Here, we investigate whether twelve genomic annotation classes were enriched or depleted for significant associations in genome wide association studies for complex traits in beef and dairy cattle. We also describe a variance component approach to determine the proportion of genetic variance captured by each annotation class.In livestock, as in humans, the number of genetic variants that can be tested for association with complex quantitative traits, or used in genomic predictions, is increasing exponentially as whole genome sequencing becomes more common. The power to identify variants associated with traits, particularly those of small effects, could be increased if certain regions of the genome were known P-values from large GWAS using 700K SNP in both dairy and beef cattle were available for 11 and 10 traits respectively. We found significant enrichment for trait associated variants (SNP significant in the GWAS) in the missense class along with regions 5 kilobases upstream and downstream of coding genes. We found that the non-coding conserved regions  were not enriched for trait associated variants. The results from the enrichment or depletion analysis were not in complete agreement with the results from variance component analysis, where the missense and synonymous classes gave the greatest increase in variance explained, while the upstream and downstream classes showed a more modest increase in the variance explained.Our results indicate that functional annotations could assist in prioritization of variants to a subset more likely to be associated with complex traits; including missense variants, and upstream and downstream regions. The differences in two sets of results (GWAS enrichment depletion versus variance component approaches) might be explained by the fact that the variance component approach has greater power to capture the cumulative effect of mutations of small effect, while the enrichment or depletion approach only captures the variants that are significant in GWAS, which is restricted to a limited number of common variants of moderate effects.The online version of this article (doi: 10.1186/1471-2164-15-436) contains supplementary material, which is available to authorized users. There is increasing evidence that genetic variation in most complex quantitative traits is the result of many mutations of small effect that individually explain only a small portion of the genetic variance –5. IdentBos taurus cattle, 28.3 million SNPs, insertions and deletions have been detected, and could be imputed into large data sets for GWAS studies or used for genomic prediction [The number of genetic variants  that can be tested for association with traits or used in genomic prediction is growing very rapidly, particularly with the increasing numbers of whole genome sequenced individuals currently available. In humans, the 1000 genomes project consortium has reported 38 million SNPs along with 1.4 million short insertion and deletions . In Bos ediction . This prediction , 14.a priori probability of containing trait associated variants (TAV), e.g. those variants significant in a GWAS. In humans, some of the variants that have significant impacts on traits and diseases are found in the protein coding regions of the genome [One way to reduce the number of variants for either genomic predictions or GWAS is to use the underlying biological information to annotate the variants into classes and prioritize for testing the functional classes with a higher e genome , with mae genome , 15, 16.e genome , 18. Howe genome , studiese genome –25. In lIn this paper, our aim was to identify regions of the bovine genome that are more likely to contain TAVs. This information could be used for prioritizing variants for future GWAS or for genomic prediction. We annotated variants on the Illumina Bovine HD array (777K SNP) into 17 classes; intergenic (variants that occur in-between genes), intragenic (variants found within genes), variants found 5 kilobases (kb) upstream of a TSS, variants found 5 kilobases downstream of a gene, introns, exons, protein coding sequence (CDS), synonymous variants, missense variants (non-synonymous variants), non-coding conserved variants, microRNA predicted target variants , both 5’We annotated SNP from 777K Illumina Bovine HD array into 17 classes (Methods). Of the 17 classes, 5 classes; frameshft SNPs, exons, mature microRNA SNPs, all splice sites and all stop sites were not included for further analysis due to having very small numbers of variants or in the case of exons already been represented by the CDS and UTR classes. This leaves us with a total of 12 classes for further analysis  and composite animals [P-value <0.0001 to define trait associated variants (TAVs) for further analysis since this gave enough power to identify enrichment or depletion while limiting the number of false positives , this class was significantly depleted for TAVs for majority of traits Table . AlthougThe synonymous class was enriched for 4 dairy traits while in beef we see enrichment in only one trait (LLPF). The missense class, however, had one of the highest levels of enrichment after the upstream and downstream classes in dairy with 7 traits to be significantly enriched for TAVs, while in beef we see no enrichment or depletion. Similarly, in the CDS class (the protein coding sequence class which includes both synonymous and missense variants) we find 7 dairy traits to be significantly enriched while for the beef traits we see only the trait LLPF to be enriched. The UTR class (both 3 prime and 5 prime) on the other hand is minimally enrichment for only 2 dairy traits and one beef trait (LLPF).The intron class was enriched for just as many traits as it was depleted, with 4 dairy and 3 beef traits showing both enrichment and depletion for TAVs. The enrichment for TAVs in this class is perhaps a surprising result given that in human studies intron variants are found to be minimally enriched or depleted , 17, 18.Overall, in dairy, the traits fat percent and protein percent were enriched in most of the protein coding annotation classes. The power to detect enrichment for these traits is greatest because they have the highest heritability of all the traits studied here.Of the non-coding regions the upstream and downstream classes were the most significantly enriched, in fact, they were enriched for more dairy traits than the protein coding classes. Similarly for the beef traits we see significant enrichment in the upstream class for most traits, with the downstream class having equal highest level of enrichment to the intragenic and intron classes, Figure . The traThe non-coding conserved class had little enrichment for TAVs. One dairy trait (protein) was actually depleted for TAVs in this class Table , while fP-value <0.0001 for our traits, and converted the data to binary, where if a variant has a GWAS P <0.0001 we code it as a “1” otherwise it is coded as a “0” (for not significant). Similarly for our annotation data, if a variant is found in an annotation class we code it as a “1” otherwise if it is not found we code it as a “0”. We recorded the P-value along with the regression coefficient from the logistic regression analysis (Methods). Results from this analysis were generally similar to the analysis above, however there were some important differences.In the enrichment or depletion analysis above, we examined our annotations independently. However, due to linkage disequilibrium, signals from one annotation may be present in another annotation. A logistic regression analysis was performed to determine the significance and estimated effect that an annotation has on the dairy and beef traits when fitted with all annotation classes together. We selected our significance level at For both dairy and beef traits, our logistic regression analysis shows the upstream and downstream classes to be very significant, with high estimated effects and positive regression coefficients on most traits Table . In partThe intron, UTR, non-coding conserved, TFBS and microRNA target classes show very minimal effects and were not significant, with a few exceptions .We used a variance component analysis to determine how much variance can be explained by the variants in each class, over and above the same number of randomly chosen intergenic variants. This was achieved by deriving a genomic relationship matrix (GRM)  for eachTo illustrate the difference, or similarity in information that would be captured by the GRM for each class, we calculated the Euclidean distance between each GRM. We found, not surprisingly, that the genomic relationship matrices between the CDS, missense and the synonymous classes are highly similar since the CDS class consists of both synonymous and missense variants (with 68% of the CDS class consisting of synonymous variants) Figure . We alsoWe find that variants in the protein coding classes explained the most variance, over and above the variance explained by the same number of randomly chosen variants, for most of our traits Figure , Table 5We find that the upstream and downstream classes do not significantly explain more of the variance, which is surprising given the number of enriched traits in the previous analysis. Of all dairy traits, only fat percent was significant in both the upstream and downstream classes, although there was a trend for the other annotations to explain more of the variance when compared to the random intergenic set for the traits fat percent, milk production and protein percent Table . For theTo extend on the above analysis, we also examined the variance explained per SNP for each annotation class to assess the genetic variance explained by each class in the presence of all the other classes. This assists with eliminating signals from one classes having on another class, and allows us to determine the contributing variance each SNPs have on our traits when categorized in each annotation. We fitted all the GRM simultaneously using GCTA , and recOur variance explained per SNP analysis reveals that the missense and synonymous classes explain the greatest proportion of the genetic variance, when the results are expressed as heritability per SNP, compared to all of our classes combined Figure , particuWe find that the upstream and downstream classes do not explain as much of the variance per SNP as the missense and synonymous classes do, however they do capture more genetic variance per SNP than the intergenic, intron, UTR, non-coding conserved and miRNA target site classes. The UTR (for both 5’ & 3’) annotations surprisingly capture a much smaller portion of the genetic variance per SNP, except for the trait fat percent.The intron, intergenic and non-coding conserved annotations explain the lowest proportion of the genetic variance per SNP while the miRNA target site annotations capture more of genetic variance per SNP than the CDS, intron, intergenic and non-coding conserved classes Figure . These rWe tested whether 12 genome annotation classes contained variants affecting complex traits in dairy and beef cattle, using a trait associated variant enrichment or depletion analysis and two variance component approaches. In the TAV enrichment or depletion analysis we observed that the upstream and downstream classes were significantly enriched for most dairy traits and many beef traits, while the protein coding classes are also enriched in dairy, but for fewer traits. In the first variance component approach, where the proportion of variance captured by the SNP in that class was compared to the proportion of variance captured by the same number of random SNP, the protein coding classes  were significant, while the upstream and downstream classes were not for most traits. The second variance component approach, where the GRM for all annotation classes were fitted at once, demonstrated that the missense class explained the most variance on a per SNP basis.The main difference in results between the TAV enrichment or depletion analysis and the variance component approaches was that the upstream and downstream classes are highly significant in the first approach, but not in the variance component approach across most traits. This difference may arise for two possible reasons. The first reason arises because the actual causal mutations are unlikely to be in our data set. In the upstream and downstream classes, there are a very large number of SNP, so that the chance of at least one of them being in high linkage disequilibrium with a causal mutation (a TAV) is higher than for classes with a lower number of SNP. Secondly, in the TAV enrichment or depletion analysis, a significance threshold is set, while in the variance component approach there is no such threshold. By setting a significance threshold, only variants in linkage disequilibrium (LD) with undiscovered mutations of moderate effect size are effectively considered in the enrichment or depletion analysis. However, given the typical genetic architecture of complex traits, there are likely to be very few of these . In contProtein coding regions are known to harbor variants affecting complex traits and by focusing on these regions, particularly exons, offers some promise for identifying the genetic variants that are associated with complex traits . The proOne major limitation of our study is that the variants we investigated are a set of SNPs selected for high minor allele frequency (MAF) and are evenly spaced across the genome (they are SNPs on the Illumina Bovine HD array), rather than the complete set of sequence variants in the population. This can lead to some annotated variants to be in high linkage disequilibrium with variants that can be causative. For example a synonymous variant (a class enriched for some traits) that is associated with a trait is unlikely to be the causative mutation but is more likely to be in high linkage disequilibrium with a nearby causative mutation. This limitation has been reported in many studies , 17 and This limitation is most prominent in the intron class (which is enrichment for some traits), where a very large number of variants are annotated for this class, increasing the chance of these variants to be in LD with causative mutations that might not be in introns, particularly for variants near the intron/exon boundaries. However in the variance component analysis, this class explains less of the genetic variance when compared to the same number of randomly selected intergenic SNPs. As mentioned earlier, this class has a large enough number of variants where most of the heritability is captured and it would be difficult to capture any more or less of the variance using this heritability alone when compared to an annotation class with a smaller number of SNPs. Fitting the intron class simultaneously in the model with all other annotations to examine the proportion of variance explained on a per SNP basis confirms that the intron class explains very little variance Figure . In otheThe UTRs are implicated to have regulatory functions in controlling gene expression and are known to harbor binding sites for microRNAs . StudiesThe microRNAs (miRNAs) are known to have important regulatory functions with claims that about a third of mammalian genes are actually regulated by miRNAs . In our The upstream and downstream classes are significantly enriched for TAVs for most beef and dairy traits in our analysis Figure , suggestStudies in human genetics are beginning to uncover the importance that the regulatory genome has on traits and how variants found in the non-coding regions are as significant as those found within genes. The regulatory genome is highly complex with many interactions contributing to the expression and regulation of genes. Large scale projects such as the ENCODE (Encyclopedia of DNA elements) project  are begiRelated research found that GWAS SNPs are significantly enriched for promoter annotated variants  while encis regulators for genes [Variants in the genome for which the sequence is conserved across species have been proposed as an important annotation class for prioritization and are potentially causative , 47. Theor genes . Studiesor genes . It has or genes . We haveAs previously mentioned, one of the major limitations in our study, is ascertainment bias and the selection of SNPs with high minor allele frequencies that are evenly spaced across the genome resulting in many variants to be in high LD with a nearby causative variant, complicating the interpretation of the analysis. The best way to eliminate this high level of LD in our data and greatly improve this study would be to use whole genome sequence data . Whole gOur findings suggest annotation of variants based on biological function can assist in prioritization of variants more likely to be associated with complex traits in dairy and beef cattle. The variance component approach indicates protein coding regions explain significantly more variation than a similar number of randomly chosen SNP across many traits. We also found significant enrichment of TAVs in upstream and downstream classes suggesting that these classes can have potentially important regulatory functions. The non-coding conserved regions were, in some cases, actually depleted for TAVs, leading us to assume that these variants are not highly significant in our traits. On a per SNP basis, missense variants explained the greatest variation for many traits. Finally, while protein coding regions are highly important in predicting complex traits, the importance of regulatory function cannot be over emphasized and having reliable regulatory information for further studies in bovine genomics is paramount.We annotated dairy and beef SNPs from the 777K Illumina Bovine HD array into; intergenic, intragenic, exons, CDS, UTRs (both 5’ & 3’), 5 kb upstream of TSS, 5 kb downstream of genes, missense, synonymous, frame-shift variants, splice sites and stop codon classes by querying the Ensembl variant database version 73 .We used the miRBase database , 52, 53 Non-coding conserved variants were obtained through phastCons  for convP-values for dairy traits were from a multi-breed GWAS including 17,425 bulls and cows, of either the Holstein or Jersey breeds [P-values for beef traits were from a GWAS of Bos indicus, Bos taurus and composite animals [P-value; < 0.1, < 0.01, < 0.001, < 0.0001, < 0.00001, < 0.000001, < 0.0000001 and < 0.00000001 and calculated false discovery rates (FDR) for all thresholds to estimate the number of false positives. The FDR was calculated with the following formula:y breeds . P-value animals . For eacp-value is one of the P-value thresholds , m is the number of significant variants found in the each threshold and n is the total number of variants in the class. We chose a GWAS threshold of P < 0.0001 for our analysis since we found most enrichment or depletion at that P-value while FDRs were low, and there were enough significant SNP for analysis in most classes  were given. An annotation with a low P-value and a high estimated effect is considered as enriched for that trait, while an annotation negative estimated effects is considered depleted for that trait.Logistic regression was applied to model annotations as variables that influenced trait-association status of the SNPs thus to eliminate signals from one class been present in another. The trait-association status was modelled as a binary variable where if a SNP has a GWAS g ASREML . The estet al.[The purpose of the variance component analysis was to determine if SNP in an annotation classes explain more of the variance than the same number of randomly chosen intergenic SNP. We calculated the genomic relationship matrix (GRM) for each class according to Yang et al., using tWe determined the measure of similarities between the GRMs belonging to the annotations, to see if variants found in one class are also present in another. We did this by calculating the Euclidean distance between each of the annotation GRMs using the following formula:m and p are the corresponding GRMs for each functional class.Where To calculate the proportion of variance explained for each independent annotation class we performed a REML analysis by fitting the following model to the data:Where y is a vector of phenotypic records, b is a vector of fixed effects including the breed and sex, X is a design matrix allocating records to fixed effects, Z is a design matrix allocating records to breeding values, and g is a vector of random breeding values, g ASREML , and then SNP but from the intergenic variant set. Similar to what was done above, we calculated the GRM for the random intergenic data sets and used ASREML to determine the heritability. This random sampling was run a total of 5 times to get a standard error and we calculated the average of these 5 runs to obtain the random set heritability.To determine the proportion of variance explained by the same number of random SNP we selected the same number of Some classes that have a large enough number of variants, can capture most of the heritability for each trait. This can be a limitation since it would be difficult to determine the true additional variance explained compared to that of the randomly selected intergenic set  for each trait along with the standard error (S.E). To determine the genetic variance explained on a per SNP basis we used the following formula for each class:h2 is the heritability and n is the total number of SNPs in the annotation class. We multiplied this result by 100 to get a percent (%) of the genetic variance explained and divided this results by 10−4 for visualization of the data.Where http://www.labarchives.com/) with DOI: http://dx.doi.org/10.6070/H4N58J9R. Files are stored in .xlsx format.Dairy and beef cattle SNP annotations for the 777K Illumina bovine HD array are provided in the repository hosted by LabArchives, LLC Additional file 1: Beef cattle enrichment or depletion for trait associated variants. Results from the enrichment or depletion analysis across all P-Value thresholds tested from the beef GWAS. Enriched beef traits are labeled as “+”. Depleted beef traits are labeled as “-“. Non-significant beef traits are labeled as “ns”. Table is attached in. xlsx format. (XLSX 17 KB)Additional file 2: Logistic regression analysis on dairy and beef traits with the CDS class eliminated. We performed a logistic regression analysis on both beef and dairy traits without the CDS class to determine the impact this has on the missense and synonymous classes. The CDS class consist of both missense and synonymous variants, thus signals form these classes are already represented in the CDS class. By eliminating the CDS class in our logistic regression analysis, we clearly see an increase in the significance and the estimated effect for both classes. Table is attached in. xlsx format. (XLSX 12 KB)Additional file 3: Matrix of similarities between the genomic relationship matrices. The matrix shows the level of similarities between the genomic relationship matrices for each function class by calculating the Euclidean distance (measure) between each annotation GRM. The more similar a GRM is to another the lower the Euclidean distance measure is. (XLSX 12 KB)Additional file 4: Genetic variance explained per SNP in each annotation class for dairy traits. The following table shows the heritability for each trait (as a percentage (%)) in each annotation class along with the average genetic variance explained per SNP /10−4). Classes that have higher values capture more of the genetic variance that contributes to the heritability. Lower values indicate that they only contribute a much smaller amount to the heritability for that class. Table is attached in .xlsx format. (XLSX 14 KB)Additional file 5: Variance component analysis using ASREML in dairy traits. The following tables show the class heritability for each trait along with the permutated heritability from the same number but randomly chosen SNPs (which was replicated 5 times and significance is determined as greater or less than the average of the proportion of variance explained by the randomly chosen SNP +/− 2*standard error). The heritability percent difference is simply the difference between the class heritability and the permutated heritability. S.E is an abbreviation for standard error. Table is attached in .xlsx format. (XLSX 15 KB)Additional file 6:


SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the  The common variant-common disease hypothesis has successfully been investigated using genome-wide association studies (GWAS) with unrelated individuals in the past decade , variants are weighted inversely proportional to their MAF, e.g., in controls  method  pathway and from cardiovascular disease risk and inflammation causing genes. In total, 36 of the 3205 genes contained causal variants with a negative correlation between effect size and MAF.Two criteria were used to collapse variants of a gene to one ROI. For functionality, only non-synonymous or gene-based variants were considered. For collapsing based on MAF threshold, variants were restricted to MAF thresholds of either 0.01 or 0.05. Furthermore, only chromosomal regions were considered, where variants were present at a minimum of two different positions. This resulted in four different collapsing scenarios which differed by the numbers of genes and variants Table .Q2 as associated phenotype. If the method allowed for the inclusion of covariates, Q1, Q4, smoking status and age were utilized. However, in this simulation study some of the given method features were not considered due to software limitations or great computational costs. In case of RVT2, covariates were not included in the analysis because the software CCRaVaT does not allow for this feature. Furthermore, for VT and PWST, only the quantitative phenotype was considered because calculations for the affection status were computationally intractable. For SKAT and SKAT-O the small sample adjustment was used in the analysis.Most of the collapsing methods analyzed here test for association between an ROI and a binary affection status Table . Only a p-values which are computer processing unit time intensive. We therefore used a four stage p-value estimation approach. Two thousand permutations were performed first, and an ROI was taken to the next higher number of permutations only if the estimated p-value was smaller than the upper bound of the corresponding confidence interval of the p-value. In the next stages, 10,000, 100,000, and 400,000 permutations were done to increase precision of p-value estimates.Many collapsing methods require permutation for obtaining Within every replicate, we determined the proportion of not associated ROIs not exceeding the α-level. To estimate the type I error, this proportion was averaged over the 200 independent replicates. To evaluate whether an error was acceptable, we applied the threshold as suggested by Bradley  of a libSLCO1B1 gene was detected using a GWAS , an association between methotrexate clearence and the To compare the 15 methods in this data set, genotype data of 673 children with ALL from different populations were made available. Phenotype data of methotraxate clearence were adjusted by sex, age, and genetic ancestry information. For the 5 collapsing methods investigating quantitative traits, information from all 673 individuals was used. For the methods analyzing binary traits, the first and the last decile of ordered adjusted methotrexate clearence were used to create two distinct groups  of the simulation study, and further studies are required to thoroughly investigate the effect of differing sample sizes on type I and II error. The simulation data possibly match the underlying assumptions of some methods better than those of others. Specifically, in the present simulation study, only positive effects of rare variants were simulated, thus fitting in with the assumptions of the burden tests. Therefore, results for burden tests might have been even more negative (and better for non-burden tests) if truly associated variants had been simulated with both positive and negative effects. In addition, disease status was simulated to be influenced by covariates such as smoking status and age. This aspect could only be considered by few of the considered methods in this work and may be a disadvantage for methods assuming only genetic effects. Despite these limitations, this work provides one of the most comprehensive performance comparisons of a wide range of collapsing concepts developed so far.Most of the permutation-based tests were computationally very intensive. To give an example, the average calculation time of PWST for one ROI was about 20 min in one replicate, and this resulted in a CPU time of about 1900 days for the analysis of PWST in a single collapsing scenario.p-value distribution of PWST, SKAT and SKAT-O observed here were in line with the results of the original work by Zhang et al. (p-value distribution from the angle bisector (Zhang et al., An important aspect is that the inflated type I error levels have been reported for some collapsing methods before even by the authors of the original articles. Specifically, the g et al.  and Lee g et al. . These aOur analyses showed that pre-information such as functionality or deleterious effects predicted from biotechnology software of the considered variants are extremely relevant to form ROIs for detecting true associations. Therefore, improvement and refinement of chosen ROIs for association analyses should be further developed. Moreover, performance of the considered collapsing methods in this work should be additionally investigated in studies with larger sample sizes.Irrespective of the collapsing method used, studies with small to moderate sample sizes suffer from low power. Even in the case of sufficiently large samples, many of the investigated collapsing approaches might lack feasibility as they rely on permutations, requiring huge computational efforts. Large sample sizes, a substantial proportion of causing rare variants together with valid asymptotic methods are therefore required in gene-based analysis to reliably detect associations with large power.Carmen Dering and Andreas Ziegler designed the study. Carmen Dering performed the analyses. Results were critically reviewed by all authors. Carmen Dering and Andreas Ziegler wrote the first draft of the manuscript, which was critically reviewed by all other authors. Laura B. Ramsey, Mary V. Relling and Wenjian Yang collected the real data set and conducted the whole corresponding pre-analysis and annotation. All authors have seen and approved this final version of the manuscript.Andreas Ziegler is member of the editorial board of the Biometrical Journal, Human Genetics, Methods of Information in Medicine and Statistics in Medicine. 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.


In the present study, biometric traits , forelimb length (FLL), hind limb length (HLL), face length, forehead width, forehead length, height at hump, hump length (HL), hook to hook distance, pin to pin distance, tail length (TL), TL up to switch, horn length, horn circumference, and ear length were studied in 218 adult hill cattle of Himachal Pradesh for phenotypic characterization.Morphological and biometrical observations were recorded on 218 hill cattle randomly selected from different districts within the breeding tract. Multivariate statistics and principal component analysis are used to account for the maximum portion of variation present in the original set of variables with a minimum number of composite variables through Statistical software, SAS 9.2.Five components were extracted which accounted for 65.9% of variance. The first component explained general body confirmation and explained 34.7% variation. It was represented by significant loading for BL, HG, PG, FLL, and HLL. Communality estimate ranged from 0.41 (HL) to 0.88 (TL). Second, third, fourth, and fifth component had a high loading for tail characteristics, horn characteristics, facial biometrics, and rear body, respectively.The result of component analysis of biometric traits suggested that indigenous hill cattle of Himachal Pradesh are small and compact size cattle with a medium hump, horizontally placed short ears, and a long tail. The study also revealed that factors extracted from the present investigation could be used in breeding programs with sufficient reduction in the number of biometric traits to be recorded to explain the body confirmation. Biometric traits are used to characterize body confirmation of different breeds of livestock, compare growth in different animals and also describe an individual or population in a better way than the conventional methods of weighing and grading . CharactAnalysis of variance and correlation are widely used to characterize phenotypic and genetic relationship among body measurements of animals -5. HowevIn spite of such unique characteristics hill cattle is among such cattle population of India which are not properly documented. Local cattle have their specific utilities in their native tract but in the last few decades, there is marked decline in their population due to widespread use of crossbreeding, destruction of the traditional production system and general thrust towards high input management system. To prevent the rich biological heritage from genetic erosion, it is imperative to redefine the breeding strategies and conservation programs. Biometric characterization is one of the important prerequisite along with other factors such as population size, geographical location, utility and management practices for implementation of breed improvement program. Furthermore, different biometric measurements, which represent the size of the animal is important criteria for selection of elite animals.Therefore, the present investigation is planned with the objective of biometrical documentation of hill cattle by studying different body measurements and to develop latent factor to define which of these measures best represent body confirmation in local hill cattle of Himachal Pradesh.During biometric data collection, attention has been paid to minimize the discomfort to the animal and animal handling was carried out in accordance with guidelines laid down by the International Animal Ethics Committee and prevailing local laws and regulation.The study was carried out in breeding tract of hill cattle  of HimacData collected were analyzed using fixed effect model, by considering district effect as fixed so as to adjust the data for significant effect of district if any as per the following statistical model .i is the fixed effect of district, while eij is the random residual error associated with each observation which is normally and independently distributed with mean zero and unit variance. Means, standard error and coefficient of variation were calculated. Pearson’s correlations (r) among various morphometric traits were estimated.Where, Y is the phenotypic observation for one of the 17 biometric traits, µ is the overall mean; DThe objective of PCA is to account for the maximum portion of variation present in the original set of variables with a minimum number of composite variables. Anti-image correlation, Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were computed to test the validity of the factor analysis of the data sets. The Eigen values were obtained by spectral decomposition of the data matrix and arrange in decreasing order of the corresponding eigenvalues, which equals to the variance of components. Thus, the first component has the largest variance. Varimax rotation was used for rotation of principal factors through the transformation of factors to approximate a simple structure. The estimate of communality for each variable measure the proportion of variance of that variable explained by all other factors jointly. All the analysis was performed using statistical software, SAS version 9.2.et al. [There was no significant effect of the district on all the traits studied in the hill cattle. Mean of biometric traits (cm) studied were 101.80±0.62 for BL, 137.38±1.08 for HG, 159.70±1.07 for PG, 26.70±0.11 for FLL, 38.57±0.21 for HLL, 39.31±0.22 for FL, 11.12±0.12 for FOL, 12.20±0.10 for FOW, 97.59±0.52 for HH, 3.24±0.07 for HL, 27.22±0.30 HHD, 12.43±0.08 for PPD, 68.04±0.55 for TL, 83.48±0.71 for TUS, 14.32±0.15 for HOL, 22.85±0.38 for HC 17.78±0.08 for EL. The morphometric characteristics observed in the present study suggested that indigenous hill cattle of Himachal Pradesh are small and compact size cattle with a medium hump, horizontally placed short ears and a long tail. The morphometric characteristics observed in the present study were more or less similar to other hill cattle of the country. Earlier study Pundir Anti-image correlation analysis revealed that partial correlation was low, indicating that true factors existed in the data, which was further supported by KMO measure of sampling adequacy. The KMO observed in the present investigation was 0.75, comparatively higher than earlier reports on hill cattle  of AssamThe first component describes the general body size of hill cattle. Various other studies on beef cattle , in blacThe first factor contributes effectively to explain general body confirmation of hill cattle of Himachal Pradesh. The positive and significant correlation among different biometrical traits also makes them amenable for analysis. The five factors extracted from the present investigation could be used to select animals based on a group of variables rather than isolated traits. The similar selection strategy has been recommended by earlier findings ,15. The SK and YP designed the plan and organized the study. DV carried out the study and collected the data, VS coordinated the data- analysis and drafted and revised the manuscript, All authors read and approved the final manuscript.


In family-based association analysis, each family is typically ascertained from a single proband, which renders the effects of ascertainment bias heterogeneous among family members. This is contrary to case–control studies, and may introduce sample or ascertainment bias. Statistical efficiency is affected by ascertainment bias, and careful adjustment can lead to substantial improvements in statistical power. However, genetic association analysis has often been conducted using family-based designs, without addressing the fact that each proband in a family has had a great influence on the probability for each family member to be affected.We propose a powerful and efficient statistic for genetic association analysis that considered the heterogeneity of ascertainment bias among family members, under the assumption that both prevalence and heritability of disease are available. With extensive simulation studies, we showed that the proposed method performed better than the existing methods, particularly for diseases with large heritability.We applied the proposed method to the genome-wide association analysis of Alzheimer’s disease. Four significant associations with the proposed method were found.Our significant findings illustrated the practical importance of this new analysis method.The online version of this article (doi:10.1186/s12881-015-0198-6) contains supplementary material, which is available to authorized users. Genome-wide association studies (GWASs) have been used to identify many genes involved in human diseases, and during the last decade, many disease-susceptibility variants have been identified. However, despite these successes, we have found that variants discovered from GWASs often explain only a small proportion of the heritability of diseases , 2. For Careful selection of samples based on phenotypes can lead to improved power for the discovery of risk variants –11. One vice versa. By the nature of these statistics, it performs well for ascertained family-based samples and it can be an efficient choice, even for a case–control design, if the relatives’ phenotype information is available. However, their statistical efficiency is affected by the heterogeneous effect of the ascertainment bias on family members, and for extended families, its effects on statistical efficiency can be substantial.Recently, the CA trend test was extended for association analysis of dichotomous phenotypes with family-based samples , 18. TheIn this report, we consider the heterogeneous effects of the ascertainment bias on family members for dichotomous phenotypes. By the nature of the proposed methods, individuals with missing genotypes and non-missing phenotypes can be utilized, and incorporation of the estimated kinship matrix to the proposed statistic provided robustness against the population substructure. The proposed method consists of two steps; the probability for each family member to be affected was calculated using a latent continuous liability , and then families and ni family members in each family. We considered the situation where the family of size ni was ascertained because it contained a particular set of pi members, and we let qi = ni – pi. We called the members of the set of pi family members “probands”, and the remaining qi individuals “non-probands”. To provide a clearer motivation on this concept, we randomly selected two families, family 1 and 2, from our AD data  and ranges from 0 to 1. Several approaches [dij have been proposed. We letWe also denoted the proaches , 21 thatRi is defined byand qA be the disease allele frequency, E(Xi) was qA is estimated with the best linear unbiased estimator (BLUE). var(Xi) is expressed by σ2Ri, and σ2 is equal to 2qA(1 –qA) under HWE.If we let μijP and μi'j'N be offsets for proband j and non-proband j' in family i and family i', respectively, the offset vector for family i is defined asWhen we analyzes the distribution of genotypes as in the FBAT approach, the statistical efficiency of the test statistic could be improved by adjustments of the phenotype with the so-called offset . If we lTi = Yi–μi, we can defineSetting q. We assumed [We denoted a minor allele frequency (MAF) of a variant in unaffected individuals by  assumed  that forp + γ < 1. Then, the score for a variant [S iswhere 0 < 2 variant , 23 can tatistic , 18:\docWL in the remainder of this report.This statistic will be denoted by WL as offset lead to asymptotically smaller variance and therefore the adjustment of heterogeneous ascertainment bias is required to improve the statistical power of WL. This probability could be estimated with the liability model if the heritabilities, h2, and prevalence, p, were available. We let lijP and li ' j 'N be the liability of proband j and non-proband j' in family i and family i', respectively, and let Families are often selected based on some probands, and the probability for family members to be affected depends on their relationship with the probands. Additional file MiP * and ViP * are the expectation and variances of LiP when their disease statuses are conditioned. If all probands are affected, they becomesBenchek and Morris  reportedandpi is 1, both can be simply calculated. We denote the cumulative and probability density function of standard normal distribution by Ф(·) and ϕ(·). If we let c be the (1–p)th quantile of the standard normal distribution, MiP * and ViP * becomesThey can be calculated with the numerical algorithms . If pi iLiN given With Pearson-Aitken formula , 27, we andjth element in MiN * by mjN * and the jth diagonal element in ViN * by vjN *. Then the probability of being affected for a non-proband under multivariate normality of the liabilities could be calculated asWe denoted the pi individuals who were “probands”, and for this situation, we calculated the statistic as indicated and denoted FQLS1. However in practice, different ascertainment condition such as sequential sampling frame [pi individuals will not be well defined. For this situation, we calculated the probability for each individual to be affected under the assumption that all the other family members were “probands”, and thus pi = ni – 1 and qi = 1. The statistic calculated this way was denoted by FQLS2.and this will be incorporated into the proposed statistic as offset. Thus far, we have assumed that there was a well-designed set of ng frame  are oftep, was assumed to be 0.2. If we denoted the disease allele frequency by qA, the genotype frequencies for AA, Aa, and aa became qA2, 2qA(1 – qA), and (1 – qA)2 under HWE, respectively, and founders’ genotypes were generated under the corresponding multinomial distribution. The genotypes for non-founders were generated with randomly generated Mendelian transmission. The disease status was generated with the liability threshold model. Once continuous liabilities that consisted of polygenic effects and random errors were generated, they were transformed to being affected if they were larger than the threshold; and otherwise, they were considered to be unaffected. The threshold was chosen to preserve the prevalence, and prevalence was assumed to be 0.2. Continuous liability was determined by combining the phenotypic mean, polygenic effect, main genetic effect, and random error. The main genetic effect for each individual was the product of β and the number of disease alleles. If we denoted the relative proportion of the phenotypic variance attributable to the main disease gene by ha2, and h2 was a heritability for continuous liability, β was calculated byIn our simulation studies, we considered two types of family structures; nuclear families with five offspring and the extended families that consist of 13 individuals along 3 generations see Fig. . The latha2 was assumed to be 0 and 0.005, respectively. Phenotypic correlations between family-members were explained by the polygenic effects. Parental polygenic effects were generated from N, and h2 was assumed to be 0.2, 0.5, or 0.8. For non-founders, the average of maternal and paternal polygenic effects was combined with the values independently sampled from N for the polygenic effects of offspring. Random errors were generated from N. For each replicate, sampling was repeated until a given number of ascertained families was generated. Type-1 error estimates were calculated with 5000 replicates, and empirical power estimates were calculated with 1000 replicates.For the evaluation of type-1 errors and power, FQLS1 and FQLS2 were evaluated from 5000 replicates under the situation of no association (ha2 = 0), and 900 nuclear families with five offspring in Fig. FQLS1 and FQLS2 preserved these nominal significance levels β2, was assumed to be 0.005, and nuclear and extended families in Fig. nproband, and the other half of the families were ascertained if the number of unaffected family members was larger than or equal to nproband. Therefore, if 100 nuclear families were generated, half of nuclear families should have more than or equal to nproband affected family members, and the other half should have at least nproband unaffected family members. We assumed that the heritabilities were 0.2, 0.5, and 0.8, and results are shown in Tables nproband. Empirical power estimates for scenario 2 were calculated when h2 = 0.2, 0.5, and 0.8, and the data are shown in Tables FQLS1 or FQLS2 was usually the most efficient statistic, and the least efficiency was provided from WL. In particular, the power gap between the proposed methods and WL was largest if h2 was 0.8, which indicates that power improvement may be proportional to the heritability. If h2 was 0.2, the proposed methods were only slightly better than WL. While all methods in our power comparison focused on the distribution of genotypes to calculate statistics, the proposed methods uniquely considered the heterogeneous effects of ascertainment bias among family members which were proportional to the magnitude of heritability; this explained the power improvement of the proposed methods. Furthermore the differences of empirical power estimates from WL and the proposed methods are larger for Tables FQLS2 was slightly better than FQLS1, and this may be induced by the uncertainty of probands in our simulation studies. Therefore, we concluded that the incorporation of a sampling scheme to the offset could make a substantial difference, and test statistic should be carefully selected depending on type of sampling scheme.The empirical powers at the various significance levels were measured based on 1000 replicates at the 0.01 and 0.001 significance levels. The relative proportion, ha2 and h2 were assumed to be 0.05 and 0.8, and nuclear families  was tested, and MAFs were estimated. SNPs for which p-values for HWE were less than 10−6 or MAFs less than 0.05 were excluded, and therefore, 417,680 SNPs were analyzed for genetic association analysis.AD is an irreversible, progressive brain disorder characterized by genetic heterogeneity. However, the genetic variations that contribute to AD still remain elusive. Thus, we applied the proposed method for identification of the disease susceptibility loci for AD. The heritability and prevalence of AD are approximately 0.8 , 30 and FQLS2 and WL. Fig. FQLS2 and WL; these plots revealed that the presence of population substructure was appropriately adjusted. Results showed four genome-wide significant results for FQLS2 and one significant result for WL.The choice of kinship coefficient matrix for the proposed method depends on the presence of population substructure. To confirm the presence of population substructure, multidimensional-scaling  analysisFQLS2 identified four genome-wide significant SNPs, while WL and FBAT identified one genome-wide significant SNP. In addition, one SNP that was significant according to WL was more significant according to FQLS2. The most significant result acquired using FQLS2 was SNP4 (p = 2.10× 10−10). The other three SNPs, i.e., SNP1, SNP2 and SNP3, reached the genome-wide significance level.Detailed information for these significant results is provided in Table FQLS1 and FQLS2 were suggested, and FQLS1 is an efficient choice if probands for each family are clearly defined and all remaining family members are incorporated to the genetic analysis. However, these conditions are often not satisfied, and different methods such as sequential sampling frame [FQLS2 is usually better than FQLS1 if the ascertaining condition is not clearly defined and thus we recommend FQLS2 unless probands are clearly defined. However we considered the limited ascertainment conditions and comprehensive simulation studies are still necessary.Although major advances in high-density genome scans have enabled the genetic association analysis of more than 10,000 individuals, disappointing results in the mapping of many common diseases have illustrated the need for more powerful methods for detecting disease susceptibility loci. Statistical efficiency is known to being affected by the ascertainment bias, and its careful adjustment can lead to substantial improvement of statistical power –38. In png frame  are usuaM markers and N individuals, the time complexity is O(N3 + MN2) for the proposed method. The proposed method was implemented with C++, and can be downloaded from http://healthstat.snu.ac.kr/mfqls/.Furthermore, the proposed method was conceptually simple and can be applied to the large families. Our methods require only a single calculation of offset for all markers, and the real data analysis could be completed with a single CPU in a few hours. For Heterogeneity between samples is an important issue in large-scale genetic analysis, and the proposed method can likely be applied to various additional scenarios with some modifications. For instance, the disease status of relatives reveals the importance of genetic components for each individual, and for this reason, such information has been used, albeit only on occasion, in genetic association analysis. The effect of relatives’ disease statuses is dependent on prevalence and heritability, and the probability for each individual to be affected could be calculated with the proposed method. This probability can be used to improve the statistical efficiency of genetic association analysis. In addition, the heterogeneity of the ascertainment bias is often an important issue for genome-wide meta-analysis because samples are collected from multiple medical centers , 40, andFQLS method to adjust this heterogeneity with known prevalence and heritability and the software was implemented with C++. We identified several significant associations between AD and SNPs, and their potential functional information will provide the better understanding of the pathogenesis of AD. Although this study has some limitations, our proposed methods illustrated important features required for genetic analysis with family-based samples, and an extension of the proposed method to rare variant association analysis such as FARVAT [We proposed s FARVAT  will be


We identified a common coding variant in the 4th exon of APOB (rs1367117) with a significant maternally-derived effect on BMI  and WC . The corresponding paternally-derived effects were non-significant (p>0.6). Suggestive maternally-derived associations of rs1367117 were observed with fasting glucose  and insulin . Bioinformatic annotation for rs1367117 revealed a variety of regulatory functions in this region in liver and adipose tissues and a 50% methylation pattern in liver only, consistent with allelic-specific methylation, which may indicate tissue-specific POE. Our findings demonstrate a maternal-specific association between a common APOB variant and adiposity, an association that was not previously detected in GWAS. These results provide evidence for the role of regulatory mechanisms, POEs specifically, in adiposity. In addition this study highlights the benefit of utilizing family studies for deciphering the genetic architecture of complex traits.Loci identified in genome-wide association studies (GWAS) of cardio-metabolic traits account for a small proportion of the traits' heritability. To date, most association studies have not considered parent-of-origin effects (POEs). Here we report investigation of POEs on adiposity and glycemic traits in young adults. The Jerusalem Perinatal Family Follow-Up Study (JPS), comprising 1250 young adults and their mothers was used for discovery. Focusing on 18 genes identified by previous GWAS as associated with cardio-metabolic traits, we used linear regression to examine the associations of maternally- and paternally-derived offspring minor alleles with body mass index (BMI), waist circumference (WC), fasting glucose and insulin. We replicated and meta-analyzed JPS findings in individuals of European ancestry aged ≤50 belonging to pedigrees from the Framingham Heart Study, Family Heart Study and Erasmus Rucphen Family study . We considered p<2.7x10 APOB gene on chromosome 2 is related to body mass index and waist circumference when inherited from mother and not from father. This maternal effect is not restricted to Jerusalemites, but is also seen in a large sample of individuals of European descent from independent family studies worldwide. Our findings provide support of the role of complex genetic mechanisms in obesity, and highlight the benefit of utilizing family studies for uncovering genetic pathways underlying common risk factors and diseases.To date, genetic variants identified in large-scale genetic studies using recent technical and methodological advances explain only a small proportion of the genetic basis of obesity, diabetes and other cardiovascular risk factors. These studies were typically conducted in samples of unrelated individuals. Here we utilize a family-based approach to identify genetic variants associated with obesity-related traits. Specifically, we examined the separate contribution of maternally- vs. paternally-inherited common genetic variants to these traits. By examining 1250 young adults and their mothers from Jerusalem, we show that a specific genetic variant, rs1367117, located in the  Genome-wide association studies (GWAS) have identified multiple loci associated with cardio-metabolic risk (CMR) phenotypes and traits, such as adiposity and glycemic traits e.g –4). GWAS. GWAS4])POEs are non-Mendelian transmittable genetic effects on phenotypes, where the phenotype in offspring depends on whether transmission originated from the mother or father. In mammals, POEs can be caused by genomic imprinting, maternal genetic effects on the intrauterine environment, or maternally inherited mitochondrial genes . The mosIn the past decade, linkage studies have demonstrated POEs on both binary and continuous CMR outcomes –17. Yet,To examine POEs on adiposity and glycemic traits in young adults, we leveraged existing genotype data and CMR trait measurements on mother-offspring pairs from a longitudinal study nested within a historical birth cohort, together with GWAS and bioinformatics databases. We replicated our findings using extended pedigrees from three family studies. Our hypothesis was that, in genes where variants are known to be associated with CMR traits, knowledge of the parental source of genetic variants would reveal POEs on body mass index (BMI), waist circumference (WC), and fasting glucose and insulin levels.−5). Associations between offspring genotypes, with and without considering parental source of alleles, for the 182 SNPs and the four traits BMI, WC, fasting glucose and fasting insulin are presented in APOB gene  and WC . The corresponding paternally-derived effects were non-significant (p>0.6). As expected in the setting of POEs, in JPS associations seen with the maternally-derived minor allele were approximately twice as strong as those that did not consider the parental source of the allele  and 1.5  for BMI and WC respectively)  . In factAPOB rs1367117 POE on BMI , WC  and fasting insulin  compared to those using estimated dosage  instead of using probability-based estimated dosage in these pairs. These analyses resulted in similar or larger estimates for d dosage  suggestiAPOB SNP rs1367117 in the replication and meta-analysis stage, also showed a significant maternally-derived effect in FHS and FamHS separately, but not in ERF, and in all four studies combined on both BMI  and WC  (-4 and 1.0x10-3 for BMI and WC respectively).Testing for POEs for .1x10-7) . The effAPOB SNP with borderline significance were also seen with fasting glucose and insulin levels in the combined results , whereas no associations were observed with the paternally-derived allele , triglycerides (TG) and systolic and diastolic BP . Significant maternal and paternal associations, of similar magnitudes, were shown with both LDL-C  and TC  , reflecting offspring genotype effect, irrespective of the parental source (additive genetic effect is discussed below).Because  process , 24, we tolic BP . A materAPOB with CMR traits, we examined the influence of offspring BMI on these associations. Further adjustment for BMI resulted in attenuation of the observed maternally-derived effects of APOB rs1367117 on waist, SBP, glucose and insulin to the null  or that are in moderate linkage disequilibrium (LD) with the SNP of interest . Forty two SNPs were identified and we then examined POEs of these selected SNPs on BMI in the 3 studies where genome-wide data was available  and meta-analyzed the results . Thprinting .APOB gene has a significant maternally-derived POE on CMR traits, and adiposity in particular. Our findings for APOB gene provide support for the potential importance of POEs on CMR traits.This study tested the hypothesis that including the parental origin of the allele will reveal parent-of-origin genetic effects on adiposity and glycemic traits among young adults. We have shown that a common coding variant in IGF2, INS and GNAS, have been associated with CMR traits, including adult obesity (BMI) and T2D [SLC2A10 and KCNK9, were replicated in five family studies. POE was not identified for APOB by that study and we were not able to examine SLC2A10 and KCNK9 in our study as genetic data for these genes were not available in JPS.Several findings from linkage studies provide evidence for loci demonstrating POEs on CMR traits and phenotypes –17. Yet  and T2D –36. GWAS and T2D , 38 and  and T2D  in human and T2D . Using tAPOB gene related to lipid and lipoprotein levels, and to coronary artery disease and myocardial infarction. Specifically, APOB rs1367117, identified here, was shown to be associated with levels of LDL-C, TC, APOB and non-HDL in GWAS [APOB rs1367117 and VCAM1 rs1041163 [Multiple candidate gene studies (e.g. reviewed in –45) and  in GWAS , 54 and  in GWAS –62 and rAPOB gene, causing a Thr→Ile amino acid change. A study using samples from both Finnish and Mexican subjects has shown that rs1367117 is associated with serum apoB levels, and that another variant identified in GWAS (rs7575840), which is in high LD with rs1367117 (r2 = 0.93 in CEU HapMap sample), is associated with expression levels of APOB gene in adipose tissue [SNP rs1367117 is a nonsynonymous missense variant, located in exon 4 of e tissue . Obesitye tissue , 65.APOB gene are associated with body size and obesity in children [APOB rs1367117 on lipid traits and a highly significant maternal-specific effect on adiposity traits, in fact demonstrate why genetic associations studies overlooking POE conducted to date have typically identified associations of APOB gene variants with lipid traits but not with adiposity.Only several studies have shown that genetic variants in children  and adulchildren –70 and wchildren . Our resAPOB gene has not been previously reported as demonstrating POEs on CMR or on other traits nor has it been identified as imprinted . Several explanations may account for the lack of previous support for POEs related to APOB. First, both imprinting and maternal genotype effects on the intrauterine environment are recognized as parent-of-origin mechanisms [APOB variant has an additive genotype effect on lipids, and yet a maternal POE on adiposity. In addition, context-specificity may underlie the lack of POEs of APOB in the ERF study, possibly due to a specific environmental factor in the Dutch population that could not be accounted for in the models. These hypotheses need to be further explored.To our knowledge, chanisms , 72, thuchanisms . Whetherchanisms –76 and gchanisms . Lastly,chanisms . It has chanisms . For exachanisms . Of notechanisms . SimilarThe major strengths of this study are the use of common genetic variation in mother-offspring pairs from a population-based cohort with quantitative CMR traits measured at age 32 and the opportunity to replicate our findings in extended pedigrees from three large family studies. Notably, the family design also minimizes confounding due to population stratification , 82. AddAPOB variant indentified here has not been previously reported in large GWAS consortia as related with adiposity. Although this is likely the result of GWAS typically ignoring the parental source of alleles due to lack of family data, there is a possibility that our POE results reflect false-positive findings. To address this, we have conducted simulations applying two different approaches, one for a mother-offspring design (using JPS) and the other for an extended pedigrees design (using FHS). Based on these simulations, maternal permutation p-values were similar to our observed p-values providing further support for the significance of the reported maternal APOB effect. Lastly, environmental exposures around the time of birth may contribute to the context-specificity of POEs. These data were unavailable for most of the participating studies and therefore their effects could not be assessed in this work.There are also several limitations to our study. First, genetic data on fathers were unavailable in JPS resulting in the use of probability-based imputation of the parental source of minor alleles in heterozygous mother-offspring pairs. However, as we have shown in sensitivity analyses, this approach is likely conservative and therefore preferable compared to excluding heterozygous mother-offspring pairs. Furthermore, replication in other studies where genetic data in extended families were used minimizes this source of bias. Second, the In summary, we have demonstrated that taking into account the parental origin of offspring alleles compared to examining the offspring genotype as a whole may enhance our understanding of genetic associations with CMR traits. Our results highlight the potential contribution of POEs to uncovering complex relations between genetic variants and common traits, and motivate further research in this area, including assessment of the impact of POEs on common traits and investigation of the mechanisms underlying these associations.The Jerusalem Perinatal Study (JPS) population-based cohort includes a sub-cohort of all 17,003 births to residents of Jerusalem, between years 1974 and 1976 –85. DataThe Jerusalem Perinatal Family Follow-Up Study includes a sample of 1,250 mother-offspring pairs  from the original 1974–1976 birth cohort, who were interviewed and examined between 2007 and 2009. Information on sampling and data collection in the JPS Family Follow-Up study was previously described . BrieflyIndividuals who reported taking medication to treat diabetes (n = 13), lipid-lowering medication (n = 13) or BP-lowering medication (n = 11), were excluded from the corresponding analyses.Blood was collected in tubes containing EDTA. DNA was extracted from white blood cells using standard salting-out extraction procedures. Common tag SNPs from candidate genes in molecular pathways potentially related to both birth weight and CMR phenotypes were previously genotyped in mother-offspring pairs using Illumina 1536 SNPs panel . Ofwww.genome.gov/gwastudies—accessed July 1, 2012) [-8) with CMR traits were reported in GWAS  , out of  in GWAS  Tables.  in GWAS .2)); waist circumference ); fasting glucose (mg/dL) and fasting insulin (mean of two repeated measures (mU/mL), natural log-transformed).The following adiposity and glycemic traits were measured in offspring at age 32: body mass index  by squared height ); high-density lipoprotein cholesterol (HDL-C (mmol/L)), triglycerides (TG (mmol/L), natural log-transformed), systolic and diastolic blood pressure ).All cardio-metabolic outcomes were treated as continuous variables .To assess the separate contribution of the maternally- and paternally-inherited variants to offspring CMR phenotypes, we first determined the parental origin of minor alleles, based on mother-offspring pairs, since genetic data on fathers were not available in JPS. For every given SNP we constructed two variables; maternally-derived minor allele (M-D) and paternally-derived minor allele (P-D) indicators. These indicators count the number of minor alleles inherited from mother and/or father; each count is a zero or one . When off(AA) = p2, f(Aa) = 2pq and f(aa) = q2. Under random mating and transmission equilibrium, the probability that the minor allele was derived from the mother is 1-MAF and from the father is MAF, where MAF = minor allele frequency. Therefore, for heterozygous mothers-offspring pairs, we used the estimated dosage of M-D and P-D indicators, i.e. 1-MAF and MAF.When offspring and mother are both heterozygous at a given SNP, the source of the minor allele is ambiguous. For any given SNP with allele frequencies of p and q for the common (A) and minor (a) alleles respectively, the expected paternal genotype frequencies assuming Hardy-Weinberg Equilibrium are We used linear regression to examine each SNP-trait association. We first assessed the associations of offspring genotype with trait, using an additive genetic model. Then the association of the parental origin of the minor allele on the trait was assessed by including the M-D and P-D indicators together in a single model.The following mean models were used:GO = offspring genotype, GMD = indicator of maternally-derived minor allele, GPD = indicator of paternally-derived allele, and Z = other covariates.Where Y denotes trait, Existence of POE in model 2 was assessed via a test of the following null hypotheses:βMD = 0; (2) βPD = 0; (3) βMD = βPD. The significance threshold for rejection of the null hypotheses was set at a within-gene corrected Bonferroni p-value<0.05 for the separate effects of the maternally- or paternally derived indicators (i.e. hypotheses 1 or 2) and nominal p-values  for the F-test examining the differences between these effects (i.e. hypothesis 3). Findings that met these criteria for any one of the examined outcomes were moved forward for replication and meta-analysis.(1) All models were adjusted for offspring sex and ethnicity. Following an approach suggested by Thomas and Witte to correct for population stratification in mixed-ethnicity families , ethniciTo account for the stratified sampling, we used Stata’s ‘pweight’ option to weight estimates by individuals’ inverse probability of being sampled .Findings exceeding the aforementioned threshold of significance in JPS were followed up in three additional family studies with extended pedigrees: 1) Framingham Heart Study (FHS) (max N = 2225 probands); 2) Family Heart Study (FamHS) (max N = 773 probands); and 3) Erasmus Rucphen Family (ERF) study (max N = 631 probands). Probands were restricted to individuals of European decent, aged≤50, with available genetic and CMR data and genetic data in at least one parent. Probands on medication to treat diabetes, lipid-lowering medication or BP-lowering medication were excluded from the corresponding analyses. Detailed description of each of the studies, including study sample, genotyping techniques and CMR trait measurements and distributions, is provided in -4. We also applied an inverse variance weighted meta-analysis approach to obtain a pooled estimate of effect sizes (betas) across studies. Since the QTDT approach does not provide estimates of standard errors (SEs), SEs were estimated by converting p-values into a z-statistic and setting: SE = beta/z.Additionally, z-based meta-analysis combining results from all 4 studies  was conducted, as a joint analysis approach was shown to be more efficient than a two-stage approach for genetic association studies . A BonfeThe JPS study was approved by the Institutional Review Board of the Hadassah-Hebrew University Medical Center  and the University of Washington Human Subject Review Committee . FHS was approved by Boston University Medical Center Institutional Review Board , FamHS was approved by the Institutional Review Board of the Washington University School of Medicine in St. Louis  and ERF study was approved by the Medical Ethics Committee of Erasmus MC, Rotterdam . All participants provided written informed consent for genetic studies.S1 Text(DOC)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.


K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and  Genomic selection (GS) is a modern breeding approach whereby genome-wide single-nucleotide polymorphisms (SNP) marker profiles are used to estimate individual breeding values of untested genotypes –3. This GS in plant breeding is considered more challenging than in animal breeding due to the complex nature of genotype-by-environment interactions and their strong influence on plant reproduction . On the The ridge regression best linear unbiased prediction (RR-BLUP) approach , 16 is bBrassica napus L., commonly known as canola, oilseed rape or rapeseed, is one of the world’s most important oilseed crops because it delivers a rich source of high-quality edible oil and animal feed as extracted seed meal  was used in R , a, a16], ay is a N × 1 vector of phenotype (vector of BLUEs across locations);μ is the overall mean;Nm is the number of SNPs;aj is the effect of the jth marker;Xi is a N × 1 vector of genotypes  of the lines for each marker j, and variance of aj is assumed to be uniformly distributed and is σ2G / Nm [σ2G / Nm .R [GPA was calculated for each trait. In some previous studies, prediction accuracies have been standardised by dividing the square root of the heritability to remove the corrected influence of heritability [GPA) as the Pearson correlation, r, between the predicted values (ŷ) and observed BLUE values (y), using the rr-BLUP package [Monomorphic SNPs and markers having more than 20% missing data were removed from the dataset. The rr-BLUP package in R  was usedtability . In our  package . BecauseFor determination of the optimum composition of training population size, we tested the prediction accuracies for each of the seven traits in the whole population under incremental increase of the training population from 10% up to 90% of the 475 lines. Based on the results of this test (see below), the training population for all further analyses and scenarios testing was set up at 70% of the total lines in the given dataset. Hence, in each run, the dataset was divided into a random 70 percent training population (TP) containing both genotyped and phenotyped data, and 30 percent validation (VP) or prediction population having only SNP data and SNP effects with no consideration of phenotype values. For each scenario the data for each trait was cross-validated for 500 rounds and a mean value was subsequently considered.The results of the principal coordinate analysis (PCoA) between the parental inbred lines are shown in K-means clustering revealed a tendency to two main clusters and one relatively smaller cluster. This assumption was supported by the results of the Caliński-Harabasz [The PCoA indicated the existence of subpopulations within the dataset. The Harabasz  clusteriBroad sense heritabilities, along with summary statistics for all the traits under consideration, are shown in GPA = 0.81) followed by oil yield (rGPA = 0.75), seed glucosinolate content (rGPA = 0.61), days to flowering (rGPA = 0.56), seed yield (rGPA = 0.45), lodging resistance (rGPA = 0.39) and the least heritable trait, seedling emergence (rGPA = 0.29). Scatter plots showing the correlations between true observed trait values and genomic predicted values for all the traits are shown in Prediction across whole population: GPA = 0.39) was observed for the low-heritability trait seedling emergence within subpopulation C1, the largest subpopulation but also the narrowest in terms of genetic diversity. Predictions accuracies also improved for two other traits with low to moderate heritability, seed yield (rGPA = 0.47) and DTF (rGPA = 0.59)  , S3 Fileectively .As expected, increasing the size of the TP resulted in improvement of the genomic prediction accuracy . All theB. napus breeding [The first investigation of the potential of genomic selection in breeding  investigbreeding .We investigated genomic prediction accuracies for seven key agronomic traits including seed yield, oil content and quality related traits using a diverse population of spring-type canola. The RR-BLUP method used for the prediction modeling has been shown to be effective in accounting for both major and minor effect quantitative trait loci (QTL) in plant breeding –20, 63.First we investigated genomic prediction accuracy for each trait across the whole-population based on GCA values. Taking the whole population under consideration, the lowest genomic prediction accuracy was estimated for seedling emergence and highest for seed oil content. The low genomic prediction accuracy for seedling emergence under scenario 1 may be explained by the low heritability and genetic variance for this trait. One strategy to increase prediction accuracy in such traits could be to combine these with other correlated high heritable traits in a multi-trait genomic prediction model which has been shown in a previous study . In the Riedelsheimer et al.  and SaatFor the high-value traits of seed oil content, oil yield and seed glucosinolate content, for which high heritabilities can be attributed to a good rank correlation among locations, we consistently obtained very high prediction accuracies in predictions across the entire population regardless of substructure. This may be further due to the modulating maternal influence of the two common male-sterile testers on embryo-related traits like seed size and oil content.In simulation studies  as well B. napus, with multiple interacting homoeologous copies of almost all genes [a priori knowledge of their underlying genetics.At the dawn of canola hybrid breeding various authors reported considerable heterosis in F1 hybrids –72. The ll genes , increasll genes . Hence, The main purpose of genomic selection is the utilisation of large and inexpensive DNA marker datasets to bring an improvement to the mean performance of a certain population . Seed yiFrom a breeder’s perspective the implementation of genomic prediction is only worthwhile if equivalent or greater selection gain can be achieved with equal or reduced time and/or cost than using conventional selection methods . Depending on the selection intensity, the results presented in this paper clearly demonstrates the value of performance predictions based on high-density SNP markers in hybrid canola. Relatively higher genomic prediction accuracies in the majority of the traits, based on the additive genetic effects in our study, indicate a lack of distinct heterotic pools in our sample. Even where no improvement on phenotypic selection gain is achieved through genomic prediction, the method is still of considerable value for traits like seedling emergence, where the very low heritability seedlots generated in multiple maternal environments combined with multi-location field evaluations. In such cases an increase in genetic gain might still be achieved if the early pre-selection approach enables a shortening of the breeding cycle. The results of our study suggest that prediction of testcross performance in canola breeding, based on genome-wide SNP markers, can be a powerful, fast and low-cost method to pre-select promising pollinators for combinations with available male-sterile maternal lines. Genomic testcross performance prediction can hence allow breeders to optimise the allocation of breeding resources. Despite the absence of clearly defined heterotic pools in canola, predictions within large subpopulations can in some cases improve prediction accuracy for selected traits with low heritability.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.