Varroa destructor mites than colonies whose queens were inseminated with 15 drones, suggesting benefits of polyandry at rates higher than those normally obtaining in nature. Our results are consistent with two hypotheses that posit conditions that reward such high expressions of polyandry: (1) a queen may mate with many males in order to promote beneficial non-additive genetic interactions among subfamilies, and (2) a queen may mate with many males in order to capture a large number of rare alleles that regulate resistance to pathogens and parasites in a breeding population. Our results are unique for identifying the highest levels of polyandry yet detected that confer colony-level benefit and for showing a benefit of polyandry in particular toward the parasitic mite V. destructor.A honey bee queen mates on wing with an average of 12 males and stores their sperm to produce progeny of mixed paternity. The degree of a queen’s polyandry is positively associated with measures of her colony’s fitness, and observed distributions of mating number are evolutionary optima balancing risks of mating flights against benefits to the colony. Effective mating numbers as high as 40 have been documented, begging the question of the upper bounds of this behavior that can be expected to confer colony benefit. In this study we used instrumental insemination to create three classes of queens with exaggerated range of polyandry– 15, 30, or 60 drones. Colonies headed by queens inseminated with 30 or 60 drones produced more brood per bee and had a lower proportion of samples positive for  Apis mellifera L) mates with many males, stores their sperm in her spermatheca, and thereafter uses the sperm to fertilize her life’s output of eggs. This habit, called polyandry, results in daughters of mixed paternity, with the number of patrilines in a colony potentially equaling the number of males represented in the queen’s spermatheca. Field studies have shown a positive relationship between a queen’s expression of polyandry and measures of her colony’s fitness, including population growth, weight gain [The honey bee queen  ight gain , (3) redght gain , and (4)ght gain . It was ght gain  who poinght gain ,10 or geght gain . And altIn the case of hypothesis (4), support has come in a string of papers arguing that parasites and pathogens are among the most powerful selectors favoring polyandry in social insects ,13,14,15But in the case of rare specialist alleles such as those conferring disease or parasite resistance, a simple GV model for polyandry’s benefits may require more nuance. Theory shows that over 90% of the change in within-colony genetic relatedness is explained by a queen’s first 6 matings , but honIn the context of genetic resistance to novel parasites, the rare allele model may be of key importance and begs the question of the constraints limiting higher mating numbers in nature. These constraints are thought to be risks to the queen from sexually-transmitted diseases  and hazaVarroa destructor Anderson and Trueman provides a model of such a virulent parasite. On its newest host A. mellifera this mite triggers multiple pathologies at the level of individual bee and colony. Left untreated, most managed colonies in temperate zones die within 3–4 years of initial infestation [V. destructor also provides a good system for testing a rare allele model because A. mellifera has relatively few resistance alleles for it.The mite estation . Being aVarroa. In this study we created three classes of honey bee queens with exaggerated ranges of polyandry by instrumentally inseminating each with semen from 15, 30, or 60 drones. We measured colonies headed by these queens for various fitness indicators including presence of V. destructor. Our purpose was to identify lower ranges of effective polyandry in the context of this highly damaging parasite. Moreover, given the ongoing honey bee health crisis, it is important to examine the extent to which benefits of polyandry can be delivered to managed systems.A first step in analyzing the rare allele model is to confirm if high degrees of polyandry do in fact confer benefit against a model parasite such as n below). Because our independent variable was polyandry and not genetic variation per se, we wanted to narrow the range of genetic variation available from our drone pool. We did this each year by identifying 15 pre-existing colonies to serve as drone sources, their number chosen to match the multiples in our experimental design. Queens inseminated with 15 drones received semen from one drone from each of the 15 colonies; queens receiving 30 drones got two drones per source colony, and queens receiving 60 drones got four drones per source colony. Queens were instrumentally inseminated with standard methods [This experiment was conducted in the summers of 2012 and 2013 at research apiaries maintained by Fera . Each year, sister virgin queens were reared from one common mother and emerged and maintained individually in polystyrene mating nuclei  during and after the insemination procedure. Each virgin queen was instrumentally inseminated with semen of 15 drones, 30 drones, or 60 drones . Polyan methods ; admixtuca 1 kg adult bees, three frames of brood of mixed ages, and two combs of honey. Colonies were established in single brood-chamber British national hives, fed supplemental sugar solution and protein as necessary, and provisioned with honey supers as needed to accommodate incoming nectar collected by foragers. Each colony was assigned one of the three experimental treatments: (1) queen inseminated with semen of 15 drones, (2) 30 drones, or (3) 60 drones. After inseminated queens were confirmed as egg-layers in their respective nuclei, each was caged, placed into one of the full-sized field colonies, and released after 1–3 days. After all queens were released, all colonies were maintained for another 6 weeks to ensure adequate queen egg-laying performance and to allow time for incipient worker bee populations (of uncontrolled genetic backgrounds) to die off. This resulted in beginning treatment replicate numbers of (1) 15 drones: n = 62012 and 52013, (2) 30 drones: n = 82012 and 52013, or (3) 60 drones: n = 92012 and 42013.Each year one dedicated apiary was established, each colony of which comprised of Varroa mites. For colonies set up in 2012, samplings were made on 25 Jul 2012, 13 Aug 2012, 30 Aug 2012, 26 Sep 2012, 1 Nov 2012, 3 Dec 2012, 7 Jan 2013, 5 Feb 2013, 27 Mar 2013, 2 Apr 2013, 26 Apr 2013, 5 Jun 2013, and 20 Jun 2013. For colonies set up in 2013, samplings were made on 14 Aug, 27 Aug, and 4 Sep. It was not possible to measure every dependent variable on every sampling date.Each year after week 6 we sampled surviving colonies to determine worker bee population, brood production, daily comb construction, and presence of 2 by the observation that one deep British National comb (both sides) = 1663 cm2.Worker bee population and brood production were derived by visually summing proportions of whole deep frames covered by workers or brood , convert2 per day) was determined during periods of nectar flow following the methods of Matilla and Seeley [Comb construction (cmd Seeley . A honeyBrood production and comb construction were determined relative to worker bee population per 100 bees) because adjusting strength variables to bee population gives insight into the relative efficiency with which collective tasks in the colony are undertaken; we predict that increasing polyandry improves efficiency of task allocation among subfamilies 00 bees b.Varroa mites was determined by collecting a sample of ca 300 adult bees (and their phoretic mites) from the middle brood combs of each colony, agitating the sample in alcohol, straining it, and counting the number of bees and mites [The presence of nd mites . Mite saVarroa mite data, we ran a non-parametric test because of a large number of zeros. Data were summarized across all observation points and classified as either containing mites or not. The proportions of observations positive for mites were then compared across the three treatments using a chi-square permutation test. All analyses were carried out using GenStat 17.1 [Brood production and daily comb construction were analysed by the Restricted Maximum Likelihood method (REML) to account for repeated measures on colonies as well as unequal replication among treatments. In this case, the random terms of the model (colony and time point) were used to define a uniform correlation across all time points within each colony. Treatment (level of polyandry) was the fixed effect. When treatment was found to have a significant effect, we separated individual treatments with pairwise comparisons and means separating groups. Predicted treatment means and 95% confidence intervals are presented. In the case of the tat 17.1 .2) produced per 100 bees was significantly higher in colonies with queens inseminated with 30 or 60 drones than in colonies with queens inseminated with 15 drones . No effect of insemination treatment was detected for cm2 comb constructed per 100 bees per day . The chi-square permutation test .n≥30 males) that confer colony-level benefit and for showing a benefit of polyandry in particular toward the parasitic mite V. destructor. By expanding the upper range of known queen mating number associated with colony benefits, our results underscore the importance of understanding how natural selection can reward polyandry at levels significantly higher than the species average. We believe two models are plausible starting points:Our results are unique for identifying the highest levels of polyandry yet detected  in Introduction). Indeed, in those cases documenting natural evolution of involved ,32. To tVarroa for which there are few resistance alleles. The fact that Varroa resistance in A. mellifera is under partial genetic control is well-documented , but benz et al , reducednz et al , and beenz et al . Varroa nce 1992 , furtherVarroa levels. When examining 30 colonies in Germany whose queens were naturally mated with a range of 10–28 males, Neumann and Moritz [Varroa mite levels and queen mating number. In New York state, Tarpy et al [Varroa) colonies in the Arnot Forest (mean mating frequency 17.4 ± 9.8) and queens from two nearest managed apiaries (20.8 ± 5.9 or 16.0 ± 6.4). From the vantage of an uncomplicated GV model, neither study supports the expectation that polyandry increases in response to a virulent parasite such as Varroa. Indeed, Tarpy et al [sensu stricto). Indeed, the model seems most promising for explaining polyandry at the extreme highs of known distributions. Our experiment shows benefit at these high ranges. Perhaps we need to rethink what constitutes hyperpolyandry.Two field studies give valuable insight into the association of hyperpolyandry and d Moritz  failed tpy et al  found nopy et al  concludesensu stricto), her degree of polyandry, and presence or absence of beneficial alleles.A direct test of the rare allele model would require the disambiguation and control of three potentially interacting factors: the genetic variation represented in the queen’s spermatheca (GV A. mellifera around the world. The pursuit of genetic host resistance, traditionally focused on targeted character-based selection programs, is championed as the best sustainable solution to the non-natural Varroa / A. mellifera relationship [Finally, it cannot be overlooked that polyandry may have an expanding role to play in mitigating health problems facing managed tionship . But it


To characterize the genetic variation of alternative splicing, we develop GLiMMPS, arobust statistical method for detecting splicing quantitative trait loci (sQTLs) fromRNA-seq data. GLiMMPS takes into account the individual variation in sequencingcoverage and the noise prevalent in RNA-seq data. Analyses of simulated and realRNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models.Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validationrate of 100%. As population-scale RNA-seq studies become increasingly affordable andpopular, GLiMMPS provides a useful tool for elucidating the genetic variation ofalternative splicing in humans and model organisms. Alternative splicing (AS) is the process by which exons from precursor mRNA transcriptsare differentially included during splicing, resulting in different mature mRNA isoformsfrom a single gene locus . AS is acis regulatory elements and trans acting factors 17]. Bothψ). Given that wehave an observed number of splice junction reads for one isoform (y) andtotal splice junction read counts (n), then n<5) in the population, and obtained 18,267 AS events from CEU and 7,747 ASevents from CEU2 for the downstream sQTL analysis.To search for sQTLs, we first identified all alternative splicing events and RNA-seqreads mapped to splice junctions using the MATS pipeline as described previously . We thencis SNPs within 200kb upstream or downstream of the target exon splice sites when searching for sQTLs.For the CEU2 dataset, the 60 individuals were all included in the 1000 Genomesproject. Fifty-eight of them were sequenced in low coverage and two were in highcoverage. To avoid genotype calling bias, we only included the 58 low-coverageindividuals with genotypes taken directly from the 1000 Genomes project data (10/2010release). The same MAF filtering was used as in the CEU dataset.The genotype data for the 41 individuals in the CEU dataset were taken from thelatest HapMap3 release (#28). Of these 41 individuals, 23 were also genotyped in the1000 Genomes project . For SNPψ as a random effect foreach individual i in the regression model with random effects,j βdenoting the fixed effectfor SNP j, the second level of the model can be written as:β,All statistical analyses were done in the R statistical environment . We evalN. Let uijPvalues for the fixed effect j βfor each SNPj.where P values. The details arein Supplementary Methods in Additional file For both the CEU and CEU2 datasets, using each of the statistical models  mentioned above, we carried out the analysis for each exon with SNPswithin 200 kb of the exon. To estimate the false discovery rate, we used the samepermutation approach as in  to obtaiP values and those with P value <0.05 were called asvalidated. All RT-PCR primer sequences are listed in Additional file To validate the sQTLs found in the CEU datasets, we randomly selected 26 significantsQTL exons (FDR ≤0.1) for RT-PCR validation. We performed the validationexperiments on an independent panel of 86 lymphoblastoid cell lines from the HapMap3project  for our minigene splicing reporter assays.Genomic DNAs were extracted from LCLs using UltraClean™ Tissue&Cells DNAIsolation kit . The target exon and itsflanking 350-500 bp intronic regions were amplified by PCR  [2>0.8 inthe CEU population and within 200 kb window of the GWAS SNP). Because of the high SNPdensity and high recombination rate around the MHC region, we excluded genes fromthis region in this part of the analysis. We then identified sQTL signal SNPsoverlapped with this expanded list of GWAS linked SNPs.We obtained 7,523 GWAS SNPs at genome-wide significance level of 30/2012) . Using aThe GLiMMPS model has been implemented and released in an easy to use package. Thesplice junction read counts, genotypes, and validation datasets, as well as thesource code used for sQTL processing and analysis are provided at the companionwebsite of this article .A3SS, alternative 3' splice site; A5SS, alternative 5' splice site; AS, alternativesplicing; CEU, Utah residents of European descent from CEPH-Centre d'Etude duPolymporphisme Humain; CV, coefficient of variation; GLiMMPS, Generalized Linear MixedModel Prediction of sQTL; glm, generalized linear model; lm, linear model; LCL,lymphoblastoid B cell line; MAF, minor allele frequency; MXE, mutually exclusive exons;QTL, Quantitative Trait Loci; RNA-seq, RNA sequencing; ROC, receiver operatingcharacteristic; SE, skipped exon; SNP, single-nucleotide polymorphism; sQTL, splicingQuantitative Trait Loci; SS, splice site.All authors declare that they have no competing interests.KZ and YX designed the project; KZ developed the algorithm; KZ, ZXL, and JWP carried outall the experiments; KZ, QZ, and YX wrote the manuscript. All authors have read andapproved the final manuscript.Supplementary Methods and Supplementary Figures S1-S9.Click here for fileSupplementary table S1. sQTL exons (FDR ≤0.1) and information ofthe most proximal sQTL SNPs.Click here for fileSupplementary table S2. HapMap3 samples used for RT-PCR validation ofsQTLs.Click here for fileSupplementary table S3. Primers and RT-PCR results for validation ofsQTLs.Click here for fileSupplementary table S4. The individual exon inclusion levels for RT-PCRvalidation of sQTLs.Click here for fileSupplementary table S5. Primers used for constructing minigene splicingreporters.Click here for file


Oncorhynchus mykiss) exhibit remarkable life history diversity throughout their native range, and among the most evident is variation in migratory propensity. Although some populations and ecotypes will remain resident in freshwater habitats throughout their life history, others have the ability to undertake tremendous marine migrations. Those that migrate undergo a suite of behavioral, morphological, and physiological adaptations in a process called smoltification. We describe a quantitative genetic analysis of 22 growth, size, and morphological traits in addition to overall life history classification (resident or migrant) over the temporal process of smoltification in a large multi-generation experimental pedigree  of migratory and resident rainbow trout derived from a wild population, which naturally segregates for migratory propensity. We identify significant additive genetic variance and covariance among the suite of traits that make up a component of the migratory syndrome in this species. Additionally, we identify high heritability estimates for the life history classifications and observe a strong negative genetic correlation between the migratory and resident life history trajectories. Given the large heritability estimates of all of the traits that segregate between migratory and resident rainbow trout, we conclude that these traits can respond to selection. However, given the high degree of genetic correlation between these traits, they do not evolve in isolation, but rather as a suite of coordinated characters in a predictable manner.Rainbow trout ( Migration is a complex life history composed of physiological, morphological, and behavioral traits working in synchrony with environmental cues to move animals over great distances in what has been termed a migratory syndrome . Traits Morphological characteristics play a critical role in the ability of an individual to successfully migrate. Several studies have identified morphological variation between resident and migratory forms of animals, including variation in wing morphology of birds and insects , the strAlthough life history and morphological variation are pervasive between all manner of migratory and resident forms of animals, this variation is perhaps best appreciated among the salmonid  family of fishes, wherein great variation exists in the propensity, timing, duration, and distance of their marine migrations . Two genThe morphological changes that take place during the smoltification process in salmonids include changes in body shape, growth rates, body condition, and coloration . Smolts Oncorhynchus mykiss) (Oncorhynchus tshawytscha). Moreover, anthropogenic and climate-induced selection may be tremendous driving forces in shaping individual traits and behaviors . Spawning efforts to produce the second generation included the creation of full and half-sib families derived from crosses of presumably unrelated anadromous females to anadromous males (A×A), anadromous females to resident males (A×R), resident females to anadromous males (R×A), and resident females to resident males (R×R). F1 offspring were raised in captivity in freshwater as described by 1 generation, all fish that were categorized as residents were reared in freshwater, whereas those fish deemed to be anadromous smolts were transferred to saltwater net pens. In 2004, unrelated mature fish from the captive F1 generation were bred to yield F2 generation fish. In addition to the crosses outlined above , two additional cross-types were made where an A×R steelhead female was crossed to an A×R steelhead male (AR×AR) and an R×A steelhead female was crossed to an R×A steelhead male (RA×RA). Thus, our pedigree consisted of three generations: the original wild-caught P1 adults used to produce the initial crosses; the F1 progeny derived from the P1 and raised in captivity to adulthood; and the F2 progeny derived from crosses of unrelated F1 parents also raised in captivity. Juvenile phenotypic traits were sampled from F1 and F2 progeny, as described below.The pedigree used in this study is derived from experimental crosses within and between anadromous steelhead and resident rainbow trout from the Sashin Creek watershed in southeastern Alaska , which is described extensively elsewhere  was scored as a categorical trait with four levels, the first being precocious mature resident rainbow trout (“mature”) identified by the expression of gametes (sperm or eggs) with the application of gentle pressure to the abdomen. The second classification was for immature resident rainbow trout parr (“parr”), which were identified by an overall dark and colorful body with visible parr marks along the lateral line, reduced fork length relative to the other life history classifications, and failure to express gametes at the time of sampling. Migratory smolts (“smolt”) were the third classification and were identified by having a more streamlined body form  with sil1 and F2 generation fish at 12 (“mo12Length” and “mo12Weight”), 15 (“mo15Length” and “mo15Weight”), and 24 (“mo24Length” and “mo24Weight”) months after fertilization. Body condition factor  is a measure of the contribution of body length to weight, and is a general measure of fish health and condition. Smolting juvenile salmonids have been shown to have a lower body condition when compared to non-smolts (3) *100,000], where W is the body weight (g) and L is fork length (mm) at a given time point (2) − ln(L1)]/[t2 − t1] × 100, where L1 and L2 are lengths (mm) or weights (g) at the first  and second  time points in the interval being calculated. Length, weight, body condition factor, and instantaneous growth rates were calculated for both F1 and F2 generations.Fork length (mm) and body weight (g) were recorded for Fn-smolts . Body co2 generation fish including an F2 family used for a QTL analysis as described by Perhaps the most striking difference between mature and resident parr and marine adapted smolts can be observed in the level of silvering or reflectance in the skin. To quantify skin reflectance, we measured the average white pixel intensity (“AvgPix”) of a defined region behind the pectoral fin, below the lateral line, and anterior to the dorsal fin  in a sub2 generation fish including an F2 family used for a QTL analysis, as described by Results for details). Values for these measures were small, so each relative warp score was multiplied by 1000 for analyses and presentation purposes. In addition to measures of shape variation, this analysis yielded a measure of the overall body size of the fish in the form of a centroid size (“CentroidSize”), which is the square root of the sum of the squared distances from each point to the shape centroid and was calculated at 24 months after fertilization. To quantify variation in body shape we used a morphometric analysis of 13 landmarks along the left side of a subset of 2057 FAll statistical analyses were conducted using packages in the R statistical computing environment developed by the R Core t-tests between the means for each life history class were estimated using a Tukey-Kramer adjustment for the multiple comparisons. Phenotypic correlations were conducted using a Pearson’s coefficient for quantitative trait correlations or a Spearman’s coefficient for correlations with the binary life history classifications. Discriminant function analyses (DFA) were conducted using the R package MASS  and the traits mo12Length, mo12Weight, mo12Kfact, mo15Length, mo15Weight, mo15Kfact, IGRL1, IGRW1, mo24Length, mo24Weight, mo24Kfact, IGRL2, and IGRW2 to predict the four class categorical life history assignment . The second DFA was conducted using the same predictive traits as the first, but it was conducted only on F2 samples (n = 8925) to determine whether environmental or year effects contributed to the prediction. A third DFA was conducted using all of the predictive traits from the first two DFAs, but it also included the traits AvgPix, CentroidSize, and RelW2–RelW6 and was only conducted in the F2 samples where these additional morphological characters were collected (n = 1882) to test the additional predictive power of the morphological traits.To test for significant differences in trait means between life history classes and to determine the overall significance of life history classification on each trait, a single factor ANOVA was conducted. Pairwise age MASS  in threey is the phenotypic value of individual i, u is the population mean value, a is the breeding value of individual i, and e is a vector of residual errors (reviewed in P) of each trait can then be described as VP = VA + VR, where VA is the additive genetic variance and VR is the residual variance, which consists of environmental variance unaccounted for by additional fixed or random effects, nonadditive genetic variance, and error variance (2h) is the ratio of the additive genetic variance to the total phenotypic variance: 2h = VA/VP (rA) among traits were estimated in the same framework of the animal model using a bivariate analysis of two traits (1 and 2) at one time to generate genetic covariance estimates (rA) were calculated as:i.e., all R×R offspring were pooled into one raceway, all A×A offspring were pooled into one raceway within a given year). In models for relative warps (RelW2–RelW6), CentroidSize was included as a covariate to account for allometric variation in body morphology, so that shape and size could be investigated in separate models. Random effects included a “family” term (f), which accounts for the rearing tank environment (micro or Vf) of each family from fertilization until 12 months after fertilization before they were combined into tanks based on their cross-type. Additional random effects included maternal environment (m or Dam) and paternal environment (s or Sire) effects and the random effect of animal (a).Variance components used to also estimate trait heritabilities were calculated using a mixed model called an animal model (reviewed in variance . The nar = VA/VP . Genetic2 distribution with 1 degree of freedom. To test the hypothesis that the additive genetic variance of each trait was significantly different from zero (Va >0), LRT was performed where the log-likelihood of a full model with random animal effect  was compared to that of a reduced model without the random animal effect as outlined above. Genetic correlations were determined to be significant by comparing the log-likelihoods of full models with genetic covariance unconstrained to those of a reduced model where the genetic covariance was constrained to be 0.Animal models for continuous traits  and genetic correlations between them were modeled in a residual maximum likelihood (REML) framework in the software program ASReml v3.0 (2h = VA/ [VP + (π2/3)], as described in the course notes for the program MCMCglmm  approach where the posterior distribution was sampled using a Gibbs sampler as implemented in the R package MCMCglmm . When usMCMCglmm .1 founders, 6593 F1, and 9473 F2 samples. The F1 generation was derived from 42 dams and 31 sires and partitioned into 75 full-sib and half-sib families with an average family size of 88 offspring. The F2 generation was derived from 41 dams and 53 sires and partitioned into 69 full-sib and half-sib families with an average of 137 offspring per family. Families were partitioned further by cross-type, with the A×A cross-type broken down into 38 families (3804 offspring) across both generations, A×R broken down into 25 families (2779 offspring) across both generations, R×R broken down into 29 families (2709 offspring) across both generations, R×A broken down into 32 families (3108 offspring) across both generations, AR×AR broken down into 10 families (1800 offspring) in the F2 generation, and RA×RA broken down into 10 families (1866 offspring) in the F2 generation.In total, 16,139 samples across three generations were used in quantitative genetic analyses, including 73 P1 generation as 60%  and the average percent in the F2 generation as 78%   and the average percent in the F2 generation as 12%   of the total juveniles categorized in this group. Indeterminate juveniles represented approximately 4% of the total (n = 614), whereas immature resident parr represented 11.7% (n = 1837) and mature fish represented 12.7% (n = 1993) of the total. The overall average percent of offspring per family classified as smolts at age 24 months after fertilization was 68% , with the average percent in the Fmax 98%) . The ovemax 41%) .1 and F2 generations and are summarized in P < 0.0001), with t-tests showing significant differences between the classes as summarized in Supporting Information, Table S1. In general, throughout the three sampling time points smolts had the longest body lengths and largest weights, whereas resident parr displayed the shortest lengths and lowest weights (Table S1). Condition factor, however, was greatest in mature resident fish at all three time points, with resident parr displaying the lowest condition factors at the first two points and smolts displaying the lowest condition factor at the final time point (Table S1). Differences in mean growth rates were also significant between the life history classes, with smolts having the greatest growth rate in length between 12 and 15 months after fertilization and resident parr having the lowest. During the second interval between 15 and 24 months after fertilization, resident parr had the greatest mean growth rate in length, whereas resident mature had the lowest  with mean values significantly different between the life history classes, smolts having the largest mean CentroidSize, and resident parr having the smallest (Table S1).Body morphology was analyzed in a subset of 1985 F2 = 0.03 ± 0.02, P = 0.05), it was removed from further analysis and discussion because the shape variation predominantly consisted of what was considered to be an unnatural morphology. The heritability of RelW1 likely reflects true heritable shape variation captured in this warp, but that cannot be dissected from the variation correlated with the bending of the tail. The remaining relative warps 2–6 collectively explained 44% of the total body shape variation, with each explaining 21.06–3.89% of the variation. When comparing the extreme positive and negative values of a warp with the consensus shape, variation in RelW2–RelW6 can largely be described as variation in dorsal-ventral body depth , caudal peduncle length and depth (landmarks 4–11), and head morphology   explained 80% of the total variation in body shape and were considered for further analysis. The first relative warp (RelW1) explained the most variation in shape (36.69%) but corresponded to extreme sagging of the caudal fin and peduncle, which is believed to be largely an artifact of anesthesia (see  and 13) . DetailsP < 0.0001) of the variation in RelW2, 3, 5, and 6, but not for RelW4 (Table S1). RelW2 explained 21.1% of the total shape variation and is explained by variation in the length and depth of the caudal peduncle, dorsal-ventral body depth, and head depth (Table S1). RelW3 explained 8.1% of total shape variation and encompasses variation in anterior body length, dorsal-ventral body depth, and length of the caudal peduncle (Table S1). RelW4 explained 5.8% of the total shape variation in dorsal-ventral body depth, dorsal fin base length, snout shape, and caudal peduncle length (Table S1). RelW5 explained 5.2% of the total shape variation and includes variation in anterior body length, lower snout morphology, and caudal peduncle length, with the most positive values indicating longer anterior bodies, shorter lower snout morphology, and deeper caudal peduncles, whereas the negative values of this warp indicate shorter anterior bodies, longer lower snout morphology, and shallower and more pointed caudal peduncles (Table S1). RelW6 explained 3.9% of the total shape variation and involves variation in head morphology, caudal peduncle length, and ventral body length in the anterior end. Positive values of this warp indicate shorter head morphology, longer ventral features, and a narrow and pointy caudal peduncle, whereas negative values represent longer head morphology, shorter ventral features, and a broader caudal peduncle (Table S1).Life history classification explained a significant proportion (ad depth . Positivpeduncle . Positive length . There weduncles . The mospeduncle . The mosTable S1).Silvering and reflectance in the skin is one of the most prominent morphological changes that occur in smolts and was measured by quantifying the average white pixel intensity of a defined region on the left lateral side of the body  in 2057 1 and F2 generations and all of the size and growth-related traits . This model was able to properly assign 87.0% of individuals to their life history classification. The second model included the same traits as the first, but on a reduced dataset, which only included 8925 F2 individuals. This model properly assigned 89.5% of the individuals to their life history class and demonstrates that the ability to assign life history in both generations based on the size and growth-related phenotypes was approximately the same, although the DFA based only on the F2 dataset was slightly more accurate. The third model included the size and growth traits from the first two models, but also incorporated all of the morphological traits  and was run on 1882 F2 individuals that had complete observations for each of the traits. This model correctly assigned 90.2% of the individuals to their life history class and indicates that the addition of the morphological data did not detectably improve our ability to classify individuals into life history classes (89.5% vs. 90.2%) in the F2. These results also suggest that the phenotypic traits measured herein can place an individual in its life history classification with a high degree of accuracy, and that a large part of the phenotypic differentiation among the life history types has been captured by measuring these traits.The DFA were used to determine the predictive power of the individual phenotypic traits for estimating the life history classification of an individual and were run using three subsets of the data. The first dataset included 15,471 individuals from both FTable S2). This pattern reflects the complex nature of this migratory syndrome with many correlations between several different size, growth, and morphological related traits. Among the strongest correlations with the LHSmolt binary life history classification was a negative correlation with body condition factor at month 24 , indicating that smolts were slimmer for their length than non-smolts. Not surprisingly, LHSmolt also had a strong positive correlation with large values of skin reflectance , indicating that smolts have more reflective skin with higher white pixel intensity levels than the other classes. Smolts also had a strong negative correlation with RelW2 , which explains variation in posterior body length and dorsal-ventral body depth, negative values of which capture more slender fusiform body shapes expected in migratory fish. Phenotypic correlations with LHMature are generally the opposite of those with LHSmolt in sign, which would be expected because the phenotypic traits measured here were intended to capture differences between resident rainbow trout and migratory steelhead smolts.Phenotypic correlations were measured between the 20 continuous and the two binary traits. Correlations between these 22 traits ranged from being strongly negative (max −0.76) to strongly positive (max 0.96), with most correlations being moderate in size. In total, 192 significant correlations were detected from 231 off-diagonal correlations  of any of the quantitative traits, whereas body weight at month 12 (mo12Weight) had the lowest significant estimate of heritability . Significant estimates of fixed and random effects varied by phenotype and generally were conditioned on a fixed generation effect, a fixed cross-type effect, and a random effect of micro , and some of the relative warps were conditioned on a fixed effect of CentroidSize , and none of the traits explored here showed evidence of a paternal environmental effect. Cross-type was included as a fixed effect in animal models to condition on the effects of the experimental mating design used to produce the offspring, which might also include environmental variation experienced by both parents and offspring of each cross-type. Including cross-type as a fixed effect may control for some of the additive genetic variation in our models resulting in a downward estimate of heritability, but this was ultimately a more conservative approach than not controlling for the potentially confounding effects. Analyses for a subset of traits suggest a minor increase in heritability for models in which cross-type was purposely excluded from animal models relative to a model in which it was included as a fixed effect (data not shown).Heritability was measured in 22 traits, including 20 quantitative traits and two binary life history classifications. Significant heritability estimates for size and growth-related traits  ranged from 0.1 to 0.62, whereas those for morphological traits  ranged from 0.25 to 0.52  using throidSize . The onl2 = Vm/Vp = 0.163 (0.078 – 0.28)], which is consistent with the estimates from the REML models . In total, 91 significant genetic correlations were detected from 231 off-diagonal correlations, including 64 positive and 27 negative correlations. The binary trait LHSmolt had strong positive genetic correlations (rG > 0.5) with the traits mo12Length, mo24Length, mo24Weight, IGRL2, AvgPix, and RelW6, but had strong negative genetic correlations (rG < −0.5) with the traits mo12Kfact, mo15Kfact, mo24Kfact, RelW2, RelW3, and RelW5. Conversely, the binary trait LHMature had strong positive genetic correlations (rG > 0.5) with the traits mo12Kfact, mo15Kfact, mo24Kfact, RelW2, RelW3, and RelW5, but strong negative genetic correlations (rG < −0.5) with the traits mo24Length, IGRL2, IGRW2, and AvgPix. In total, the binary trait LHSmolt had 18 significant genetic correlations, lacking only correlations with the traits mo12Weight, mo15Weight, and RelW3, whereas LHMature had 12 significant genetic correlations. The trait with the fewest significant genetic correlations was mo12Weight, having only genetic correlations with mo12Length and RelW4. Considering all genetic correlations as a whole provides insight into the complexity of this life history, wherein size, growth, and morphological traits are strongly genetically correlated with one another, and with the propensity to migrate. A complete dataset including pedigree and family identification and quantitative traits is provided in Table S3.Genetic correlations were measured between 22 traits, including 20 continuous traits and two binary life history classifications in two statistical frameworks, REML and Bayesian, respectively. Genetic correlations between the 22 continuous traits ranged from being strongly negative (max −0.99) to strongly positive (max 0.98), with most correlations being moderate in size , which supports the hypothesis that there is a strongly canalized trade-off during development between early maturation and smoltification (1 families from this same study population (2 = 0.16–0.37) and weight (h2 = 0.1–0.21), which are similar but lower than the median of a meta-analysis of the same traits across salmonid fishes , a related salmonid species (Table S1), suggesting that the developmental decision to smolt or remain resident may have occurred sometime within the first year of growth in this experimental population, and time points earlier than 12 months after fertilization are likely critical to the development of a life history tactic. It has been shown that high levels of growth within the first year of rearing led to an increase in precocious male maturation in Chinook salmon  for the binary migratory life history (LHSmolt) and 0.51 (0.335–0.72) for precocious maturation (LHMature). Although caution is advised in comparing heritability estimates between species and between wild and experimental populations given that heritability estimates are a component of the population and environment studied , our resfication . Given td fishes . Body led fishes . A stronMuch like the traits of body size and condition, growth rates in length and weight also harbor substantial additive genetic variance. Moreover, the positive genetic correlations between growth rates over the course of this study and the smolt life history type, and the negative correlations with growth and the precocious mature life history, suggest not only that smolts experience more rapid growth than mature fish over their second year of development but also that the genetic mechanisms associated with growth in length and weight are key components of life history trajectory. Life history classification at 24 months after fertilization is highly genetically correlated with characters at early time points in development, and we see segregation in size and growth traits between the life history classes at our earliest sampling periods at age 1. This suggests that the proximate genetic mechanisms underlying life history divergence might already be put in motion as early as 12 months after fertilization. This idea was proposed by 1 generation (2 = 0.29 ± 0.07) but strongly positively correlated  with the smolt life history, and strongly negatively correlated  with the mature life history, almost to the point of unity. This is not unexpected because the qualitative assignment of fish to individual life history categories is heavily influenced by subjective judgment of body coloration and morphology. In the case of morphology, skin reflectance is so dramatically different between resident and migratory life history types at 24 months after fertilization that this character likely contributes the most to the overall qualitative morphological classification. Given the moderate heritability and the strong genetic correlation with life history tactic, selection on skin reflectance in either the freshwater or the marine environment could consequently cause a response in life history decision.Although many of the quantitative genetic parameters estimated here have previously been explored, albeit in a reduced pedigree from the F2 = 0.31 ± 0.08) and has a positive genetic correlation with the smolt life history. This trait represents an overall measure of complete body size, and thus it is not surprising that it is also highly genetically correlated with lengths and weights at 24 months after fertilization. Measures of body shape variation captured in relative warps are all found to have moderate levels of heritability (h2 = 0.25–0.52) and fall within the range of heritability estimates previously reported for morphometric variance in salmonids . In this analysis, several genetic regions associated with multiple QTL were identified, although of particular interest were regions on rainbow trout chromosomes Omy12 and Omy14, which had QTL for more than two traits. Additionally, in a population genomic screen of thousands of markers in wild steelhead and rainbow trout from Sashin Creek, rG > 0.3) between 13 of the 21 pairwise correlations of those traits. On chromosome Omy14, the traits IGRL1, IGRW1, mo15Kfact, mo24Kfact, RelW3, and RelW4 all localize to the same region with overlapping QTL support intervals. Here, we find support for moderate to strong positive genetic correlations (rG = 0.32–0.95) for 5 of the 15 pairwise correlations. Although it is difficult to know whether the genetic correlations and overlapping QTL represent the pleiotropic effect or close linkage of genes . This threshold trait expressed considerable additive genetic variance as well as some appreciable phenotypic and genetic covariance with size and growth, a result consistent with the conclusions of


Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach. The broad sense heritability H2 is defined by the proportion of phenotypic variance due to all genetic variation.Identifying genetic variation that underlies complex phenotypes has important implications for genetics and biology , 2. The Many complex phenotypes comprise a variety of low level traits (or phenotypic features) that are often highly variable. Association analysis of such a complex phenotype is impeded by this phenotypic heterogeneity . For exaΣa, the variance due to additive genetic effects taking into account the relationships of individuals (family structure); and Σ, the covariance matrix due to effects other than additive genetic effects. If there are d low level traits in x, this means that two d-by-d matrices need to be estimated from the sample. Once the two covariance matrices are computed, a generalized eigenproblem is solved to identify the combination coefficients w so that the ratio of w⊤Σaw/w⊤Σw is maximized, leading to high heritability for the combined trait w⊤x.Heritable component analysis methods identify principal components of the data, i.e., linear combinations of low level traits, that are heritable –16. All d = 20 low level traits are used, this method can run for days, and as observed in our experiments, the method may not converge. It requires very large sample to obtain reliable estimates of two covariance matrices and d combination coefficients, totally 2d2 + d parameters, from a sample.A few methods have been developed in the literature to estimate the two covariance matrices. In , 15, thesequential quadratic programming algorithm is developed to optimize the problem. We then extend the basic formulation to correct fixed effects of covariates in the component analysis. Because we do not estimate any covariance matrix, our approach is computationally much more efficient than those in ⊤. Note that μi is a vector of length of the number of family members with corresponding entries equal to either μm or μf depending on the gender of the family member. We can simplify Hi is defined byi. For males in the family, −1 is assigned at their corresponding entries in i has three members included in a study, and they are ordered as a male member, a female member and then another male member. The vector Hi matrices of different families in columns, we get another matrix H. Similarly, we can form a matrix X, so the trait values of all subjects y = X⊤w. The sample variance of the trait y is, by definition, (1/n)(y − μ)⊤(y − μ). It is equal to (1/n)(X⊤w − μ)⊤(X⊤w − μ) = (1/n)β⊤HH⊤β. Then, the condition of β: (1/n)β⊤HH⊤β = 1, which can be rewritten as β⊤HH⊤β − n = 0.We then consolidate the parameters μm and μf are not free parameters, as they are determined once w is determined. They are equal to the sample means of the trait, i.e., Mean(x⊤w), for male and female, respectively. Let xm and xf be the two means of the data vector x respectively over male and female samples. Then, β state that As a matter of fact, Imposing all of these constraints on y = x⊤w has low validation heritability despite a high training heritability. To enhance the generalizability of the derived model to new samples, a regularization condition on w, R(w), is required to control the complexity of the model. The objective function in λ is a pre-specified tuning parameter for balancing the two terms in the objective function, and R(w) can be realized in different forms and be application-specific. For example, R(w) can be implemented with the ℓ1 vector norm: w as shown in the Least Absolute Shrinkage and Selection Operator (LASSO) method ⊤, and replace Hi byHiβ = Kiγ.The objective function in Ki’s in columns leads to the full matrix K. The quadratic constraint in γ⊤KK⊤γ − n = 0. By setting u ≥ 0 and v ≥ 0. We hence design a matrix J = d2, [0]d2] where Id × 2d2 is the identity matrix of dimension 2d × 2d, and [0]d2 is a column vector of all zero entries with length of 2d. Then, the bound constraints can be written as Jγ ≥ 0. Overall, with the new variables u and v, e = 2d + 3 is the total number of constraints in the problem.Stacking all w = u − v. Note that the regularization condition in uj = 0 or vj = 0 for the jth feature because otherwise they would not be optimal. If both uj > 0 and vj > 0 and assume uj ≥ vj, then we have another solution,  because the first term of f remains the same whereas the second term of f is reduced by 2vj. Thus, at optimality, the regularizer of It can be proved mathematically that the optimal solution of g1, we can solve it efficiently by the framework of sequential quadratic programming (SQP)  with a step size of 1. For each method, the choice of λ that gave the best cross validated heritability was used in the subsequent analysis.The three previous methods evaluated in our comparison all used a regularization condition in their eigenproblem, so they also had a tuning parameter y1, all methods did not use covariate data as the trait was not simulated with fixed effects. In the experiments with the trait y2, because Ott  in the analysis.All subjects were reported to have used cocaine in their lifetime, and were assessed on the following 13 features of cocaine use and related behaviors:http://www.1000genomes.org). Both subjects and SNPs were undergone stringent quality control . Genotypes for additional 37,427,733 SNPs were imputed using IMPUTE2 [λ, which was subsequently used to find a linearly combined trait from the 13 features based on the entire SSADDA data. The heritability of the derived trait was estimated and compared to that of individual quantitative features in the data, including the cocaine symptom count (F8). The feature F8 was recognized as a better trait than the binary trait induced by the diagnosis of cocaine dependence in a recent genomewide association study (GWAS) [p < 5 × 10−6. We then computed the derived trait for the subjects in the replication SAGE sample. The markers identified from the SSADDA data were tested using the replication subjects. All tests included age, sex and the first three ancestral principal components as covariates. The association test results on discovery and replication data were combined by performing meta analysis using Metal [We derived a multivariate trait based on the 13 features of cocaine use and related behaviors. This trait was derived from the SSADDA data by Algorithm 1 with a correction for the fixed effects of age and race. Three-fold cross validation was performed to find the optimal y (GWAS) . We compng Metal . Genomewλ varied from 1 to 50 with a step size 1. When λ = 2, we observed the highest heritability on average in the cross validation. We hence used λ = 2 in Algorithm 1, and derived a linear combination of the features from the entire SSADDA data. The heritability of the derived trait and all individual quantitative features was estimated using SOLAR and reported in 1 vector norm created shrinkage effects on our model. In other words, our approach selected parsimonious features to use in the linear combination. age when first used cocaine received the largest positive weight and therefore had the strongest impact on the derived trait. The other four important features were F11—age onset of DSM4 CD diagnosis, F4—persistent desire or unsuccessful efforts to cut down or control cocaine use, F5—great amount of time spent in activities necessary to obtain, use or recover from the effects of cocaine, and F3—using cocaine in larger amounts or over longer period than intended. Features F6, F1 and F2 had some but limited effect on the derived trait.Using a regularization condition based on the sparsity-favoring ℓp-value threshold (5 × 10−6) in the genomewide association tests with the discovery sample. These SNPs are listed in −8 is an appropriate p-value threshold for use in a GWAS that employs imputed SNPs [−8< meta p-value < 5 × 10−6) associated with the derived trait only. In other words, using the standard phenotype in association tests would not discover these SNPs that are associated with a specific subtype (a quantitative subphenotype) of cocaine dependence. The marker rs833936 is located at the TXNIP gene which may act as an oxidative stress mediator when its expression is suppressed by synaptic activity in brain [PTRF gene which has been identified to be associated with cocaine abuse in an early transcriptional change study [EFEMP1 gene has not been reported in the genetic analysis of cocaine dependence. Since all the identified SNP markers have not been thoroughly studied in genetics of cocaine dependence, our findings may promote subsequent investigations for these genes as well as subtypes of cocaine dependence. The proposed heritable component analysis for multivariate phenotypes may provide a new strategy to improve genomewide association studies of complex disorders.We identified three SNPs for the AA population and four SNPs for the EA population that passed our ted SNPs . Based oin brain . Two marge study . The EFEy = x⊤w of lower level traits x by explicitly maximizing its heritability. Specifically, we search for the optimal w that maximizes the likelihood of observing a high value of heritability. This is equivalent to finding the best w, so that the projected trait x⊤w will be best aligned with the kinship matrix Φ of the pedigree. An efficient algorithm based on sequential quadratic programming has been developed to optimize the proposed formulation. The algorithm is extended to allow the correction for covariate effects when deriving a heritable component.In this paper, we have proposed a quadratic optimization formulation that is capable of identifying highly heritable components of complex phenotypes. The multivariate trait is derived as a linear function p = 4.36 × 10−22, std = 0.06), which was much higher than a standard cocaine-use phenotype, e.g., the symptom-count trait, with heritability of 0.41. The subsequent phenotype-genotype association study demonstrated important utility of the derived trait for use in association analysis. Our results show that seven SNPs were significantly or nominally significantly associated with the derived subphenotype, but were not associated with the symptom count phenotype. Two out of the seven associated SNPs reached genome-wide significant level after correction for multi-testing following the procedure in [Our simulation study provides evidence of the effectiveness of the proposed approach as a means to find highly heritable components of multivariate phenotypes. Then a case study on the phenotypes of cocaine use and dependence was conducted. A quantitative trait was identified based on thirteen cocaine use symptoms and behaviors. The trait had a heritability estimate of 0.7 (with edure in .λ. Using a hyper-parameter is common in machine learning algorithms such as support vector machines [λ is not determined by solving the formulation itself and instead needs to be pre-specified. Both our simulation study and our case study showed that our formulation is fairly robust to the value of λ when it is chosen from a reasonably wide range. In real-world applications, hyper-parameters are often determined by a cross-validation process, which was used in our experiments.Our formulation has a hyper-parameter machines . As a hyDiscovering heritable components of a multivariate phenotype can also improve genomic prediction . If a tr2,1 vector norm. Algorithms that can solve the formulations with alternative regularization terms need to be developed. Additional empirical studies across different disciplines are needed to evaluate the capability and effectiveness of the proposed approach.There are limitations of our proposed technique. The non-convex quadratic optimization formulation requires a complex solver, such as sequential quadratic programming. For a sample that contains millions of subjects, it may become computationally prohibitive. More efficient solvers or approximations may be needed to scale up the proposed approach. In some applications, complex grouping structures may exist in the data between different lower level traits. A formulation that takes into account the special data structure may be more useful in producing biologically and clinically meaningful traits. As discussed in the paper, alternative regularization conditions exist, including some that may deal well with complex data structures, such as the one based on ℓ


Electrocardiogram (ECG) measurements are a powerful tool for evaluating cardiac function and are widely used for the diagnosis and prediction of a variety of conditions, including myocardial infarction, cardiac arrhythmias, and sudden cardiac death. Recently, genome-wide association studies (GWASs) identified a large number of genes related to ECG parameter variability, specifically for the QT, QRS, and PR intervals. The aims of this study were to establish the heritability of ECG traits, including indices of left ventricular hypertrophy, and to directly assess the proportion of those heritabilities explained by GWAS variants. These analyses were conducted in a large, Dutch family-based cohort study, the Erasmus Rucphen Family study using variance component methods implemented in the SOLAR  software package. Heritability estimates ranged from 34 % for QRS and Cornell voltage product to 49 % for 12-lead sum. Trait-specific GWAS findings for each trait explained a fraction of their heritability . The inclusion of all ECG-associated single nucleotide polymorphisms explained an additional 6 % of the heritability of PR. In conclusion, this study shows that, although GWAS explain a portion of ECG trait variability, a large amount of heritability remains to be explained. In addition, larger GWAS for PR are likely to detect loci already identified, particularly those observed for QRS and 12-lead sum.The online version of this article (doi:10.1007/s00439-015-1595-9) contains supplementary material, which is available to authorized users. Parameters describing electrical activity in the heart, measured by the electrocardiogram (ECG), are important tools for diagnosing, monitoring, and evaluating risk in patients with cardiovascular disease  and moderate heritabilities for QT/QTc (25–67 %), PR (34–46 %), and QRS (33–43 %)   for ECG phenotypes identified 65 loci harboring both novel and previously described ECG genes, including two loci influencing electrocardiographic indices related to left ventricular hypertrophy  was computed. Blood pressure was measured twice on the right arm in a sitting position after at least 5 min rest, using an automated device . The average of the two measures was used in the analyses. Hypertension was defined through the use of antihypertensive medication and/or through the assessment of blood pressure measurements according to the World Health Organization guidelines  (This study was embedded in the Erasmus Rucphen Family study (ERF), a cohort derived from a region in the southwest of the Netherlands. The population was established in the middle of the 18th century by a limited number of founders, has experienced minimal immigration and emigration, and has exponentially increased in size in the last century. The ERF study was instituted in this population to determine the genes underlying quantitative trait variation in humans  . The MedExaminations included 12-lead ECG measurements. A 10-s 12-lead ECG  was recorded with an ACTA-ECG electrocardiograph  with a sampling frequency of 500 Hz. Digital measurements of the ECG parameters were made using the Modular ECG Analysis System (MEANS)  and the three LVH proxies . Sokolow–Lyon was defined as the sum of the S wave in V1 plus the R wave in V5 or V6, Cornell as the sum of R in aVL and the S in V3, and 12-lead as the sum of R–S in all 12 leads; these three voltages were then multiplied by QRS duration to obtain voltage-duration products as an approximation of the area under the QRS complex  as a reference panel with MACH version 1.0.16  . These SNPs were extracted from our imputed dataset for further analysis, with the exception of a single QRS SNP (rs991014). If multiple SNPs in a given locus were described in the GWAS, only the SNP with the lowest P value was selected for inclusion.Genotyping in ERF was performed using Illumina 318/370 K, Affymetrix 250 K, and Illumina 6 K micro-arrays. Individuals were excluded for excess autosomal heterozygosity, mismatches between called and phenotypic gender, and if there were outliers identified by an IBS clustering analysis. The exclusion criteria for SNPs were Hardy–Weinberg equilibrium (HWE) Individuals were excluded from analysis if their ECG showed evidence of atrial fibrillation, myocardial infarction, left or right bundle branch block, or atrioventricular block. Additional exclusion criteria consisted of pacemaker implantation, Wolff–Parkinson–White syndrome, pregnancy, and use of Type I or III antiarrhythmic medications or digoxin, which may shorten the QT interval . A narrow-sense heritability estimate (h2) represents the fraction of variation in a trait attributable to additive genetic factors. To determine the proportion of variance due to genotypes associated with ECG trait variability, narrow-sense heritabilities were computed with and without genotypic data  software package  of the pair-wise kinship coefficients for the analyzed sample was 0.004 (0.007); the number of pairs for a broad range of kinship levels are presented in Supplementary Table 1. The median (inter-quartile range) of the inbreeding coefficient was 0.003 (0.009). Correlations between the analyzed traits are presented in Table After exclusions, 1396–1474 phenotyped and genotyped ERF participants were available for analysis. Table P = 1.14 × 10−8). There was no evidence for recessive effects, as the inclusion of inbreeding coefficient did not alter the heritability estimates. The inclusion of SNPs specifically identified for QT (model 2) explained approximately 4 % of the trait’s heritability (leaving 96 % unexplained) (Table P = 2.58 × 10−4). The additional inclusion of SNPs identified in GWAS of other ECG phenotypes further decreased the heritability by 15 %, although this difference (between model 2 and 3) was not significant (P = 0.15).The heritability for heart rate-adjusted QT, prior to the inclusion of SNP information, was 36 % (P = 2.32 × 10−9). QRS-specific GWAS SNPs explained 17 % of the heritability of QRS (P = 1.30 × 10−6). Inclusion of non-QRS ECG SNPs did not further explain the heritability (P = 0.28). Two percent of the heritability of PR  could be explained by the inclusion of known PR variants (P = 2.64 × 10−4). The inclusion of SNPs associated with the other ECG phenotypes explained a further 6 % of PR heritability, reducing it to 37 % (P = 1.00 × 10−3) (Table P = 7.09 × 10−4), while the two 12LS SNPs explained an additional 0.5 % (P = 4.03 × 10−4).The heritability of QRS was similar to that of QT, 34 % (P < 5 × 10−8) are known for these traits, and since QRS is a crucial component of all three, QRS SNPs were included in model 2 for each of these measures. 12LS showed the highest heritability of these outcomes , while CV showed the lowest . The heritability estimate for SL was 46 % (P = 1.00 × 10−13); inclusion of QRS SNPs did not significantly alter this estimate (P = 0.42), nor did the inclusion of all ECG SNPs (P = 0.15). The inclusion of the two 12LS SNPs and the QRS SNPs explained 4 % of the heritability of 12LS (P = 5.77 × 10−3). The inclusion of the remainder of the SNPs did not further explain the heritability (P = 0.15). For CV, the inclusion of the QRS SNPs increased the estimate slightly, but significantly , while the inclusion of all SNPs did not influence the heritability (P = 0.18). The additional inclusion of inbreeding coefficient did not impact any of these models (data not shown).Heritability estimates were also calculated for three LVH proxies ; since only two GWAS associations  and ECG-derived LVH indices . Highly significant estimates of heritability, ranging from 0.34 to 0.49, were observed for all traits, several of which needed replication in a population-based study. The inclusion of known trait-specific GWAS loci explained a fraction of the heritability of each trait except for SL and CV . Inclusion of all ECG-associated SNPs further explained an additional proportion of the heritability for PR (6 %), clearly suggesting cross-phenotype effects for some loci.This study benefits from a large, well-characterized, family-based population, selected on the basis of genealogy and not phenotype. It is well powered for this type of analysis and is not biased due to phenotypic selection for, as an example, cardiovascular disease. The complex genealogy allowed for the assessment of inbreeding and, furthermore, accounts for relatedness within families.Despite these strengths, there are also limitations to this study. One is that ERF was part of the discovery analyses for the QRS and QT GWAS. However, ERF accounts for only a small proportion of those efforts, making it unlikely that over-fitting has a large impact on these findings. Discovery of genetic variants is still in progress. In all likelihood, larger GWAS efforts will lead to the identification of additional SNPs and, therefore, larger proportions of explained heritability. Finally, medication use, abundant in the population, may directly affect variability in ECG measurements. Some medications are known to induce such effects  strongly suggests the presence of variants with effects across these traits. These cross-phenotype effects are a common phenomenon in complex trait genetics  influence PR, despite failing to achieve genome-wide significance in PR GWAS to date. Increasing GWAS sample sizes, searching for cross-phenotype effects, and identifying less common variants are likely to increase the explicable portion of ECG trait heritability.Supplementary material 1 (DOCX 19 kb)Below is the link to the electronic supplementary material.


Bos taurus and Bos indicus. Here we design a minimal genome wide SNP panel for tracking ancestry in recent hybrids of Holstein–Friesian and local Arsi zebu in a field sample from a region of high bovine tuberculosis (BTB) endemicity in the central Ethiopian highlands. We first demonstrate the utility of this approach by mapping the red coat color phenotype, uncovering a highly significant peak over the MC1R gene and a second peak with no previously known candidate gene. Secondly, we exploit the described differential susceptibility to BTB between the ancestral strains to identify a region in which Bos taurus ancestry associates, at suggestive significance, with skin test positivity. Interestingly, this association peak contains the toll-like receptor gene cluster on chromosome 6. With this work we have shown the potential of admixture mapping in hybrid domestic animals with divergent ancestral genomes, a recurring condition in domesticated species.Admixture mapping affords a powerful approach to genetic mapping of complex traits and may be particularly suited to investigation in cattle where many breeds and populations are hybrids of the two divergent ancestral genomes, derived from  Admixture mapping forms a powerful alternative approach to the ubiquitous genome wide association study design for the discovery of genes that contribute to complex traits. Rather than identifying individual SNP variants that show significant association with phenotype, it seeks to identify segments of chromosome in admixed populations within which ancestry from one parental group diverges from expectation .This approach has several advantages. First, with appropriate trait-population combinations it displays high power. This is manifest most clearly when the ancestral populations to the admixed subjects are divergent in distributions of both genotypes and the targeted phenotypes. Second, although dependent on the generation time-depth since admixture, the SNP density required for a genome wide scan is greatly reduced compared to a typical GWAS experiment, often by over two orders of magnitude. However, a corollary is that genetic mapping resolution is comparatively low. Third, admixture mapping is relatively robust to genetic heterogeneity as a confounder of association .Although the advantage of admixed subjects for gene detection has been recognized for several decades , it is oBos taurus and Bos indicus that were domesticated from distinct wild populations  as compared to the local Bos indicus (Ethiopian Arsi zebu) cattle (taurus than zebu cattle (One such trait is bovine tuberculosis (BTB), a chronic respiratory infection caused by lication . Ethiopin cattle . Also, aase risk . Studies) cattle . Both tuu cattle . BTB relu cattle . Two genu cattle .taurus-zebu hybrids that are of several generations depth of admixture. Their ancestral populations, Ethiopian Arsi zebu and European Holstein–Friesian, diverge in BTB susceptibility as well as in other phenotypes, including coat color. We first demonstrate the utility of our assay by locating with high significance two loci controlling coat pigmentation differences between these ancestral strains. One of these localization peaks includes the known trait gene MC1R. Secondly, we uncover suggestive evidence for a locus influencing the described divergence in tuberculosis susceptibility between parental strains, as assessed using data from skin testing of herds exposed to known BTB transmission under natural conditions. Interestingly, this peak includes, among other potential causative genes, the toll-like receptor gene cluster on chromosome 6.In this study we develop a low-density genome wide scan for ancestry in cattle and use this to genotype The major focus of the project was BTB – the coat color trait was included as an incidental measurement but which nevertheless served as a useful proof of principle of application of the approach in this sample.Crossbred animals (hybrids) from Ethiopian field herds were used for the admixture mapping the owner of each animal was interviewed and the following information were recorded:, age, sex, physical condition score , pedigreThis work was carried out as part of a large Wellcome Trustproject (see Acknowledgments) investigating TB in field situations in humans and cattle in Ethiopia. The CIDT test results from this larger work have been published in numerous publications, including four referenced here , 2010. ETM DNA purification kit (5 PRIME GmbH) at the Armauer Hansen Research Institute (AHRI), Addis Ababa, Ethiopia. DNA concentration of each sample was quantified using the fluorescence method, QubitTM. A total of 400–700 ng of DNA was used for analysis.A total of 10 ml of whole blood was collected from the jugular vein of 585 hybrid Holstein–Friesian/Ethiopian zebus. DNA was extracted from blood using the Archive PureThe breeds used to determine which markers were informative (see SNP Mapping Set) were Ethiopian–Arsizebu (40), Boran (8), Holstein–Friesian from the Bovine Hapmap sample collection plus individuals collected in Ireland (56), Hariana (10), Sahiwal (8), Tharparker (7), plus Bovine Hapmap samples from Brahman (20), and Gir (20).M. bovis (PPD-B) and Mycobacterium avium (PPD-A). 0.1 ml of each PPD-B and PPD-A  were injected into two different sites on the animal neck. The subject was classified as “reactor” (CIDT positive) if the skin thickness at the PPD-B site was higher than at the PPD-A site by at least 4 mm. In order to increase the sensitivity of the test without affecting the specificity, animals with a PPD-B site thickness greater than 2 mm were classified as “possible reactors” and were also included in the “reactor” group for the analysis. This was based on an extensive study in the Selalle region, Ethiopia  or “non-reactor” (control) to tuberculosis infection using a single comparative intra dermal tuberculin test (CIDT). The test was performed via skin injection of purified protein derivatives (PPDs) which are crude extracts of Ethiopia , 2010. TBos taurus and Bos indicus of 0.6 and above, absolute allele frequency difference between African Bos taurus and Bos indicus of 0.6 and above and inter marker distance between two consecutive SNPs of on average 3.6 Mb  with the aim of even coverage across all autosomes (sex chromosomes were excluded). Furthermore, several additional SNPs from genomic regions of biological importance were added. Genotyping was carried out using the Illumina Golden gate assay and was performed at the Wellcome Trust Centre for Human Genetics, Roosevelt Dr., Oxford, UK.Selection of informative markers was carried out by first examining data from the Bovine Hapmap consortium . These mBos taurus, was estimated using ANCESTRYMAP . The initial set of 585 Holstein–African zebu hybrid genotypes were reduced to 502 by exclusion of 67 samples with an estimated percentage of either East African zebu or European Bos taurus ancestry greater than 0.90 and an additional 16 samples due to low genotype call rates, suspected duplication or because they were closely related. The BTB trait admixture analysis was based on a final set of 341 cases and 161 controls and the coat color calculations featured 76 red and 406 not-red individuals.After quality control measures the final set of genome-spaced markers comprised 662 autosomal SNPs. The proxy parental samples for the admixture under examination were East African zebu Arsi and Boran breeds (48 samples), and European Figure 1 shows the hybrid case-control animals in the center of the plot while the parental populations, the European Bos taurus (HOL) and the East African zebu (ETZ and BRN) cluster at alternate sides of the main distribution, as expected. Non-African zebu separate out with the African zebu on eigenvector 1 but form a clearly distinct cluster. The plot also shows that the hybrid animals have dominant zebu ancestry since most of the individuals are concentrated in close proximity to the zebu parental populations. BTB trait cases and controls are labeled separately and no visible sorting of these was evident.Principal component analysis was carried out to ascertain population variation in relation to the parental populations. The plot in Bos taurus and Bos indicus, of a depth of at least two generations, were used in the first admixture analysis using a binary coat color phenotype. For a single trait variable analysis all the samples were divided into two groups: 76 red, which included animals displaying light-red or red coat color and not-red, the 406 remaining animals. The ANCESTRYMAP analysis was performed using the local Ethiopian zebu (Arsi breed) as one of the ancestral populations. This breed has a variable coat color; animals are mostly dark brown but may also be found with a wide spectrum of black, red, and spotted derivate of colors. The second ancestral population is the European Bos taurus, Holstein–Friesian. This breed is majority black and white.A total of 482 hybrids of Figure 2 the Z and LOD-scores from the ANCESTRYMAP analysis are plotted. The former is based on a comparison of inferred ancestry levels between cases and controls. The latter is a complementary analysis which assesses locus ancestry levels versus those inferred genomewide for each individual. In both analyses the highest positive score centered in a region of chromosome 18 that contains the melanocortin 1 receptor (MC1R) gene . This gene is known to regulate the level of tyrosinase enzyme production which is responsible for the switch between phaeomelanin (red pigment) and eumelanin (black pigment) production. The dominant MC1R gene has been described as associating with black coat color and a recessive allele results in red coat color  in both analyses and was located on chromosome 12. No genes known to be associated with coat color phenotypes were identifiable within this peak region.In at color . The Z-sFigure 3 plots the LOD scores associated with risk models ranging from 0.1 to 3-fold increased risk for European Bos taurus (Holstein) ancestry. These values are averaged across the genome and correspond to the increase in risk with addition of one copy of a Bos taurus ancestry allele. In effect this model tests the association of genomewide European ancestry with tuberculin reaction. The maximum log-likelihood ratio (LOD) was 4.04 for a risk value of 1.7. The average LOD value over each of the risk models considered was 3.17. This overall risk model analysis gives significant evidence of association between European Bos taurus ancestry and tuberculin positivity.The association between overall genome ancestry and tuberculin reaction was initially tested; As with red coat color, we implemented two approaches in order to search for genome regions associated with TB susceptibility. First, inferred ancestry at each position was compared in 341 CIDT skin test positives (cases) and 161 test negative controls from the hybrid herds, with 48 East African zebu and 56 European Holstein genotypes used as parental reference samples. Second, inferred ancestry at each position was considered versus a genomewide average for case samples without reference to the control genotypes. These are two non-independent approaches of different power and propensity to false discoveries. We consider only signals that appear with both methods.Figure 4 with panel A showing LOD scores for the case only analysis and panel B indicating case control comparisons assessed as Z-scores. The significance threshold of Z = 3.0 and the suggestive significance level of LOD = 4.0 are indicated, respectively. In the case-control comparison, the only significant association found was on chromosome 6 for the marker BTA76573 with a Z-score value of 3.11 which corresponds to a nominal P = 1.8 × 10-3 (Table 1).These admixture mapping scans are plotted for genome position in Z-score was on chromosome 5 for the marker BTA149284 ; however, here the LOD score for case only analysis was not significant (Table 1). Within the case only analysis the highest signal observed was for the marker rs29019760 on chromosome 11 (LOD = 6.87), but the value for the case-control comparison analysis was well below significance . No other LOD score peaks have corresponding case control Z-scores with any support for significance.This locus also has a strong suggestive peak signal for the case only analysis with a LOD score of 4.65. The second highest http://genome.ucsc.edu/) was used to extract genes from the associated region using a one LOD interval  that showed support for association in each analysis was examined to list possible candidate genes for association with susceptibility. The Bos_taurus_UMD_3.1.1/bosTau8build of the bovine genome data  were at least two times more susceptible than the local breeds .Interestingly, this chromosome 6 peak harborsa TLR gene cluster of potential importance to cumented . They arcumented . Mutatiocumented , human tcumented , and malcumented  specificcumented . Importacumented . HoweverPrior mapping work for BTB susceptibility in cattle has identified association with the SLC11A1 locus (formerly named NRAMP1) including in African cattle, however, this genome region showed no signal of note in this present study . AdditioIn summary, we have used a genome wide marker set chosen for high allele frequency divergence between the two genomes segregating in modern cattle typed in a hybrid animal sample to conduct admixture mapping on two traits: susceptibility to tuberculosis and coat color pigmentation. Both approaches give significance peaks that correspond with known candidate loci: MC1R for pigmentation and the TLR cluster on chromosome 6 for BTB susceptibility. This work is a first illustration of the potential of this approach in domestic animals which may have wide and efficient applicability given the prevalence of divergent ancestral genomes and their hybrids in the most important domesticates.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.