To minimize inbreeding, the lines were maintained by rotational mating. Pedigree records predict that the inbreeding coefficient increases at a rate of <1% per generation, ~37-38% slower than expected for random mating. Genome-wide 10K SNP genotype data for generations 5, 14, and 26 demonstrate substantial genomic evolution: between-line differentiation increased progressively, while within-line diversity deceased. Genome-wide average heterozygosity decreased at a rate of <1% per generation, consistent with pedigree-based predictions and confirming the effectiveness of rotational breeding. Linkage disequilibrium index r2 decreases to 0.3 at ~3 Mb, suggesting that the resolution for mapping quantitative trait loci (QTL) can be as high as 2-3 cM. To establish a test population for QTL mapping, we conducted an HCR-LCR intercross. Running capacity of the F1 population (n=176) was intermediate of the HCR and LCR parentals (28 pairs); and the F2 population (n=645) showed a wider range of phenotypic distribution. Importantly, heritability in the F0-F2 pedigree remained high (h2~0.6). These results suggest that the HCR-LCR lines can serve as a valuable system for studying genomic evolution, and a powerful resource for mapping QTL for a host of characters relevant to human health. Aerobic capacity is a strong predictor of all-cause mortality and can influence many complex traits. To explore the biological basis underlying this connection, we developed via artificial selection two rat lines that diverge for intrinsic (i.e. inborn) aerobic capacity and differ in risk for complex disease traits. Here we conduct the first in-depth pedigree and molecular genetic analysis of these lines, the high capacity runners (HCR) and low capacity runners (LCR). Our results show that both HCR and LCR lines maintain considerable narrow-sense heritability (h Increased intrinsic exercise capacity or aerobic capacity, generally measured as maximal work with a standardized treadmill test, is an excellent predictor of disease risk in humans, with higher capacities associated with enhanced health and resistance to metabolic disease –9. Peak To understand the genetic and functional basis of aerobic capacity we sought to establish an animal model that allows in-depth analyses of the biology and health impact of innate aerobic capacity. In 1996, a long-term experiment was initiated to create two lines of rats through bi-directional selection for untrained aerobic running capacity . The twoOne of the original aims for establishing the HCR-LCR lines was to test the hypothesis that artificial selection based on intrinsic aerobic capacity would yield models that also exhibit contrasts in disease risks. This hypothesis has been proven correct: after 28 generations of selection, the HCR and LCR diverged not only for running capacity, but also in other physiological measures, including blood pressure, body mass index, lung capacity, lipid and glucose metabolism . The LCRA major advantage of the HCR-LCR system is that the pedigree and running phenotype data  are completely known; and tissue samples for most breeding members  have been archived. This combination of existing data and reagents, combined with over 70 published physiological studies of the two lines, represents a valuable resource that allows comprehensive analyses of the effects of selection on genomic and phenotypic evolution. In this study, we carried out a systematic analysis of the running phenotype and related traits over the known pedigree of 0-28 generations. We also collected a genome-wide 10K SNP dataset for a subset of breeding members from three generations (G) , and used these data to examine patterns of genomic evolution in the two lines as they undergo selection. Finally, we performed the first intercross experiments between the HCR and LCR, and analyzed the phenotypic distribution and heritability of the F1 (n = 176) and F2 populations (n = 645). These analyses provided new insights into the genealogical structure, inbreeding patterns, and genetic variability of the two lines, and characterized the intercross animals to assess their suitability as a mapping population for identifying quantitative trait loci (QTL).The protocols of animal maintenance, phenotyping, and rotational breeding have been described previously  see als1. The bFor each animal, we collected phenotype data that include maximal running distance, body weight at the time of running trial, and vertical work performed during each run. All animals were tested at 11-12 weeks of age. While both lines were derived from the same base population (indicated in yellow in 2) for the logarithm of running distance, which measures the proportion of total phenotypic variance explained by additive effects of genes, was calculated for each line separately, and was 0.47 ± 0.02 in HCRs and 0.43 ± 0.03 in LCRs when all 28 generations were considered. To evaluate potential change in heritability over time, we also calculated h2 over four-generation, partially overlapping, intervals and found that while h2 was variable across intervals, it maintained positive values, with no sign of abatement in later generations  loci for 142 animals, consisting of 22-25 animals in each of three non-adjacent generations  in both lines (see Methods). We chose these three generations to profile the long-term genomic changes in the two lines.. We used the average heterozygosity of 61 X chromosome (ChrX) markers to infer sex S3A, andTo measure the apportionment of total genetic variance into between-line and within-line components we performed an Analysis of Molecular Variance (AMOVA) . The pro0) across the 2,518 Panel-2 markers for each animal, and averaging within each of the six groups  (0 averaged 0.379 in HCR and 0.372 in LCR. At G14 it decreased to 0.338 (-10.8%) in HCR and 0.327 (-12.1%) in LCR. At G26 it decreased further to 0.303 (-10.2% from G14) in HCR and to 0.296 (-9.4%) in LCR. Note that the absolute values of H0 are influenced by the allele frequencies of the genotyped SNP markers, and it is the relative changes of H0 that reflect the altered genomic diversity. The observed rates of decrease are slower than in random mating populations. Using the known numbers of effective breeders at each generation we calculated the expected H0 at each generation assuming random mating, and found that the expected rate of decrease in H0 is on average 1.53 per generation for both HCR and LCR,. The observed rate of decrease, 0.95% and 0.97% per generation for HCR and LCR, respectively, is lower by 37-38%, consistent with the pedigree-based predictions  5. At G5dictions 1 and co0 over time primarily reflects higher levels of inbreeding, a majority of the increase of homozygote genotypes should be accounted for by the emergence or expansion of long runs of homozygosity (ROH). Using 10,185 SNPs in SNP Panel-1 (see Methods), we found that for HCR, ROH covered an average of 46% of the genome in G5 animals, and this rate increased to 54.8% in G26. For LCR, ROH covered 46.8% of the genome in G5, and 55% in G26. Thus the non-ROH regions shrink by 0.73-0.77% per generation in the two lines, accounting for most of the decrease of H0.As the reduction of H2 decays to 0.3 at ~3 Mb in both HCR and LCR  patterns using the Panel-1 SNPs on Chromosome 1 (n = 978). The LD index r and LCR 6. The lus Stock . These rus Stock .As HCR and LCR have evolved separately, a direct between-line comparison of phenotypes and genotypes would incur the effect of population stratification. To create a QTL mapping population with randomized genomes, we performed "F2" intercross experiments using 28 HCR-LCR pairs and obtained 242 F1 animals (176 phenotyped). From the phenotyped F1 population we set up 63 mating pairs and produced 645 F2 animals. The term "F1" or “F2” is applied loosely in this context because our crosses were not based on inbred lines. However, we use F0, F1, and F2 to indicate the generations. The running phenotype of F1 fell in an intermediate range between that of their HCR and LCR parents, and the F2 animals exhibited larger variations than the F1 rats 7. This 2 of the maximal running distance in the F0-F2 pedigree remained high, at 0.60 ± 0.05. The h2 for vertical work is comparable to those of the ancestral lines under selection (0.61 ± 0.05), while the h2 for body weight is lower (0.03 ± 0.04). In addition, we phenotyped a number of other physiological measures in a subset of F2 animals, including heart/body mass ratio (n=380), extensor digitorum longus (EDL) mass/body mass ratio (n=387), and percent body fat (n=490), which showed strong heritability . These results indicate that the running capacity and related traits are influenced by genetic factors, confirming that it is feasible to use the F2s as a mapping population for QTL.Importantly, the hWhile previous studies have focused on functional, physiological comparisons between HCR and LCR rats, here we conducted the first in-depth pedigree and molecular genetic analysis of the two lines. Phenotypic data over the 28-generation pedigree not only revealed substantial heritability for the running capacity trait, but also showed that the heritability is maintained in later generations, suggesting continued selection. In addition, the strong heritability is recapitulated in the F2 intercross. These findings suggest that causal variants continue to segregate in both HCRs and LCRs and persist in the F2 population. Our study generates the first direct evidence that the trait under selection is highly heritable, providing justification for intercross-based QTL mapping. In addition, the heritability for vertical work, heart/body ratio, EDL/body ratio, and percent body fat suggests that the model can be used for simultaneous QTL mapping for multiple traits.within one or both lines. We observed continued response to selection in HCR during 20-28 generations. This observation is notable because it can be interpreted in two possible scenarios. The first is that the aerobic running capacity may be influenced by many interacting QTLs, and as variants in some loci become fixed under selection, the previously hidden phenotypic effects of other variants can be "released" and come under selection, thereby fueling prolonged responsiveness. This agrees with previous observations in similar systems that long-term selection did not exhaust the genetic variation for the selected trait due to the graduate shifts in the capacitors of cryptic genetic variation ,43. The HCRs exhibited accelerated improvement of running capacity during G12-G15 2. To idde novo mutation is unlikely, as prodigious running capacity arose in multiple families concurrently. Such a pattern, however, is compatible with a scenario in which causal "high" alleles in multiple genes interact in a non-linear fashion. Various combinations of the high alleles could have undergone gradual enrichment and in G12-G15, began to manifest as improved phenotype when the most favored combinations were formed. Future studies, including linkage analysis of these intermediate generations, are needed to characterize the genetic changes accompanying the apparent varying tempo of trait evolution. The accelerated improvement of running capacity in HCRs during G12-G15 could also reflect genetic changes. However, emergence of a single high-impact In the F2 generation of the intercross, the running distance distribution is wider than in F1, but did not reach the full range seen in F0 animals. The fact that none of the F2 animals performed as well as their HCR grandparents, and very few performed as poorly as the LCR grandparents, strongly suggests that multiple genetic loci are involved. The Castle-Wright estimator of the effective number of QTLs is calculated as 4-10 using our F2 data ,45. CautThe HCR-LCR system was initiated in 1996  and reacTaken as a whole, the results presented above suggest that the HCR-LCR system is well-suited to serve as a novel model system for studying genome evolution under sustained selection and for dissecting the functional and genetic basis of polygenic traits. The model exhibits large phenotypic divergence, sustained heritability for a wide range of cardiovascular and metabolic traits, and maintained outbred character. The complete pedigree is known, with running phenotype for all animals already collected, and tissue sample for most breeders archived. Compared to inbred line-based gene mapping, our system offers some additional advantages. First, while the F2 generation could be subjected to conventional linkage mapping , the twoThis study was approved by the University Committee on Use and Care of Animals, Ann Arbor, Michigan . The proposed animal use procedures are in compliance with University guidelines, and State and Federal regulations. In practice, each line contains at least 13 mating pairs through all generations. From each of the family produced, one male and one female are selected as breeders for the subsequent generation. For HCR, the male and female with the greatest running distance are selected, whereas in LCR, those with the lowest distance are selected. The breeders are paired between different families to avoid brother-sister mating, and the pairings rotate in successive generations to minimize inbreeding . When thOne inevitable consequence of this breeding scheme is the mating of first-cousins at every half interval S7B. ForEleven week old animals are subjected to run-to-exhaustion tests without prior training, except for brief sessions of treadmill education during the week prior to the tests. The purpose of such education sessions is to familiarize the rats to the experimenters and the testing equipment and to ensure that each rat has the ability to achieve a minimal level of continual running for 5 minutes at least once, which constitutes the threshold performance necessary for inclusion in the actual running tests the following week. During education, the rats learn to keep running in order to avoid a mild shock (1.2 mA of current at 3 Hz) induced by the electrified grid located at the back of the treadmill. For all sessions the treadmill is set at a 15-degree upward slope.During the run-to-exhaustion test, each rat was evaluated on five consecutive days (Mon-Fri) for G0-G16 and on three alternating days (Mon-Wed-Fri) for G17-28. Each trial starts at a velocity of 10 m/min, which increases by 1 m/min every 2 min until the rat reaches exhaustion. Exhaustion point is defined as the third time a rat can no longer keep pace with the treadmill and remains on the shock grid for two seconds rather than resuming running. At this point the rat is removed from the treadmill and weighed. For each rat, the best distance out of the multiple trials is taken as the best estimate of its intrinsic capacity, and used as the criterion for breeder selection. The vertical work during each trial is estimated using the equation:work = (running distance) x (body weight) x (sin[15°]) x (9.8m/s2)/1000 in which the unit for work is joule (J=kg•m2/s2). Unit for running distance and body weight is meter and gram, respectively.vioplot function from the Vioplot package in R. To calculate the Spearman's rank correlation coefficient (ρ) between maximal running distance and body weight, separately for two sexes within each line, we used the cor and cor.test functions in R. The ρ values were calculated for each generation, and averaged over G1-G28.To display phenotype distribution we produced violin plots  breeding patterns in the preceding generations. To calculate the expected F under random mating, we used the equation Fn+1 = Fn+(Nf+Nm)/(8*Nf*Nm)-Fn(Nf+Nm)/(8*Nf*Nm), where the Fn and Fn+1 are the inbreeding coefficients at the n-th and (n+1)-th generation, respectively, and Nf and Nm are the numbers of male and female breeders at the n-th generation (Nf = Nm = 13 in every generation for a 13-family breeding scheme). To calculate the expected F under perfect adherence to the rotational scheme, we generated an idealized pedigree of 13 mating pairs of exact mating patterns as intended, and used the pedigree package to calculate F for every member of the pedigree.We calculated the inbreeding coefficient (F) for each animal in the pedigree using the age in R . The ped2), we applied the variance and covariance component models as implemented in SOLAR version 4.3.1 [2 for maximal running distance both over the entire pedigree and for four-generation intervals (with one and three generation overlap) to assess the h2 variation over time. We also estimated h2 for body weight and work for the G0-G28 pedigree, and for additional traits for the F2 intercross. For the G0-G28 analysis, we included sex and operator as covariates; and for the F2 intercross we included sex and batch because the breeding was performed in two batches, containing 154 and 491 F2 animals, respectively.To calculate the narrow-sense heritability , and 572 SNPs with minor allele frequency (MAF) <5%. After these steps, 2,518 SNPs remained and formed the "Panel-2" markers. The Panel-2 markers were used in calculations of IBD, AMOVA, and genome-wide average heterozygosity. Pairwise Identity-by-State (IBS) matrix was estimated in PLINK [-genome command and Panel-2 markers. Multidimensional scaling analysis of the IBS matrix was performed in R [Arlequin, was used to calculate the within- and among-group differentiation [DNA from 22-25 breeders from both lines in three non-adjacent generations  was extracted from frozen liver tissue, and genotyped across 10,846 SNP loci using the Affymetrix Rat Mapping 10K GeneChip. Attempts to extract DNA from generations earlier than G5 revealed that many samples in G0 and G4 were degraded. We therefore chose G5 as the earliest generation in our analysis due to its assured DNA quality. In assessing the quality of SNP markers we removed 28 duplicate SNPs, 496 SNPs with genotype missing rate >10%, and 137 SNPs with Hardy-Weinberg Equilibrium test p < 0.001. These steps led to 10,185 SNPs that formed the "Panel-1" markers. As some analyses require a reduced set of SNPs without rare variants and without strong linkage disequilibrium, we removed from Panel-1 an addition set of 7,284 SNPs selected by trimming SNP pairs in linkage disequilibrium with rin PLINK  using thmed in R . AnalysiPLINK using the -genome command.We assessed the accuracy of recorded sex for each genotyped animal by calculating the average heterozygosity of the X chromosome SNPs. Male and female animals are confirmed by non-overlapping distributions of ChrX heterozygosity values. We confirmed known sibling pairs among the genotyped animals by plotting pairwise Z0 vs Z1 values in R. Z0 and Z1 values were determined in PLINK using the -homozyg command. We defined ROHs as genomic segments with at least 4 homozygous markers and having a density of at least 1 SNP per 500Kb. Total ROH length in each animal was obtained by summing over all ROH and also reported as the fraction of the rat genome (2.75 Gb).Using the 10,185 Panel-1 markers, we identified long runs of homozygosity (ROH), in -hardy command, and compared across genotyped lines and generations using boxplots. The expected heterozygosity values were calculated using the equation Hn+1 = Hn(1-((Nf+Nm)/(8*Nf*Nm))), where the Hn and Hn+1 are the heterozygosity at the n-th and (n+1)-th generation, respectively, and Nf and Nm are the numbers of male and female breeders at the n-th generation, respectively (Nf = Nm = 13 in every generation for a 13-family breeding scheme)  3. 2) were calculated for marker pairs within 5 Mb on Chromosome 1 using Haploview [2 and inter-marker distance, we calculated average r2 values for groups of marker pairs falling in discrete bins of inter-marker distance, in 500Kb increments, and plotted the values for G5, G14, and G26 in both HCR and LCR . This generated 154 F2 rats. For the second batch, we selected 9 males and 9 females from G28 of each line to form 18 mating pairs, which generated 163 F1 rats, from which 97 were phenotyped. Out of the 97 F1 animals, 40 males and 40 females were selected across the 18 families with equal representation between HCR/LCR parentage, such that we had 4 combinations  and generated 491 F2 rats. The two batches together yielded 645 F2 rats 7. PhenoFigure S1A) and femalesDistribution of maximal running distance for generations 0 to 28 for males ( (B). Shown are "violin-plots" for individual generations for females and males separately. The blue tick marks on the y axis indicate the maximal running distance for eleven inbred lines, which are ordered, from top to bottom for males (A) as DA (968m), PVG (754m), SR (615m), AUG (594m), ACI (447m), LEW (405m), WKY (387m), BUF (355m), F344 (332m), MNS (302m) and COP (262m), and for females (B) as AUG (805m), DA (712m), PVG (682m), F344 (606m), LEW (479m), ACI (453m), WKY (441m), SR (409m), BUF (391m), COP (333m), and MNS (315m). (TIFF)Click here for additional data file.Figure S2Narrow-sense heritability of running capacity remained positive over time. Shown are heritability (h2) estimates and standard errors for maximal running distance in four-generation intervals that overlap by one-generation for HCR (A) and LCR (B). (TIFF)Click here for additional data file.Figure S3Sample quality assessment in 10K SNP genotype data. (A) Average heterozygosity over 61 SNPs on the X chromosome for 142 genotyped animals, ordered by line and generation. Males and females fall in two non-overlapping clusters, indicating that the observed X-chromosome heterozygosity is consistent with the reported sex of the animals. (B) Scatter plot of Z0  versus Z1 , showing that known sib pairs form a separate cluster than more distant relatives. (TIFF)Click here for additional data file.Figure S4Assessment of litter size, missing phenotype, and rotational breeding schedule over the generations. (A) Average litter size over G1-G28 for the HCR animals in the recorded pedigree, accompanied by standard deviations among all the families for each generation. No significant change in litter size is observed over the course of selection. (B) Number of animals in each generation without recorded running phenotype for HCR (blue) and LCR (red). The increase in missing phenotypes between G7-G13 overlaps operator 3 (shown as the orange bar below). (C) Percent of mating pairs out-of-schedule (off-rotation) per generation for HCR (green) and LCR (red). (TIFF)Click here for additional data file.Figure S5Between-line synchrony and short-term heritability estimates in HCR. (A) Dates of birth (x-axis) for G1-G28 animals (y-axis). The horizontal bars indicate the range of birth dates, and the dots indicate the average. The close match between HCR and LCR shows that the two lines are synchronized. (B) HCR narrow-sense heritability for adjacent 3-generation intervals, showing no significant change over time.(TIFF)Click here for additional data file.Figure S6A), G13 (B), G14 (C), and G15 (D).Running performance of mid-parents and offspring for on/off-rotation mating types for G12 ( Shown are the scatterplots of mid-parent running distance (x-axis) versus male and female offspring running distance (y-axis) for on-rotation mating (indicated by XX.0), off-rotation mating due to mother (XX.1), off-rotation mating due to father (XX.2), off-rotation mating due to both mother and father (XX.3), and either father or mother from the enrichment cohort (XX.4). Data points above the solid diagonal line indicate offspring with better running performance than their mid-parent.(TIFF)Click here for additional data file.Figure S7Rotational breeding scheme. (A) Mate pairing matrix for breeding rotations 1 through 12, where females from families 1 through 13 are designated in columns (from left to right), while males are designated in rows (from top to bottom). Male-female pairs are formed differently in successive generations as indicated by the rotation numbers in the matrix. (B) An example to show how, at the mid-cycle through rotational breeding among 13 families, every breeding pair is a first-cousin mating. The example shown is rotation 7 between Family 1 and Family 7. A male from family 1 (1M) to be mated with a female from family 7 (7F) are both offspring of breeding members of family 7 in rotation 6, thus making them first cousins. (TIFF)Click here for additional data file.File S1HCR pedigree in G1-G28 and select phenotypes.(XLSX)Click here for additional data file.File S2LCR pedigree in G1-G28 and select phenotypes.(XLSX)Click here for additional data file.


Callosobruchus maculatus. Heritability was significant for all traits under observation and genetic correlations between traits (rA) were low. Interestingly, we found substantial sex-specific genetic effects and low genetic correlations between sexes (rMF) in traits that are only moderately (weight at emergence) to slightly (longevity) sexually dimorphic. Furthermore, we found an increased sire (Knowledge of heritability and genetic correlations are of central importance in the study of adaptive trait evolution and genetic constraints. We use a paternal half-sib-full-sib breeding design to investigate the genetic architecture of three life-history and morphological traits in the seed beetle,  VA) or heritability  has been increasingly used to estimate h2 in natural populations , examining sexes separately. Several previous studies . Populations had been collected in large numbers from three adjacent locations in Nigeria , Africa and had been kept in the laboratory prior to their transfer to our laboratory for 2 years (approximately 24–30 generations). Beetles from the Nigerian mixed strain were kept on black-eyed cowpeas (Vigna unguiculata) as a host with 250–350 randomly chosen adult beetles transferred to 120–140 g of host medium every new generation in incubators under constant conditions at 30°C and 45% (±10%) relative humidity.We used the seed beetle C. maculatus, which is a cosmopolitan pest of stored legumes (Fabaceae). Mated female seed beetles cement their eggs to the surface of host bean  and newlVA) and can therefore be used to estimate VA and the narrow-sense heritability h2  and weight at emergence , we conducted a paternal half-sib breeding design . A randoility h2 . Virgin and male . IndividIn total 5732 offspring  were reared. Developmental time and weight at emergence data (Group 1) were recorded from 2908 offspring , reared from 165 full-sib families (45 sires and 165 dams). Longevity data (Group 2) were collected from 2824 offspring , reared from 203 full-sib families (47 sires and 203 dams).http://r-forge.r-project.org/). The significance of variance components was assessed using LRTs.All statistical analysis was conducted in R version 2.13.0 . We fittVA) and is unbiased by dominance, maternal, and environmental (offspring randomized across the environment) effects  and maternal or other environmental effects (VEc)  from hereon. Confidence intervals of the heritability estimates were assessed using parametric bootstrapping. We conducted a randomization test in order to investigate sex differences in variance components .We calculated heritability based on sire and dam variance components by dividing four times the sire or dam variance by the phenotypic variance . The least biased estimated of heritability in this breeding design is based on the sire variance component . Therefomponents ; first, We compared sire and dam variance components for each trait and sex by evaluating their confidence intervals. This gives an estimate of eventual presence of nonadditive genetic and environmental effects in either variance component. A dam variance component that is significantly larger than the sire variance component indicates the presence of nonadditive effects  e.g., . AnalogorMF) for each trait was assessed by dividing the covariance between sexes by the square root of the product of variance for each sex; we calculated the genetic correlation between traits (rA) for each sex separately by dividing the covariance between traits by the square root of the product of variance for each trait. Significance of estimates of the genetic correlation was assessed by their 95% highest posterior density (HPD) interval not overlapping zero or one, respectively.In order to calculate genetic correlations between sexes and traits, we fitted three two-trait linear mixed random slope models  to the data using a Bayesian approach , developmental time , and longevity  with males being 27% smaller, hatching approximately 3.5 h earlier and dying 9% earlier than females. Sire and dam effects were significant for weight at emergence, developmental time, and longevity  for each trait and between traits (rA) for each sex were generally low and differed significantly from one; only the rMF for developmental time was significantly different from zero  for life-history traits that are only moderately (weight at emergence) to slightly (longevity) sexually dimorphic. These interesting results highlight that sexes should not be assumed to be the same, and have great implications for the study of sexual dimorphism and its evolutionary dynamics. Third, we found sire variance components to be significantly higher than dam variance components for weight at emergence for males and for longevity for females, suggesting that nonadditive genetic and/or environmental effects might play a role in the estimation of heritability.In this study, we used a very common method  to estimate the genetic architecture of life-history and morphological traits in the seed beetle rA) to be generally low and not significantly different from zero  for these traits to be low and not different from zero. Our findings are surprising and contradict results of previous studies, where genetic correlation between the sexes have been shown to be significantly different from zero and generally high (rMF > 0.80) for both weight at emergence  is significantly higher than the sire variance component in the opposite sex . Our res females , 2004. Fmergence  and adulmergence .Callosobruchus maculatus is known to be sexually dimorphic for the traits under observation , which is known to result in higher levels of additive genetic variance .Our results could also be explained by sex differences that arise through sex-specific responses to the environment. The environment can influence males and females differently; one sex might be more vulnerable to a environmental condition than the other on both a genetic and phenotypic level. Sexes might for instance differ in how environmental factors affect and alter gene expression , the sexh2 estimates. We suggest that this effect could possibly be explained by the biology of the species. Callosobruchus maculatus is a species without direct paternal or maternal care. However, males are known to provide females with nuptial jpgts during mating , they were able to estimate separate variance components for maternal and paternal genotype and common environment effects. Interestingly, We found an increased sire compared to dam variance component within trait and sex. This increase was only detected in the traits and sexes where we also observed sex-specific variance components. As discussed, the difference in sire and dam variances suggests potential presence of environmental variance in the sire variance component. This in turn implies that an assumption of the breeding design is violated. In a paternal half-sib-full-sib design, the sire observational component is assumed to only include the additive genetic variance (barring epistasis), since maternal effects, dominance, and environmental effects  are controlled for with the breeding design and paternal effects are assumed to be absent. If now the sire variance component includes environmental variation  sexual genetic conflicts  to slightly (longevity) sexually dimorphic. This highlights that sexes should not be treated the same when it comes to estimates of quantitative genetic parameters. Many quantitative genetic studies have focused on one sex only  or simply ignored sexes. However, if sex differences are present, then estimates cannot be extrapolated to the population as a whole. Sex-specific genetic variance and onflicts . Moreoveonflicts . Thus, f


Genome-wide association studies (GWAS) have been used successfully in detecting associations between common genetic variants and complex diseases. However, common SNPs detected by current GWAS only explain a small proportion of heritable variability. With the development of next-generation sequencing technologies, researchers find more and more evidence to support the role played by rare variants in heritable variability. However, rare and common variants are often studied separately. The objective of this paper is to develop a robust strategy to analyze association between complex traits and genetic regions using both common and rare variants.wSC Bis sensitive to the directions of genetic effects, and it separates the deleterious and protective effects into two components. Another denoted by wSCd Bis robust in the directions of genetic effects, and it considers the difference of the two components. In our simulation studies, wSC Bachieves a higher power when the casual variants have the same genetic effect, while wSCd Bis as powerful as several existing tests when a mixed genetic effect exists. Both of the proposed tests work well with and without the existence of genetic effects from common variants.We propose a weighted selective collapsing strategy for both candidate gene studies and genome-wide association scans. The strategy considers genetic information from both common and rare variants, selectively collapses all variants in a given region by a forward selection procedure, and uses an adaptive weight to favor more likely causal rare variants. Under this strategy, two tests are proposed. One test denoted by Two tests using a weighted selective collapsing strategy provide potentially powerful methods for association studies of sequencing data. The tests have a higher power when both common and rare variants contribute to the heritable variability and the effect of common variants is not strong enough to be detected by traditional methods. Our simulation studies have demonstrated a substantially higher power for both tests in all scenarios regardless whether the common SNPs are associated with the trait or not. Genome-wide association studies (GWAS) have been used successfully in detecting associations between common genetic variants and complex diseases. However, common SNPs detected by current GWAS only explain a small proportion of heritable variability . These iRecently, a strategy that collapses all rare variants across a causal region was proposed . The ideHowever, collapsing methods have their own limitations and may not be robust. One limitation is that the classification of rare variants is subjective based on a certain threshold. Tests considering only rare variants cannot utilize genetic information of common variants and lose some power as a consequence. Weighted sum statistics ,13 were The proposed strategy tries to consider all information in a genetic region, including both common and rare variants. It addresses the genetic direction problem by using deleterious and protective components and overcomes the issue of non-causal variants by applying a forward selection procedure. To avoid selection bias, a permutation procedure is employed to find the P-value. The method is designed for candidate gene studies of qualitative traits, but it can also be used for genome wide association scan by applying a sliding window strategy and be used for any type of traits through a generalized linear model.wSC Band wSCdB) with several other tests under various scenarios. The tests are classified into three categories based on genetic resources: rare variants only, common variants only, and both rare and common variants, denoted by R, C, and B, respectively. There are three traditional collapsing methods: the indicator, the sum, and the weighted sum, denoted byind, sum, and wSum, respectively. For example, ind Rrepresents the test considering only rare variants in a genetic region using an indicator function as collapsing method for all rare variants without any selection. wSum Bis the test using weighted sum collapsing method combining all variants, where the weights are based on minor allele frequencies. The logistic-based single marker test of a common SNP with Bonferroni correction is denoted by bonC. The logistic-based multiple marker test for common SNPs is denoted by logitC. Let ind Band sum Brepresent the logistic-based multiple marker tests using all the common SNPs with an extra fake "common SNP", which is obtained by collapsing all rare variants through the indicator and the sum functions as collapsing methods. The selective collapsing method is denoted by SC, and the weighted selective collapsing method is denoted by wSC. The tests which only selectively collapse rare variants are denoted by as wOR Bbe the odds ratio based weighted sum test. SSU Band SSUw Bare SSU and SSUw tests. aSSU Band aSSUw Bare both adaptive sum tests using SSU and SSUw as test statistics for all variants. aSSUOrd Band aSSUwOrd Bare adaptive sum tests for ordered variants. KML Bis Logistic Kernel-Machine Test. Our proposed test are denoted by wSC Band wSCdB, which selectively collapse both common and rare SNPs according to the squared correlation coefficients and with data driven weights.In our simulation studies, we check the type-1 error rate and compare the power of the weighted selective collapsing method ' from a multivariate normal distribution with covariance structure Corr = 0.4 between any two observed components. Each observed common SNP has the same chance to correlate with the underlying causal SNP with Corr = a * 0.4, where a takes values 1 and -1 with probability 0.5. Each allele on the haplotype is generated with a minor allele frequency obtained from a uniform distribution between 0.1 and 0.3. Rare variants are generated independently with common SNPs, which are also from a multivariate normal distribution. Within each group of no causal rare variants and causal rare variants, LD structure is defined by Corr = 0.4i-j||. Each allele on a haplotype is generated with the cut-off of the minor allele frequency obtained from a uniform distribution between 0.001 and 0.005. Next, genotypes i X= ' for each individual are generated by the sum of two haplotypes. Last, the phenotype Yi is generated based on the logistic regression model with a given odds ratio and the order of genotypes have been shuffled. We consider five scenarios here. Scenario A is the null case where the odds ratios for all variants are set at 1. In Scenario B, rare variants are associated with the trait but common variants do not. We randomly selected eight with the customized odd ratio by parameter, OR between 1.3 and 3.1. Odd ratio of the half rare variants is defined as OR and another half is defined as OR plus one. For example, if OR is 2, then we consider Odds Ratio =  for eight casual rare variants. In Scenario C, both common and rare variants have effects on the traits, but effects from common variants are not significant enough to be detected by traditional association approaches. The odds ratio of the unobserved causal common SNP is set at 1.5. The odds ratios for rare variants are set in the same fashion as in Scenario B. Scenario D, which is quite similar to Scenario B, has a different odds ratio structure for rare variants. The odds ratio for half of them is set to be positive, while it is set to be negative for the rest. For example, if OR is 2, then we consider Simulated data are generated based on the strategies used in previous studies ,22. A ta- values is to simulate a large sample of test statistics from the asymptotic null distribution. We randomly select 1,000 simulation replicates and shuffle the phenotype data 1,000 times to generate data under the null hypotheses and compute the tests statistics for the asymptotic null distribution. We first consider Scenario A to check the type-I error rate. In Table For tests requiring a permutation procedure, a quicker way for calculating PwSC Bachieves the highest power under different OR. Roughly speaking, wSCB, wSumB, aSSUwOrd Bare the top five tests among 17 tests. Multivariate tests with common variants and an extra component from rare variants, ind Band sumB, have low power as expected, because common variants do not contribute to the trait variability so they are just noise. sum Rhas a consistently better performance than Rind. However, among all variants, more than half of them are non-causal, which are also noise in this case. Directly collapsing without any selection would lead to a loss of power. ind Rand sum Rby a selection procedure to remove the noise from the non-causal rare variants. wSumB, on the other hand, puts more weight on the rare variants to reduce noise in this scenario, resulting a better performance than previous tests. However, as shown in the appendix, the weights based on the estimated minor allele frequencies from controls tend to favor those deleterious rare variants and to ignore the protective rare variants. Thus, scenario B, where all causal variants are deleterious, is the optimal case for wSumB. wSC Bachieves the highest power by considering both common and rare variants with a selection procedure and a data driven weight which could benefit both deleterious and protective rare variants and reduce noise. wOR Bhas a lower power in this simulation study, because, for a region with the limited number of variants, we used the weights from log odds ratios without additional threshold. This may not be significantly enough to distinguish the true signal and noise. In this simulation study, the order of all variants is shuffled to have a fair comparison with adaptive tests. aSSUwOrd Bachieves a higher power by sorting the genotypes according to single test statistics and performs an adaptive SSUw test. aSSUwOrd Bhas a consistently better performance than SSUw Band aSSUw Bin both cases. SSUw based tests have a consistently better performance than SSU based tests.Under the alternative hypothesis, we first consider the case where all rare variants have the same genetic effect on the trait. In scenario B, where only rare variants are associated with the trait, we consider tests R and B, a total of 17 tests. The result is shown in Table ind Rand sum Rperform better than aSSUwOrdB. KML Band SSU Bhave the lowest power in this simulation study. KML Bhas a consistently better performance than SSUB. In scenario C, both common and rare variants are associated with the trait, but the association between common SNPs and the trait is not strong enough to be detected by the traditional association methods. We considered all 19 tests, the results are shown in Table wSCB, achieves the highest power in most case of OR, except when OR = 1.9, wSum Bhas a slight higher power. Roughly speaking, wSC BwSumB, aSSUwOrd Bare the top five among 19 tests. KML Band SSUB, either using a linear kernel or without using any weights on rare variants, result in the same power as bon Cand logit Cin this simulation study. The results of selected tests in scenario B and C, where all rare variants have the same genetic effect on the trait, are shown in Figure When the effect of rare variants is relatively weak (OR is from 1.3 to 2.2), wSCdB, achieves the highest power in scenario D for most case of OR. When OR = 1.9 and 2.8, aSSUwOrd Bachieves the highest power. When OR = 3.1, aSSUOrd Bachieves the highest power. Roughly speaking, wSCdB, aSSUwOrdB, aSSUOrdB, aSSUw Band wSC Bare the top five tests among 17 tests in scenario D. In scenario E, wSCd Band aSSUwOrd Bachieve the highest power in most cases. When OR = 1.3 and 1.6, wOR Bachieves the highest power. When OR = 1.3, 2.8 and 3.1, aSSUwOrd Bhas a higher power than wSCdB. When OR = 1.6, 1.9 and 2.5, wSCd Bhas a higher power. When OR = 2.2, they both achieve the same power. Roughly speaking, wSCdB, aSSUwOrdB, wSCB, aSSUw Band SSUw Bare the top five test among 19 tests. Being different from the results of scenarios B and C, the power of wSum Bdrops significantly, because the weights in wSum Bonly favor those deleterious rare variants and ignore the protective rare variants, which are as important as deleterious ones in this simulation. Although wOR Bachieves a low power because of limit number of variants, wOR Bhas performed consistently better than wSum Bin most cases under both scenarios. Due to the presence of the causal rare variants with opposite association directions and non-causal rare variants, other tests involving directly collapsing methods also have a lower power. On the other hand, SSU and SSUw based tests tend to perform well under these scenarios. aSSUwOrd Bbecomes one of the most powerful test in these two scenarios. We find that SSUw based tests combine both deleterious and protective genetic variations into the test statistic SSUw, while most collapsing methods only consider one of them. Having the same merit of aSSUwOrdB, our second proposed method wSCdB, which is based on the difference of the two components, achieves the higher power in most cases. The results of selected tests in scenario D and E, where rare variants have different genetic effect on the trait, are shown in Figure Now, we consider scenarios D and E where rare variants have different genetic effect on the trait. Tables wSC Bselectively collapses common and rare variants into two separate components with data-driven weights. The test statistic is derived by comparing these components, which is robust in situations with or without common variants. A permutation procedure is employed to find the P-value. Simulation studies show that the proposed tests achieve a higher power than other commonly used tests for rare variants in most cases. The optimal scenario for the proposed test is that when the common and rare variants both contribute to the heritable variability and effects of common variants are not detectable by traditional methods using common variants alone. If there is no association between the common variants and the trait, the proposed method also performs robustly as well as demonstrated by our simulation studies. We believe that the improved power comes from three sources. First, the test considers more genetic information by combining both common and rare variants instead of dealing with rare variants alone. Second, the test filters out the suspicious non-causal variants as noise and separates the variants into deleterious ones and protective ones by the selective collapsing method. Distinguishing deleterious and protective sources can improve the power when variants have different genetic effect on the trait. For example, in the worst case scenario, common variants have a deleterious effect, while rare variants collectively have a protective effect on the trait. The effects from the two sources will be neutralized if the effect directions are not distinguished. Our test can achieve a high power by choosing the strongest source in any cases instead of neutralizing them. The third reason for the improvement of the power comes from the data driven weights. Instead of using weights based on estimates of the minor allele frequencies from control data, which favor those deleterious rare variants and ignore the protective rare variants, the proposed test uses weights based on an estimate of the disease risk, which is the probability of an individual with disease mutation. The proposed weights tend to favor both deleterious and protective rare variants.In this paper, we proposed two novel association tests for candidate gene studies and genome wide association studies. The test wSCB) has many advantages, it is certainly not universally better than other tests. For example, in scenarios D and E, when the mixed genetic effect exists, wSC Bcan only capture the genetic effect in one direction. It can be used for detecting variants with the same genetic effect direction. Therefore, we also propose another test wSCdB, which can capture all genetic effect. It can be used for detecting a region of variants with opposite directions of genetic effects. We also would like to point out that the proposed test can be easily extended to include covariates since the tests are based on a logistic regression model. It can also be applied to quantitative traits by using a linear regression model. The strategy that collapsing rare variants based on common variants for qualitative trait in GWAS has been successfully applied to the simulated sequencing data from Genetic Analysis Workshop 17[Although the proposed test  function ,14. Let i Y= 1) = β0, + β1iS. The null hypothesis of no genetic effect is H0 : β1 = 0. In a candidate gene study, we employed the likelihood ratio test. Because the score test is computationally faster than the likelihood ratio test, we use the following tests for the genome wide association study. LetIn a case control study, it is natural to consider the logistic regression model for tests, and those collapsing methods can be achieved by: Logit Pr  solves tThe null hypothesis of no genetic effect is Another collapsing method uses a data-driven weight considering both common and rare variants.N0 is the number of controls in the study [where the weight is calculated by he study . By usinFor a region with both common and rare variants, the above two approaches consider all the genetic information. However, it is impossible that all variants in this region contribute to the heritable variability, and it is more likely that only some of them are causal. If many of rare variants are non-casual, collapsing will inevitably introduce noise and lose power of the test.2, which was used by the original authors, we considered the squared correlation coefficient R2 as the screening test statistic. Starting from a score without any rare variants, each rare variant is examined, and it is added into this score if it improves the test statistic the most. An optimal subset was obtained by a forward selection procedure to achieve the highest squared correlation between the collapsed score and traits. The test statistic then can be derived from a logistic regression model between the trait and the collapsed score as before. P-value can be found by permutation. However, this method does not consider genetic information from the common variants in this region and it ignores the direction of the rare variants by using either the squared correlation coefficient R2 or Pearson's χ2.A covering method called RareCover , has beeWe also compared some recently proposed methods, SSU tests, adaptivU = , where each component Let the score vector SSU = U'U And SSUw = U'Diag(If)-1U, Where f I= Cov(U) is the expected fisher information matrix. Asymptotic distributions of the above two test statistics are scaled χ2 distributions[ributions.m U= , where m<K, is the vector containing the first m components. Adaptive test statistics isFor the Adaptive test, suppose that Pval(T(mU)) is the p-value of the test statistic, T. For the Adaptive test, we used SSU and SSUw as the score of the test statistics T. The adaptive tests are called aSSU and aSSUw tests. More generally, one can order the SNPs based on the single test statistics and repeat the adaptive test process, resulting in the aSSU-Ord and aSSUw-Ord. The P-value of aT is calculated by a permutation procedure.where For the ORWSS test, the score is constructed in the same way as other weighted sum test.but the weight is calculated as follow.k γ= log(kOR), where k ORis the odds ratio for the kth marker.The amended estimator of the odds ratio is computed by adding 0.5 to each cell of the 2 by 2 table for case control studies. If we define σ is the standard deviation calculated from γk, k = 1, . . . , K, c is a parameter and where ds ratios. In the Then the test statistic is defined asP-value of ORWSS is calculated by a permutation procedure.For the Logistic Kernel-Machine Test, the test statistics is based on logistic regression with a kernel function of the SNPs.0 : h(X) = 0. The test statistics has been developed asSome commonly used kernels include linear, identity-by-descent (IBS) and quadratic kernels. We only consider the linear kernel here. In order to test whether there is a true genetic effect, the null hypothesis is Hχ2 distribution[which follows a scaled tribution.For all the tests above, we considered both common and rare variants, since we want to develop a robust strategy to detect any association between complex traits and genetic regions considering both common and rare variants.Now, we propose a new collapsing strategy, which considers genetic information from both common and rare variants. The new strategy tries to remove the noise generated by the non-causal variants and to improve the power by considering both deleterious and protective components of this region. In brief, our strategy is as follows. We defined rare variants as SNPs with minor allele frequencies less than 0.01, others as common variants. Starting from a null model without any variants, by a forward selection procedure, common SNPs are first selectively collapsed into two components, which will serve as bases for the rare variants. One is a deleterious component having an extremely positive correlation coefficient with the trait. Another is a protective component having an extremely negative correlation coefficient. Because rare variants have high genetic effects, they were added into the collapsed set one at a time by a weighted sum function until either there were no variants remaining, or there was no improvement of the correlation coefficient. Repeat the forward selection procedure without common variants as the basis, two more components were generated. Last, the collapsed score was obtained from the four components according to the measure of squared correlation coefficient with the trait. The test statistic then can be derived from a logistic regression model between the trait and the collapsed score as before. P-values can be computed by permutation.J common variants and K rare variants within a certain predefined genomic region. Let j = 1, . . . , J, and k = 1, . . . , K. Let S+ denote the deleterious component, which is a vector collapsed by the subset of the SNPs to achieve an extremely positive correlation. Let S- denote the protective component, which is a vector collapsed by the subset of the SNPs to achieve an extremely negative correlation.Now, we describe the procedure in details. Assume there are Step 1: Forward selection on common SNPs with sum collapsing.a) Calculate the correlation coefficient R for each common SNP with the trait. The common SNP with the largest correlation coefficient is added into andS+ and j = 1, . . . , J.where S+ and S- with j take values only from the remaining common SNPs. Repeat a) until either all common variants are collapsed into components or there is no improvement for the correlation coefficient of each component.b) Update Step 2: Forward Selection on rare SNPs with weighted sum collapsing.a) Because rare variants have high genetic effects, the data driven weight is derived as follows to favor the rare variants with high genetic effect in both deleterious and protective way.where ith sample in the kth rare variant. k pis the empirical estimate of the probability that an individual with the mutation will have the disease. wk is adjusted based on pk with the constraint that the sum of the weights is the number of rare variants.b) Calculate the correlation coefficient R for each rare SNP with the trait. The rare SNP with the largest correlation coefficient is added into andS+ and k = 1, . . . , K.where S+ and S- with c) Update Step 3: Construct the final collapsed score. Repeat Step2 considering rare variants only without the bases from common variants. Thus, our test can be robust when common SNPs are not associated with the trait. It will generate another two components, where The test statistic (wSC) can be derived from a logistic regression model between the trait and the collapsed score as before. P-values can be computed by permutation.wSC is constructed by comparing the potential effect of components in different directions. As an alternative, we also propose a method (wSCd) to detect the genetic effects and it is robust when the effects are in different directions. To find wSCd, we will follow all the same steps described before in deriving wSC, but the final collapsed score isSwhere YD contributed in the development of the statistical tests, provided simulation strategies, and drafted the manuscript. RJ and JD both supervised the whole process and participated in drafting the manuscript. All authors read and approved the manuscript.w and wc should have similar behavior.In the appendix, we show that the weight defined by  ^(1-q ^) tends toBy its definition w is a decreasing function of q, where q ∈ . Let R denote the odds ratio of case and control groups and r be the minor allele frequency in all samples for a given SNP. We haveandcaseN, control Nare the number of samples in cases, controls, respectively. The above equation can be written aswhere The relationship between p and q can be easily derived based on the value of R as follows.0. If rare variants potentially have protective genetic effect for the disease, then R < 1 and w < w0. This shows that the weight defined by Let  ^(1-q ^) tends to


The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate.In this paper, we propose joint analyses based on two robust tests, MERT and MAX3, for GWASs under a two-stage design. We developed computationally efficient procedures and formulas for significant level evaluation and power calculation. The performances of the proposed approaches are investigated through the extensive simulation studies and a real example. Numerical results show that the joint analysis based on the MAX3 test statistic has the best overall performance.MAX3 joint analysis is the most robust procedure among the considered joint analyses, and we recommend using it in a two-stage genome-wide association study. More details could be referred to p-values)π = 0.5, γ = 0.0001, the powers of ALLEJ, CATAJ, MERTJ and MAX3J are 0.070, 0.058, 0.385 and 0.759, respectively. Under the additive model, CATAJ and ALLEJ have comparable power and are more powerful than the other two tests. However, the power difference between CATAJ and MAX3J is mostly at the level of 6.6%, with the largest discrepancy of 7%. Under the dominant model, CATAJ and MAX3J are more powerful than ALLEJ and MERTJ. Both tests have comparable power when MAF = 0.15, and MAX3J is much more powerful than CATAJ when MAF = 0.25, 0.35 and 0.45. In summary, it appears that MAX3J has the best overall performance.For convenience, we refer to the aforementioned four joint analysis approaches as, respectively, ALLEJ , CATAJ , MERTJ , MAX3J . Table -4, 57 SNPs were selected and further screened on 2,617 cases and 2,894 controls in Stage 2. We applied the above four considered methods to two SNPs, rs1005316 and rs2876711, which were not reported in their Table p-values of these two SNPs. We found a genome-wide significant association between rs2876711 and the outcome. Although the association between rs2876711 and type 2 diabetes mellitus has not been reported by [Type 2 diabetes mellitus is one of the most common diseases, and has been found to be associated with environmental factors and genetic variants. A two-stage GWAS for type 2 diabetes mellitus was reported in . In thisIn genetic association studies, the underlying genetic inheritance model is often unknown, and thus hinders the use of methods such as CATT, which has to be derived under an assumed genetic model. Robust tests, such as MERT and MAX3, had been proposed to relax the dependence on the underlying genetic models. Extending these tests to a two-stage setting, we construct two robust joint analyses based on MERT and MAX3. Numerical results show that MAX3J has the best overall performance among the four considered joint analysis approaches. For type 2 diabetes mellitus, based on MAX3J, we found that SNP rs2876711 was significantly associated with type 2 diabetes mellitus besides their findings.e.g., [Pearson Chi-square test is a robust test that was used in genetic association studies . Recente.g., . They reRecently, a joint analysis based on genetic model selection  to overcGWAS: genome-wide association study; SNP: single nucleotide polymorphism; MAF: minor allele frequency; AFDT: allele-frequency-difference-based test; CATT: Cochran-Armitage trend test; MERT: maximin efficiency robust test; MAX3: maximum values of Cochran-Armitage trend tests under recessive, additive and dominant models; ALLEJ: allele-frequency-difference-based joint analysis; CATAJ: Cochran-Armitage trend test under the additive model-based joint analysis; MERTJ: MERT-based joint analysis; MAX3J: MAX3-based joint analysis.PD and LQ implemented the model. All authors read and approved the final manuscript.Appendix for the main text. The file  is a Microsoft Word document. Appendix A gives a detailed description of the joint distribution of the additive trend test statistic Click here for file


P<10−4) that cannot be explained by main effects, but no genome-wide significant signals. Some of these maternal and maternal-child associations were in or adjacent to autism candidate genes including: PCDH9, FOXP1, GABRB3, NRXN1, RELN, MACROD2, FHIT, RORA, CNTN4, CNTNAP2, FAM135B, LAMA1, NFIA, NLGN4X, RAPGEF4, and SDK1. We attempted validation of potential autism association under maternal-specific models using maternal-paternal comparison in family-based GWAS datasets. Our results suggest that further study of parental genetic effects and parent-child interaction in autism is warranted.Effects of parental genotype or parent-offspring genetic interaction are well established in model organisms for a variety of traits. However, these transgenerational genetic models are rarely studied in humans. We have utilized an autism case-control study with 735 mother-child pairs to perform genome-wide screening for maternal genetic effects and maternal-offspring genetic interaction. We used simple models of single locus parent-child interaction and identified suggestive results ( Autism is a heterogeneous neurodevelopmental disorder defined by deficits in language and social behavior as well as patterns of repetitive behaviors. Twin, sibling, and broader autism phenotype research has demonstrated the high heritability of autism spectrum disorders as well as autism-related traits Genetic studies of autism to date have been focused on genetic risk carried by the affected proband. The possibility that parental genotypes influence risk for autism, separately or in conjunction with the genotype of the proband, has yet to be investigated. There are two main types of effects that we consider here: parental main effects, where the genes in one generation have an effect on the phenotype in the next generation independently of inheritance, and transgenerational epistasis, where a combination of genes in the parent and the offspring lead to a phenotype. These effects deviate from classical Mendelian models and have not been widely studied. However, there are several examples in model organisms that demonstrate their importance 12 in the mother increase the risk of neural tube defects. Additionally, several groups have demonstrated that maternal genetic polymorphisms in MTHFR, which has a role in folate metabolism, and TCN2, which has a role in vitamin B12 transport, have an effect on the risk of NTDs There are several models through which transgenerational effects could contribute to autism risk. The first model is in relation to maternal main effects; specifically, this model postulates that genetic variation in the mother alters the fetal environment and impacts early neurodevelopment. One well-documented example of this type of maternal effect can be seen in neural tube defects (NTDs). It has been shown that low folate and vitamin BThe second model is that of maternal-fetal genetic interaction. One example of this might be genetic incompatibility. It is long-recognized that maternal-fetal cellular exchange occurs during pregnancy and birth and can have consequences for the fetus/neonate or the mother. Further, research has shown that fetal cells exist and survive long-term in the maternal system in a phenomenon known as microchimerism et al. found that rhesus macaques infused mid-gestation with serum from human mothers of children with autism went on to have offspring with autism-like phenotypes A well-understood example of transgenerational epistasis leading to a damaging maternal immune response in humans is Rh disease. Rh disease occurs when the fetus inherits an Rh+ allele from the father and the mother is Rh negative, causing a maternal immune response against the foreign Rh antigen expressed by the fetal blood cells. Some evidence has pointed to Rh incompatibility as a risk factor for neuropsychiatric disorders, such as schizophrenia In this study, we have used novel analyses to test specific models of maternal genetic effects in autism. We report the results of a case-control study using mother-child pairs to test novel models to investigate proband genetic main effects, maternal genetic main effects, and maternal-fetal transgenerational epistasis in autism.The committee for the Protection of Human Subjects (CPHS), California Health and Human Services Agency, an institutional review board that operates in compliance with the Common Rule under the authority of a Federalwide Assurance, reviewed the project and granted a waiver of HIPAA authorization. A designated member of the Kaiser Permanente Northern California (KPNC) Institutional Review Board (IRB) approved this study, including waiving the requirement for informed consent and waiving the requirement that Privacy Rule authorization be obtained from study participants. The Utah State University IRB granted this project (Protocol #1548) an Exemption because the samples were pre-existing and no identifying information accompanied the samples handled at the USU laboratory. The UCSF Committee on Human Research considers this to be non-human subject research, meeting the criteria that 1) The coded private information and specimens were not collected specifically for the current research project and 2) UCSF PI and holder of the key have an agreement prohibiting the release of the key (EMA) or there are IRB-approved written policies for the repository or data management that prohibit the release of the key (replication data).Our samples are part of the Early Markers for Autism (EMA) study, which has been previously described ASD and DD diagnoses were verified by abstraction and expert review of Regional Center medical records following a protocol initially developed by the Metropolitan Atlanta Developmental Disabilities Surveillance Program Specimens to be tested were coded with a study identification number. For maternal samples, blood cell pellets collected in 4ml serum separator tubes were shipped on dry ice to the biomedical laboratory at Utah State University for processing. The cell pellets were broken up with pipette tips and about 400 ul from each sample was removed and placed into test tubes. The QIAGEN QIAamp 96 DNA Blood Kit was used following the manufacturer's protocol.For newborn samples, one dried bloodspot (about 14 mm in diameter) collected on filter paper was entirely punched with a 3.2 mm paper punch. Since there is little DNA in a 3.2 mm paper punch, 15 to 18 punches were used for DNA extraction. Again, the QIAGEN QIAamp 96 DNA Blood Kit was used following the manufacturer's protocol. Three of the 3.2 mm punches were used per well.The Invitrogen Quant-iT DNA Assay Kit was used to measure the DNA concentration and the spectrophotometer absorbance was measured at 260/280 and 260/230 on 3 ul of sample to determine DNA purity (Bio-Tek Synergy H instrument using the Take3 plate).All maternal and neonatal samples were genotyped using the Affymetrix Axiom EUR array by the Genomics Core Facility (GCF) at UCSF, using standard protocols. The Axiom EUR array assays approximately 675,000 SNPs across the genome, and is optimized for genome-wide, gene-based, and candidate-SNP coverage Genotype calling and quality control were carried out separately for the neonatal bloodspot-derived DNA and maternal blood pellet DNA samples. For samples to be included in analysis, we required a genotype call rate >97%. We checked genetic relationships using PLINK's ‘–genome’ module to calculate pair-wise genetic relatedness between all individuals in our sample We performed Axiom genotyping on DNA from 1,706 EMA samples. Among our neonatal bloodspot samples (N = 852), 3 (0.03%) were excluded due to failed genotyping, 36 (4.2%) were excluded due to low genotype call rate, 13 (1.5%) were excluded due to apparent contamination, 26 (3.1%) were excluded due to sample misidentification, and 10 (1.2%) were removed due to unresolved affection status resulting in 88 (10%) total excluded samples. Among our maternal blood pellet samples (N = 854), 14 (1.6%) were excluded due to low genotype call rate, 18 (2.1%) were excluded due to apparent contamination, 22 (2.6%) were excluded due to sample misidentification, and 10 (1.2%) were removed due to their child's unresolved diagnosis, resulting in 64 (7.5%) total excluded samples. The higher rate of samples excluded due to low call rate among the neonates (4.2%) compared to the mothers (1.5%) is consistent with the quality of DNA extracted from the bloodspot samples being on average lower than the quality available from the blood pellet samples. After quality control, we were left with 764 offspring samples  and 790 maternal samples . These samples comprise 735 complete maternal-offspring pairs .P-value <10−10 using the Hardy-Weinberg exact test statistic We filtered out markers with genotype call rate <97%, Fisher's Linear Discriminant (FLD) score <3.6, and Heterozygous cluster strength offset (HetSO) value <−0.1 as outlined in the Axiom Advanced Analysis Workflow. After these steps were carried out separately in the maternal and neonatal sample sets, the samples were combined and markers that were excluded in either set were subsequently excluded from transgenerational analysis. After combining the maternal and neonatal samples, we tabulated Mendelian errors of inheritance in all of our complete maternal-baby pairs and additionally excluded markers with >10 of these errors. We also excluded markers that violated Hardy Weinberg Equilibrium (HWE) in control mothers, as judged by a Replication data are comprised of five datasets that have been used in previous and ongoing GWAS of autism. The first dataset is comprised of samples genotyped by the Autism Genome Project (AGP) and includes 4,074 samples; these samples were genotyped using the Illumina Human 1M-single Infinium BeadChip array http://faculty.washington.edu/browning/beagle/beagle.html) with 1000genomes reference data (http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes.phase1_release_v3/) In order to replicate our results using datasets genotyped on different array platforms, we imputed our replication samples using BEAGLE  test for repeated tests of independence to compare the relative proportions of allele frequencies or binary classifiers between cases and controls. In order to stratify pairs for the purposes of this CMH test we clustered samples using PLINK's ‘–cluster’ module We carried out case/control tests separately in the maternal and neonatal samples using PLINK's ‘– cmh’ module To test the hypotheses of transgenerational epistasis, we created three binary classifiers, as follows: 1) pairs where the offspring has an allele the mother does not vs. pairs where the mother possesses at least one copy of each allele present in the offspring (“Offspring Heterozygous” model), 2) pairs where the mother has an allele the offspring does not vs. pairs where the offspring possesses at least one copy of each allele present in the mother , 3) pairs where the mother and offspring genotypes are identical vs. pairs where they are not identical (“Difference” model) as shown in Using the determined population strata, our genome wide CMH tests in the EMA discovery sample of proband main effects (λ = 1.002), maternal main effects (λ = 1.011), “Offspring-Heterozygous” (λ = 0.991), “Maternal-Heterozygous” (λ = 0.997), and “Difference” transgenerational epistatic effects (λ = 0.998) had no genomic inflation with accompanying q-q plots shown in http://www.staff.ncl.ac.uk/richard.howey/emim) and described extensively In order to demonstrate that maternal effects are not driven by proband main effects, and that transgenerational effects are not driven by a combination of maternal and proband main effects, we employed a multinomial maximum likelihood model (MMLM) as implemented in the EMIM software package . These parameters are estimated from the pedigree data assuming HWE and random mating. To assess fit in our proposed transgenerational effect models, we define the null model to account for neonatal and maternal genetic effects and our model of interest includes the additional interaction parameter. For example, in our Difference model where a mother is homozygous and the neonate is heterozygous, we would consider 1S2α as the null model and contrast it to our model of interest 1S2jcα using a LRT:To assess fit in our proposed transgenerational effect models, the EMIM package estimates probabilities for Further methods and parameters are given in In order to replicate the results of case/control tests in the probands, we carried out transmission disequilibrium tests (using PLINK's TDT module) in available family-based autism datasets We devised equivalent tests to our transgenerational epistasis models for replication in the available family-based autism datasets. To replicate the “Offspring Heterozygous” model, we considered all affected offspring trios with heterozygous offspring and compared the number of homozygous mothers to homozygous fathers using a chi-square test. To replicate the “Mother Heterozygous” model, we considered all affected offspring trios with homozygous offspring and compared the number of heterozygous mothers to heterozygous fathers. To replicate the “Difference” model, we considered all affected offspring trios and compared the number of mothers vs. fathers with the opposite genotype as the affected proband .P – values, odds ratios and standard errors as input for all models in each dataset P-value is from the random-effects meta-analysis statistic. In cases where an allelic combination had zero observations in any single dataset, the replication datasets were considered jointly and the merged replication P-value is presented.Several datasets genotyped on different platforms and collected by research consortia with disparate recruitment methods were used for replication. Therefore, we carried out random-effects meta-analysis in PLINK to combine the results into one meta-replication statistic specifying P<10−5) were on chromosome 8p between CSMD1 and MCPH1 , 13q in the intron of SPATA13 , 8q in the intron of COL22A1  and14q between GCH1 and WDHD1   . All ProP-value <0.05). Our strongest associations (P<10−5) that also showed evidence for maternal main effect in our MMLM (LRT P<0.05) were on chromosome 11q in the intron of MAML2 , 17p in the intron of GAS7 , 1p between PRKACB and SAMD13 , 17q between TMEM97 and NLK  and 13q between KLF5 and KLF12   including both maternal and proband main effects and only proband main effects using a likelihood ratio test (LRT). We found that in 93% (54/58) of our top results under the maternal main effect test, a model which included both maternal and neonatal main effects showed a significantly better fit than a model including only neonatal main effects (LRT 24×10−4) . All MatP-value <0.05). Our most suggestive associations (P<10−5) which also showed evidence for “Offspring Heterozygous” effect in our multinomial maximum likelihood model (LRT P<0.05) were on chromosome 7q in the 3′UTR of SEMA3C , 17q in the intron of ACCN1  and 18q in the intron of SLC14A2   of top association results showed that a model which included both offspring-heterozygous and main effects constituted a significantly better fit than a model including only main effects (LRT 41×10−4) . All OffP-value <0.05). Our most suggestive associations (P<10−5) which also showed evidence for “Maternal Heterozygous” effect in our multinomial maximum likelihood model (LRT P<0.05) were on chromosome 9q in the intron of PALM2  and on 16p in the intron of KIAA0430  (P<10−4 are listed in We found that in 71% (20/28) of our top maternal-heterozygous effect SNPs, a model which included both maternal-heterozygous and main effects constituted a significantly better fit than a model including only main effects (LRT  = 0.01) . All MatP-value <0.05). For the “Difference” test, our lowest P-values (P<10−5) which also showed evidence for “Difference” effect in our multinomial maximum likelihood model (likelihood ratio test P<0.05) were on chromosomes 4q between C4orf37 and UNC5C , on 11p between INSC and SOX6 , on 14q in the intron of SLC7A8  and on 15q in the intron of GABRB3   of our top difference effect SNPs a model which included both difference and main effects constituted a significantly better fit than a model including only main effects (LRT  0.0326) . All DifOverall, we observed that the MMLM LRT corresponding to the appropriate model provided the best fit for the majority of the top SNPs from each case control test, i.e. for the top offspring-heterozygous effect SNPs a LRT including maternal-heterozygous effects did not produce a greater proportion of significant results than the LRT including the appropriate offspring-heterozygous effect.P<10−4 in our discovery samples using meta-analysis of five imputed family-based autism datasets . For the proband main effects, we found a nominal association by TDT on 2q between NXPH2 and LRP1B  (We attempted replication of results that achieved a significance level of = 0.024) . HoweverCD83   to assess maternal-specific effects, we found a significant replication (meeting a Bonferroni threshold) on 6p in the intron of = 0.001) .P<0.05 and with an effect size in the same direction as in the discovery samples   . This reP<10−4 level in or near several genes previously implicated in autism: NLGN4X, RAPGEF4, RORA, FAM135B and CNTNAP2NXPH2 and LRP1BP<5×10−8), they are not definitive. We cannot rule out the possibility that maternal genetic risk factors make no contribution to ASD. Alternatively, our lack of conclusive results may be a direct result of our study size (385 cases and 379 controls); an analysis of power shows that we have 80% power to detect only common variants  with genotype relative risk of at least 2 at genome-wide significance, or at least 2.5–3.5 for less common alleles. These effects are larger than those observed for SNP effects in most complex genetic disease, and even in our main effect proband case-control test we did not identify significant findings or replication in family-based data, despite strong evidence that common SNPs contribute to ASD risk Our analysis of proband genetic main effects constitutes a case/control GWAS of autism; previous autism studies employ primarily family-based designs et al. estimate that the shared environmental component of autism accounts for more of the variability in autism liability than previously appreciated KLF5, a transcription factor regulated by mastermind-like signaling and placentally expressed P<10−4 level near several genes previously implicated in autism, although not in our analysis of proband effects: PCDH9 and FOXP1We conducted the first case/control GWAS of maternal genetic main effects which considers having an affected child to be the relevant phenotype. In a recent twin study of autism, Hallmayer KIAA0430CD83, an immunomodulator also expressed neuronally P<10−4 level, such as MACROD2, RORA, SDK1, GABRB3, PCDH9, NRXN1, RELN, FHIT, CNTN4 and LAMA1Relevant to our models of maternal-fetal interaction, interestingly, one top signal is near a gene known to be immunogenic, We have presented a novel study design and methodology for surveying transgenerational genetic effects on a genome-wide level. By using matched case and control pairs, we can potentially detect over- or under-representation of certain maternal-offspring genotype pairings in autism, and establish the directionality and magnitude of these effects. When we see a putative ‘maternal effect’, we first need to rule out that this result is simply driven by direct risk contributed by the alleles inherited by the proband, which we have done by use of a likelihood ratio test. The second possibility is that there is a main effect of maternal genotype, for example determining the prenatal developmental environment, which we have tested in our maternal main effect model. Finally, we could be observing maternal-fetal genetic interaction, which we have tested in several specific models. We believe that our methods are easy to interpret within this framework on a genome-wide scale and take full advantage of our case/control study design.One challenge we encountered was the lack of a replication dataset that uses the same study design as our discovery samples. We were able to address this issue by implementing methods that would allow us to detect similar effects using family-based replication datasets. In order to do this, we make the assumption that maternal genetic effects will be distinct from paternal genetic effects, of which there are demonstrated examples P-value surviving Bonferroni correction and an effect size in the same direction as in the discovery data, was not more than expected. One factor in this could be the limitations of our imputed replication data. Because the design of the Axiom array is so different from the design of previous arrays, many of the SNPs it assays are poorly covered by the arrays used in our replication datasets. As a result, not all of the top discovery results had equivalent imputed genotype calls with high enough imputation quality to be included in our study, effectively reducing our ability to analyze all markers in our replication data. In our replication method for transgenerational tests this effect is even more exaggerated, since only trios where one parent is heterozygous and one is homozygous are informative. This, combined with data lost during our post-imputation QC, makes our effective replication sample size and marker coverage modest even though we used over 3,000 unique trios for replication.While we do report nominal associations in our replication data, the number of results which were successfully replicated, as indicated by a In conclusion, we have presented a new model for genetic studies of autism that could also be applied to other diseases in order to search for parental genetic effects. Parental main effects of genotype and maternal-fetal incompatibility are important mechanisms to be considered in human disease, and we believe that our methods are a straightforward way to search for these effects on a genome-wide level in case-control or family-based datasets. Although we have identified some promising initial results, individual associations detected here will require replication and further study. Future study of maternal genetic effects could also consider the interaction of maternal genetics with the external environment, for example, maternal exposures during pregnancy.Figure S1Multidimensional-scaling plot of the EMA discovery cohort.(DOCX)Click here for additional data file.Figure S2Quantile-quantile plots for genome-wide application of our EMA discovery cohort tests.(DOCX)Click here for additional data file.Figure S3Quantile-quantile plots for genome-wide application of our transgenerational epistasis replication methods.(DOCX)Click here for additional data file.Methods S1Replication Datasets.(DOCX)Click here for additional data file.Methods S2Imputation of Replication Datasets.(DOCX)Click here for additional data file.Methods S3Stratification of the Discovery Sample.(DOCX)Click here for additional data file.Methods S4Multinomial Maximum Likelihood Modeling.(DOCX)Click here for additional data file.Methods S5Replication Methods.(DOCX)Click here for additional data file.Table S1Replication Datasets.(DOCX)Click here for additional data file.Table S2Parameterization of Multinomial Model.(DOCX)Click here for additional data file.Table S3Parameters Estimated for the Likelihood Ratio Test.(DOCX)Click here for additional data file.Table S4Top results (P−4) from the CMH test of allele frequencies in the proband samples.<10(DOCX)Click here for additional data file.Table S5Top results (P−4) from the CMH test of allele frequencies in the maternal samples.<10(DOCX)Click here for additional data file.Table S6Top results (P−4) using the “Offspring Heterozygous” model of transgenerational epistasis.<10(DOCX)Click here for additional data file.Table S7Top results (P−4) using the “Maternal Heterozygous” model of transgenerational epistasis.<10(DOCX)Click here for additional data file.Table S8Top results (P−4) using the “Difference” model of transgenerational epistasis.<10(DOCX)Click here for additional data file.


Carcass fatness is an important trait in most pig breeding programs. Following market requests, breeding plans for fresh pork consumption are usually designed to reduce carcass fat content and increase lean meat deposition. However, the Italian pig industry is mainly devoted to the production of Protected Designation of Origin dry cured hams: pigs are slaughtered at around 160 kg of live weight and the breeding goal aims at maintaining fat coverage, measured as backfat thickness to avoid excessive desiccation of the hams. This objective has shaped the genetic pool of Italian heavy pig breeds for a few decades. In this study we applied a selective genotyping approach within a population of ~ 12,000 performance tested Italian Large White pigs. Within this population, we selectively genotyped 304 pigs with extreme and divergent backfat thickness estimated breeding value by the Illumina PorcineSNP60 BeadChip and performed a genome wide association study to identify loci associated to this trait.We identified 4 single nucleotide polymorphisms with P≤5.0E-07 and additional 119 ones with 5.0E-07<P≤5.0E-05. These markers were located throughout all chromosomes. The largest numbers were found on porcine chromosomes 6 and 9 (n=15), 4 (n=13), and 7 (n=12) while the most significant marker was located on chromosome 18. Twenty-two single nucleotide polymorphisms were in intronic regions of genes already recognized by the Pre-Ensembl Sscrofa10.2 assembly. Gene Ontology analysis indicated an enrichment of Gene Ontology terms associated with nervous system development and regulation in concordance with results of large genome wide association studies for human obesity.Further investigations are needed to evaluate the effects of the identified single nucleotide polymorphisms associated with backfat thickness on other traits as a pre-requisite for practical applications in breeding programs. Reported results could improve our understanding of the biology of fat metabolism and deposition that could also be relevant for other mammalian species including humans, confirming the role of neuronal genes on obesity. Fat deposition is a key biological process that has important similarities between humans and pigs, potentially useful to elucidate mechanisms determining human obesity. This trait has practical and economical implications in pig breeding as it indirectly affects feeding efficiency and determines carcass value and consumers’ acceptance of pork.Following consumer demands, breeding goals for fresh pork generally aim at reducing carcass fatness and increasing lean meat content which has adversely affected pork quality (e.g.).The Italian pig breeding industry is mainly devoted to the production of high quality Protected Designation of Origin (PDO) dry cured hams for which pigs are raised until they reach about 160 kg live weight and appropriate fat coverage of the hams is required,3. ThereIGF2)In our study, genotyped pigs had extreme and divergent EBV for BFT: 151 had the lowest (thinnest BFT) and 152 the highest (thickest BFT) EBV. These animals were two generation unrelated gilts taken from the performance test of the National selection program of the Italian Large White breed carried out by the National Pig Breeders Association (ANAS).nominal value thresholds for significant results were those indicated by the Wellcome Trust Case Control Consortium, WTCCC)Genomic DNA was extracted from dried-blood by standard protocols. Based on quality control, all animals were used for genotyping using the PorcineSNP60 BeadChip developeEstimated breeding values for BFT were calculated in the population using a BLUP-Multiple Trait-Animal Model that included the fixed effect of sex (considering the triplets of pigs from the same litter), batch on trial, inbreeding coefficient of the animal, interaction of sex by age at slaughtering, date of slaughtering and random effect of litter and animal. Three criteria were used to filter animals and SNP before association analysis: call rate >0.9 both at the 1) animal and 2) SNP level, and 3) MAF>0.05. Animals and SNPs that passed these filters were taken for association analysis treating the two groups as cases and controls. Full pedigree information available was used to obtain a kinship matrix. In order to correct for possible family-based stratification  and the phenotype of animal j, respectively, a PC-based adjustment was performed on genotypes and phenotypes according to the following formulas:For ckj is the score of the k-th component on animal j, βki and γk are the partial regression coefficients for predicting the i-th genotype and the phenotype, respectively, on the basis of the k-th component .where n − K − 1)ri2, whereThe association test-statistic is computed as  and Pre-Ensembl (http://pre.ensembl.org/Sus_scrofa/Info/Index) databases (February 2012). Coordinates for the Sscrofa10 genome preliminary version (September 2010) were downloaded from the Animal Genome repository web sitehttp://www.animalgenome.org/repository/. Identification of the closest genes to SNPs with P<5.0E-05 was obtained using Pre-Ensembl annotation of Sscrofa10.2 genome version and verified using Ensembl Sscrofa9.2 genome version (February 2012). Starting from the corresponding protein sequences retrieved from these databases, the corresponding gene symbols were extracted from NCBI Gene section (http://www.ncbi.nlm.nih.gov/) and/or Uniprot (http://www.uniprot.org/) databases (February 2012). Gene annotation was verified by BLAST analysis (http://blast.ncbi.nlm.nih.gov/). Gene Ontology analysis was carried out using DAVID Bioinformatics Resources 6.7 , their chromosome positions and their closest genes in Sscrofa10.2 (Pre-Ensembl). Notes are the same as those reported for TableClick here for fileFigure S1. Two-dimensional graphical representation of relatedness among animals based on a multidimensional scaling representation of pedigree-based kinship matrix. Different symbols are used to denote pigs with positive or negative backfat thickness estimated breeding values.Click here for file


A genetic predisposition to cluster headache (CH) has long been debated. Genetic epidemiological studies have reported an increased risk of CH in relatives of CH sufferers. The familial clustering supports a model of autosomal dominant inheritance with reduced penetrance. Some candidate gene studies have been performed, detecting associations with HCRTR2 and ADH4, but these have been limited by small sample size. We have established a large cohort of CH families in which we have previously reported a genome-wide linkage scan, isolating a number of putative linkage loci. DespiteTo further delineate the genetic architecture underlying CH, we have used an exome sequencing strategy in a subset of Northern European families.Exome target enrichment and paired-end sequencing were performed for ten probands. Annotated variants were filtered to exclude known polymorphisms, leaving a total of 1711 novel variants. Exome data from related affected subjects were examined to limit the analysis to variants segregating with the CH phenotype. Segregation analysis by Sanger sequencing in all family members reduced the candidate list to a total of 45 genes (range: 1-13 genes per pedigree). These genes are now being screened in our extended cohort to provide further insight into the role of each gene in CH pathogenesis.Whilst exome sequencing for rare monogenic disorders is now well-established, approaches to detect pathogenic variation with complexities such as locus heterogeneity and incomplete penetrance remain challenging. We have combined exome and Sanger sequencing to isolate novel coding variation segregating across CH families, highlighting the need for large homogeneous cohorts to elucidate the molecular genetic basis of CH. The significance of these variants in CH pathogenesis remains to be determined; however these results provide further evidence for a potential genetic predisposition to this debilitating disorder.