The puzzle of the disparity between molecular- and traditional behaviour genetic study findings has prompted widespread discussion. Fundamental questions have been raised across the whole field of complex genetic traits as well as for behavioural traits. We consider explanations for recent findings and discuss what they mean for the field of developmental psychopathology. Findings from a recent twin-based molecular genetic study of cognitive and behavioural traits . A new method of analysis assesses heritability that is attributable to the measured SNPs. Recent genome-wide association (GWA) analyses have provoked debate because they generate ‘SNP heritability’ estimates for behavioural traits that appear to be dramatically lower than those observed for other traits and when using traditional designs; although SNP heritability  and typically have exploited case–control comparisons or the correlation of DNA variation with trait variation in a population.Traditional behaviour genetic approaches statistically infer familial risk and heritability from observed patterns of phenotypic similarities between relatives who differ in their degree of biological relatedness . Whilst there are a number of important assumptions and limitations inherent in these designs , sometimes referred to as GCTA after a software program in which this method is implemented . Variation in the whole genome is not measured; this is important as there are many other classes of DNA variation. Also, the measured SNPs should be viewed as risk markers because they are selected only to ‘tag’ or provide an indirect index of DNA variation that will include the truly causal SNPs. Whole genome molecular genetic designs have utilised three key approaches [see Wray et al.  for a sch2 estimates for behavioural traits so much lower than those generated from traditional behaviour genetic designs and why does SNP h2 appear to be especially low for behavioural traits in comparison to other characteristics such as cognitive ability  and sequence (nucleotide) variants, gene–gene interaction and gene–environment interaction effects. GCTA assesses additive effects of the measured common SNPs and is thus known as SNP heritability; additive SNP variance provides a more specific description. ‘SNP heritability’ is thus expected to be lower than twin heritability because they refer to different measures.Building from this brief synopsis of molecular genetic approaches to genetic risk estimation for psychopathology, the issues we are focusing on in the present contribution is why are SNP based Drosophila melanogaster (fruit fly). Gene-by-environment (G × E) interactions also occur across multiple biological levels. All such effects would be encompassed in a broad-sense heritability estimate from traditional studies but not by SNP heritability.Would we be surprised, for example, if multiple measures of adversity in a twin study failed to explain all the environmental variance? They would be unlikely to explain all population-trait variance. We would realise that (a) our environmental measures likely provide an index rather than a direct assessment of each causal risk; (b) we have not captured all relevant environmental exposures and that (c) interactions between different environmental risks are also likely to be operating. That is also the case for GWAS where SNPs do not provide a direct assessment of every variety of causal genetic risk, and testing multiple interaction effects for every risk marker across the genome is not statistically feasible. Moreover; we would realise that the effects of environment and genes, whilst neatly partitioned for statistical purposes in behaviour genetic studies, are of course highly correlated and interdependent Plomin, . These fThe study conducted by Trzaskowski et al.  is, howeDo these findings mean that common gene-variant contributions are restricted to those with a psychiatric diagnosis and are very much less prominent for behavioural traits? In general, statistical power is enhanced when selecting extremes. Also, those with a psychiatric diagnosis, especially those who are referred to clinics, likely score high on multiple trait measures . Theoretically these factors could also contribute to higher enrichment of common genetic risk variants in those with a diagnosis. Nevertheless, there is evidence of SNP additive variance contributing to questionnaire-generated trait/behavioural measures from some studies. For example, a recent study showed significant ‘SNP heritability’ (0.18) for social communication/ASD traits (St Pourcain et al., Having established that SNP additive genetic variance has been found to contribute to some traits as well as psychiatric disorders, we can ask is it possible that different behavioural traits have different genetic architectures? That is, is SNP additive variance low for some traits, because other types of risk factors are more important? For example, it is possible that some traits are more strongly explained by rare genetic variants such as copy number variants (CNVs; Thapar & Cooper, Another issue to consider is whether measure and rater effects contribute to inflation of twin heritability estimates, differing patterns of SNP heritability for different traits as observed by Trzaskowski et al.  as well In conclusion, as discussed in this issue (Wray et al.,


CPT1A, CPT1B, CPT1C, CPT2, CRAT and CROT, encode for three carnitine acyltransferases that are important for the oxidation of fatty acids, a critical step in their metabolism.Nunavik Inuit  reside along the arctic coastline where for generations their daily energy intake has mainly been derived from animal fat. Given this particular diet it has been hypothesized that natural selection would lead to population specific allele frequency differences and unique variants in genes related to fatty acid metabolism. A group of genes, namely Exome sequencing and SNP array genotyping were used to examine the genetic variations in the six genes encoding for the carnitine acyltransferases in 113 Nunavik Inuit individuals.CPT1A, CPT1B, CPT1C, CPT2 and CRAT, including three novel variants and one Inuit specific variant CPT1A p.P479L (rs80356779). The latter has the highest frequency (0.955) compared to other Inuit populations. We found that by comparison to Asians or Europeans, the Nunavik Inuit have an increased mutation burden in CPT1A, CPT2 and CRAT; there is also a high level of population differentiation based on carnitine acyltransferase gene variations between Nunavik Inuit and Asians.Altogether ten missense variants were found in genes The increased number and frequency of deleterious variants in these fatty acid metabolism genes in Nunavik Inuit may be the result of genetic adaptation to their diet and/or the extremely cold climate. In addition, the identification of these variants may help to understand some of the specific health risks of Nunavik Inuit. Modern Inuit are descendants of the Dorset peoples from the second wave of migrations which took place over 1000 years ago , and belCPT1A, CPT1B, CPT1C and CPT2; (2) carnitine acetyltransferase (CrAT) encoded by CRAT; and (3) carnitine octanoyltransferase (CrOT) encoded by CROT [ CPTs include two members, CPT-I and CPT-II. As a rate-limiting step in fatty acid oxidation, CPT-I converts long-chain fatty acyl molecules into their corresponding acylcarnitines, which are then transported across the inner mitochondrial membrane for beta-oxidation. CPT-II is responsible for the reversal of this process [The oxidative processing of fatty acids is critical for generating energy from a diet enriched with animal fat. Several enzymes are involved in the metabolism of fatty acids, including the carnitine acyltransferases . The car by CROT . The gen process . CrOT an process .CPT1A and CPT2, heterozygous carriers of CPTs mutations can also be symptomatic [CPT-I and II deficiencies are genetic disorders that are characterized by the reduced CPT enzyme activity which can lead to severe consequences, such as liver failure and renal problems , 7, 8. Aptomatic , 10.Considering their critical roles in lipid metabolism and energy generation, the six genes encoding carnitine acyltransferases may be subjected to evolutionary pressure, especially in populations such as the Inuit, which require an increased enzyme activity because of their special diet and extreme living conditions. Recent studies have shown that the diet of Nunavik Inuit led to changes in specific biomarkers. For instance, Nunavik Inuit have very high levels of n-3 polyunsaturated fatty acids (PUFA), especially in the elderly , as wellWe recruited 113 Inuit individuals  from 13 inhabited villages of Nunavik. The study was approved by Comité d'éthique de la recherche du Centre hospitalier de l'Université de Montréal (ND 04.101)  and the Nunavik Nutrition and Health Committee . Individual written consent was obtained from each participant before entering this study.The genomic DNA of 113 Nunavik Inuit individuals was extracted from peripheral blood lymphocytes using Gentra Systems PUREGENE DNA purification kit (Qiagen). A 50 μl DNA sample at a concentration of 100 ng/μl from each sample was captured by Agilent SureSelect 50mb/V4 capture kit. The library was subsequently sequenced at 100 bp pair-end using Illumina Hiseq 2000, with 3 samples per lane to ensure an average coverage depth of 100-fold.Raw fastq files were aligned to NCBI human reference GRCh37 using Burrows-Wheeler Aligner (BWA) , with alCPT1A, CPT1B, CPT1C, CPT2, CRAT and CROT were examined, and were further validated by manual inspection using the Integrative Genomics Viewer 1.4 (IGV) [In the variant analysis, intronic, intergenic and non-coding variants were excluded because of insufficient coverage. Quality filters were set at sequencing depth ≥20x and variant frequency ≥25%, with genotype quality ≥10. Rare variants were defined as variants with minor allele frequency (MAF) ≤0.01 in the aforementioned public databases. All potential missense and splicing site variants from the genes .4 (IGV)  and SangSNP genotyping was performed on 113 Nunavik Inuit, using Illumina HumanOmniExpress-12 SNP array, which contains 730,525 SNPs. Raw data was processed and analyzed using Illumina GenomeStudio and quality control was performed by PLINK 1.07 . SNPs wiMultidimensional scaling (MDS) and Admixture analyses  were perMissense variations of the carnitine acyltransferase genes were retrieved from 286 Chinese (CHB) and Japanese (JPT) from the 1KGP database and European descendants from EVS, to serve as controls.Mutation burden is defined as the average number of rare (MAF ≤0.01) missense variant alleles per person. To examine whether there is an increased mutation burden in the selected genes in Nunavik Inuit compared to other populations, we performed a binomial test to compare rare allele frequencies between Nunavik Inuit and 1KGP Asians. The Adaptive Permutation test was also performed to compare the mutation burden in Nunavik Inuit with Asians. The Permutation test was performed five times based on the number of variant-containing genes, with Bonferroni correction for multiple comparisons applied and p <0.01 was set as statistically significant.FST and FIS) scores based on variants identified in the carnitine acyltransferase genes. The FST value provides an indication about the population genetic difference that is due to the genetic drift between subpopulations. The FIS value measures the proportion of total inbreeding within a population that is due to non-random mating within subpopulations [CPT1A, CPT1B, CPT1C, CPT2 and CRAT in Nunavik Inuit, 1KGP Asians and EVS Europeans. Mann-Whitney U test was performed to compare the PolyPhen-2 scores of all rare missense variants found in Nunavik Inuit and 1KGP Asian populations, with p <0.05 as significant.We also calculated two F-statistics , the Polyphen-2 scores of all the rare variants found in Nunavik Inuit and 1KGP Asian population were still significantly higher  for Nunavik Inuit . Permutation test showed similar results with marginally significant p values in CPT2 and CRAT (not significant after Bonferroni correction)  from 1KGP. Nunavik Inuit had significantly more mutated alleles per individual in the rection) .FST and FIS scores were calculated for 100 Nunavik Inuit and 83 HapMap Asians (CHB and JPT). The CPT1A p. P479L variant had the highest FST value (0.92), and the mean FST of the 4 Inuit specific variants was also statistically significant . However, the mean FIS of all variants did not significantly deviate from zero after 1,000 randomizations (-0.0092) (-0.0092) .CPT1B missense variants  were in an 8 kb LD block. The frequency of this haplotype in the Inuit (0.66) was significantly higher than in Asians (0.46) . In CPT2, two common missense variants rs2229291 (p.F352C) and rs1799821 (p.V368I) are in a 1 kb LD block  of all variants indicates a great genetic differentiation between Nunavik Inuit and Asians (FST >0.15); it is also noteworthy that the 4 Inuit specific variants are indicating an even higher degree of differentiation for this population (FST >0.25)  . HoweverCRAT p.S78F mutation is located at the splicing site and is predicted to affect splicing, potentially to leading to a loss of function of CrAT and carnitine acetyltransferase deficiency. Moreover, in a muscle-specific CrAT knockout mouse model, CrAT acts as a modulator of whole-body glucose homeostasis and metabolic flexibility [CPT2. The CPT2 p.R477W mutation found in the Nunavik Inuit is likely to lead to the loss of function as it is located in a highly conserved region where mutations known to cause CPT-II deficiency occur [CPT2 may have a beneficial effect on the enzyme activity in Nunavik Inuit.The xibility . The Inucy occur , 29. It CPT1A; yet in some Inuit populations, the p.P479L loss of function mutation has a high frequency, ranging from 44% to 83% from Alaska to Greenland [CPT1A p.P479L mutation is significantly more frequent among Nunavik Inuit compared to 243 Kivalliq Inuit (western Nunavut)  [CPT1A p.P479L mutation is absent from all other worldwide populations, including East Asians  anAlthough different disorders and traits associated with this variant were reported in different studies, the presence of this variant in very high frequency in Nunavik Inuit suggests that it is likely to be beneficial in this population. For example, it may help the arctic residents to adapt to the extremely cold environment and/or their ketogenic diet. This assumption is supported by the mean age difference found in previous studies and in the current study. Previously, it was reported that the p.P479L variant frequency is higher among Inuit newborns and children , 35. HowCPT1B variant in Nunavik Inuit. Nevertheless, the relative frequencies of different CPT1B locus rs470117/rs8142477/rs3213445 containing haplotypes found in the Nunavik Inuit are different from those seen in Asian populations. Interestingly, this haplotype is in the same LD block as the SNP rs5770917, which was reported to be associated with narcolepsy in the Japanese population [The mutation burden and tolerance tests did not show any excess of rare missense pulation , 42. GivCPT2 missense mutations are rare in the general population, suggesting that the variations in the CPT2 gene seen in Nunavik Inuit may be functional, possibly related to the energy metabolism requirements unique to this population. The haplotype containing p.F352C and p.V386I variants was previously named as a thermolabile CPT-II variant [CPT2 p.F352C variant was also reported in three individuals from one Inuit family with CPT-II deficiency [ variant  with dec variant , 43, 44.ficiency . HoweverIn this study, we observed an increased frequency of rare divergent variants in the carnitine acyltransferases family of genes in Nunavik Inuit in Quebec, as compared to other populations. There are few missense mutations in these genes, suggesting that they do not tolerate variation. As Asians are thought to have common ancestors with the Inuit , the excSince data on metabolic measurements was not available in our cohort, we could not determine whether the high frequency of variations in the carnitine acyltransferase genes in Nunavik Inuit affects their enzyme levels and activities. Our cohort was comprised of healthy Nunavik Inuit individuals and individuals with a family history of brain aneurysms; none of them showed any of the severe symptoms caused by CPTs or CrAT deficiencies. We hypothesize that these variants, while being part of the Inuit ‘healthy genomes’, could be harmful in other populations. Further studies in other Inuit populations, with the inclusion of measurements of these enzymes’ levels and activity are necessary to confirm our results and conclusions.S1 Archive(7Z)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigCPT1A p.P479L variant was only shown in a selected of samples.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 FigCanada's Relationship with Inuit: A History of Policy and Program Development”.Original map from “(TIF)Click here for additional data file.S1 Table1Variant only in individuals with mixed ethnicity. 2Variant with MAF = 1 in Inuit, Asians and Europeans.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Barley, globally the fourth most important cereal, provides food and beverages for humans and feed for animal husbandry. Maximizing grain yield under varying climate conditions largely depends on the optimal timing of flowering. Therefore, regulation of flowering time is of extraordinary importance to meet future food and feed demands. We developed the first barley nested association mapping (NAM) population, HEB-25, by crossing 25 wild barleys with one elite barley cultivar, and used it to dissect the genetic architecture of flowering time.G). The strongest single QTL effect corresponded to the known photoperiod response gene Ppd-H1. After sequencing the causative part of Ppd-H1, we differentiated twelve haplotypes in HEB-25, whereof the strongest exotic haplotype accelerated flowering time by 11 days compared to the elite barley haplotype. Applying a whole genome prediction model including main effects and epistatic interactions allowed predicting flowering time with an unmatched accuracy of 77% of cross-validated pG.Upon cultivation of 1,420 lines in multi-field trials and applying a genome-wide association study, eight major quantitative trait loci (QTL) were identified as main determinants to control flowering time in barley. These QTL accounted for 64% of the cross-validated proportion of explained genotypic variance  contains supplementary material, which is available to authorized users. Barley is among the oldest crop species human civilization was built on. Approximately 10,500 years ago, barley was domesticated in the Fertile Crescent , presumaPpd-H1, a PSEUDO-RESPONSE REGULATOR 7 (PRR7) gene, in its center [Ppd-H1, through mediation of CONSTANS (CO), promotes the expression of the floral inducer Vrn-H3, a homolog of the Arabidopsis thaliana FLOWERING LOCUS T (FT) gene [Vrn-H2, a zinc-finger CONSTANS, CO-like and TOC1 (CCT)-domain protein (ZCCT1) acts as a repressor of Vrn-H3 [Vrn-H2, in turn, is repressed by Vrn-H1, an APETALA1 family MADS-box transcription factor [Vrn-H3 is abolished and flowering is induced. Based on its vernalization requirement, winter barley and spring barley can be distinguished. Spring barley lacks the vernalization requirement due to a deletion of Vrn-H2 [In this regard, time of flowering is expected to play a major role in future crop improvement. It is a key trait for the successful completion of a plant’s life cycle and, therefore, it has a strong impact on grain yield . Floweris center . Under lFT) gene . On the f Vrn-H3 . Vrn-H2,n factor , which if Vrn-H2 .per se [A. thaliana [Besides photoperiod and vernalization, there are also genetic mechanisms acting independently of environmental cues, so-called earliness per se . Althougthaliana ,21-23. Sthaliana ,24,25.Hordeum vulgare ssp. vulgare, Hv) and 25 highly divergent exotic barley accessions, contributing an ideal instrument to study biodiversity. The exotic donors comprise 24 wild barley accessions of H. vulgare ssp. spontaneum (Hsp), the progenitor of domesticated barley, and one Tibetian H. vulgare ssp. agriocrithon (Hag) accession. Barke was selected since it was also used as a parent of a barley high-resolution mapping population [et al. [1 plants were backcrossed to Barke and, subsequently, selfed three times  emerged as a multi-parental mapping design to investigate genomic regions with unprecedented genetic resolution and allelic variation by combining the advantages of linkage analysis and association mapping . Hence, pulation  and as apulation . The exo [et al.  to repreIn the present study we investigated the genetic architecture of flowering time in barley. For this purpose, the NAM population HEB-25 was grown from 2011 to 2013 in multi-field trials to gather data on flowering time. By combining these data with high-density SNP marker information via genome-wide association studies and genomic prediction models, we could show that flowering time in barley mainly depends on a low number of large-effect QTL and epistatic interactions.The inheritance of parental segments across the genomes of the 1,420 HEB lines was characterized through genotyping 5,709 informative, genic single nucleotide polymorphism (SNP) markers . Marker Diversity in HEB-25 was also visible phenotypically. During the seasons 2011 through 2013, HEB-25 was cultivated at the Halle University Experimental Field Station to collect flowering time data. The HEB lines flowered on average 68.1 days after sowing with a range from 51.0 to 98.9 days and a standard deviation of 6.5 days  was observed on the short arm of chromosome 2H and explained a pG of 36%. This SNP is directly located within Ppd-H1, the major determinant of photoperiod response in barley under long day condition [Ppd-H1, also the vernalization genes Vrn-H1 and Vrn-H2, as well as the floral inducer Vrn-H3 and its putative paralog HvCEN [denso [HvELF3 [For GWAS, we initially applied the multiple linear regression Model-B with step-wise selection of cofactors, as outlined in Liu et al. . Model-Bet al. , where a10 Table  and 7. TTable G, ) of 64% Table G, , affectsdetected ,42. The ondition . Seven fog HvCEN  exhibiteog HvCEN  through N [denso  and HvEL [HvELF3 ,47 as tw [HvELF3 ,48. So fPpd-H1 and Vrn-H3), exactly those SNPs revealed the highest significance within the respective QTL window  and 25 highly divergent exotic wild barley accessions. The latter were used as pollen donors. Twenty-four accessions, originating from Afghanistan, Iran, Iraq, Israel, Lebanon, Turkey, and Syria (Hordeum vulgare ssp. spontaneum), were selected to maximize the genetic diversity in HEB-25. One further accession, HID380, originating from Tibet, China, was classified as Hordeum vulgare ssp. agriocrithon (Åberg). F1 plants of the initial crosses were backcrossed with Barke as the female parent. Twenty BC1 plants per cross were subsequently selfed three times, using the single seed descent (SSD) technique to generate the next generations. The resulting BC1S3 generation consists of 1,420 individual lines, classified in 25 HEB families with 22 to 75 individual lines per family  was initiated in 2007 conducting crosses between the spring barley cultivar Barke , heterozygous and homozygous donor genotypes, respectively. In total, 1,027 monomorphic SNPs and 1,128 SNPs with high failure rates  were excluded from the dataset, resulting in 5,709 informative SNPs for further analyses.SNP genotype data were collected at TraitGenetics, Gatersleben, Germany, for all 1,420 individual BC864 SNPs . At each1S3. The subsequent seed propagation of HEB lines was based on these founder HEB plants. For Barke and the wild barley accessions leaf material from three to four plants was used to create bulked samples. The plants were cultivated in a glasshouse and 50 to 100 mg of leaf material was harvested for each sample. DNA was extracted according to the manufacturer’s protocol, using the BioSprint 96 DNA Plant Kit and a BioSprint work station , and finally dissolved in distilled water at approximately 50 ng/μl.DNA was extracted from leaf tissue of 1,420 single founder HEB plants in generation BCet al. [P-value likely indicate a tight linkage. Therefore, we assigned the position of the SNP with the lowest P-value (minimum: P < 0.001) to the non-mapped SNP under investigation. If there were more than one SNP with the same P-value, the position of the unmapped SNP was defined as the average of the minimum and the maximum position of the respective markers. In this way, all except six of the non-mapped SNPs were placed into the Comadran map.The chromosomal positions of 3,391 out of 5,709 SNPs were taken from Comadran et al. . The remThe differentiation of the HEB genotypes was based on an identity-by-state approach. Based on parental genotype information, the exotic allele could be specified in each segregating family. Thus, HEB lines that showed a homozygous exotic genotype were assigned a value of 2 and HEB lines that showed a homozygous Barke genotype were assigned a value of 0. Consequently, heterozygous HEB lines were assigned a value of 1. If a SNP was monomorphic in one HEB family but polymorphic in a second family, lines of the first HEB family were assigned a genotype value of 0 to keep a full genotype data set, which is a pre-requisite for the subsequent multiple regression analysis. For the same reason, missing genotypes were estimated applying the mean imputation (MNI) approach . For thiProc Distance, based on a simple matching comparison between the three possible genotype states across all informative SNPs. In addition, we performed principal component analysis (PCA) using R [SAS 9.4 Software  was used to evaluate genetic diversity among parents and progenies of the HEB-25 population. Genetic similarities (GS) between HEB lines and their parents and among HEB lines were calculated with  using R . First w using R .2 [2 between SNP pairs against their genetic distance. A second-degree smoothed loess curve [Proc Loess. The population-specific baseline r2 was defined as the 95% percentile of the distribution of r2 for unlinked markers [LD was calculated as r2  between 2 . For thiss curve  was fitt markers . LD decaPpd-H1 locus on chromosome 2HS we used the following primers: PP05 (forward) 5′-GTGCAAAGCATAATATCAGTGTCC-3′ and PP04 (reverse) 5′-GGCCAAAGACACAAGAATCAG-3′. These primers amplify the last two exons and three introns of Ppd-H1 covering the CCT domain that contains SNP22, the causal SNP of Ppd-H1 [et al. [For sequencing of the f Ppd-H1 . Identic [et al. .1S3 lines . Sowing occurred in single to five row plots with a length of 1.50 m and a distance of 0.20 m between rows. The number of rows per HEB line and the position inside the field trial depended on the number of available BC1S3:4 seeds. Lines with seed numbers lower than ten were planted in plots with a length of 0.50 m. In 2012 and 2013, the field trials were conducted with the selfed progenies in BC1S3:5 and BC1S3:6, respectively. Two replications per HEB line, arranged in two randomized complete blocks, were cultivated in 2012 and 2013. The plots consisted of two rows (30 seeds each) with a length of 1.50 m and a distance of 0.20 m between rows. All field trials were sown in spring between March and April with fertilization and pest management following local practice.Between 2011 and 2013, three field trials were conducted at the ‘Kühnfeld Experimental Station’ of the University of Halle to gather phenotype data on flowering time. In 2011, the field trial was conducted with selfed progenies of BC2) was estimated on an entry-mean basis. Best linear unbiased estimates (BLUEs) of flowering time were calculated for each genotype assuming fixed genotype effects.The occurrence of flowering time was recorded as days after sowing, when the first awns were visible (BBCH49 ) for 50%et al. [Proc Glmselect in SAS and minimizing the Schwarz Bayesian Criterion [BON-HOLM < 0.05. Additive effects for each SNP were estimated based on regression across but also within families. Significant marker trait associations were grouped to a singled QTL if the significant SNPs were linked by less than 5 cM and revealed additive effects of the same direction, i.e. both exotic alleles increased or decreased flowering time. In addition, a two-dimensional epistasis scan was carried out to identify pairwise marker interactions. For this, the GWAS Model-B was extended to cover a second main SNP effect and the interaction effect between the two SNPs.For GWAS, we applied Model-B as outlined in detail by Liu et al. . This moet al. . It is bet al. . Cofactoriterion . The genriterion , excludiriterion  was usedPpd-H1. We used the same GWAS procedure with cofactor selection as mentioned before. However, bi-allelic SNPs covering the region of Ppd-H1 were replaced by a qualitative variable containing the defined Ppd-H1 haplotype. BLUEs were determined for each haplotype. Subsequently, pairwise comparisons between all haplotype BLUEs were performed using the Tukey-Kramer [A haplotype-based association mapping test was implemented in HEB-25 to test for effects of haplotypes at y-Kramer  multipleBased on BLUEs of the 1,420 HEB genotypes, two approaches for genomic prediction were applied considering additive effects: ridge regression best linear unbiased prediction (RR-BLUP ) and Bayn be the number of genotypes, m be the number of markers and l be the number of environments. The RR-BLUP model has the form y = 1nμ + Xg + e, where y is the vector of BLUEs of flowering time scores for all HEB genotypes across environments, 1n denotes the vector of 1’s, μ is the overall mean, g is the vector of marker effects , X is the corresponding design matrix and e is the residual term. In the model we assumed that Let quations .g from all later cycles were averaged to obtain estimates of the marker effects.The basic model of BayesC π is the same as RR-BLUP. However, all parameters are treated as random variables in a Bayesian framework. First, we defined the prior distributions as G), we estimated the effects of the significant peak markers within the estimation set and predicted the genotypic value of the lines in the test set [G as the squared Pearson product–moment correlation between predicted and observed genotypic values in the test set standardized with the heritability. The mean pG in 100 cross-validation runs  was taken as the final record. In addition, the number of significances for each SNP was cumulated across all runs and is referred to as QTL detection rate.The accuracy of the prediction of flowering time by GWAS and the two genomic prediction approaches were evaluated using five-fold cross-validations . In eachtest set . We thenG was calculated as in GWAS.For genomic prediction we estimated the effects for all markers using the estimation set and predicted the genotypic value of the lines in the test set. The cross-validated py is the vector of BLUEs of flowering time for all HEB genotypes, 1n denotes the vector of 1’s, μ is the overall mean, gi is the main additive effect of the i-th marker, Xi is the vector of marker indices, fjl is the epistatic effects of the j- and the l-th marker, XjXl is the point-wise product of the two vectors Xj and Xl, and e is the vector of residual terms. Note that in the third term of the right hand side of the formula, the sum is not taken over all pairs of markers but only pairs of markers exhibiting a significant additive effect in the GWAS study performed previously. Hence, in different cross-validation runs, different pairs of markers were considered in the model. The model assumptions are similar to the usual RR-BLUP, except treating additive and epistatic effects separately. We assumed PG is the cross-validated proportion of explained genotypic variance for genomic prediction, obtained previously by the RR-BLUP, only considering additive effects, m is the number of markers, p is the number of pairs of markers having significant additive effect. Therefore the penalty parameter λ is different for additive and epistatic effects.We extended the RR-BLUP based on main effects to model also epistasis for markers with significant main effects in the GWAS. The model is G in the same way as outlined above.Using the above extended model, for each cross-validation run we estimated the additive effects of all markers and epistatic effects of all pairs of markers exhibiting significant additive effects in GWAS using the estimation set. Then we predicted genotypic values of the lines in the test set and calculated the pPpd-H1 haplotypes and GWAS, and all other supporting data are provided as additional files.Raw data, including data on SNPs,


E. coli densities, genetic markers). We addressed this research gap by investigating the association between swimming-related illness frequency and water quality determined from multiple bacterial and viral genetic markers.Recreational waters impacted by fecal contamination have been linked to gastrointestinal illness in swimmer populations. To date, few epidemiologic studies examine the risk for swimming-related illnesses based upon simultaneous exposure to more than one microbial surrogate . These genetic marker data were paired with previously determined human exposure data gathered as part of a cohort study carried out among beach users at East Fork Lake in Ohio, USA in 2009. Using previously unavailable genetic marker data in logistic regression models, single- and multi-marker/multi-water quality indicator approaches for predicting swimming-related illness were evaluated for associations with swimming-associated gastrointestinal illness.E. coli densities in multivariable models. No significant associations between bacterial genetic markers and swimming-associated illness were observed.Data pertaining to genetic marker exposure and 8- or 9-day health outcomes were available for a total of 600 healthy susceptible swimmers, and with this population we observed a significant positive association between human adenovirus (HAdV) exposure and diarrhea  as well as gastrointestinal illness  upon adjusting for culturable This study provides evidence that a combined measure of recreational water quality that simultaneously considers both bacterial and viral densities, particularly HAdV, may improve prediction of disease risk than a measure of a single agent in a beach environment likely influenced by nonpoint source human fecal contamination. E. coli or enterococci) are observed or predicted to be in excess of single-day maximum criteria. Prior to November 26, 2012, the U.S. Environmental Protection Agency (USEPA) had established single-day maximum criteria, which were derived from several large epidemiological studies performed in the early 1980s E. coli criteria were supported by a comprehensive meta-analysis demonstrating E. coli was the most consistent indicator for predicting gastrointestinal illness in freshwater E. coli were problematic for same-day water quality advisories. The method required 18–24 h incubation and therefore was untimely for communication of risk to susceptible beachgoers Beach water advisories are issued to discourage human contact  when water is potentially harmful to human health. In the United States, many of these advisories are issued at freshwater beaches when densities of fecal indicator bacteria (Enterococcus via quantitative polymerase chain reaction (qPCR) for predicting human illness at sewage-impacted Great Lakes and marine beaches Since the 2003 meta-analysis, several epidemiological studies evaluating associations between illness and more rapid measures of fecal indicators have been performed with an emphasis on genetic markers of bacterial or viral contamination, which have demonstrated the effectiveness of rapidly measured To date, few epidemiological studies on beachgoers have been performed at areas dominated by human-associated non-point source contamination, particularly in freshwater environments. Several peer-reviewed epidemiological studies have evaluated illness associations with human enteric viruses in European recreational waters To date, the majority of recreational water-related epidemiological studies have been performed to identify effective single indicators, often bacterial, for practical application among those making beach management decisions. Multiple fecal indicators and multiple genetic markers have been evaluated in recreational waters with respect to their association with each other as well as GI illness frequency among swimmers. There is some inconsistency in the findings of studies that have studied associations (or the lack thereof) between fecal indicator densities and measured pathogen and/or viral levels in freshwater E. coli and human exposure data. In our 2010 study E. coli densities at East Fork Lake ; however, no assessment of associations between genetic marker densities and human health occurred. In 2012, the U.S. Environmental Protection Agency (EPA) emphasized a need for additional evaluation of rapid molecular methods for the timely determination of water quality to protect recreational water users To address these knowledge gaps, this study examined previously undescribed samples which were archived during our prospective cohort study E. coli by uidA and 23S, Enterococci by 23S, Bacteroides-Prevotella by HuBac) markers from 23 samples collected over 23 weekend days from 2009 using qPCR and paired them with human exposure data.The exposure and health data were collected using the prospective cohort study approach adopted by Wade et al. in their epidemiological studies related to recreational water Approvals for the study design, questionnaires, verbal consent procedures, and related materials were obtained from the Institutional Review Board (IRB) at The Ohio State University (IRB Protocol #2009H0107). For this study, adult participants (18 years of age or older) capable of providing verbal consent were welcomed into the study by investigators or key personnel trained and authorized by the university to obtain consent. The written consent waiver permitting verbal consent was granted given the low study risk and because written consent would have required surname information and the gathering of consent documentation in a periodically windy and wet beach environment. Thus, written consent procedures would have increased the likelihood of losing or damaging documentation which would have then included participant surnames that could potentially be matched with telephone numbers and health data. Verbal consent was recorded on the datasheet for each household interviewed. In addition, participants were provided with a card sharing contact information for the principal investigator and the ethics coordinator in the Office of Responsible Research Practices at the university.E. coli genetic marker density data to further establish our exposure classification of each beach user.The East Fork Beach Study Health outcome data were obtained by Marion et al. E. coli, a 1 L sample aliquot was temporarily stored (24–48 h) at 4°C and then immediately filtered for bacterial marker analysis, and a 1 L aliquot was stored at −20°C for virus analysis.Beach water samples used for obtaining genetic marker data were collected on the same day as enrollment and administration of the exposure questionnaire on summer weekends during the time of day generally used for swimming. In total, 91% of samples were collected at the median time of 13:45±3 h. Two samples (9%) were collected after 16:00 on days when daily swimming attendance was low  in anticipation of more swimmers late in the day. For each day, 3 L was collected into multiple autoclaved 500 mL bottles  and sterile Whirl-Pak bags  over 23 weekend days. Samples were collected near the beach center in water with an approximate depth of one meter by sweeping containers 30 cm below the water surface. Within 30 minutes after collection, a 1 L sample aliquot was used for membrane filtration for culturable E. coli densities were quantified for three separate filtration volumes  for each sample using the culture-based method described in EPA Method 1603 Text S1), including a description of the primers and probes used in this study (Table S1). In brief, samples were filtered to capture bacteria for quantifying the following bacterial markers: uidA genes (uidA) and 23S rRNA genes (23S E. coli) of E. coli, 16S rRNA genes of Bacteroides-Prevotella (HuBac), and 23S rRNA genes of Enterococcus spp. (23S Enterococcus). For concentrating viruses , membrane filtration was performed using cation (Al3+)-coated membranes. DNA or RNA was then extracted from the microbes captured on the membranes using a QIAmp DNA Stool Kit or the RNeasy Mini Kit , respectively. RNA extracts for HEntV were amplified by reverse transcription (RT)-PCR and quantified using TaqMan real-time RT-PCR (RT-qPCR) with the QIAGEN OneStep RT-PCR Kit . DNA extracts for HAdV and all bacterial markers were amplified by PCR and quantified using a TaqMan-based real-time qPCR system. All RT-qPCR and qPCR reactions except HNoV GI and HNoV GII were carried out in an ABI 48-well StepOne Real Time System . RT-qPCR assays for HNoV GI and HNoV GII were carried out with an Eppendorf Mastercycler RealPlex2 . Quantification of the markers was then determined using standard curves generated by plotting Ct values (y) versus the log gene copy numbers of the target microbes (x) and each limit of detection (LOD) was determined based on the lowest gene copy number from which above 90% of the replicates could be amplified in each qPCR assay. Additionally, with regards to these samples, the presence/absence of qPCR inhibition was previously evaluated for East Fork samples A detailed description of sample preparation, water concentration, and quantification of the bacterial and viral marker densities by qPCR is provided as -transformed. Prior to log-transforming E. coli results, samples with 0 CFU/100 mL were assigned a value of 1 CFU/100 mL. In scatterplots and models containing genetic marker densities, for markers not detected after 45 cycles, densities were assigned a value of half the detection limit in log CFUs per 100 mL or log gene equivalents (GE) per 100 mL for bacterial and viral markers, respectively.The analysis focused on healthy susceptible individuals, and persons who were classified as prevalent or existing cases based upon responses to the enrollment questionnaire were excluded in analyses using incident cases. Logistic regression was employed to evaluate crude associations between genetic marker exposure and new illness among swimmers reporting head immersion. The data for the microbiological terms, including E. coli and the various genetic markers observed from the single sample collection for that day. Densities of E. coli and the various genetic markers were determined from the single mid-day water sample collection.Multivariable logistic regression was used for estimating odds ratios for any GI illness, diarrhea, and HCGI among swimmers and non-swimmers (negative controls) as recommended in observational studies The multivariable logistic regression models considered potential confounders and/or modifying influences, including covariates related to demographic, exposure, meteorological, and water quality factors. Since data were obtained from households, for the purpose of statistical analysis, the data were treated as clustered by household Multivariable logistic regression models were evaluated for fit via the Hosmer-Lemeshow Goodness-of-Fit Test E. coli densities greater than 0 CFU/100 mL were observed in 91% (21/23) of all samples. The time-series plot of culturable E. coli and HAdV densities by day (p = 0.503). Overall, culturable E. coli densities exceeded Ohio single-day maximum criteria (>235 CFU/100 mL) in 8.6% (2/23) of our samples; however, no beach advisories were issued by local or state authorities due to their adherence to the one sample per week guideline used by the Ohio Department of Health for inland waters. Other water quality parameters are summarized in Marion et al. Among the 23 weekend water samples collected for qPCR analysis, bacterial markers were more readily detected than viruses . HEntV as by day  demonstrA total of 891 individuals were included in this study (from 278 households). The study population was 93% white, 2.5% black, 1% Hispanic and 3.5% other. The average study participant age was 24 years (median  = 21 years). Young children (≤5 years) represented 13% of the participants on whom data were available. Females were more represented (56%) than males (44%). The population described in this report is a subset of the 965 individuals from 300 households in our previous study Among the participants in this study, 618 (69%) individuals reported head immersion in East Fork beach waters. Prevalent GI illness was reported for 18 (2.9%) of these swimmers at enrollment . Chronicp<0.05) with HCGI, GI, and diarrhea in single marker models using univariable and multivariable logistic regression when treating marker densities as continuous terms or binary terms in models . When evaluating the association of either HEntV or HAdV detection with HCGI, GI, and diarrhea incidence, no association was observed (p>0.05). The covariates used in the illness models are summarized in Table S2 and Table S3, respectively. The most significant covariate, food consumption at the beach (see Table S2 and Table S3), was used in all genetic marker and E. coli models except the HCGI model, as food consumption at the beach was common to all HCGI cases. Beyond food consumption at the beach, specific conductivity and previous 72-hour UV results were the most significant covariates in all of the GI illness models. With respect to the diarrhea illness model, food consumption and previous 72-hour UV data were used as covariates, as they were identified as most significant through the backward elimination approach. In evaluating HCGI models relying on two microbiological terms, the only two-term model achieving significance was a combined model using HAdV and culturable E. coli . None of the bacterial markers were significant predictors of HCGI in models using an additional marker.The qPCR-based densities for bacteria and viruses do not show any significant associations (n models . Among ap<0.05) or near significant odds ratios (p = 0.056) were observed for all covariates in this model ; whereas, food consumption, increasing HAdV, and increasing E. coli were identified as risk factors for GI (aOR>1). The use of two microbial terms was supported in model building as the removal of the HAdV term resulted in significantly lower log-likelihood values for HCGI, GI, and diarrheal models (p<0.05), and the two microbial terms had the low p-values throughout the entire backward selection procedure allowing their retention in the model. The final four-term model was evaluated using a negative control group (the non-swimmer group), and no significant associations were observed for any model terms, suggesting no day-effects, and implying a water quality-related association in our exposed group. Lastly, all possible models for predicting GI illness using a single bacterial marker in tandem with either culturable E. coli or viral marker levels were developed, and no bacterial markers were associated with illness.Using this same bacteria and virus approach for GI illness, we observed a similar association. Since more GI cases were detected than HCGI, the model was able to contain an additional two covariates. In this model, significant  when adjusting E. coli density by including E. coli density in the model. Compared to the HCGI and GI models, the diarrheal model provided poor discrimination (AUC <0.70) with respect to the ability of the model to properly classify diarrhea cases  compared to swimmers with the least exposure to E. coli and HAdV. Exposure to the highest levels of E. coli when HAdV levels were low also presented increased GI illness risk  compared to those swimmers with low E. coli and low HAdV exposure. In the diarrheal illness model, significantly increased odds of diarrhea were only observed in the group who swam on days with elevated E. coli and HAdV compared to the reference group . Model discrimination for each model (GI and diarrhea) was evaluated and the AUC values were 0.63 and 0.65, respectively.For considering possible interaction effects, a water quality index was constructed based upon the detection limit of HAdV and the median E. coli) and increased odds of GI and HCGI among swimmers at the studied beach. More importantly, the two-indicator model (HAdV and E. coli) represented a significant improvement over single health indicator approaches with this data. Although speculative, particularly due to the small sample size of the study, the findings of this two-indicator approach are etiologically plausible as a combined viral + bacterial (ViBac) approach may account for GI and HCGI illness associations with viral and bacterial pathogens. Based upon our cohort study results, we have some reason to speculate that a multiple health-indicator approach may be beneficial for natural recreational waters such as the East Fork beach, which is part of a watershed comprised of mixed land uses and a high density of septic systems, all of which may promote a more diffuse type of fecal contamination than observed in point-source impacted waters. Within the beach watershed of interest, two municipal wastewater treatment plants (Williamsburg and Bethel WWTPs) and several smaller WWTPs operate, while malfunctioning septic systems are presumed to be a very significant source of contamination 2) among tributaries investigated suggesting diffuse septic system-associated fecal contamination impacts this beach.This prospective cohort study from an inland U.S. beach demonstrates the predictive potential of an integrative, multi-microbial approach for estimating recreational waterborne disease risk from viral and bacterial indicators. The term ‘indicator’ used here does not imply fecal indicator, but instead refers to ‘health-relevant’ indicators. Our results showed a positive association between increasing densities of individual health-relevant indicators  has clear winter peak seasonality E. coli and enterococci as well as human-specific markers explain the potential pathogenic bacterial genera  as well as gastrointestinal illness among swimmers. However, the need for data attempting to address and/or quantify viral-associated human health risks is significant, and some attempt has recently been made using a site-specific quantitative microbial risk assessment (QMRA) for adenovirus illness Accordingly, water quality advisories should be protective for preventing human illness from all potential infectious agents as deemed practical with the available methods. The need for bacteria-based indicators of health risk are warranted, as the historical paradigm supports the notion that individual fecal indicators like Future studies with larger populations are needed for drawing less speculative conclusions. The total swimmer population after exclusion is relatively small (n = 600) for rigorous analysis and may be leading us to spurious conclusions. Given the low number of cases presented, model construction was limited to two to four covariates, which therefore limited our ability to adequately assess multiple confounders, interaction effects, and linearity. The cohort study approach used here and in other U.S. studies Despite this sample size limitation, several constructed models did provide acceptable discrimination Click here for additional data file.Table S2Adjusted odds ratios for covariates used in GI illness models from .(DOCX)Click here for additional data file.Table S3Adjusted odds ratios for covariates used in diarrheal illness models from .(DOCX)Click here for additional data file.Text S1Detailed methods for sample processing and qPCR procedures.(DOCX)Click here for additional data file.


Drosophila melanogaster Genetic Reference Panel (DGRP) to map genetic variants affecting recombination rate. We used a two-step crossing scheme and visible markers to measure rates of recombination in a 33 cM interval on the X chromosome and in a 20.4 cM interval on chromosome 3R for 205 DGRP lines. Though we cannot exclude that some biases exist due to viability effects associated with the visible markers used in this study, we find ~2-fold variation in recombination rate among lines. Interestingly, we further find that recombination rates are uncorrelated between the two chromosomal intervals. We performed a genome-wide association study to identify genetic variants associated with recombination rate in each of the two intervals surveyed. We refined our list of candidate variants and genes associated with recombination rate variation and selected twenty genes for functional assessment. We present strong evidence that five genes are likely to contribute to natural variation in recombination rate in D. melanogaster; these genes lie outside the canonical meiotic recombination pathway. We also find a weak effect of Wolbachia infection on recombination rate and we confirm the interchromosomal effect. Our results highlight the magnitude of population variation in recombination rate present in D. melanogaster and implicate new genetic factors mediating natural variation in this quantitative trait.Meiotic recombination ensures proper chromosome segregation in many sexually reproducing organisms. Despite this crucial function, rates of recombination are highly variable within and between taxa, and the genetic basis of this variation remains poorly understood. Here, we exploit natural variation in the inbred, sequenced lines of the  Drosophila melanogaster. Here, we use the D. melanogaster Genetic Reference Panel to determine the scale of population-level variation in recombination rate and to identify genes significantly associated with this variation. We estimated rates of recombination on two different chromosomes in 205 strains of D. melanogaster. We also used genome-wide association mapping to identify genetic factors associated with recombination rate variation. We find that recombination rate on the two chromosomes are independent traits. We further find that population-level variation in recombination is mediated by many loci of small effect, and that the genes contributing to variation in recombination rate are outside of the well-characterized meiotic recombination pathway.During meiosis, homologous chromosomes exchange genetic material through recombination. In most sexually reproducing species, recombination is necessary for chromosomes to properly segregate. Recombination defects can generate gametes with an incorrect number of chromosomes, which is devastating for organismal fitness. Despite the central role of recombination for chromosome segregation, recombination is highly variable process both within and between species. Though it is clear that this variation is due at least in part to genetics, the specific genes contributing to variation in recombination within and between species remain largely unknown. This is particularly true in the model organism,  Meiotic recombination, the reciprocal exchange of genetic information between homologous chromosomes during meiosis, is necessary for proper chromosome segregation in many organisms . InteresIn addition to its role in preserving genomic integrity between generations, recombination is a pivotal force in evolution. Recombination can reduce interference between a genetic variant and the genetic background in which it resides, thereby increasing the efficacy of natural selection –29. MorePrdm9 also modulates hotspot activity in humans [Given the importance of recombination and the pervasive natural variation in recombination rate, it is perhaps unsurprising that the genetic basis of this variation has been an active area of research for the last decade. With respect to the genetic basis of the distribution of crossover events, the first known determinant of recombination distribution in metazoans was discovered recently –45. Thisn humans . PRDM9 in humans . This ran humans  was firsn humans , and evin humans ,50–55.Comparatively less is known in other systems such as Drosophila. Several studies have identified sequence motifs associated with recombination events ,12,56–59Recent work in mammals has also provided insight into the genetic architecture of global recombination rate. RNF212 has been repeatedly associated with natural variation in recombination rate in several systems including humans ,62, cattDrosophila melanogaster. However, it is well-documented that recombination rate is a variable and heritable trait in Drosophila. For instance, classical genetic experiments indicate that the amount of crossing-over as well as the distribution of crossover events can vary among lines of D. melanogaster [The genetic architecture of recombination rate variation outside of mammals remains poorly understood, even in the model organism nogaster ,13,71,72nogaster –85. Finanogaster , DDT resnogaster , geotaxinogaster , and resnogaster , which iD. melanogaster, we used an association mapping approach. We favored an unbiased approach in part because D. melanogaster lacks homologs of the three known determinants of recombination rate in mammals noted above: RNF212, REC8, and PRDM9. We measured recombination rates on both the 3R and X chromosomes in the 205 fully-sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) [X and 3rd chromosomes. We leveraged this pervasive population-level variation in recombination rate for genome-wide association (GWA) mapping to identify dominant or semi-dominant variants associated with phenotypic variation in recombination rate on each chromosome. We selected the top 20 most promising candidate genes associated with recombination rate and subjected these candidates to both gene-level and allele-level functional assessment. Our functional assays implicate five highly promising candidates as novel mediators of recombination rate variation in D. melanogaster: CG10864, CG33970, Eip75B, lola, and Ptp61F. Our results provide new insight into the scale and scope of population level variation in rates of recombination and more importantly implicate new determinants of natural variation in recombination rate in Drosophila.To gain the first insight into the genetic basis of population-level variation in recombination rate in l (DGRP) ,91 usingebony (e) and rough (ro) on chromosome 3R and the 33 cM interval between yellow (y) and vermilion (v) on the X chromosome. In total, 506,045 progeny were scored for recombinant phenotypes . On average, each replicate (there were three replicates per DGRP line per chromosome assay) contained ~368 progeny  and ~499 progeny . We first verified that our data conformed to expectations under Mendelian inheritance. Deviations from these expectations would be consistent with viability defects associated with the visible markers used in this study. To do so, for each line we compared the number of wild-type progeny to the number of progeny possessing both markers (either e ro or y v), summing across all three replicates (e + versus + ro or y + versus + v)  and 8 lines that deviate from the expected 1:1 wild-type: y v ratio . In all but one case, the deviation is in the direction of a relative excess of wild type flies. Only one line deviated significantly in both intervals (DGRP_819), with more wild-type progeny in both intervals. When comparing the ratios of the two recombinant haplotypes, we find that DGRP_31 deviates significantly from the expected 1:1 e +/ + ro ratio  and that DGRP_819 deviates significantly from the expected 1:1 y +/ + v ratio .To assay recombination rate variation in the DGRP, we used a classic two-step crossing scheme . We measplicates . We alsosus + v) . The nule ro interval . For the y v interval, only two lines significantly deviate from expectation (DGRP_41 AND DGRP_801) , both in the direction of a relative excess of females.Similarly, we tested for sex ratio unity by comparing the numbers of female and male progeny. There are no deviations from the expected 1:1 male:female ratio in the 205 lines for the m1m2 individuals, or m1 + individuals versus + m2 individuals are correlated with our estimates of recombination within the DGRP , the average crossover rate for e ro is 20.9 ± 0.2 cM (ranging from 14.2 cM to 26.12 cM) (y v is 31.2 ± 0.3 cM (ranging from 23.6 cM to 39.30 cM)  . This ag9.30 cM) , compareFe ro = 1.34, Pe ro = 0.038 and Fy v = 3.00, Py v < 0.0001, ANOVA). Using only lines with a standard karyotype (112 lines), we estimated broad-sense heritability (H2) of recombination rate for the e ro interval as 0.12 and for y v interval as 0.41  were correlated with various traits including organismal fitness. While the majority of correlations were weak and not statistically significant, we elaborate on several interesting significant correlations . This trend is echoed in the e ro interval (20.9 cM vs. 20.7 cM) but the difference in recombination frequency between standard and inverted karyotypes is not statistically significant . These results are discussed in the context of previous work in Recombination is suppressed within inverted regions, and recombination elsewhere in the genome increases through what is known as the interchromosomal effect ,97. A laWolbachia pipientis [y v interval , such that Wolbachia-infected lines have a higher crossover frequency (31.8 cM) than uninfected lines (30.0 cM). No effect of Wolbachia infection was found for the e ro interval . Importantly, estimates of heritability are not driven by Wolbachia infection in either interval  lines with no inversion on 3R (n = 156), 2) lines with neither 3R inversions nor inversion polymorphisms elsewhere in the genome (n = 130), and 3) lines with the standard karyotype . The three datasets used for the X chromosome analyses were: 1) all lines (n = 205), 2) lines without inversion polymorphisms (n = 152) and 3) lines with a standard karyotype (n = 112).The continuous variation for recombination rate among lines described above  suggestsur study , we are ion rate . We thuse ro interval, there is a significant effect of the NS inversion . It appears that in both intervals, there is an enrichment of associated variants on chromosome 2R .The full results for all six GWA analyses are presented as supplementary tables  Tables. e ro interval, the three GWAS yielded a combined total of 688 unique variants at a nominal significance threshold of P < 10−5. For the y v interval, combining results from all three GWA analyses, we identified 160 unique variants at a nominal significance threshold of P < 10−5. A description of types and locations of these variants is included in e ro GWAS, and variants in 111 genes were associated with recombination rate variation in the y v GWAS. There is very little overlap between these gene lists; a total of fifteen genes showed overlapping (gene-level) associations between the e ro and y v GWAS . The effect sizes for these variants were moderate, averaging ~2.32 cM for both intervals  with jing and Ptp61F having the most significantly associated variants (17 and 13 respectively). The full list of variants within these genes and associated P-values are listed in We sought to functionally assess a subset of the genes identified by our association mapping. We used several criteria to refine our list of candidate associations to a tractable set of 20 candidate genes. First, we restricted our focus to protein-coding genes harboring significantly associated genetic variants. We then integrated the  ovaries –103. Oure ro and y v intervals for available mutants and RNAi lines for all 20 candidate genes in the same way as described earlier. We used a combination of P-element insertions, chromosomal deletions, as well as any available RNAi lines. For the RNAi experiments, we used a nanos GAL4 driver, which should target the effects of knockdown to oogenesis. For assessment using the e ro markers, the only line tested that produced a significant difference from control line was a deletion line, Df(3R)ED2  . Using the y v markers, seven lines tested show a significant deviation in recombination frequency relative to the appropriate control . Eip7B, and CG9650 showed a decrease in recombination relative to the control while alph, CG33970, Eip75B, lola, and Ptp61F showed an increase in recombination relative to the control. Interestingly, one P-element insertion in grp showed a significant increase of recombination while a different P-element insertion in grp showed a significant decrease of recombination.If these identified candidate genes mediate recombination rate in some way, we expect that perturbing these genes will affect recombination rate. We used both mutant analysis and RNAi to either knock out or knock down expression of each of these genes, and compared recombination rate in the knock out/down lines to an appropriate genetic background control. We measured recombination rate in the ’s test) ; this li control . These iGAPDH) reveal significant differential expression for 11 of our 20 candidate genes  display higher expression levels than lines with the minor alleles. Conversely, DGRP lines with the major alleles of CG1273, CG10864, dpr6, Eip75B, lola, Oaz, and Ubx  display lower expression levels than lines with the minor alleles. It should be noted for variants in these eleven candidate genes, all minor alleles are associated with reduced rates of recombination. Comparisons of un-normalized data . For each of our twenty candidate genes, we selected three DGRP lines containing the major allele and three lines containing the minor allele . For cante genes . DGRP lipression ,105) larD. melanogaster for two independent genomic intervals. At the broadest scope, these data are consistent with previous work from other systems. Indeed, a wealth of data indicate that recombination rate varies between and within populations in species such as Drosophila [Here we report the largest population-level survey of recombination rate variation in Drosophila to date. We find significant genetic variation for recombination rate in this North American population of osophila ,13, miceosophila , and humosophila ,51,106.D. melanogaster. For instance, we observe 1.67 fold-variation for the y v interval, and previous work in this interval shows ~1.2-fold variation [The magnitude of population-level variation in recombination rate exposed by our survey is comparable to what has been previously shown in ariation ,72. Otheariation . Althougariation . Work frariation , which aariation ,107.e ro interval (excepting lines with an inversion on 3R) and 3.5-fold for the y v interval. This range of variation in recombination frequency is remarkable, nearly doubling previous estimates from Drosophila, mouse and humans. However, it also bears mentioning that we cannot exclude that our estimates of recombination may be biased by the weak viability effects associated with our visible markers (see above).It should be noted that the ~2 fold variation in recombination frequency that we report above is biased downward and is not truly reflective of segregating natural variation in recombination rate in Drosophila. When we include lines with inversions, which clearly segregate in natural populations, we see a much greater span in recombination rates in the DGRP: 5.2-fold for the e ro interval as compared to the genetic component of phenotypic variation in recombination rate in the y v interval is driving the lack of correlation between recombination rates in the two intervals. In contrast to what we observe here, previous work in humans showed a significant positive correlation between the number of maternal recombination events on individual chromosomes and the number of maternal recombination events in the remaining genome complement for 20 out of 23 chromosomes, as well as a strong, significant correlation for the first eight chromosomes compared to chromosomes nine through twenty-two and the X chromosome [Our results indicate that recombination rate at the two intervals surveyed are uncorrelated in the DGRP. It is certainly possible that the weaker genetic component of phenotypic variation in recombination rate in the romosome , though romosome ) and thePrevious work has estimated heritability for recombination rate in many different species. While estimates of heritability are necessarily population-specific, mammalian estimates encompass a wide range, from as small as 0.14  and 0.30Wolbachia pipientis is a common endosymbiont that infects the reproductive tissues of many arthropods [W. pipientis [Drosophila Population Genomics Project (n = 116) [W. pipientis [thropods . Evidencipientis –138. Appipientis  are infen = 116) . In the ipientis . In Drosipientis ,142 and ipientis .y v interval but not in the e ro interval. This discrepancy between the two intervals surveyed is difficult to explain, and merits further investigation. More curious yet is the contrast with previous results. It has been shown that Wolbachia infection has no effect on rates of crossing over in the w ct interval (18.5 cM) in the laboratory wild-type strain Canton S [w ct interval is actually within the y v interval surveyed in this study, so the discrepancy between the two studies is puzzling. It may be that the effect of Wolbachia infection on recombination frequency is sufficiently minor that the previous study, using a single genetic background and smaller sample sizes than the present study, was underpowered to detect this small effect (an average increase of 1.8 cM associated with Wolbachia infection in our study). Our results, coupled with previous findings, suggest that W. pipientis might have differential effects on recombination frequencies in different parts of the genome. Testing explicitly for this heterogeneity will be a topic of future exploration. In the future, it will also be interesting to see if infecting DGRP lines with Wolbachia causes an increase of crossover rates and if curing DGRP lines via tetracycline yields a corresponding decrease in crossover rates.Interestingly, we see a significant association between Wolbachia infection and crossover rates in the Canton S . The w cThe DGRP allows us to couple phenotypic variation with genetic variation such that the genetic basis of complex traits of interest can be dissected. One benefit of this association mapping approach is that it is unbiased, which means that new genes, outside of known pathways playing a role in the phenotype of interest, can be identified. For example, a recent study using the DGRP dissecting the genetic architecture of abdominal pigmentation yielded associations with several variants in the known pigmentation pathway but importantly, also functionally validated seventeen out of twenty-eight candidate genes that had not been previously associated with pigmentation . Because2His2 zinc fingers . This is particularly intriguing due to the link between the zinc-finger domain containing PRDM9 and hotspot determination, and it is tempting to speculate that these proteins bind to DNA and designate crossover sites in a way that is vaguely reminiscent of the role of PRDM9 in mammalian recombination [D. pseudoobscura ortholog of Oaz, GA14502, was previously identified as a possible candidate gene involved in recombination as the frequency of its zinc finger binding motif was significantly negatively associated with recombination on a broad scale [Consistent with the power of GWAS to uncover novel genes associated with phenotypic variation, our top candidate genes significantly associated with recombination rate variation contain genes outside of the meiotic recombination pathways, which have been characterized in exquisite detail  which showed neither a change in recombination frequency in the e ro or y v intervals when perturbed nor a difference in expression level between the major and minor allelic variants. However, lack of functional confirmation does not imply that a candidate gene has no role in modulating recombination rate in Drosophila. Indeed, validation of candidate genes is challenging. The effect sizes of the genetic variants are moderate at best . Finally, the process of recombination is likely to be highly buffered, and one could hypothesize that there is redundancy for maintaining the number of crossovers required. It is also possible that these statistical associations are false positives due to our lenient P-value.Overall, there were 5 genes  were identified in GWAS in both the e ro and y v interval.However, integrating across both the gene- and allele-level functional analysis, we find five high quality candidate genes for further investigation. These genes show significant perturbations in recombination frequency relative to the appropriate genetic background control in addition to differential expression specifically in ovaries between allelic variants at these loci. These were CG10864 is involved in potassium channel function [function . In humafunction . While ifunction , it is uCG33970 is predicted to be involved with ATP binding and transporter activity [RecA [Escherichia coli, DMC1 [Rad51 and Rad55 in yeast [XRCC3 in humans [CG33970 may contribute to meiotic recombination. Further work is aimed at dissecting this link.activity . A directy [RecA  in Escheli, DMC1 , Rad51 ain yeast ,156 and n humans  cause deEip75B (Ecdysone-induced protein 75B) is involved in mediating ecdysone signaling, a steroid hormone. Defective ecdysone signaling affects the early germarium, causing defects with meiotic entry [Eip75B in oogenesis makes it a particularly exciting candidate for further work.ic entry . Interesic entry –161. Droic entry ,163. It lola, or longitudinals lacking, is BTB zinc finger-containing transcription factor that is required for axon growth and guidance [ZBTB46 or BZEL, was shown to repress a desumoylase [lola is involved in early processes that could ultimately lead to crossover formation.guidance ,165. As umoylase . Sumoylaumoylase  and therPtp61F (Protein tyrosine phosphatase 61F) is a member of the protein tyrosine phosphatase family. Ptp61F is an induced antagonist of the JAK/STAT pathway [Ptp61F is targeted to the nucleus and cytoplasmic organelles [Ptp61F and DNA damage can be made in mammals; Ptp61F is the Drosophila homolog of human PTP1B and knockout PTP1B mice show a higher sensitivity to irradiation and an upregulation of many genes in the DNA excision/repair pathway [Ptp61F in meiotic recombination is not obvious, the clear function of this gene in oogenesis coupled with its tentative connection to DNA damage repair is promising. pathway ,169 and  pathway . In the ganelles  and thisganelles . Tentati pathway . HomologD. melanogaster and have shown that genetic background significantly drives phenotypic variation in this critically important phenotype. The magnitude of observed phenotypic variation in recombination rate is large, with almost 2-fold variation present in each genomic interval analyzed. We demonstrate that inversions play a large role in mediating rates of recombination, indicative of the interchromosomal effect, and provide the first evidence that Wolbachia infection can significantly increase rates of recombination. Through our GWA approach, we show that recombination rate is a highly polygenic trait, with many genetic factors of small effect associating with phenotypic variation. We show that a subset of our candidate genes  play putative roles in modulating recombination rate variation in Drosophila through both gene-level and expression-level functional assessment. Future work will be aimed at determining the role of these candidate genes in the molecular process of recombination.In conclusion, we have quantified the extent of recombination rate variation in a natural population of Drosophila Genetic Reference Panel is a collection of 205 fully-sequenced inbred lines [Drosophila melanogaster females were originally collected in Raleigh, NC, USA in 2003. Their progeny were subjected to 20 generations of full-sibling matings. The resulting inbred lines were then fully sequenced. A total of 4,853,802 single nucleotide polymorphisms (SNPs) and 1,296,080 non-SNP variants were identified among these lines [The ed lines ,91. MateD. melanogaster. To measure recombination rates on the 3R chromosome, we used a strain marked with ebony (e4) and rough (ro1); these markers are 20.4 cM apart [X chromosome, we used a strain marked with yellow (y1) and vermillion (v1); these markers are 33 cM apart [P-elements [To assay recombination rate, we took advantage of visible, recessive markers in cM apart . To meascM apart . These mcM apart ,95. Eachelements , to alloe ro or y v). For each replicate, all 205 lines were crossed simultaneously to avoid conflating block effects with variation among lines. This yielded three replicate estimates of recombination frequency per line per interval. For the first cross, ten virgin females from every DGRP line were crossed to ten doubly-marked males (either e ro or y v) in eight ounce bottles. Males and females were allowed to mate for five days, after which all adults were cleared from the bottles. F1 females resulting from this cross are doubly heterozygous; these females are the individuals in which recombination is occurring. To uncover these recombination events we backcross F1 females to doubly-marked males. For this second cross, twenty heterozygous virgin females were collected and backcrossed to twenty doubly-marked males. Males and females were allowed to mate for five days, after which all adults were cleared from the bottles. After eighteen days, BC1 progeny were collected, frozen, and scored for sex and for visible phenotypes. Previous work in our lab has demonstrated that freezing flies has no effect on the visible markers we scored. Recombinant progeny were then identified as having only one visible marker (m1 + or + m2). For each replicate, recombination rates were estimated by taking the ratio of recombinant progeny to the total number of progeny. Double crossovers cannot be recovered with this assay, so our estimates of recombination frequency are likely to be biased downwards slightly. The estimated recombination for a given strain and interval was calculated as the average across the three replicates.To assay recombination rate variation in the DGRP, we used a classic two-step crossing scheme . All croX chromosome (in this population of flies) lacks inversions while 49 lines contain an inversion on chromosome arm 3R which spans at least part of the e ro interval used to assay recombination rate [3R interval. The three datasets used for the 3R analyses were: 1) lines with no inversion on 3R (n = 156), 2) lines with neither 3R inversions nor inversion polymorphisms elsewhere in the genome (n = 130), and 3) lines with the standard karyotype (n = 112). The three datasets used for the X chromosome analyses were: 1) all lines (n = 205), 2) lines without inversion polymorphisms (n = 152) and 3) lines with a standard karyotype (n = 112).Freeze 2 of the DGRP contains information relating to 16 segregating autosomal inversions verified by cytological methods . We therion rate . We thusH2) of recombination rate, we used an ANOVA framework on line means . The ANOVA followed the form of Y = μ + L + ϵ for each chromosome assayed where Y is recombination rate, μ is the overall mean, L is the random effect of line and ϵ is the residual. Additionally, we ran a similar ANOVA, adding the genomic region as a fixed factor, to test for a significant interaction between line and genomic region. That ANOVA followed the form of Y = μ + L + R + L × R + ϵ, with the terms the same as above and R is the genomic region assayed. To estimate H2, we follow the formula H2 = σ2L / (σ2L + σ2ϵ) where σ2L is the variance component among lines and σ2ϵ is the residual variance or variance component attributed to error. The variance components were calculated using REML. All H2 estimates were calculated using R Statistical Software, v3.2.1 and RStudio v0.99.467.To estimate the broad-sense heritability (Y = μ + W + ϵ for each chromosome assayed where Y is recombination rate (measured in cM), μ is the overall mean, W is fixed effect of Wolbachia infection status and ϵ is the residual, including all individual measurements.To test for a significant effect of Wolbachia infection, we used an ANOVA framework as well. The ANOVA follows the form http://dgrp2.gnets.ncsu.edu/) [Wolbachia pipientis infection as well as the presence of inversions that are segregating in the DGRP. The adjusted line means are then used to fit a linear mixed model, Y = Xb + Zu + e. Y is the adjusted phenotypic value, X is the design matrix for the fixed variant effect b, Z is the incidence matrix for the random polygenic effect u and e is the residual. The vector of polygenic effects u has a covariance matrix in the form of Aσ2, where σ2 is the polygenic variance component and A is the genomic relatedness. Additionally, Manhattan plots were constructed using the qqman package in R [To identify genetic variants that are associated with differences in mean crossover number in two different intervals of the Drosophila genome, we performed a GWAS using the established web-based pipeline developed by the Mackay lab at NC State University, Raleigh, NC (su.edu/) ,91. The age in R .P-value of the variant located within or near the gene, effect size of the variant, the number of GWAS that a variant within or near the gene was implicated in and available expression data. To functionally explore these candidate genes with respect to their roles in recombination, we took advantage of available P-element insertion lines and chromosomal deletions as well as RNAi lines  as well as appropriate controls  were used in the same crossing scheme  in eight oz. bottles. Males and females were allowed to mate for five days, after which all adults were cleared from the bottles. For the second cross, ten virgin heterozygous females were collected and backcrossed to ten doubly-marked males in vials. Males and females were allowed to mate for five days, after which all adults were cleared from vials. BC1 progeny were collected from each vial, frozen, and scored for sex and for visible phenotypes. For each P-element insertion or chromosomal deletion, there were 30 replicates. For each replicate, recombination rates were estimated by taking the ratio of recombinant progeny to the total number of progeny.As described in the text, we selected 20 candidate genes to functionally assess that contained at least one significantly associated genetic variant within them. We selected these genes based on Ai lines . Lines cg scheme  detailed0 cross. These males contained the doubly-marked chromosome (e ro) along with nanos GAL4 driver [nanos is expressed throughout Drosophila oogenesis [P-element insertions, chromosomal deletions or RNAi lines were compared to appropriate controls using Dunnett’s Test [The RNAi lines followed an identical crossing scheme except for the males used in the F4 driver ,177. nanogenesis . All P-et’s Test ,180 usinTo test the hypothesis that gene expression differences between alleles drive phenotypic variation in recombination rate, we analyzed ovarian mRNA abundance differences between the major and minor allele for each of our 20 candidate genes using quantitative RT-PCR (qPCR). For each candidate gene, three DGRP lines containing the major allele and three DGRP lines containing the minor allele were chosen . For theGAPDH for normalization due to its relatively consistent expression [GAPDH control. Measurements from each DGRP line were normalized by dividing by the average Cq value of GAPDH for the corresponding DGRP line, modeled after common normalization procedures [GAPDH expression was not biasing results.Samples were analyzed using pression . For eacocedures . These 9S1 Text(PDF)Click here for additional data file.S1 Fige ro interval on chromosome 3R and (B) the y v interval on the X chromosome. The lines for each panel are ordered by recombination rate. Error bars depict standard error. For reference, the reported map distance for the e ro interval is 20.4 cM, while the reported map distance for the y v interval is 33 cM, denoted by a horizontal line in both graphs.Variation in crossover frequency in the all lines of the DGRP in (A) the (TIF)Click here for additional data file.S2 Fige ro and (G-L) y v intervals. Spearman’s rho values for all comparisons are included in Scatterplots showing pairwise relationships between replicates as well as between each replicate and the overall average for the (A-F) (TIF)Click here for additional data file.S3 FigOverlap of significantly associated genetic variants from the three different data sets for each chromosomal interval assayed.(TIF)Click here for additional data file.S4 Fige ro and (B) y v intervals from all GWA analyses.Distribution of combined effect sizes for the (A) (TIF)Click here for additional data file.S5 Fig3R inversions) for the e ro interval and (B) all lines for the y v interval. A significance threshold of P ≤ 10−5 is displayed with a horizontal line. Brackets within the Manhattan plot highlight the chromosomal interval assayed. The triangular heat map displays the amount of linkage disequilibrium  between variants. Each major chromosome is depicted. Red denotes complete LD and blue denotes absence of LD.Results are depicted for (A) all lines (excluding those with (TIF)Click here for additional data file.S6 Fig3R inversions and/or polymorphic inversions for the e ro interval and (B) lines excluding those with polymorphic inversions for the y v interval. A significance threshold of P ≤ 10−5 is displayed. Brackets within the Manhattan plot highlight the chromosomal interval assayed. The triangular heat map displays the amount of linkage disequilibrium  between variants. Each major chromosome is depicted. Red denotes complete LD and blue denotes absence of LD.Results are depicted for (A) lines excluding those with (TIF)Click here for additional data file.S7 FigP < 0.05, ** indicates P < 0.01 and *** indicates P < 0.001.For each candidate gene, the raw  average expression of three lines with major allele (blue bars) and three lines with minor allele (red bars) are shown. Error bars denote standard error. * indicates a (TIF)Click here for additional data file.S1 TableG-test P-values for number of males versus females, number of wild-type progeny versus doubly marked individuals (m1m2), and number of recombinant progeny (either + m2 or m1 +).Raw counts, expected numbers and (XLSX)Click here for additional data file.S2 Tablee ro and y v) are included along with inversion status [Three replicates and average  for both intervals (n status .and Wolbn status .(XLSX)Click here for additional data file.S3 Tablee ro and y v intervals.Spearman's rho values for correlations between replicates as well as between each replicate and the overall average for the (XLSX)Click here for additional data file.S4 TableANOVA testing for the effects of Line, Interval, and the interaction of the two. % is the percentage of variance explained by factor.(XLSX)Click here for additional data file.S5 Tablee ro and y v intervals.Spearman's rho values for correlations between replicates as well as between each replicate and the overall average for the (XLSX)Click here for additional data file.S6 Tablee ro data set including all DGRP lines (except those containing an inversion on 3R).Results from GWA on (XLSX)Click here for additional data file.S7 Tablee ro data set excluding DGRP lines with an inversion on 3R or any polymorphic inversions.Results from GWA on (XLSX)Click here for additional data file.S8 Tablee ro data set of DGRP lines only with standard karyotypes.Results from GWA on (XLSX)Click here for additional data file.S9 Tabley v data set including all DGRP lines.Results from GWA on (XLSX)Click here for additional data file.S10 Tabley v data set excluding DGRP lines with any polymorphic inversions.Results from GWA on (XLSX)Click here for additional data file.S11 Tabley v data set of DGRP lines only with standard karyotypes.Results from GWA on (XLSX)Click here for additional data file.S12 TableBreakdown of genetic variants identified in 6 GWAS see  Tables.(XLSX)Click here for additional data file.S13 TableP-value.List of significantly associated genetic variants within candidate genes with corresponding (XLSX)Click here for additional data file.S14 TableGenotype of each DGRP line at each significantly associated genetic variant within candidate genes surveyed.(XLSX)Click here for additional data file.S15 Tablee ro interval.Raw counts from gene-level assessment in the (XLSX)Click here for additional data file.S16 Tabley v interval.Raw counts from gene-level assessment in the (XLSX)Click here for additional data file.S17 TableList of DGRP lines selected for each candidate gene for qPCR expression analysis.(XLSX)Click here for additional data file.S18 TableGenotype of DGRP lines selected for qPCR expression analysis at respective gene of choice.(XLSX)Click here for additional data file.S19 TableCq values from qPCR expression analysis, normalized to GAPDH expression. Lines 1–3 for the major and minor allele correspond to lines in (XLSX)Click here for additional data file.S20 TableCq values from qPCR expression analysis. Lines 1–3 for the major and minor allele correspond to lines in (XLSX)Click here for additional data file.S21 TableLines used for functional assessment.(XLSX)Click here for additional data file.S22 TableForward and reverse primers of candidate genes for qPCR expression analysis.(XLSX)Click here for additional data file.S23 Tablee ro and y v intervals (using only standard karyotypes) compared to other DGRP published phenotypes. Sources of data are listed both within the main text as well as in the chart. Significant correlations are in bold.Spearman’s rank correlation for crossover rates in the (XLSX)Click here for additional data file.