent cohort, our study has some limitations. Most importantly, we used validated semi-quantitative or visual scores for the characterization of emphysema because CT scans were obtained in clinical routine with different CT equipment, different acquisitions and variable use of intravenous contrast. When using automated density measures of emphysema (defined as the percentage of voxels below −950 Hounsfield Units (HU) at inspiration), we found the expected poor relationships between AC and emphysema percentage (R 0.0001) . Althoug 0.0001) ,45. Surp 0.0001) . WhetherTaken together, our data provide strong evidence that airway collapse is one of the best lung functional correlates of visually assessed emphysema on CT scan. In primary care, detection of emphysema on spirometry may identify a population at risk for clinical deterioration and specific follow-up.AC: Angle of collapse; BMI: Body mass index; COPD: Chronic obstructive pulmonary disease; DL,CO: Carbon monoxide diffusing capacity; FEV1: Forced expiratory volume in one second; FVC: Forced vital capacity; HU: Hounsfield units; I CC: Intraclass correlation coefficient; KCO: Carbon monoxide transfer coefficient; MSE: Mean square error; MTT: Mean transit time; ROC: Receiver operating characteristic.The authors declare that they have no competing interests.Study concept and design: MT, KJ, DB, MD, WJ; Analysis and interpretation of the data: MT, VE, AP, WJ; Radiology data interpretation and scoring: JC, WD, MH, PS; Writing of the manuscript: MT, MD, WJ; Critical revision of the manuscript. All authors read and approved the final manuscript.Supplementary material.Click here for file


P = 0.029 and P = 0.018) on LRP2 and ZNF133, respectively. The transmission ratios were as high as 1.7~1.8:1. Genotyping validation and further replication is still necessary to confirm the TRD. This study shows that there may be large-effect maternal-specific TRD loci of common SNPs in the human genome but that these are rare.Transmission ratio distortion (TRD) is indicated by the recovery of alleles in offspring in non-Mendelian proportions. An assumption of Mendelian proportion is central to many methods to identify disease-associated markers. This seems reasonable as, while TRD cases have been occasionally observed in various species few instances have been identified in humans. Here we search for evidence of paternal or maternal TRD with genome-wide SNP data of pedigrees from the Framingham Heart Study. After excluding many examples as better explained by genotyping errors we identified two maternal-specific TRD loci for autosomal SNPs rs6733122 and rs926716 (corrected  DrosophilaTransmission ratio distortion (TRD) is indicted when the transmission ratios between different alleles deviate from the 1:1 ratio that is expected under Mendelian inheritance. TRD has been observed and studied in some model organisms, such as the segregation distorter system in A few TRD loci have been identified in humans to date, and most of them were observed on rare variants such as the Robertsonian translocations, which were often directly associated with human diseaseMethods) to identify loci subject to paternal-specific and maternal-specific TRD. After quality-control of the data and statistical tests (see Methods), a total of 41 paternal and/or maternal TRD SNPs reached the 5% significance threshold after Bonferroni correction, equivalent to a raw p-value of 7.2 × 10−8 .We used the large-scale genome-wide SNP data of individuals from the Framingham Heart Study (FHS) pedigrees (see 2 × 10−8 . After v2 × 10−8 . The genr2 = 0.98). This SNP showed a good clustering . Therefore, it is still possible that the TRD of rs6733122 was affected by genotyping artifact that yielded more transmissions for the major allele, and additional efforts in genotyping for this SNP would be helpful to confirm the TRD. The other remaining SNP rs926716 showed a good clustering , and we observed significant sex-differentiated TRD effects for the two identified SNPs . Therefore, the paternal TRD effects should be minimal and we considered that the two identified TRD loci were maternal-specific.The remaining two SNPs gave genome-wide-significant maternal TRD ectively . Interesied SNPs . In addiMethods). The overall cross validation consistencies in the 100 repetitions of ten-fold cross validation were 953 and 962 (out of 1000) for rs6733122 and rs926716, respectively.To avoid the situation that the TRDs were generated by a few pedigrees and to evaluate the robustness of the TRDs, we performed pedigree-based cross validation for the two maternal TRD loci (see Methods). Eight of the phenotypes are quantitative traits; namely, height, weight, systolic blood pressure, diastolic blood pressure, cholesterol level, high-density lipoprotein level, triglycerides level and blood sugar level, and the other two phenotypes are two disease statuses of diabetes and hard coronary heart disease. No significant association was identified . Furthermore, we used the Genetic Association DatabaseIt is possible that the identified TRDs were confounded by actual phenotypic association if the studied individuals, especially offspring, were sampled with enrichment of certain disease  or extreme trait, rather than randomly sampled from the population. We confirmed that the individuals of the FHS were sampled in a random way89LRP2  TRD. A previous study also used the FHS data to search for human TRD loci of SNPs, but without separately considered for paternal and maternal TRD ref. A recent study also used the FHS data to detect general and sex-specific TRD loci in humansAn early sib-pair linkage study suggested that there was the trend of extensive TRD throughout the human genome, and the trend was contributed by many small-effect TRD loci, without evidence of sex-differentiated TRD ref. LRP2 is caused by postzygotic viability due to a maternal effectThe identified maternal-specific TRD loci may result from either meiotic drive or postzygotic viability due to maternal effects (including disruptions to imprints). It is unknown what the mechanism of action might be in either of the cases that we identified. Neither marker is in a cytogenetic band with any connection to imprinting, there being no predicted imprinted genes in either 20p11 or in 2q31DLG5 is transmission-distorted among healthy male offspringATG16L1, which is also associated with Crohn's disease, was subject to maternal TRD in healthy controlsP = 0.70), which may be due to the fact that the TRD observed was population-specific.The TRD loci identified in this study are probably of functional significance, and they may also relate to human diseases. Notably, rs926716 is associated with blood cholesterol level, which is an intermediate phenotype related to many complex diseases. A previous study found that a Crohn's disease-associated allele on P > 0.05, data not shown). Rather than declaring that the identified TRD might be statistical false positives, we speculate that large-effect TRD are likely to be observed in some specific population rather than all populations, because large-effect TRD loci are unstable, that is, the preferred alleles should quickly dominate the population after the TRD started so it is extremely unlikely that one TRD can be observed in many populations simultaneously.In addition, we could not replicate the identified two maternal TRD loci (rs6733122 and rs926716) using two other datasets, namely, the HapMap Phase II dataFinally, it can be seen from this study that genome-wide-significant TRD loci could be identified in pedigrees without any enrichment of a certain disease in offspring, when the available sample size is large enough. It is possible that, in a TDT with genome-wide SNP data for a certain disease, there may be spurious TRD loci identified that are not necessarily relevant to the specified disease. Therefore, TDT results should be interpreted with caution, and an appropriate control for the identified TRD loci, such as to sample and test a number of unaffected trios from the studied population, would help to avoid the situation mentioned above.The data analyzed here were obtained from the GAW16  Problem 2: the Framingham Heart Study dataThe data mentioned above were processed with a series of quality-control procedures, as follows. (1) Removal of any individual with missing sex information; (2) removal of twins; (3) removal of any individual with a call rate < 97%; (3) exclude any SNP with > 5% missing genotypes; (4) removal of any individual with problematic sex information according to the sex inferred by the X chromosome homozygosity estimate F ; (5) removal of out-of-pedigree singletons; (6) removal of potential duplicated samples if the estimated proportion of identical-by-decent allele sharing between any two individuals was larger than 0.9 (none removed); (7) removal of any offspring with an individual-wise Mendelian error rate > 0.01 (none removed); (8) exclude any SNP with a SNP-wise Mendelian error rate > 0.01; (9) exclude any SNP for which the proportion of "informative"  paternal or maternal transmissions was lower than 0.05; this is a similar control to that for minor allele frequency, but it was more appropriate in the TRD analysis to avoid sparse data; (10) remove individuals that were not in any complete parent-offspring trios. Procedures (1–6) were performed with the PLINK software2 test to determine if the paternal and maternal transmission ratio of two alleles of a certain SNP deviated from the expected ratio of 1:1, and used Bonferroni correction to adjust the p-values employing the total number of multiple tests, including the separate tests for paternal and maternal TRDs. A standard χ2 test was used to evaluate the difference between the paternal and maternal transmission ratio for any given SNP. The analyses were performed with customized programs and the results of the two identified TRD loci were verified with the S.A.G.E. softwareAs each child corresponds to a parental transmission event, a total of 1,241 parent-offspring trios  were derived from the 319 pedigrees for the analyses of paternal and maternal TRDs. For each SNP, the numbers of paternal and maternal allele transmissions were respectively recorded if the transmissions were informative. Transmissions in ambiguous trios with all heterozygous genotypes were not included. We used the goodness-of-fit χFor each identified TRD locus, ten-fold cross validation was performed and the cross validation was repeated 100 times. In each cross validation, the pedigrees were randomly divided into ten groups, and every group was labeled by turn as testing data while the other nine groups were combined and labeled as training data. In each turn, the validation was considered consistent if the direction of the TRD in the testing data was the same as that in the training data .We performed population-based phenotypic association analyses using unrelated individuals of the FHS data. Here the individuals removed in quanlity-control procedure (10) were retained because the individuals that are not in complete trios can still be used in population-based association analyses. Founders were extracted and then 444 individuals were removed due to relatedness . A total of 1,330 individuals remained for association analyses. Eight quantitative traits and the two disease statuses were analyzed using linear regression and logistic regression, respectively, on the two identified SNPs for the evaluation of allelic additive effects. All traits except height were log-transformed to approximate normal distribution. Sex, age and smoking status were included as covariates.2 tests to test for differences in allele transmission ratio between those trios with affected offspring and the other trios. Paternal and maternal allele transmission ratios were analyzed separately.Family-based association analyses were also performed using the same data as in the TRD analyses. We used QTDTY.L., X.K. and L.D.H. conceived and designed the experiments. Y.L. performed all the statistical and computational analyses. Y.L. and L.D.H. wrote the paper. Y.L., L.Z., S.X., L.H., L.D.H., X.K. analyzed the results and reviewed the manuscript.Supplementary Table S1 and Figure S1


Unlike CMS, the 2sh had significant positive cytoplasmic effects on the seed set rate, weight per panicle, yield, and general combining ability (GCA) for plant height, seed set rate, weight per panicle, and yield. These effects indicated that 2sh cytoplasm may show promise for the improvement of rice yield. Genetic analysis suggested that the phenotype of 2sh was controlled by a single recessive locus. We mapped 2sh to a 152 kb region on chromosome 6, where 22 candidate genes were predicted. None of the 22 genes had previously been reported to be responsible for the phenotypes of 2sh. Sequencing analysis showed a 12 bp deletion in the sixth exon of Loc_Os06g40550 in 2sh in comparison to wild type, suggesting that Loc_Os06g40550 is the best candidate gene. These results lay a strong foundation for cloning of the 2SH gene to elucidate the molecular mechanism of male reproduction.Cytoplasm has substantial genetic effects on progeny and is important for yield improvement in rice breeding. Studies on the cytoplasmic effects of cytoplasmic male sterility (CMS) show that most types of CMS have negative effects on yield-related traits and that these negative effects vary among CMS. Some types of genic male sterility (GMS), including photo-thermo sensitive male sterility (PTMS), have been widely used in rice breeding, but the cytoplasmic effects of GMS remain unknown. Here, we identified a GMS mutant line,  IR19661-283-3-2 has a significant negative effect on both yield and days to flowering but does not affect plant height. The male sterile cytoplasms of IR46828, IR46831, IR48483, IR54752, IR17492-18-10-2-2-3, IR54753, IR19661-283-1-3-2, IR54758, and IR54756 have been found to have different effects on grain yield Cytoplasmic male sterility (CMS) is essential to the exploitation of heterosis in three-line hybrid rice breeding programs. CMS cytoplasm has been reported to have substantial genetic effects on various traits of hybrid rice 1 hybrid. Genetic interaction limits the flexibility of germplasm selection and increases production costs 2sh) identified in our breeding program to evaluate cytoplasmic effects on plant height, yield-related traits, and general combining ability (GCA) for these traits.In the three-line system of hybrid rice, male sterility (MS) and its restoration are governed by genetic interaction between male sterile cytoplasm and the nucleus. The interaction maintains the MS, allows the regeneration of the MS line, and restores MS for enjoyment of yield advantage in the FPMS3  P/TMS12-1  japonica background and TMS in indica background. Recently, a number of non-photo-thermo-sensitive genic MS genes have been identified in rice, including WDA1 (wax-deficient anther1) CYP704B2TDR (tapetum degeneration retardation) GAMYBDPW  PTC1  2SH gene and for elucidating the molecular mechanism of anther and pollen development.GMS includes photoperiod-sensitive male sterility (PMS), temperature-sensitive male sterility (TMS), photo-thermo-sensitive male sterility (PTMS), and non-photo-thermo-sensitive genic male sterility. The MS in PMS, TMS, and PTMS can be maintained and reproduced under certain light and temperature conditions. PMS and PTMS have been successfully applied to the production of hybrid seeds, such as Nongken58s, Peiai64s and Y58s 2sh mutant (a spontaneous mutant identified from an indica maintainer 2SChuannong H) and F2 mapping populations generated by respectively crossing 2sh as female with Zhenshan97B and Nipponbare were planted in a paddy field at Sichuan Agricultural University  and at the Hainan rice station . Five isonuclear alloplasmic lines were generated by crossing heterozygote (2sh+/−) as male with Zhenshan97A, D702A, G46A, K18A and XieqingzaoA and then backcrossing 8 times using 2sh+/− as the recurrent parent to substitute the nuclei of Zhenshan97A, D702A, G46A, K18A, and XieqingzaoA with the 2sh nucleus. The 2sh mutant was named A1, and the resultant isonuclear alloplasmic lines were A2, A3, A4, A5, and A6, respectively  and R1–R5 as parent 2 (P2).The ectively . The nuc2sh mutant were cytologically examined for anthers at the meiosis, post-meiosis, young microspore, and vacuolated pollen stages. The preparation for chromosome spreads followed the method described in Chen et al. The wild type and the 1s and their parents were sown on April 4 and transplanted on May 20, 2006 in the Sichuan Agricultural University paddy  to evaluate cytoplasmic effects. A randomized complete block design with three replications was used. For each replication, three rows with 12 plants in each row were planted in plots 2.12 m×0.8 m in size. Rows were 26.6 cm apart and plants within rows were 17.7 cm apart. Ten randomly selected plants from each plot were measured for plant height (cm), effective panicle number, 1,000-grain weight (g), grain number per panicle, seed set rate (%), and weight per panicle (g). Mean values were used for analysis of variance (ANOVA). Most of the plants from each plot were bulk harvested for grain yield, but one plant from each end of each row was omitted to exclude any border effect. This experiment was repeated in the summer of 2008, with the same design. A fixed model with type III sum of squares in SPSS software  was adopted and used to perform an ANOVA. Least significant difference (LSD) was estimated for mean comparison of general combining ability (GCA) effects. The A1–A6 GCA was calculated by the difference between a mean over all combinations for a line A such as A1 to be a parent with all restores (R1–R5) and the overall mean for a trait. The effects of 2sh cytoplasm on plant height and yield-related traits were estimated as follows: [2sh (A1)/R (R1–R5)] F1– [isonuclear alloplasmic line (A2–A6)/R (R1–R5)] F1.Thirty F2sh gene was mapped using a procedure described previously by Chen et al. 2 populations were generated by crossing 2sh as a female with Zhenshan97B and Nipponbare, respectively. We used 342 SSR markers to examine polymorphism between 2sh and Zhenshan97B. Preliminary mapping was based on 1532 male sterile F2 individuals from the cross between 2sh and Zhenshan97B. For fine mapping, 35 SSRs and 74 indel markers were used to examine polymorphism for the target region between 2sh and Nipponbare, and 2400 male sterile F2 individuals produced from this cross were analyzed. Among the 74 indel markers, the closest marker to the mutation locus was W11 . Genetic distance was estimated using Mapmaker 3.0 software http://bar.utoronto.ca/efprice/cgi-bin/efpWeb.cgi) and the rice anther expression database The 2O was used as a template for PCR. The RNA used for RT-PCR was from wild type and mutant anthers at the post-meiosis stage, and the primer pair for Loc_Os06g40550 was W70  and OsActin1 .Total RNA was isolated using a Qiagen RNA plant mini kit with on-column DNAse digestion (Qiagen). Two micrograms of RNA was used for reverse transcription using M-MLVRT (Promega) with oligo (dT18) primer, and 1 µL RT product diluted with 20 µL ddH2sh mutant showed shorter spikelets with smaller anthers than the wild type , indicating a single recessive mutation in the spontaneous 2sh mutant. 2sh also showed stable MS in both Sichuan  and Hainan , suggesting that 2sh was not a PTMS.All F2sh nucleus. This allowed reliable comparison of the cytoplasmic effects among A1–A6.No differences in genetic background among the six isonuclear alloplasmic lines (A1–A6) were detected , indicat2sh on plant height and yield-related traits were estimated by comparing two hybrids with the same male parent. Out of 25 paired comparisons, all but two showed that 2sh cytoplasm had a positive effect on yield. These positive effects were significant in 12 (48%) and 10 (40%) paired comparisons in 2006 and 2008, respectively (2sh cytoplasm had positive effects on the seed set rate. Thirteen (52%) and fourteen (56%) of such comparisons were found to be significant in 2006 and 2008, respectively. Six (24%) and seven (28%) paired comparisons showed significant positive cytoplasmic effect on weight per panicle in 2006 and 2008, respectively. For plant height, effective panicle number, and 1000-grain weight, there were fewer comparisons with significant positive cytoplasmic effects, even though the high proportion of paired comparisons showed positive cytoplasmic effects    and pland yield)  in both her year . The sig2sh had considerable potential for the improvement of rice yield, the male sterility of 2sh severely restricts its utility in the production of hybrid seeds. This is because 2sh is a non-photoperiod/thermo-sensitive genic male sterile mutant. However, one previously described approach provides a possibility to maintain the MS of recessive GMS, so that 2sh could be used to produce hybrid seed in breeding practices 2SH gene using a mapping-based approach.Although cytoplasmic analysis of agronomic traits and GCA indicated that 2 population was generated from a cross between 2sh and Zhensan97B to map the 2sh gene. A bulk segregant analysis was conducted using 52 polymorphic SSR markers between 2sh and Zhensan97B with two DNA bulks, each with ten sterile F2 plants and ten fertile F2 plants. Of the 52 SSRs, only RM3 on chromosome 6 was polymorphic between the two DNA bulks, indicating that the putative 2sh gene was located on chromosome 6. We then examined 23 SSRs flanking RM3 for polymorphisms between 2sh and Zhensan97B, and the polymorphic SSRs were used to analyze 612 male sterile F2 individuals. Both RM454 (located at 27832 kb) and RM528 (located at 26544 kb) were found to be linked with the 2sh locus, with 7.47 and 9.19 cM genetic distance, respectively  and RM20366 (located at 24196 kb) with 0.2 and 0.3 cM genetic distance, respectively  transporter domain. Its homolog in Arabidopsis, ABCG26/WBC27, has been reported to be involved in pollen exine formation, and the abcg26/wbc27 mutant exhibits impaired pollen wall formation that causes MS in ArabidopsisSNF7 (sucrose non-fermenting 7) protein encoded by Loc_Os06g40620 in Arabidopsis is a subunit of the ESCRT III complex. It may be involved in vesicle-mediated transport Loc_Os06g40630 encodes a protein containing a SFT2 domain, its homolog in yeast is involved in trafficking to the Golgi complex Loc_Os06g40650 encodes copine-1, whose homolog in Arabidopsis (ring domain ligase 2) negatively regulates drought stress response LOC_Os06g40540 and LOC_Os06g40660) are predicted to be retro-transposons, and the genetic functions of three other genes  are unknown at present. Among the ten candidate genes, Loc_Os06g40550 is the only one to be expressed in anthers around the tetrad stage Loc_Os06g40550 homolog in Arabidopsis, abcg26/wbc27, also exhibits MS, and the spatial and temporal expression of Loc_Os06g40550 was consistent with 2sh phenotype, we chose Loc_Os06g40550 as a top candidate gene for further study.Analysis of expression patterns for the 22 candidate genes showed that ten are expressed in the inflorescence GCCACCCTCCTC) deletion in the predicted sixth exon of Loc_Os06g40550 was detected in the 2sh genomic DNA and RNA by sequencing and RT-PCR analysis  of 30 combinations of 6 isonuclear alloplasmic lines (A1–A6) with 5 restorers (R1–R5) during both years.PPTXClick here for additional data file.Figure S3Mean grain number per panicle and CV (coefficient of variation) of 30 combinations of 6 isonuclear alloplasmic lines (A1–A6) with 5 restorers (R1–R5) during both years.PPTXClick here for additional data file.Figure S4Mean 1000-grain weight and CV (coefficient of variation) of 30 combinations of 6 isonuclear alloplasmic lines (A1–A6) with 5 restorers (R1–R5) during both years.PPTXClick here for additional data file.Figure S5Mean effective panicle number and CV (coefficient of variation) of 30 combinations of 6 isonuclear alloplasmic lines (A1–A6) with 5 restorers (R1–R5) during both years.PPTXClick here for additional data file.Figure S6Mean plant height and CV (coefficient of variation) of 30 combinations of 6 isonuclear alloplasmic lines (A1–A6) with 5 restorers (R1–R5) during both years.PPTXClick here for additional data file.Figure S7Mean yield and CV (coefficient of variation) of 30 combinations of 6 isonuclear alloplasmic lines (A1–A6) with 5 restorers (R1–R5) during both years.PPTXClick here for additional data file.Figure S8Mean weight per panicle and CV (coefficient of variation) of 30 combinations of 6 isonuclear alloplasmic lines (A1–A6) with 5 restorers (R1–R5) during both years.PPTXClick here for additional data file.Table S1Isonuclear alloplasmic lines used in cytoplasm effects analysis.(DOCX)Click here for additional data file.Table S22sh cytoplasmic effects on seed set rate, weight/panicle and yield in comparison with Zhenshan97A (A2), D702A (A3), G46A (A4), K18A (A5) and XieqingzaoA (A6).(DOCX)Click here for additional data file.Table S3GCA effects on seed set rate, weight per panicle and yield among cytoplasms of 2sh (A1), Zhenshan97A (A2), D702A (A3), G46A (A4), K18A (A5) and XieqingzaoA (A6).(DOCX)Click here for additional data file.