However, limited information is available for underlying genetic architecture of longitudinal trend in EW. Herein, we measured EWs at nine time points from onset of laying to 60 week of age, and conducted comprehensive genome-wide association studies (GWAS) in 1,534 FNCAPG harbors a non-synonymous SNP (rs14491030) causing a valine-to-alanine amino-acid substitution. Genome partitioning analysis indicated a strong linear correlation between the variance explained by each chromosome and its length, which provided evidence that EW follows a highly polygenic nature of inheritance.Egg weights at all ages except the first egg weight (FEW) exhibited high SNP-based heritability estimates (0.47 ~ 0.60). Strong pair-wise genetic correlations (0.77 ~ 1.00) were found among all EWs. Nine separate univariate genome-wide screens suggested 73 signals showing significant associations with longitudinal EWs. After multivariate and conditional analyses, four variants on three chromosomes remained independent contributions. The minor alleles at two loci exerted consistent and positive substitution effects on EWs, and other two were negative. The four loci together accounted for 3.84 % of the phenotypic variance for FEW and 7.29 ~ 11.06 % for EWs from 32 to 60 week of age. We obtained five candidate genes, of which Identification of significant genetic causes that together implicate EWs at different ages will greatly advance our understanding of the genetic basis behind longitudinal EWs, and would be helpful to illuminate the future breeding direction on how to select desired egg size.The online version of this article (doi:10.1186/s12864-015-1945-y) contains supplementary material, which is available to authorized users. In poultry industry, egg weight has always been an economically important trait, and is usually regarded as the major breeding objective and research goal –4. As a http://www.animalgenome.org/cgi-bin/QTLdb/index). However, a majority of reported QTLs are mapped with wide confidence intervals by low-density linkage analysis in the past [Multiple lines of evidence have suggested that egg weights are mainly determined by genetic factors and they all show considerable heritability estimates , 10, 11.the past . In genethe past . To imprthe past , 15. So the past , 16. Recthe past . In chicDespite the great advantages of GWAS, most studies to date utilized only phenotypes measured at a single time point , 18–20. 2 chickens with observations for egg weights at different ages. In addition, we examined the genetic architecture of egg weight by partitioning genetic variation according to chromosome. The main goals of our current work were to assess the feasibility of longitudinal data as an intriguing resource in GWAS, pinpoint associated loci and genes that contribute to the phenotypic variability and longitudinal trend in egg weight, and provide valuable insights into the genetic basis of longitudinal egg weights.In the present study, we implemented univariate, multivariate and conditional GWASs using 600 K Affymetrix Chicken SNP array in a total of 1,534 Fhsnp2 = 0.60). Moreover, bivariate GCTA analyses indicated that egg weights at multiple ages were highly and positively interrelated, especially for egg weights at two neighboring time points. As the beginning of entire laying stage, FEW showed slightly lower genetic correlations with egg weights at the following ages (rg < 0.90), compared with those among egg weights from 32 to 60 week of age (rg > 0.90).Descriptive statistics for nine egg weight variables are presented in Table rs316497033 on GGA3). Of all significant association signals, 45 SNPs were responsible for only one trait and other 28 hits affected multiple phenotypes. In particular, four loci around rs14491030 were found to be implicated in all egg weights except FEW. The global view of the putative P-values for all SNPs affecting EW36 is given in Fig. 1000 values varied from 0.987 to 1.010. These results indicated negligible inflation of the observed genome-wide association signals caused by population stratification. Notably, moderate deviations within the upper tail of the distribution were found in all QQ plots, in spite of stringent quality control and inclusion of up to top five PCs. Thus, the departure due to a great number of weakly associated SNPs was indicative of polygenic inheritance.As a preliminary, we conducted nine separate association tests using univariate method for longitudinal egg weights. In total, 73 genome-wide significant SNPs located on chromosome 1, 3 and 4  were successfully identified for these nine traits  on GGA1, survived the genome-wide suggestive threshold of multivariate model. Considering that rs314058619 and rs14916609 are separated by 1.5 Mb and the pair-wise LD between them is relatively low at D’ = 0.37 and r2 = 0.09 (a LD block extends only for a short distance), we treated rs14916609 as another separate signal. Taken together, we obtained three significantly independent loci associated with all egg weights from multivariate analysis.In total, we obtained 270 genome-wide suggestive loci from nine independent univariate analyses. To enhance the statistical power, a joint GWAS analysis of nine traits was performed by fitting these SNPs into a multivariate model. Consequently, a total of seven significant hits on GGA1 and GGA4 provided compelling evidence for associations with longitudinal egg weights . After two-step univariate conditional tests on rs314058619 and rs14491030, the third SNP rs14916609 was still significantly associated with all traits except FEW. This locus seemed to have independent effects on these traits, despite that it showed only suggestive significance inferred by two-round multivariate conditional GWASs. The finding further strengthened the validity regarding rs14916609 as an independent hit. It should be noted that the significance levels of loci nearby the peak signals were substantially attenuated after adjusting for the three independent hits. In order to provide visual support of independent associations, we interrogated the signals in the 1.0 Mb genomic region surrounding the independent loci (500 kb each side). Since rs14916609 and rs14491030 affected more phenotypes, we illustrated their regional association plots for EW36 and EW60 to compare the difference of putative significance levels before . According to the putative SIFT score (0.74), the change of amino-acid was predicted as tolerated to protein structure and function.Gene annotation of the four significant SNPs allowed us to identify genes related to longitudinal egg weights. Considering that the putative variants may be in high LD with a causal locus genuinely associated with phenotype, we inferred the LD block around the sentinel SNP using Haploview. Four blocks containing the four hits were identified and the detailed information is summarized in Table 2 population, parameter estimates for some traits in the joint model could not converge. However, the strong genetic correlations between nine egg weight phenotypes provided evidence that they had great potential to share a consistent genetic architecture. Therefore, we only exemplified the partitioning spectrum of EW36 due to its highest heritability. The estimates of variance contributed by each chromosome exhibited a strong linear relationship with the length of the chromosome for EW36  on GGA1 are segregated by 1.5 Mb and located in the intronic regions of CAB39L  and FKHR , respectively. The CAB39L gene has been proposed to participate in the mTOR signaling pathway. It is increasingly apparent that mTOR signaling can affect most cellular functions as a central controller of cell growth (mass accumulation) and proliferation [FKHR is a member of FOXO subfamily and an important mediator of the insulin signaling pathway [In total, four LD blocks tagged by the resulting SNPs were inferred, and contained five candidate genes of potentially functional relevance. Two SNPs  located within the NCAPG  was unveiled in the current study. In chickens, many independent studies reported that multiple genomic segments containing this gene were identified to be implicated in both egg weight and body weight [CCKAR (cholecystokinin A receptor) located around 2.7 Mb away from NCAPG is mainly responsible for body weight [CCKAR might be a more promising causal gene. Notably, the gene NCAPG has been suggested to be associated with many growth and weight traits in several livestock species [NCAPG for body weight in chicken, owing to a particularly interactive relationship between egg weight and body weight [NCAPG gene may exert a pleiotropic effect involved in the two phenotypes simultaneously. Consequently, the impact on body weight of the gene for egg weight identified by us requires further investigation.A non-synonymous mutation rs4491030 ly weight , 20, 53.y weight , by estay weight , CCKAR m species –60. Thery weight , i.e., tCDADC1 (cytidine and dCMP deaminase domain containing 1) is located about 18.2 kb upstream of SNP rs14916609, but its function and mechanism is not yet clear. One previous study indicated that this gene may be an important factor regulating testicular development and spermatogenesis in human [KCNG3  indexed by rs316497033 encodes a subunit of the potassium voltage-gated channel. It is known to be mainly involved in cell volume and smooth muscle contraction [Another gene in human . Due to traction , and theNCAPG may influence both egg weight and body weight simultaneously in a pleiotropic manner. These promising loci and genes could be helpful to engineer practical breeding programs and produce the desired egg size according to diverse consumption markets.In summary, we performed univariate, multivariate and conditional GWASs for longitudinal egg weight data using high-density 600 K SNP arrays, and suggested that longitudinal analysis had higher power to dig out variants that influence phenotype variability over time. Our study provided evidence that egg weight appears to be highly heritable and polygenic, and shares the similar genetic determinants and mechanisms at different ages. Four significant loci and five candidate genes were detected with significant effect on egg weights. In particular, 2 resource population was developed by reciprocal crosses between White Leghorn (WL) and Dongxiang chickens (DX), representing a popular commercial breed and a Chinese indigenous strain, respectively. The two pure lines were maintained for about ten years at the experimental farm, and WL eggs weighed about 14 g more than DX eggs at the same age. Six WL males were mated with 133 DX females and six DX males were mated with 80 WL females, to generate 1,029 and 552 birds of the F1 generation, respectively. Then, 25 males and 407 females from WL/DX cross and 24 cocks and 235 hens from DX/WL cross were selected to produce the F2 population. A total of 3,749 F2 individuals  with full pedigrees originating from 590 full-sib families were created in the same hatch. The population was maintained in three-tier single-hen cages and reared in the same environment with feed and water ad libitum at the experimental farm of Jiangsu Institute of Poultry Science. Finally, to ensure sufficient phenotypic information and accurate pedigree, we chose 1,534 hens from 49 half-sib families and 550 full-sib families for SNP genotyping.An FTo characterize the genetic architecture of egg weight developmentally, we collected egg weight data at nine different time points. Firstly, the first egg weight (FEW) was recorded when hens started laying, mainly considering that FEW is conceptually defined as weight of first egg for each hen, and it is very difficult to collect eggs on several consecutive days due to the physiologically unstable state of hens at onset of laying. Subsequently, egg weights (EW) were measured on two consecutive days per four weeks from 32 to 60 week of age, and the average for two days was regarded as the phenotypic value for each hen. Cracked, soft-shell and double-yolk eggs were not used in our study. Descriptive statistics were calculated with the MEANS procedure of the SAS software package  using all available records. For traits deviating from normality, we conducted the rank-based inverse normal transformations (INTs) using SAS ahead of association tests . BrieflyP < 1 × 10−6 using the PLINK v1.90 package [R2 > 0.5 underwent the next analyses step. Finally, a total of 1,512 samples and 435,867 SNPs were eligible for inclusion in the following GWASs.The whole blood samples were collected from brachial veins of chickens by standard venipuncture. Genomic DNA was isolated using standard phenol/chloroform extraction method and genotyped with the 600 K Affymetrix Axiom Chicken Genotyping Array . From an initial set of 580,954 SNPs , 7,883 S package . Some sp package , then on--indep-pairwise 25 5 0.2 command (PLINK), and then calculated top five PCs as covariates into the mixed model. To establish proper thresholds for genome-wide suggestive and significant associations, we corrected for multiple testing using the simpleM method [eff = 59,308, thus the genome-wide suggestive and significant P-values were 1.69 × 10−5  and 8.43 × 10−7 , respectively.Prior to GWAS, we conducted a principal component analysis (PCA) to eliminate spurious associations due to the presence of potential cryptic relatedness or hidden population stratification. Considering that clusters of highly correlated SNPs may distort the resulting PCs, we first pruned the full SNP set to 41,130 independent SNPs via the M method  accounti10-transformed observed P-values were generated using the “gap” and “qqman” packages in R [We firstly performed univariate tests of association for SNPs having MAF ≥ 0.05 using an exact mixed model approach implemented in the GEMMA v0.94 software . The cenges in R , 70. We ges in R . Furtherges in R , 73. They is an n × 1 vector of phenotypic values for n individuals, W is an n × c matrix of covariates  including a column vector of 1, α is a c × 1 vector of corresponding coefficients including the intercept, x is an n × 1 vector of marker genotypes at the locus tested, β is the corresponding effect size of the marker and all effects are reported for the minor allele in each marker, u is an n × 1 vector of random polygenic effects with a covariance structure as u ∼ N, where K represents a known n × n genetic relatedness matrix derived from SNP markers and Vg is the polygenic additive variance, and ε is an n × 1 vector of random residuals with ε ∼ N, where I is an n × n identity matrix, and Ve is the residual variance component. We used the Wald test statistic β = 0, where the best linear unbiased estimate (BLUE) of β and the corresponding sampling variance g and Ve.for the equation , y is anTi2 is the estimated Wald statistic and asymptotically distributed as a chi-square with 1 degree of freedom under the null hypothesis, and usually called observed χ2 statistics, and 0.455 is the expected median of the standard χ2 distribution under the null hypothesis of no association. For the formula  is an n × c matrix of covariates  including a column of 1 s, A is a c by d matrix of corresponding coefficients including the intercept, x is an n-vector of marker genotypes, β is a d vector of marker effect sizes for the d phenotypes. It should be noted that G is an n by d matrix of random effects with G ~ MVNn×d where Vg is a d by d symmetric positive definite matrix of genetic variance component, and E is an n by d matrix of residual errors with E ~ MVNn×d where Ve is a d by d symmetric positive definite matrix of residual variance component (K and I are the same in the two models). MVNn×d denotes the n × d matrix normal distribution with mean 0, row covariance matrix V1 (n by n) and column covariance matrix V2 (d by d).where −4 and 7.59 × 10−6 through multiplying the raw thresholds by the number of phenotypes [Owing to the different number of statistic tests, we further adjusted the significance level in multivariate testing accounting for the nine independent inquiries in usual single-phenotype strategy. For the multivariate model, we corrected the genome-wide suggestive and significant cut-offs as 1.52 × 10enotypes .Notably, a genomic region containing a cluster of neighboring SNPs in strong LD is usually associated with a phenotype for high-density array. To demarcate independent association signals across the putative regions, we run stepwise conditional analyses both in multiple and single models, through fitting the genotypes  at the strongest signal identified by the multivariate analysis as covariates . The prohsnp2) for GWAS using the same inverse-normal transformed EW values. We also quantified the pair-wise genetic and phenotypic correlations among egg weights at multiple time points with the bivariate mixed model [For egg weight at each time point, univariate restricted maximum likelihood (REML) implemented in GCTA v1.24 program  was perfed model . A genety is an n × 1 vector of phenotypic values for n individuals, b is a vector of fixed effects with its incidence matrix X, gG is a vector of aggregate effects of all SNPs, and Var(gG) = AGσG2 with AG being the SNP-derived GRM and σG2 being the additive genetic variance, e is a random residual term with e ∼ N where σe2 represents the residual variance and I represents an identity matrix. In this model, we partitioned σG2 into the contributions of selected SNP loci  with all intermediate SNPs, but the intermediate SNPs are not necessarily in LD with each other. Subsequently, we performed functional annotation on the significant SNPs and searched for candidate genes in the blocks based on the Galgal4 assembly, using Variant Effect Predictor (VEP) and Biomart tools supported by Ensembl [In general, GWAS does not distinguish a genuine causal locus from those statistically significant loci within a strong LD region. Therefore, in order to characterize potential candidate genes responsible for a trait, we conducted LD analyses and inferred the haplotype blocks containing peak SNPs by Haploview v4.2 . A block Ensembl , 80.


The relationship between glucose-6-phosphate dehydrogenase (G6PD) deficiency and clinical phenomena such as primaquine-sensitivity and protection from severe malaria remains poorly defined, with past association studies yielding inconsistent and conflicting results. One possibility is that examination of a single genetic variant might underestimate the presence of true effects in the presence of unrecognized functional allelic diversity.G6PD locus.We systematically examined this possibility in Kenya, conducting a fine-mapping association study of erythrocyte G6PD activity in 1828 Kenyan children across 30 polymorphisms at or around the P=1.5×10−200, additive model). Additionally, we identify other common variants that exert smaller, intercorrelated effects independent of c.202G>A, and haplotype analyses suggest that each variant tags one of two haplotype motifs that are opposite in sequence identity and effect direction. We posit that these effects are of biological and possible clinical significance, specifically noting that c.376A>G (rs1050829) augments 202AG heterozygote risk for deficiency trait by two-fold .We demonstrate a strong functional role for c.202G>A (rs1050828), which accounts for the majority of variance in enzyme activity observed (Our results suggest that c.202G>A is responsible for the majority of the observed prevalence of G6PD deficiency trait in Kenya, but also identify a novel role for c.376A>G as a genetic modifier which marks a common haplotype that augments the risk conferred to 202AG heterozygotes, suggesting that variation at both loci merits consideration in genetic association studies probing G6PD deficiency-associated clinical phenotypes. Glucose-6-phosphate dehydrogenase (G6PD) is a housekeeping enzyme encoded on chromosome X. It is ubiquitously expressed in all body tissues and found in eukaryotes and prokaryotes alike. G6PD catalyses the rate-limiting first step in the pentose phosphate pathway, generating NADPH, an electron donor important in a variety of metabolic processes, including fatty and nucleic acid synthesis, nitric oxide production, and the regeneration of antioxidant glutathione. Deficiency of G6PD is the most common enzymopathy in man, with prevalence especially high in sub-Saharan Africa and southeast Asia. It is a trait that is thought to be both harmful and beneficial. While clinical penetrance is generally mild, G6PD deficiency can predispose to hemolytic anemia and neonatal jaundice, generally in the context of oxidant stressors generated by certain foods, medications, and infections. Counterbalancing these deleterious effects is evidence that suggests G6PD deficiency confers partial protection against malaria, and that such protection has conferred a selective advantage to G6PD-deficient individuals in malaria-endemic regions, as reflected in the close geographical correlation between the global distribution of G6PD deficiency and that of historical malaria endemicity.Plasmodium vivax infection and one of few drugs that acts against the Plasmodium falciparum gametocyte; it therefore has high value in eliminating reservoirs of infection and reducing transmission rates, tasks that are crucial to the success of any malaria elimination program ,. Si. SiP = 1P=9.1×10−5, t-test, Figure P<0.025 similarly exhibited differential ES direction and resembled either H+ or H- in sequence identity  has been reported previously , and thiWe also examined qualitative G6PD activity status, using the intermodal trough of the bimodal activity distribution in males as an ad hoc cutoff to distinguish normal from deficient Figure . OverallP=0.0009, Figure P=0.014). This latter finding has not been previously reported, and represents the first demonstration of a genetic modifier that influences risk to deficiency state in individuals heterozygous for the A- form of G6PD deficiency.While it is known that the distribution of enzyme activity in 202AG female heterozygotes is broad, with some individuals exhibiting an effectively deficient biochemical phenotype, little is known about the genetic factors that might contribute to this risk. We tested the possibility that our H- tag SNP, c.376A>G, might reduce enzyme activity sufficiently to augment risk of phenotypic G6PD deficiency state in such individuals. The 202A allele is known to obligately lie on a 376G background in African populations to form the ‘A-’ haplotype ,22],,, bu, buG6PD G6PD, might be associated with G6PD deficiency, and that c.202G>A might only be indirectly associated with deficiency, a finding which could explain discrepancies in previous association studies. Although the number of SNPs surveyed here is relatively small and does not include rare variants, we did not observe any SNPs more strongly associated with G6PD deficiency in Kenya than the c.202G>A variant, strongly suggestive of a causal role. Additionally, while all known functional variants in sub-Saharan Africa were genotyped as part of our study, only c.202G>A was found to be polymorphic in Kenya (DAF = 17%). This allelic homogeneity contrasts with the situation in some areas of west Africa [Our study is the first to systematically consider the possibility that other SNPs, even those found several hundred kilobases away from t Africa , and sugRPL10  and G6PD, respectively. While we can only speculate as to the precise nature of the structure-function relationship mediating these effects, the biology of the locus makes for interesting possibilities for interaction between c.202G>A and the H+/H- variants, including the possibility of cell-cell interactions synergistically impacting enzyme activity distribution in 202AG mosaic females.Our work also demonstrates the power of a haplotype-based approach in resolving multiple correlated association signals in a region. Though we found two haplotype motifs that were common and of significant effect independent of c.202G>A, we were unable to further localize the signal, even when examining all possible two- and three-locus haplotypes (data not shown), suggesting that these haplotypes are merely markers for the true causal variants. Interestingly, among the twelve variants of significant effect independent of c.202G>A were c.376A>G, a nonsynonymous coding variant, as well as rs915942 and rs111827785, predicted 5’ UTR splice-site variants within At one level, the work presented here reveals how common, standing genetic variation superimposed on a Mendelian trait can significantly impact expression of that trait. More important, however, is how this finding might translate into a better understanding of how G6PD deficiency is linked to key clinical phenomena. Specifically, it will be worth considering whether two site genetic testing is more predictive of clinical outcome than single SNP testing in women heterozygous for the common A- form of G6PD deficiency in Africa. For example, in considering a public health intervention such as mass primaquine administration as part of a malaria elimination campaign, two-site testing might help to identify the subset of A- heterozygotes more likely to be susceptible to acute hemolytic anemia. Lastly, at a more general level, our work highlights the importance of measuring intermediate molecular phenotypes that can bridge the gulf between variance at the genetic level and variance in clinical phenotypes. This is especially important for association studies of G6PD deficiency given its considerable allelic heterogeneity, varying biochemical penetrance, and long history of inconsistent findings.We have surveyed genetic variation at the G6PD locus in Kenya via resequencing, and conducted a fine-mapping association study of erythrocyte G6PD activity. We find that while the c.202G>A polymorphism accounts for most of the variance in enzyme activity seen in this population, several other common polymorphisms are also relevant, specifically noting that c.376A>G acts as a genetic modifier which increases by two-fold the risk of deficiency state in 202AG heterozygotes. Our results suggest that multiple alleles at the G6PD locus play a role in determining G6PD deficiency state and therefore merit consideration in genetic association studies of clinical phenotypes such as severe malaria or drug sensitivity.The authors declare that they have no competing interests.SS, KR, TEW, DPK and TNW designed the study. SS and KR designed and validated the enzyme activity and genotyping assays. AM, JM and SU performed enzyme activity assays. SS, RC and CH performed genotyping. KK performed sequencing. SS conducted statistical analyses. SS, KR, TEW, DPK and TNW wrote the paper. All authors read and approved the final manuscript.Quality control plots. Shown here are plots that examine: (a) whether any individuals with low call rate and high heterozygosity (left), or (b) whether any SNPs with low call rate and extreme deviation from HWE (right) could be found, features indicative of low confidence samples and SNPs, respectively.Click here for fileSNP summary table. Shown here for each SNP assayed is: the genomic position (hg19/GRCh37), observed alleles , ancestral allele (according to the Ensembl EPO pipeline), G6PD exon-intron position (according to UCSC genome annotation coordinates), and derived allele frequency stratified by sex. Key: E=exon, I=intron, US=upstream, DS=downstream. NB: c.202 and c.376 are referred to by their coding strand (-) allele designations throughout for clarity.Click here for fileHaplotype distribution in males vs. females. The frequency of each haplotype is plotted in decreasing order, stratified by sex . No significant differences between male and female haplotype frequencies were found, attesting to highly accurate haplotype phasing (bottom).Click here for fileInitial association test results. Shown here for each SNP surveyed is: genomic position (hg19/GRCh37), DAF stratified by sex, P values and effect sizes (with 95% CI) for initial, uncontrolled association tests under three different genetic models.Click here for fileAssociation test results for effects independent of c.202G>A. Shown here for each SNP surveyed is: genomic position (hg19/GRCh37), DAF stratified by sex, P values and effect sizes (with 95% CI) for c.202G>A-controlled association tests under three different genetic models.Click here for fileLinkage disequilibrium (LD) across region. Pairwise LD matrix is shown in colorimetric form, using the D’ and LOD metrics. Colors indicate: D’ = 1 and LOD ≥ 2 (red), D’ = 1 and LOD ≤ 2 (blue), D’ < 1 and LOD ≥ 2 (pink), D’ < 1 and LOD ≤ 2 (white).Click here for fileAssociation test results of full-length and partial haplotypes for effects independent of c.202G>A. Shown here for each haplotype surveyed is: allele identity across all sites , population frequency, P values and effect sizes (with 95% CI) for c.202G>A-controlled association tests.Click here for fileAllele sharing with H+ or H- explains variance independent of c.202G>A. G6PD activity as stratified by sex and haplogroup, where the latter is defined by categorizing each individual according to extensive allele sharing with either the H+, H-, or neither haplotype (see Methods). In box-and-whisker plots, center of box represents median, edges of box represent upper and lower quartile bounds, and whiskers represent either minimum and maximum values or 1.5 times the interquartile range, whichever is less extreme. The P-value displayed in the figure represents the statistical significance of allele sharing under a linear model which incorporates c.202G>A genotype and gender as covariates .Click here for fileDefining qualitative G6PD deficiency. The intermodal minimum in the G6PD activity distribution in males was determined using kernel density estimation of the probability density function (red) and provided our ad hoc cutoff (blue) for distinguishing normal vs. deficient. A histogram of the G6PD activity distribution in males is superimposed for clarity.Click here for file


Previous genome-wide association analyses identified QTL regions in the X chromosome for percentage of normal sperm and scrotal circumference in Brahman and Tropical Composite cattle. These traits are important to be studied because they are indicators of male fertility and are correlated with female sexual precocity and reproductive longevity. The aim was to investigate candidate genes in these regions and to identify putative causative mutations that influence these traits. In addition, we tested the identified mutations for female fertility and growth traits.LOC100138021 and TAF7L genes; and in TEX11 and AR genes for scrotal circumference. One SNP in TEX11 was explained ~13% of the additive genetic variance for scrotal circumference at 12 months. The tested SNP were also associated with weight measurements, but not with female fertility traits.Using a combination of bioinformatics and molecular assay technology, twelve non-synonymous SNPs in eleven genes were genotyped in a cattle population. Three and nine SNPs explained more than 1% of the additive genetic variance for percentage of normal sperm and scrotal circumference, respectively. The SNPs that had a major influence in percentage of normal sperm were mapped to The strong association of SNPs located in X chromosome genes with male fertility traits validates the QTL. The implicated genes became good candidates to be used for genetic evaluation, without detrimentally influencing female fertility traits.The online version of this article (doi:10.1186/s12864-015-1595-0) contains supplementary material, which is available to authorized users. In livestock breeding, sires have an important effect in disseminating superior genetic merit, particularly in situations where artificial insemination (AI) is used ,2. SiresAndrological traits, such as scrotal circumference measured at 12 months and percentage of morphologically normal sperm measured at 24 months have a moderate and negative genetic correlation with female puberty -9 and a Using a GWAS methodology, QTL regions were identified on the bovine X chromosome that potentially influence andrological traits in cattle ,15. The LOC100138021, CENPI, TAF7L, NXF2, CYLC1, TEX11, AR, UXT and SPACA5. The gene LOC100138021 is a homolog of the TCP11 gene and plays an important role in spermatogenesis and sperm function in humans LOC100138AR and TEX11 have been studied before, and their influence on scrotal circumference traits in Brahman and Tropical Composite bulls has been documented TEX11 (Te2) was estimated using the Plink program  to determine which SNPs were more frequently inherited together. Considering two loci with two alleles for each locus (A/a and B/b), the following formula was used:The linkage disequilibrium (rr2 = [ f(AB) x f(ab) – f(Ab) x f(aB)]2/ f(A) x f(a) x f(B) x f(b)] = D2/ [f(A) x f(a) x f(B) x f(b)] where ‘f’ is the frequency and D = f(AB) - f(A) x f(B)The single marker regression was examined for genotyped animals using a mixed model analysis of variance with ASREML software. The mixed model is described below:yi represents the phenotypic measurement for the ith animal, X is the incidence matrix relating fixed effects in β with observations in y, Z is the incidence matrix relating to random additive polygenic effects of animal in μ with observations in y and Sj is the observed animal genotype for the jth SNP , if the SNPs were located in the X chromosome and males were genotyped, they were coded as 0 or 2 (since there is no heterozygous), aj is the estimated SNP effect, lastly ei is the random residual effect. For SC12, SC18, SC24 and PNS, the same fixed effects were used for each trait. These fixed effects included contemporary group , the interaction of year and month of birth and breed. For AGECL and PPAI, the fixed effects were contemporary group , herd of origin and age of dam. For bull growth traits, the fixed effects included cohort origin and age. The p-values were not corrected for multiple testing.Where th SNP was estimated according to the formula th SNP estimated across the entire population, aj is the estimated additive effect of the jth SNP on the trait under analysis, and σg2 is the REML estimate of the (poly-) genetic variance for the trait.The percentage of the genetic variance accounted by the jThe single marker regression was also done fixing the top marker (higher F statistic) for each trait. The p-values of the other markers were recalculated consecutively until no marker has a significant p-value. The aim of these analyses is to verify the independence of the effect among the markers.Animal genotypes for all markers are available as Additional file


The estimation of quantitative genetic parameters in wild populations is generally limited by the accuracy and completeness of the available pedigree information. Using relatedness at genomewide markers can potentially remove this limitation and lead to less biased and more precise estimates. We estimated heritability, maternal genetic effects and genetic correlations for body size traits in an unmanaged long-term study population of Soay sheep on St Kilda using three increasingly complete and accurate estimates of relatedness: (i) Pedigree 1, using observation-derived maternal links and microsatellite-derived paternal links; (ii) Pedigree 2, using SNP-derived assignment of both maternity and paternity; and (iii) whole-genome relatedness at 37 037 autosomal SNPs. In initial analyses, heritability estimates were strikingly similar for all three methods, while standard errors were systematically lower in analyses based on Pedigree 2 and genomic relatedness. Genetic correlations were generally strong, differed little between the three estimates of relatedness and the standard errors declined only very slightly with improved relatedness information. When partitioning maternal effects into separate genetic and environmental components, maternal genetic effects found in juvenile traits increased substantially across the three relatedness estimates. Heritability declined compared to parallel models where only a maternal environment effect was fitted, suggesting that maternal genetic effects are confounded with direct genetic effects and that more accurate estimates of relatedness were better able to separate maternal genetic effects from direct genetic effects. We found that the heritability captured by SNP markers asymptoted at about half the SNPs available, suggesting that denser marker panels are not necessarily required for precise and unbiased heritability estimates. Finally, we present guidelines for the use of genomic relatedness in future quantitative genetics studies in natural populations. Knowledge of the genetic architecture underpinning phenotypic variation in natural populations is pivotal for our understanding of evolutionary processes. Classical quantitative genetics assumes the infinitesimal model, under which trait variation is controlled by alleles of small effect at many loci as well as environmental variation, and to date, extensive searches for quantitative trait loci in various taxa give strong support for this model  and to what extent there is covariance between pairs of traits (genetic correlations). In recent years, there has been a marked increase in the application of this approach in wild populations  populations, identification of extra-pair paternities led to higher heritability estimates of tarsus length and body mass, but pedigrees with 20% paternity errors only led to heritability underestimates of 5%  population, simulations showed that, in the absence of maternal effects, downward bias of heritability increased with increasing rate of pedigree errors  would ideally be estimated at all causal loci underlying trait variance. As causal loci are unlikely to be among the genotyped markers, the accuracy of heritability estimates is dependent on linkage disequilibrium (LD) between causal loci and the genotyped markers at which relatedness is estimated  a pre-existing pedigree with paternities identified using a panel of microsatellites; (ii) a new pedigree based on a more powerful panel of SNP markers; and (iii) genomic relatedness derived from a 50K SNP chip. Body size traits are good candidates for exploring the effects of pedigree improvements and the performance of marker-based heritability in this study system. Body size is known to be heritable (Milner The objectives of the study were therefore to determine: (i) the effect of a major improvement in pedigree completeness and accuracy on estimates of heritability and genetic correlations; (ii) if improved estimates of relatedness lead to better separation of maternal genetic and nongenetic maternal effects from direct genetic effects; (iii) the effect of improved estimation of relatedness on uncertainty of maternal effects and heritability estimates; and (iv) how much of the genetic variance as estimated by the best available pedigree is captured by relatedness at a dense panel of SNP loci. With these objectives, we aim to consider prospects for genomic-relatedness-based quantitative genetic studies of natural populations in the future.et al. et al. The Soay sheep is a primitive breed that has been living on the island of Soay, in the St. Kilda archipelago, NW Scotland, for thousands of years in a largely unmanaged state. The population on Hirta has been unmanaged since its introduction from Soay in 1932  and jaw length (mm). More details about how these traits are measured can be found in Beraldi et al. The majority of lambs are ear-tagged and weighed within a couple of days of birth. Each August, approximately two-thirds of the resident population is trapped. At capture, body size traits which are measured include foreleg, hindleg and body weight. Winter mortality is monitored, and the left foreleg and both jawbones are collected, cleaned and stored. Body size traits measured from this At first live capture, all sheep are ear-punched before ear tagging, and all sheep captured live are blood sampled into lithium heparin tubes, with the blood separated into plasma and buffy coat prior to freezing at −20 °C. A sample of ear tissue is also taken from all sheep when found dead, generally providing high-quality DNA for genotyping; in some cases, DNA is also available from muscle samples collected early in the study.post-mortem ear samples using the Qiagen DNeasy 96 Blood and Tissue kit using the recommended protocol, except that final elution was in 2 × 50 μL elution buffer. For individuals, where no ear material was available, we extracted DNA from buffy coat samples using the Qiagen DNeasy Blood and Tissue kit, using the same final elution volumes; in a few individuals for which only post-mortem muscle was available, we extracted DNA using a standard phenol–chloroform method. DNA concentration was quantified using pico green , and samples in the range 20–40 ng/μL were vacuum concentrated to achieve the desired final concentration. Samples below 20 ng/μL were not used further.We attempted to extract suitable DNA (20 μL at 50ng/μL) from all individuals alive in the study population since 1990. DNA was extracted from ear punches and genomestudio (Illumina). Most loci were clustered automatically, but 634 SNPs for which clustering had been zeroed by Illumina were manually clustered. Individuals with a call rate of >95% were retained in the analysis. Further quality control was performed in plink v1.07  using an iScan instrument at the Wellcome Trust Clinical Research Facility Genetics Core . A total of 54 241 single-nucleotide polymorphisms (SNPs) distributed throughout the genome were genotyped. Results were inspected in sheep genome, http://www.livestockgenomics.csiro.au). We calculated linkage disequilibrium (LD) using the r2 statistic using all genotyped individuals in plink v1.07  and a genomic relatedness matrix (GRM) were used in our analyses.et al. et al. Pedigree 1: This pedigree was the most complete and accurate Soay sheep pedigree constructed using microsatellites to infer parentage. Maternities were assigned by field observation, and molecular parentage analysis was used to infer paternities  for all cohorts between 1980 and 2012. For each cohort maternity and paternity were inferred simultaneously using 315 high MAF, unlinked SNPs in the R package pedantics . We used observations or assignments inferred using microsatellites to fill in the gaps. In 1257 cases where no maternity was assigned using molecular markers, field observational data were used. For 222 lambs without assigned fathers, paternity data from Pedigree 1 were used if confidence of assignment was >95%. For Pedigrees 1 and 2, pairwise relatedness between all individuals was estimated using the R package gcta v1.04 which estimates the proportion of the genome identity-by-state (IBS) between individuals. At each locus, relatedness was scaled by the expected heterozygosity 2pq , me and ma are vectors containing the maternal environmental and maternal genetic effects, respectively, and e is a vector of residual effects. Additional random effects ur varied between traits and are fitted with their own corresponding incidence matrix Zr. Birth year was fitted as a random effect in all models. For adult August phenotypic data, year of measurement and permanent environment effects were also fitted as random effects in all models. Models 1 and 2 were run using each of the three relatedness matrices . Note that when maternal genetic effects were investigated in Model 2, the maternal relatedness matrix used was derived from the same source, for example, the Model 2 using genomic relatedness to detect direct genetic effects also used genomic data to detect maternal genetic effects. The variance explained by a random effect was expressed as a proportion of the total phenotypic variance for the trait after accounting for fixed effects in the model. The statistical significance of random effects was assessed using likelihood ratio tests (LRT) assuming a χ2 distribution with one degree of freedom.where idh term while fixing skeletal variance to near zero.We also estimated covariances between all five traits measured in adults using bivariate models, one for each of the ten trait combinations using each of the three relatedness matrices. Covariances were estimated using unstructured variance models. Fixed and random terms included in the models were identical to those included in univariate model 1  the three additional cohorts included in Pedigree 2; and (ii) maternities which were inferred using genetic markers in Pedigree 2 leading to slightly larger sample sizes. To eliminate this possibility, we pruned the Pedigree 2 data set for each trait to only include individuals which were present in the Pedigree 1 data set and re-analysed the data using Model 1. Second, differences between Pedigree 2 and the genomic relatedness could arise due to the fact that the Pedigree 2 analyses include individuals with phenotype data which have not been SNP genotyped. We therefore pruned the data set analysed with Pedigree 2 to include only SNP genotyped individuals and reran univariate analyses with Model 1 to check whether this made any difference to the results obtained.Finally, we explored whether the heritability captured by SNPs correlates with the number of SNPs used to estimate relatedness. In total, 37 037 autosomal SNP loci were used to estimate genomic relatedness. We randomly sampled 2.5%, 5%, 10%, 30%, 50%, 70% and 90% of the available markers, corresponding to 926, 1852, 3704, 11 111, 18 518, 25 926 and 33 333 autosomal markers, respectively. This procedure was replicated 50 times, and adult traits were analysed with each resulting genomic relatedness matrix using Model 1.r2 was 0.30, but r2 dropped to 0.11 at an interlocus distance of 1 Mb  than in Pedigree 1 (3.1 × 10−3) or using genomic relatedness (−1.7 × 10−4), but the variance in relatedness increased from Pedigree 1 (4.5 × 10−4) to Pedigree 2 (6.6 × 10−4) to genomic relatedness . Pedigree 2 relatedness correlated with genomic relatedness estimated using SNPs .The three methods  generated correlated pairwise relatedness estimates. Pedigree 1 relatedness correlated with Pedigree 2 relatedness  for all univariate models are shown in Table In Model 1, variance due to maternal effects and heritability of birth weight decreased from Pedigree 1 to Pedigree 2 to genomic relatedness  generally increased from Pedigree 1 to genomic relatedness to Pedigree 2 , Fig. 2,In Model 2, when dissecting maternal effects into maternal genetic and maternal environmental components, all estimates of relatedness allowed detection of maternal genetic effects in three traits , while maternal genetic effects were only significant in foreleg length and hindleg length in models using genomic relatedness. For hindleg length and weight, heritabilities estimated using Pedigree 1 were not significant when a maternal genetic effect was fitted , Fig. 3.In yearlings, heritabilities were higher than in lambs , Fig. 2.Using Model 2, despite the strong maternal effects in the August traits in Model 1, maternal genetic effects were only found in foreleg length, while maternal environment effects were only observed in hindleg length, both of which were only significant in models using genomic relatedness , Fig. 3.In adults using Model 1, heritabilities were high, ranging between 0.25 and 0.73 , Fig. 2 Accuracy of maternal effect, maternal genetic effect and heritability estimates, either the relationship between estimate and standard error, or standard error alone, generally increased from models using Pedigree 1 to Pedigree 2 to models using genomic relatedness, a pattern which emerged relatively systematically across all four age classes .All genetic covariances were positive , Fig 4 aPedigree 2 heritability estimates retaining only the individuals used in the Pedigree 1 analysis did not differ much from the Pedigree 2 estimates where all individuals were used Table  and werePedigree 2 heritability estimates using the subset of SNP genotyped individuals were very close to estimates using all individuals with Pedigree 2 information Table , indicatn = 18 518), suggesting that adding more loci to our current panel will not necessarily lead to improved genomic heritability estimates  lead to heritability estimates virtually indistinguishable from those estimated using improved pedigrees  maternal effects and maternal genetic effects. Standard errors generally declined from Pedigree 1 to Pedigree 2 to the GRM, suggesting that more accurate estimates of relatedness allow the estimation of variance components with more precision. Only a minor proportion of the differences can be explained by differences in sample size between the data sets, indicating that the smaller standard errors are a direct result of improvements in relatedness estimates. This suggests that improved relatedness estimates can increase power to detect significant quantitative genetic parameters.et al. et al. et al. et al. On average, genomic relatedness accounted for 84% of the genetic variance, meaning that most of the heritability as estimated using Pedigree 2 is captured by SNP markers on a 50K SNP chip. We also show that the difference in heritability estimates between Pedigree 2 and the GRM cannot be explained by the small differences in data set size and composition between the two analyses. The proportion of additive genetic variance explained by genomic markers is much higher than is found in human populations when unrelated individuals are used  are similar to previous within-male estimates obtained using Pedigree 1  was a m  effects. Furthermore, we have shown that in this population, most of the genetic variance and covariance as estimated by the pedigree is captured by SNPs on a commercially available 50K SNP chip. Increasing the number of markers is unlikely to yield improvements in the proportion of genetic variance which can be explained by SNPs. We suggest that dense marker panels can be used successfully to estimate quantitative genetic parameters in wild populations, but only if researchers (i) ensure that marker density in relation to levels of LD are high enough to tag causal variants; and (ii) account for sources of common environment in their models to avoid upwards bias of heritability estimates.


In rare variant association studies, to improve the power of the tests, we explore the advantage of the extreme phenotype sampling. At first, we dichotomize the continuous phenotypes before analysis, and the two extremes are treated as two different groups representing a dichotomous phenotype. We next compare the powers of several methods based on extreme phenotype sampling and random sampling. Extensive simulation studies show that our proposed methods by using extreme phenotype sampling are the most powerful or very close to the most powerful one in various settings of true models when the same sample size is used.Next-generation sequencing technology has made it possible to detect rare genetic variants associated with complex human traits. In recent literature, various methods specifically designed for rare variants are proposed. These tests can be broadly classified into burden and nonburden tests. In this paper, we take advantage of the burden and nonburden tests, and consider the common effect and the individual deviations from the common effect. To achieve robustness, we use two methods of combining  Hundreds of common genetic variants associated with many complex diseases and human traits have been successfully identified by the genome-wide association studies (GWAS). However, these common genetic variants with minor allele frequencies (MAF ) >3% have small to moderate effects, and explain only a small fraction of disease heritability for common disease ,2,3,4. TVarious methods have been proposed to detect association between rare variants and complex diseases. These tests can be broadly classified into burden and nonburden tests. Burden tests collapse multiple rare variants in a genetic region into a single variant, and then test the association between the single variant and the trait of interest. Burden tests include the cohort allelic sums test (CAST) , the com C-alpha , the seq C-alpha , the opt C-alpha , the mix C-alpha , and an  C-alpha . Variancet al. [p method of combining p-values.From the nonburden tests, we can see that the average association across variants is zero. However, unless the effect of all rare variants are in opposite directions with the same strength, and they cancel out, the average effect will not be zero. Thus, a model restricting the average effect to be zero may lose power. Thus, we use a more flexible model proposed by Wang et al. . In thisIn this paper, we also explore the advantage of the extreme phenotype sampling in rare variant analysis and refine this design framework for future large-scale association studies on quantitative traits. Sampling individuals with extreme phenotypes can enrich the frequency of rare variants and therefore lead to an increase in power compared to random sampling. Recently, several statistical methods have been proposed for rare variants association study when extreme phenotypes are sampled ,27,28,29n individuals and p rare variants in a genomic region. For ith individual; for ith subject carries at the jth variant site.We only consider the quantitative traits. Consider a sample of p degrees of freedom has low power.Consider a linear regression modelp rare variants into a single variant. Then, the model is simplified asY. When these individuals are randomly sampled, the burden test is denoted as RS_burden.To decrease degrees of freedom and increase the power, burden test collapses the i.e., Mendelian disorders). In such cases, the corresponding variants will be enriched in the extreme sample. Thus, we think that extreme phenotype sampling will be more powerful than random sampling. For example, in clinical practice, diseases such as hypertension and obesity, are dichotomized by setting a threshold for quantitative traits. When individuals with extreme phenotypes are sampled, the high phenotypic extremes are regarded as cases and the low phenotypic extremes are regarded as controls. The logistic model for these “case-control” data isIn practice, it may be more likely that the very extremes of phenotype distribution may consist of unknown genetic heterogeneity due to genes with large effects Yi=θ0p-value of p-value of p-values obtained from In the p-values, such as the minimum-p approach. In the minimum-p approach, the test statistic of In addition, we consider other methods for combining The two methods of combining P-values have been studied by many authors ,30. WhenNext, we consider extreme phenotype sampling. Dichotomizing the higher and the lower phenotypic extremes as cases and controls, the logistic regression model for these “case-control” data isp-values obtained from p-values: Fisher’s method and minimum-p method. The test statistics of In the p-value of the statistic et al. [We denote the two methods as ES_Fisher and ES_min-p, respectively. We use permutation approach to obtain the n et al. .et al. [N individuals, we randomly combine two haplotypes of the 697 individuals.The GAW17 provides the Mini-Exome genotype data for simulation studies. This dataset contains genotypes and phenotypes for 697 unrelated individuals on 3205 genes. We follow the simulation set-up of Sha et al. . Specifip-values less than To evaluate type I error rate, we generate quantitative trait values by using the model:N individuals by using the following model:i.e., n = 500, 1000, and 2000) and the proportions of extreme phenotypes (10% and 20%).To evaluate power, we generate phenotypes for the N individuals are simulated, for random sampling, n individuals are arbitrarily selected from the N individuals. For extreme phenotype sampling, we denote the highest N individuals as cases and the lowest et al. [After the genotype and phenotype data for g et al.  is denotp-values are estimated by 500 permutations and type I error rates are evaluated by 1000 replications. The estimated type I error rates of the seven methods  are summarized in For type I error rates, we consider different sample sizes, different proportions of extreme phenotypes, and different significance levels. In each simulation setting, p-values are estimated by 500 permutations and powers are evaluated using 500 replications at a significance level of 0.05. In all cases, the threshold value of rare variants is selected as 0.03.For power comparisons, we consider different sample sizes, different proportions of extreme phenotypes, different percentages of causal variants, and different percentages of causal variants with positive effects. In each simulation scenario, Power comparisons of the seven tests for half risk variants and half protective variants are given in Power comparisons of the seven tests for 80% risk variants and 20% protective variants are given in Power comparisons of the seven tests for the same direction effect of variants are given in In summary, the ES_Fisher and the ES_min-p are either the most powerful tests or have similar powers to the most powerful one in each setting. The powers of the ES_Fisher and the ES_min-p are relatively robust to the increase of protective variants and neutral variants. It means that in rare variants association studies, extreme phenotype sampling is superior to random sampling in the same sample size.GWAS have identified many genetic variants associated with many multifactorial diseases. However, most GWAS approaches do not consider the disease heterogeneity and the follow up functional analysis of risk variants. Recently, a new field of ‘molecular pathological epidemiology (MPE)’ has emerged as an interdisciplinary integration of ‘molecular pathology’ and epidemiology” . The MPEet al. [The idea of sampling the extremes was initially proposed in linkage analysis as a way to increase efficiency . Howeveret al.  have demith subject. Our proposed methods easily adjust for covariates, such as age, gender, and principal components for population stratifications. When considering covariates, we use the following modelIn this paper, we propose two methods for testing whether a set of variants is associated with continuous phenotypes. We use the same model with the JOINT method, in which common effects of all rare variants and individual effect deviations from the common effect are jointly considered. However, the SKAT assumes that the average effect is zero. In fact, the average effect will not be zero unless the effects of all rare variants are in opposite directions with the same strength.p method, the JOINT method is the sum of standardized p method combine the p-values of p methods are more powerful when only all rare variants have common effect on the trait or when only rare variants have individual effects on the trait. When the true underlying disease model includes risk variants and protective variants, the Fisher’s and minimum-p methods are more powerful than burden tests. In the same sample size, each of the three methods  uses random sampling and extreme phenotype sampling. Our simulation results show that sampling from extreme phenotypes outperforms random sampling methods when the same size is used.Compared with Fisher’s method and the minimum-


Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait.p-values compared to univariate analyses.It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance components and principal components, suffer from inherent limitations, such as lack of robustness or difficulty in biological interpretation of association findings. In an effort to circumvent these limitations, we propose a novel regression approach based on a conditional binomial model to detect association between a single-nucleotide polymorphism and a multivariate phenotype vector. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. We find that the bivariate analysis of the two phenotypes yields more promising results in terms of lower  Most clinical end point traits are governed by a set of quantitative precursors and a single precursor is unlikely to explain a significant amount of the variation in the end point trait. Thus, it may be a prudent strategy to analyze a multivariate vector of phenotypes for association mapping of a clinical end point trait. The major statistical challenge in the analyses of multivariate phenotypes lies in the modeling of the vector of phenotypes. Univariate analyses of the constituent phenotypes may lead to the statistical problem of multiple comparisons . LikelihFor our analyses, we use data on systolic blood pressure (SBP) levels and diastolic blood pressure (DBP) levels on 157 unrelated individuals along with their genotypes at all the available 456,752 variant sites distributed over 11 autosomal chromosomes in the genome-wide association studies data provided in GAW18. In addition to age, we use smoking status and medication indicator (both defined as binary variables) at each time point of examination as covariates, as these factors could be potential confounders in the association analyses. Both the SBP and the DBP levels are adjusted for these covariates for each time point and the tests for association are performed on the adjusted phenotypes. The adjustment and the association analyses are based on 139 individuals, as data on all the covariates are not available for the remaining individuals. The percentages of individuals with missing phenotype data at the different time points are 8%, 35%, 35%, and 75%, respectively. On tabulating the percentages of individuals with phenotype data missing at multiple time points, we do not observe any pattern in the missing data. For our association analyses, we impute the missing data using a procedure outlined in the following section.p-values are very close to the threshold of 0.05. We thus use a logarithmic transformation on each of the phenotypes to induce normality. Suppose the vector of log-transformed values of any of the two phenotypes at the 4 time points is represented as X = . If Y denotes the vector comprising those components of X that are missing and Z comprising the components that are available for an individual, Y is estimated via an expectation maximization algorithm as the expectation of Y conditioned on Z and is given by Y-∑YZ∑ZZ-1(Z-μZ), μwhere, Y μand Z μare the mean vectors of Y and Z, respectively; ∑YZis the matrix of covariance between Y and Z, while ZZ ∑is the dispersion matrix of Z. At every time point, we perform a linear regression of the log-transformed values of each of the two phenotypes (available as well as imputed) on age, smoking status, and medication indicator based only on those observations for which data were available on all the covariates and thereby obtain the residuals of each of the regressions.The assumption of multivariate normality provides a computationally elegant framework for the expectation maximization algorithm  to estimX =  denotes a vector of k phenotypes and N denotes the number of copies of the minor allele  at a SNP. We model the conditional distribution of N given X as binomial with parameters 2 and p(X) where, p(X) is a logistic link function given by:The phenotypes for our association analyses are the adjusted SBP and DBP levels at each time point obtained using the algorithm described in the preceding section. We propose a binomial regression framework to test for association of a SNP with a multivariate phenotype. Suppose H0: β1 =β2= ... =βk = 0 versus H1: not H0 and the log-likelihood ratio test statistic is distributed as chi-squares with k degrees of freedom under the null hypothesis. We compare the relative performances of 5 phenotype vectors in detecting association: (a) T1: the adjusted SBP levels at the 4 time points; (b) T2: the adjusted DBP levels at the 4 time points; (c) T3: the adjusted SBP levels summarized by the first 2 principal components across the four time points; (d) T4: the adjusted DBP levels summarized by the first two principal components across the four time points; and (e) T5: the adjusted SBP as well as the adjusted DBP levels summarized by a bivariate phenotype comprising the first 2 principal components corresponding to each of the phenotypes across the four time points. The above choice of the principal components is motivated by the fact that approximately 75% of the variation in each of the two phenotypes is explained by the corresponding first two principal components. Because the sample size is not very large, we consider only common variants for our association analyses and select only those SNPs with a minor allele frequency of at least 0.01. We also exclude those SNPs that provide significant evidence of departure from Hardy-Weinberg equilibrium (based on the Bonferroni corrected threshold at the genome-wide level). Thus, the tests for association based on the proposed binomial regression model are carried out on 426,193 SNPs. To correct for multiple testing, we use the false discovery rate (FDR) procedure [The test for association is equivalent to testing T5) provides the most promising association findings. Table rs12634258 on chromosome 3 (adjusted FDR of 0.047) with T5. We observe that this SNP also ranks among the top five SNPs in terms of lowest unadjusted p-values when T3  is analyzed.Among the five phenotype vectors considered, the one comprising the first two principal components of both SBP and DBP levels  and FHIT (fragile histidine triad) on 3p14.2. The RNA expression in PTPRG has been reported to be upregulated for arrhythmogenic right ventricular cardiomyopathy in humans [FHIT is downregulated in mouse for low blood pressure [The SNP n humans . On the rs7644778 ranks among the top two association findings with the first principal component of each of SBP and DBP levels across the four time points, albeit not significant at the genome-wide level. We note that this SNP also exhibits evidence of association with first two principal components of each of the two phenotypes. The SNP is located in the gene TPRG1 (tumor protein p63 regulated 1). The RNA expression of this gene has been reported to be upregulated for cardiomyopathy in humans. On the other hand, the five most significant findings with the first two principal components are identical to those with the first three principal components, but none of them is significant at the genome-wide level. This may be explained by the fact that the test with 3 principal components for each of the two phenotypes has two additional degrees of freedom under the null hypothesis and may suffer from loss in power.While constructing the phenotype vectors, we had considered the first two principal components of the blood pressure levels across the four time points. As mentioned earlier, these two principal components explain approximately 75% of the total variation of blood pressure levels across the time points. Even though the first principal component explains approximately 55% of the variation, the first 3 principal components explain approximately 90% of the variation. Although for brevity we do not present the association findings for the different choices of the principal components, we find that the SNP T1, T2, T3, T4 and T5), we find that T5  provides the most significant association findings . Even though the empirical findings seem to suggest that association analyses based on a multivariate phenotype comprising traits modulated by common genetic variants are likely to be more powerful than univariate analyses of the constituent traits of the multivariate phenotype vector, such a hypothesis can only be validated using extensive simulations. We have performed some independent simulations on 500 unrelated individuals under a few pleiotropic bivariate phenotype models: (a) the constituent traits have a bivariate normal distribution, (b) both are distributed as chi-squares, and (c) 1 trait has a normal distribution, while the other is binary. Our preliminary results based on 1000 replications indicate that our proposed method maintains the correct false-positive rates under all the phenotype models. We also find that simultaneous analyses of both the phenotypes as covariates consistently yield more power compared to analyzing each univariate trait using the binomial regression. On the other hand, the power of the proposed method based on both phenotypes is higher, similar, or marginally lower compared to that based on the first principal component, depending on whether the correlation between the traits is low (less than 0.25), medium (between 0.25 and 0.5) or high (greater than 0.5). The above phenomenon was observed irrespective of the bivariate phenotype model considered. When the gene effects are in different directions for the two phenotypes , the first principal component performs very poorly and may yield lower power compared to analyzing univariate phenotypes. However, further simulations based on higher dimensional phenotype vectors need to carried out to obtain greater insights into the relative advantages of the proposed method using the multivariate phenotype vector over data reduction approaches such as using a subset of the principal components of the phenotype vector.Among the five phenotype vectors (The authors declare that they have no competing interests.SG and AM developed the proposed method. AM and IM wrote the computer codes and performed the data analyses. AM participated in the compilation and interpretation of the results. All authors read and approved the final manuscript.


Family designs are naturally equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Unfortunately, pedigree likelihoods are notoriously hard to compute, and current software for association mapping in pedigrees is prohibitively slow in processing dense marker maps. In a recent release of the comprehensive genetic analysis software MENDEL, we implemented an ultra-fast score test for association mapping with pedigree-based GWAS or NGS study data. Our implementation (a) works for random sample data, pedigree data, or a mix of both;(b) allows for covariate adjustment, including correction for population stratification;(c) accommodates both univariate and multivariate quantitative traits; and (d) allows missing values in multivariate traits. In this paper, we assess the capabilities of MENDEL on the Genetic Analysis Workshop 18 sequencing data. For instance, when jointly testing the 4 longitudinally measured diastolic blood pressure traits, it takes MENDEL less than 51 minutes on a standard laptop computer to read, quality check, and analyze a data set with 959 individuals and 8.3 million single-nucleotide polymorphisms (SNPs). Our analysis reveals association of one SNP in the q32.2 region of chromosome 1. MENDEL is freely available on  Pedigree data are attractive in modern association studies because they permit control of population substructure and study of parent-of-origin effects . Relatedν and the corresponding covariances into a matrix Σ and represents the loglikelihood of a pedigree asQuantitative trait locus (QTL) association mapping typically invokes the multivariate Gaussian distribution to model the observed trait values where the covariance matrix is typically parameterized asΦ is the global kinship coefficient matrix capturing additive polygenic effects, and H has entries i and j are in the same household and 0 otherwise. Individual environmental contributions and trait measurement errors are incorporated via the identity matrix i. When one tests multiple traits, the covariance matrix has to be properly augmented by matrix Kronecker products. QTL fixed effects are captured through the mean component A and vector of regression coefficients β.Here the variance component p-values (top 50 by default) are further tested by the more accurate likelihood ratio method, thus achieving a good compromise of speed and power for large-scale QTL analysis.To implement likelihood ratio testing, iterative maximum likelihood estimation must be undertaken for each and every SNP under the alternative hypothesis. This unfortunate requirement is the major stumbling block retarding pedigree analysis. Score tests serve as convenient substitutes for likelihood ratio tests. A careful analysis shows that the basic elements of the score statistic can be quickly assembled. In MENDEL , SNPs wiOur analysis is based on the genotype calls for 959 individuals (464 directly sequenced and the rest imputed) provided in the chrX-geno.csv.gz files. Simulated traits in all 200 replicates (SIMPHEN.1-200) were used for size and power studies in the first example. The second example presents results from a pedigree GWAS performed on chromosome 3 using the traits in the first simulation replicate (SIMPHEN.1). A whole genome QTL analysis for the real phenotype diastolic blood pressure (DBP) is presented in the final example.Both the traits (blood pressures) and some environmental factors are measured (or simulated) on study individuals at 3 or 4 visits. To adjust for the environmental effects of Age, BPMed, Smoke, and Sex, we model the systolic blood pressure (SBP) by a linear mixed model (LMM):t indexes 3 time points, where decorrelated from the polygenic effects. QTL mapping can be performed on The estimated fixed effects for traits in simulation replicate 1 are summarized in Table MAP4 gene on chromosome 3 are evaluated based on the provided 200 simulation replicates. Figure p-values) for each variant using either the decorrelated (method1) or the correlated residuals (method2). Type I errors are evaluated based on the provided Q1 trait which is not genetically influenced. In general, we found that the decorrelated residuals (method1) lead to higher power but also inflated type I error. The test using the correlated residuals (method2) has well-controlled type I error, high power (0.78 ~ 0.90) for detecting the common variants 47957996 and 48040283 but low power for the rare variants 47913455 and 47957741. For comparison, we also list the power and the size of likelihood ratio test (LRT) using correlated residuals. LRT edges out the score test in a few cases, but the difference is not significant. LRT is practically infeasible for a large number of SNPs. In the following two pedigree GWAS examples, we present only the results of the score test using correlated residuals (method 2).Powers for detecting the 6 functional variants in the p-values less than 0.05, corroborating the correct size of the score test. Results for trait SBP are similar and not displayed.We performed pedigree GWAS on all available sequence variants on chromosome 3 using the correlated residuals from the traits in SIMPHEN.1.A total of 1,213,657 SNPs passed the filtering and were subject to testing.Figure The phenotypes (SBP and DBP measured at 4 time points) are available for 1389 members from 20 extended families. The largest family contains 107 individuals; the smallest, 27. Genotypes at 8,348,674 SNPs were available on 959 of the individuals. For brevity, we only present results for the multivariate DBP trait here.We adopted the strategy discussed earlierto adjust the multivariate traits for the environmental factors. The table in Figure p-value found by whole genome analysis was 1 × 10−10.5 on chromosome 1 q32.2 region at 210,338,112 base pairs. No other SNPs reached genome-wide significance.The most significant By supplying a comprehensive, fast, and easy-to-use package for GWAS on quantitative traits in general pedigrees, we hope to encourage exploitation of family-based data sets for gene mapping. A gene mapping study should collect as large a sample as possible consistent with economic constraints and consistent trait phenotyping.If the sample includes pedigrees, all the better. Here we have argued that score tests can efficiently handle unrelated individuals, pedigrees, or a mixture. For human studies, in whichcontrolling breeding is forbidden, nature has provided pedigrees segregating every conceivable genetic trait. Many of these pedigrees are known from previous linkage studies and should be treasured as valuable resources.The authors declare that they have no competing interests.HZ, EMS and KL designed the overall study. HZ and JZ conducted statistical analyses and drafted the manuscript. All authors read and approved the final manuscript.


Genotype imputation is an important tool for whole-genome prediction as it allows cost reduction of individual genotyping. However, benefits of genotype imputation have been evaluated mostly for linear additive genetic models. In this study we investigated the impact of employing imputed genotypes when using more elaborated models of phenotype prediction. Our hypothesis was that such models would be able to track genetic signals using the observed genotypes only, with no additional information to be gained from imputed genotypes.For the present study, an outbred mice population containing 1,904 individuals and genotypes for 1,809 pre-selected markers was used. The effect of imputation was evaluated for a linear model (the Bayesian LASSO - BL) and for semi and non-parametric models . The RKHS method had the best predictive accuracy. Genotype imputation had a similar impact on the effectiveness of BL and RKHS. BRANN predictions were, apparently, more sensitive to imputation errors. In scenarios where the masking rates were 75% and 50%, the genotype imputation was not beneficial. However, genotype imputation incorporated information about important markers and improved predictive ability, especially for body mass index (BMI), when genotype information was sparse (90% masking), and for body weight (BW) when the reference sample for imputation was weakly related to the target population.In conclusion, genotype imputation is not always helpful for phenotype prediction, and so it should be considered in a case-by-case basis. In summary, factors that can affect the usefulness of genotype imputation for prediction of yet-to-be observed traits are: the imputation accuracy itself, the structure of the population, the genetic architecture of the target trait and also the model used for phenotype prediction. Genome-enabled prediction of quantitative traits is a topic of current interest in genetic improvement of agricultural animal and plant species, as well as in preventive and personalized medicine in humans. In agriculture, it has been applied to prediction of genetic merit for breeding purposes  and to mA commonly used technique in this field is genotype imputation. Genotype imputation can be employed to fill in missing data from the laboratory or allow merging data sets generated from different SNP chips. Genotype imputation has been proposed also to impute from genotypes scored with low-density chips to higher densities, as a way to reduce genotyping costs ,8,9. OthThe benefit of imputing genotypes essentially depends on its imputation accuracy , which, So far, all studies conducted to evaluate the effect of genotype imputation on whole-genome prediction have assumed a linear relationship between phenotype and genotype, aimed at capturing additive genetic effects only. However, complex traits are known to be affected by complex gene effects and interactions . For thiRecently, Heslot et al.  evaluateThe objective of our study was to investigate the effect of genotype imputation in the context of whole-genome prediction of complex traits in mice using parametric, semi-parametric and non-parametric models applied to different sizes of subsets of SNPs. Our underlying hypothesis was that more elaborated prediction models, such as those capable to accommodate non-additive genetic effects, would not benefit significantly from genotype imputation for prediction of yet-to-be-observed phenotypes.Results indicated a good accuracy of imputation of unknown genotypes for all scenarios Table . The lowCorrelations between predicted and observed phenotypes in the testing set are shown in Tables As expected, the predictive ability for BW was higher than for BMI, since the latter has a lower heritability. Differences on results for each trait are also probably due to differences between their underlying genetic architectures. As discussed by Legarra et al. , in thisIn general, the method with the best prediction results was RKHS using kernel averaging, and the worst was BRANN, probably due to overfitting. BRANN showed high correlation (above 0.9) between predicted and measured phenotype for the training sets (results not shown). Table The results for predicted mean squared error (PMSE) are summarized in Tables Recently, some studies have investigated the predictive ability of models using subsets of SNPs, with and without imputation ,31,32. IThe results obtained indicated that imputation of the missing genotypes was not always advantageous for phenotypic prediction. The benefit of imputing genotypes depended on the degree of relatedness between reference and target samples, genetic architecture of the trait, number of markers available in the original panel, and the method used to predict marker effects.Weigel et al.  investigFor scenarios with different masking rates the imputed testing set gave, on average, a 4% higher correlation. For BMI, the reduced testing sets  provided 89% of the predictive ability of their respective complete imputed testing sets and 78% of the predictive ability of the complete testing sets, averaged across all scenarios tested. So, in general, the results indicated that imputation can be useful for phenotypic prediction.When comparing correlations for across and within families cross-validation strategies, genotype imputation seemed to be more effective in improving prediction accuracy in cases where there was a weaker genetic relationship among individuals in the reference and testing data sets. Other studies regarding the role of within and across-family information  also indRegarding the models considered, it was expected that the non-parametric methods would give smaller differences between the complete set with imputed markers and the reduced panel. However, our results indicated that the effect of imputation was similar for BL and RKHS predictions. An exception was the case of BRANN, which was not able to cope with imputation errors and tended to give worse predictions for the complete testing set containing imputed markers. Therefore, it seems that imputation accuracy is a fundamental factor to be considered when using BRANN for predicting phenotypes. The imputation from 905 markers to the full panel  tended to slightly improve prediction using BRANN perhaps due to the low imputation errors rates for these panels.Another discussion, beyond the scope of this paper, is on differences between chips containing either equally spaced SNPs or SNPs pre-selected based on their estimated effects for genome-enabled prediction . The maSimilar results were observed in terms of PMSE. Apparently, higher imputation errors caused higher values of PMSE, making the results from models using the reduced SNP panel better than those containing imputed marker genotypes.The results of the present study can be generalized for different scenarios, regardless the number of SNPs and/or sample size of a particular study, based on the impact of imputation accuracy on the predictive quality of genomic models. Clearly, the predictive ability of a model not only depends on how well genotypes are imputed but also on the genetic architecture of the target trait and the breeding program design. Therefore, the general reasoning provided by the results of the present study is that the use of genotype imputation should the evaluated in a case-by-case basis. For example, the use of imputed genotypes when employing the non-parametric method  is not recommended given that this model tends to approximate the noise inserted by imputation errors.Genotype imputation did not always improve the predictive ability of parametric and semi-parametric models. For BW, genotype imputation improved predictive ability when there was a relatively low genetic relatedness between the reference panel and the target population set. For BMI, the use of genotype imputation was more beneficial when the genotype set was very sparse (201 SNPs), especially for BL and RKHS. In other scenarios, imputation just slightly improved or even deteriorated predictive ability; the latter happened in cases in which the genotype imputation had low accuracy. Lastly, BRANN seemed more sensitive to imputation errors; therefore the use of imputed genotypes with this model should be carefully evaluated when using neural networks.http://mus.well.ox.ac.uk/mouse/HS/) was used. This is a sample from an outbred mice population that descended from eight inbred strains created for fine-mapping QTL and high-resolution whole-genome association analysis of quantitative traits [A publicly available dataset on mice  were discarded from the original dataset. The two traits, BW at ten weeks of age, and body mass index BMI were pre-corrected by fitting the following linear mixed model:y is the vector of observations on one of the measured phenotypes (BW or BMI); θ is an unknown vector of fixed effects of age, gender, month and cage density; c is a random vector of unknown cage effects; u is a random vector of unknown additive genetic effects; X, W and Z are the incidence matrices of fixed, random cage and additive genetic effects, respectively, and e is a vector of residual effects assumed to follow a multivariate normal distribution y* will be simply referred to as y.where After data cleaning, 10,348 SNPs remained from which 1,809 equally spaced SNPs were selected and regarded as full genotyped data due to computational limitations on number of markers that can be fitted when using Bayesian Regularized Neural Networks. Then, subsets containing 905, 453 and 201  equally spaced SNPs were taken from the full genotype set. In total, 1,881 and 1,823 individuals were included in the analysis of BW and BMI, respectively. For a cross-validation (CV) model comparison, in each case, approximately 2/3 of the individuals were designated as training set (reference sample) and 1/3 as testing set (target sample)  prior on β that is a product of p independent, zero mean, double-exponential (DE) densities [A Bayesian version of the LASSO was proposed by , who desensities . The douensities ), i.e.:\Convenient priors for the parameters of the Bayesian LASSO (BL) model have been suggested by  as:\docup normal densities with zero mean and variance j. Further d.f. degrees of freedom and scale parameter S; p is a Gamma distribution with parameters α1 and α2. The parameter λ, also called smoothing parameter, plays a central role in the model as it controls the trade-off between goodness of fit and model complexity [where H is a set of hyper-parameters. Here, mplexity . As its mplexity .Sε = d. f. ε = Su = d. f. u = 1, and α1 =1.2 and α2 = 10− 5. The package BLR [The model was fitted to the training set in all scenarios considered. Inferences were based on a Gibbs sampling chain with 70,000 samples after a burn-in of 5,000. The parameters of the prior distribution were kage BLR  developef ∈ H) is used, from which an element, The RKHS theory was introduced by Aronszajn  and has density) . The optl is a loss function representing a measure of goodness of fit; f, related to model complexity, and λ controls the trade-off between goodness of fit and model complexity.where Cov ~ K, with xi and xi ` representing vectors containing genotypes for the ith and i’th individuals, and K is the Reproducing Kernel (RK) related to a positive definite function [According to the Moore-Aronszajn theorem , each RKfunction .h is a bandwidth parameter and h is a model selection issue and must consider the observed distribution of dii’. In this study we used “kernel averaging” (multi-kernel fitting) as an automatic way of choosing the kernel based on the sample median of dii’, as described by [a was −5, −1 and −1/5, and q0.5 is the sample median of dii’, for the three kernels used for kernel averaging. In this model, the genotypic values were the sum of three components, g = f1 + f2 + f3 , with df = 5 and S =(var(y)/2 × (df − 2)), respectively. Posterior distribution samples were obtained with a Gibbs sampler as described by de los Campos et al. [The Kernel matrix (K) employed here was a Gaussian kernel, i.e. ribed by . Hence, A Bayesian Regularized Artificial Neural Network (BRANN) is a feed-forward network implemented with a maximum a posteriori approach in which the regularizer is the logarithm of the density of a prior distribution . This moA basic feed-forward network uses initial weights and biases and transforms input information  through each given connected neuron in the hidden layer using an activation function. Information is then sent to the neuron in the output layer using another activation (transformation) function generating the output or predicted value. Next, the results are backpropagated (non-linear least-squares) in order to update weights and biases using derivatives. Therefore, no assumptions about the relationship between genotypes (input) and phenotypes (target) are made in this model. After training, outputs are calculated as:ŷi is the predicted phenotype for an individual and g and f are the activation functions for output and hidden layers, respectively; wk and wk,i are the weights from neurons of the hidden to the output neuron, and from the input to the hidden neurons, respectively, and b2 are the biases of the two layers. Training is the process by which the weights are modified in light of the data while the network attempts to produce an optimal outcome [where  outcome . After tIn BRANN, in addition to the loss function given by the sum of squared errors, a penalty to large weights is also included in order to have a smoother mapping (regularization). The objective function is:D is the data (input data and target variable), α and γ are the regularization parameters that control the trade-off between goodness of fit and smoothing.where w given α, γ, D and M is [The posterior distribution of and M is :\documenP is the likelihood function, P is the prior distribution on weights under the chosen architecture, and P is the normalization factor.where To assess overfitting, network architectures and number of epochs (iterations) were tested in a first step. A network containing 5 neurons in the hidden layer with a tangent sigmoid function and 1 neuron in the output layer with a linear function was used after such tests (Figure http://gscan.well.ox.ac.uk/.The data set supporting the results of this article is available in the


APM1 to have a significant effect on marbling in Hanwoo. APM1 encodes an adipocytokine called adiponectin, which plays a significant role in lipogenesis. The aim of this study was to verify and validate the effect of the SNP and indel on marbling and other carcass traits in a large, representative, countrywide population of Hanwoo cattle. The carcass traits measured were marbling (MAR), backfat thickness (BFT), loin eye area (LEA), and carcass weight (CAW).Our previous study had identified the SNP (g.81966377T > C) and indel (g.81966364D > I) located in the promoter of P < .0001) with strong additive genetic effects. The allele frequencies was estimated at  and insertion  presenting genotypes of D/D (75.63 %), D/I (21.44 %), and I/I (2.92 %). Significant departure from Hardy-Weinberg equilibrium was not detected for both the SNP and the indel.Primers were designed to amplify 346 bp of the genomic segment that contained the targeted SNP (g.81966377) and the indel (g.81966364). After data curation, the genotypes of 8,378 individuals identified using direct sequencing analysis estimated frequencies for C (0.686) and T (0.314) respectively showing genotype frequencies for CC (0.470), CT (0.430) and TT (0.098). The genotypes were significantly associated with MAR, BFT and LEA. The indel had significant effect on marbling (The SNP genotypes showed significant association with MAR, BFT and LEA with strong additive genetic effects, while the indel was significantly associated with MAR. The results confirmed that the variants can be used as a genetic marker for improving marbling in Hanwoo. APM1 (Adipose most abundant gene transcript 1) gene codes an adipocytokine called adiponectin, and is secreted from the white adipose tissues  facilities located throughout Korea, thereby avoiding any regional bias, and so that the sample is representative of the whole country. The meat quality was graded by KAPE based on a national - grading system  according to the manufactures guidelines. The integrity of the DNA was checked by agarose gel electrophoresis and a NanoDrop 1000 spectrophotometer . The genomic DNAs were then stored at -70 °C until further use.APM1 gene sequence (GenBank: JQ7755868) was retrieved from the Genbank database to design PCR primers using DNA select program of the DNAstar package (Version 6.0). The forward and reverse sequences are CAGCTCGGTACTCATGGGGACAAG and GTGGGAGCTGATGGTGGTAACTGG, respectively. The primers were designed to amplify a 346 bp of the APM1 promoter region which included the targeted SNP (g.81966377T > C) and indel (g.81966364D > I) that was previously identified [The bovine entified .Taq DNA polymerase  in a 20 μl reaction. The PCR condition was as follows; initial-denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 45 s, annealing at 57 °C for 30 s and Extension at 72 °C for 1 min and a final extension at 72 °C for 5 mins on a thermal cycler . The PCR product was visualized on an agarose gel stained with a fluorescent dye  under UV light.The PCR reaction included 50 ng genomic DNA, 1 x reaction buffer, 2.5 mM dNTP, 10 pmoles of each primers and 1 unit The remaining PCR products were purified using a PCR purification kit  for sequencing analysis with reverse primer. The sequencing reactions were carried out in 10 μl reaction containing 1 μl of purified PCR product, 2 μl of 5 x sequencing buffer, 1.6 pico moles of reverse primer and 0.5 ul Bigdye®Teminator . The sequencing PCR were carried out for 35 cycles of 94 °C for 10 s, 57 °C for 10 s, and 60 °C for 3.5 min. The amplification product was precipitated by isopropanol-ethanol precipitation and reconstituted in 10 μl of formamide. After denaturation at 95 °C for 5 min the sequences were analyzed with ABI3730 XL Genetic Analyzer  at NIAS. The resulting sequences were aligned using SeqMan program of the DNAstar Package (Version 6.0) and the genotypes were determined according to the sequence chromatogram.After curating the data for missing phenotypes and genotypes, 8,378 samples were used for the statistical analysis.The statistical analyses were performed using the Statistical Analysis System . AnalysiY= observed for the target traitμ= overall mean of the target traitG= genotype effectS= sex effectb= the regression coefficient for ageA= age (covariate)e= random error.Where;Least squares means were compared using Fisher’s least significant difference test with a comparison error rate of 0.005. Additive genetic effects were estimated by the difference between estimates for the two homozygous genotypes and the dominance deviation was estimated by the difference between the solution for the heterozygous genotype and the average of the solutions for the two homozygous.The carcass traits of animals used in the analysis are summarized in Table The sequences were aligned with a reference sequence downloaded from NCBI DQ156119.1) and the nucleotide substitution at 81966377 (T > C) and the indel (g.81966364D > I) was verified  and LEA (P < 0.05). Animals with genotype CC (4.992) had significant effects on MAR than CT (4.942) and TT (4.693) with a significant additive genetic effect. Animals with genotypes TT  and CT  had significant effects on BFT, LEA than CC  with significant additive effects. The SNP was not significantly associated with CAW, though the genotype TT had the highest effect on CAW followed by genotypes CT and CC. The genotypes of the indel was strongly associated with MAR (P < 0.0001) with significant additive effect, while no significant association with BFT, LEA and CAW was detected . To achieve this goal, a marker based selection becomes imperative. Though genes having effect on polygenic traits like carcass and meat quality are still unclear, potential candidate have been identified by looking for associations between important traits and some physiological and biochemical processes [There has been huge demand for high quality beef from 1970s with increased marbling, taste, and texture . Effectirocesses .APM1, which is involved in lipid synthesis, fatty acid oxidation, energy homeostasis, insulin sensitivity and glucose utilization [APM1 could be a functional gene that is valuable for meat quality traits especially for fat related traits. Though the direct genetic effect of the studied SNP on the gene is still to be elucidated, variations in the promoter region could directly influence its expression thereby influencing meat quality and quantity traits. Several studies have recorded variations in the bovine APM1 promoter region [lization  encodes lization . Thus, Ar region , 8, 17.APM1 on meat quality [APM1 with growth, LEA and BFT Angus [APM1 has been confirmed to regulate bone development, yield grade and weight traits [Our previous analysis reported the genetic effect of g.81966377T > C and a 67 bp insertion (g.81966364D > I) in the promoter of  quality . HoweverFT Angus  and ChinFT Angus . Accordit traits , 18, 19.APM1 was verified in 8,378 animals. The genotypes were found to have significant association on MAR, BFT and LEA with strong additive effects, while the indel was found to have a significant association with MAR, paving the way for their use as a robus marker for selective breeding.The genetic effect of the SNP (g.81966377T > C) and indel (g.81966364D > I) located in the promoter of