OCA2, HERC2, SLC45A2, SLC24A4, IRF4, TYR, TYRP1, ASIP, and MC1R) for hair color, eye color, and freckling validate the Web-based, self-reporting paradigm. The identification of novel associations for hair morphology , freckling , the ability to smell the methanethiol produced after eating asparagus , and photic sneeze reflex  illustrates the power of the approach.Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits is the efficient, coordinated collection of genotype and phenotype data. We have developed a novel research framework that facilitates the parallel study of a wide assortment of traits within a single cohort. The approach takes advantage of the interactivity of the Web both to gather data and to present genetic information to research participants, while taking care to correct for the population structure inherent to this study design. Here we report initial results from a participant-driven study of 22 traits. Replications of associations  and freckling, hair curl, asparagus anosmia (the inability to detect certain urinary metabolites produced after eating asparagus), and photic sneeze reflex (the tendency to sneeze when entering bright light). Additionally our analysis verified the association of a large number of previously identified genes with variation in hair color, eye color, and freckling. Our analysis not only identified new genetic associations, but also showed that our novel way of doing research—collecting self-reported data over the Web from involved participants who also receive interpretations of their genetic data—is a viable alternative to traditional methods. Many common human traits have long been understood to have a genetic basis, yet in only a few cases have influential genes been identified. Even pigmentation, for which almost 40 years ago Cavalli-Sforza Data for these studies was collected within a research framework wherein research participants, derived from the customer base of 23andMe, Inc., a direct-to-consumer genetic information company, consented to the use of their data for research and were provided with access to their personal genetic information . They weThe parallel and continual nature of this research framework facilitates the rapid recruitment of participants to many studies at once. Furthermore, the presentation of interpreted genetic data to the participants creates incentive for them to return to the website, lowering the marginal cost of recontacting for additional analyses. The participant-driven nature of this study design and the resulting heterogeneity of the data sets require that care be taken to eliminate population stratification and other possible sources of bias. However, the challenges of eliminating such stratification and biases are balanced by the continuous accrual of new data as participants sign up and respond to new surveys.From the initial set of surveys released, we report results on the 22 traits meeting our sample size criteria . The phenotypes that met these criteria are described below.Pigmentation has been a fruitful area for genetic research since the 19th century, when scientists realized that the mice with varied coat colors that “mouse fanciers” had been developing for centuries provided easily tracked phenotypes for genetic analysis EDAR is associated with hair thickness in Asians Human hair varies in thickness as well as in the extent of curl, which is related to the shape of the hair (round versus flat cross-section). Over 100 years ago Davenport and Davenport The study of the ability to smell the urinary metabolites of asparagus  also dates back over 100 years Listed under “ACHOO  syndrome” in Online Mendelian Inheritance in Man (OMIM), the “photic sneeze reflex” refers to the tendency to sneeze when moving from relative darkness into bright light—most often sunlight. Aristotle discussed the trait in a section of his Book of Problems called “Problems concerning the nose,” hypothesizing that heat-generated movement led to tickling of the nose. No previous studies have reported genes or SNPs associated with this particular reflex.The other traits analyzed here fall into three broad categories. The first category consists of laterality preferences: handedness, footedness, ocular dominance, and hand-clasp (which thumb is on top when clasping one's hands). The second group consists of simple physical characteristics: whether participants have had cavities, have worn braces, have had wisdom teeth removed, have astigmatism, wear glasses, have attached earlobes, and suffer from motion sickness while riding in a car. The third group consists of personality traits and preferences: optimism, a preference for sweet versus salty food, and preference for night-time versus morning-time activity. None of these traits have well-established associations with SNPs, although handedness Of the 22 studies, eight yielded positive results, with novel associations discovered for four traits and replications for five traits . All fivThe studies were performed on multiple overlapping datasets drawn from a single cohort. The cohort was derived from the subset of the customer base of 23andMe, Inc. who took surveys relevant to the 22 traits considered here. Of these individuals, only those assessed to be of northern European ancestry were included. In addition, individuals were eliminated until any pair of participants shared at most 700 cM of full or half identity by descent (IBD), approximately the lower end of sharing between a pair of first cousins. Average IBD between a pair of participants was 0.146cM, median IBD was 0 and only 123 pairs of individuals shared more than 100 cM. The resulting cohort consisted of a total of 9126 individuals who had answered at least one of the surveys considered here. Each individual was genotyped on the Illumina HumanHap550+ BeadChip platform . Covariates showing significant correlations . As a result, none of these traits made our sample size cutoff.For the 22 phenotypes considered here, participants were shown predictions for hair color, eye color, and freckling, although they were on separate pages from the surveys for these phenotypes. There was no evidence that for any of these traits participants who saw their genotypes gave different responses from those who did not . TherefoSurvey response rates correlated with sex, age and the first (north-to-south) principal component of population structure. That is, women, people of northern European ancestry, and older people were more likely to answer more surveys than men, people of central European ancestry, and younger people , the highly cross-linked outermost layer of skin that provides mechanical protection from the environment, or are involved in cross-linking the CE with the network of keratin filaments in the cells.We found an association between a SNP near S100A11, TCHHL1 , TCHH , and RPTN (repetin). All four are putative calcium-binding proteins that contain two EF hand domains. S100A11, TCHH, and RPTN have all been shown to be associated with the CE The LD block containing the most significant SNPs includes four genes: WNT10A. The minor allele is associated with curly hair, each T is associated with a 0.2 point change. See We also found an association between rs7349332 and hair curl, with score 13.4, in an intron of OFCC1 , rs1556547 has a score of 7.8 and the minor allele is associated with straighter hair  are annotated as pseudogenes in ORDB OR2M7), has score 23.2 and appears to be acting in a dominant fashion decreasing the odds of anosmia, with an odds ratio under a dominant model of 0.48  PABPCP2 .For photic sneeze reflex, we find a novel association with rs10427255 (score 10.9 and an OR of 1.32). This SNP lies in a large intergenic region of 2q22.3 between NR2F2, some 560kb away. Details for the SNPs in these regions are shown in We also find a suggestive association with rs11856995 (score 7.13 and OR of 0.78). It also lies in a large intergenic region of 15q26.2, with the nearest gene being BNC2 (Zinc finger protein basonuclin-2), with a score of 9.4 and an estimated We find one new association for freckling and replicate two known regions. The novel association is at rs2153271, in an intron of IRF4  as well as KITLG. The SNP rs12821256 in KITLG showed a mild but significant association with hair color in our study, with a score of 4.2 and Sulem et al. MC1R region, long known to be associated with this phenotype MC1R gene. This region is strongly associated with common variation in red hair ASIP is associated with red hair  we found many associated SNPs in the OCA2/HERC2, SLC24A4, IRF4, SLC45A2, TYR, and TYRP1. In HERC2, the well-known SNP rs12913832 has a score over 300 and SLC24A4, rs12896399 has a score of 15.9 and IRF4, rs12203592 has a score of 14.7 and SLC45A2, rs16891982 has a score of 11.8, TYR, rs1393350 has a score of 8.5, TYRP1 has a score of 7.5, We confirm known associations for each of two traits: blue to brown and green versus blue eye color . For bluOCA2/HERC2, SLC24A4, and TYR. For this trait, SNP rs12913832 in OCA2/HERC2 has a score of 51.5 and an estimated allelic OR of SLC24A4, rs12896399 has a score of 22.8 and OR of 0.58 (0.52–0.65), again similar to TYR, we report rs1847134 with a score of For green versus blue eyes, three regions show associations: We have conducted genome-wide association studies for 22 traits. The sheer size of this study was possible due to an original, web-based, parallel design. We have found novel associations for hair curl, freckling, photic sneeze reflex, and the ability to smell the urinary metabolites of asparagus. In addition, we have replicated a wide array of associations for pigmentation traits; these replications show great consistency with the numbers reported in other studies.TCHH, LCE3E, WNT10A, and OFCC1. that together explain about 6% of the variance in hair curl within northern Europe. The association with TCHH replicates the finding of WNT10A as a non-significant but interesting association).We have found associations between hair curl and SNPs near the genes TCHH region, the strongly associated imputed SNP rs11803731  is of note. This position is a possible helix initiator  that could play a role.The bulk of this evidence points to WNT10A, there appears to be a direct connection to hair morphology: mutations in this gene are known to cause odonto-onycho-dermal dysplasia, which includes dry, misformed hair as a symptom For OFCC1 has been associated with orofacial clefting Finally, a translocation breakpoint in It has been debated whether variance in the production or the detection of methanethiol explains the differences in the reporting of the ability to detect asparagus metabolites in urine. One study suggested that production is an autosomal dominant trait We have identified a locus associated with this trait. This locus lies within a region containing many olfactory receptors and appears to act in a dominant fashion. Both of these facts suggest that the genetic variation in this trait is in the ability to detect the odorant.BNC2. BNC2 is a potential transcriptional regulator in keratinocytes based on its close similarity to BNC1 (basonuclin 1) BNC1 and BNC2 have different functions BNC2 and freckling.We have found a novel association between freckling and rs2153271 in an intron of IRF4 and freckling. This region has previously been associated with freckling, however this SNP appears to more closely tag the causal variant than the previous association in this region IRF4 appears to be regulated by MITFWe have also found a strong association between rs12203592 in an intron of KITLG, discovered in We replicated many known associations for hair color, both blond to black and red versus non-red. Of interest is the fact that rs12821256 in ZEB2 and a second suggestively associated about 550kb away from NR2F2.There is evidence that photic sneeze reflex is genetic: Peroutka and Peroutka ZEB2 is mutated in individuals with Mowat-Wilson syndrome, which has seizures as a common symptom. There may be a link between photosensitive epileptic seizures and photic sneeze reflex (triggered by a sudden switch from being dark-adjusted to light) NR2F2 , also has a possible link to sneezing: it interacts with NR2F6NR2F6 knockout mice show defects in the locus ceruleus (part of the brainstem) Handedness has putative genetic associations Looking at the laterality measures  as a whole, there is no overlap between the marginal hits for any of these phenotypes: no SNP has a score of above 5 for more than a single one of these measures.In this initial set of results, we have shown parallel, web-based phenotype collection to be quick and reliable. The population structure present in the wider dataset makes statistical analysis more involved than in a typical GWAS, however it does not influence the results. Furthermore, this complex population structure facilitates studies of multiple ethnicities simultaneously. It also compels the development of robust methods for genetic research in populations reflecting the complexities of human populations.This design has several statistical advantages over traditional studies. By centralizing many studies, we have the ability to avoid publication bias by reporting statistics on a large collection of independent association studies, including both positive and negative results. Such a bias is a concern Ignoring linkage disequilibrium , one would expect to see one or two suggestive associations (with scores between 7.1 and 8.4) by chance in about half of all studies similar to ours. This is a conservative estimate; the false-discovery rate (FDR) analysis estimates a FDR of about 5.2% for a cutoff of 7.1, meaning that we would expect 5.2% of the associations with scores in this range to be false positives.In addition, genotypes for cases and controls are collected and treated the same way, avoiding sources of bias cf. . In factThis new research model raises interesting methodological questions. For example, since new participants join the study continuously, results are constantly changing. For this study, we chose to set an end date and inclusion criteria in advance to avoid possible bias due to choosing a stop date according to the latest results. However, it is an interesting problem to design a criteria for significance using a continually expanding cohort to replace the traditional design of phased data collection with discovery followed by replication. For example, While we have not formalized this notion in this paper, we note that the novel associations we report here have been steadily increasing in significance cf.  and thatOur research model makes possible studies that might be infeasible otherwise due to the low marginal cost of asking additional questions over the web and the speed of broadcasting recruitment messages in parallel online. This speed and flexibility allows us to easily study traits for which funding may not be readily available, such as asparagus anosmia.We believe that providing participants with well-explained descriptions of their genetic data can substantially benefit genetic research as a whole. It is an opportunity to harness the public interest in genetics and make the participants a part of the study. This participation provides a wonderful chance to educate the public about genetics, statistics and research and to give something back to the individuals who contribute to genetic research.Participants were drawn from the customer base of 23andMe. After purchase, customers were shipped a kit for saliva collection, containing a bar-coded tube to be returned and a code to claim that kit online. They entered this code on the 23andme.com website, created an account, and agreed to a Consent and Legal Agreement. Upon signing up for 23andWe , participants were reminded that they had consented to the use of survey responses for research.The Consent and Legal Agreement stated that participants' genotype data and whatever phenotype data they entered would be used for internal research after being coded and stripped of individually identifying information (“anonymized”). Individually identifying information refers to personal information that is collected during purchase, such as name, credit card information, billing and shipping addresses, and contact information such as an email address or telephone number. The consent form stated that aggregate genetic data might be shared publicly (as it is in this paper). However, it provided that individual-level genetic data would not be shared with outside researchers without separate consent. For this study, all access to individual-level data occurred at 23andMe by full-time 23andMe employees. One author (IP) was working as a 23andMe consultant and did not access individual-level data. Another author with a secondary, non-23andMe affiliation (JM) was acting in her capacity as a 23andMe employee during her work on this study.As part of the consent form, participants were told they could withdraw from all future research at any time by emailing 23andMe. This information was also present as a FAQ on the 23andMe website. A small number of customers have elected this option and those who did so before January 30, 2010 were not included in any analysis in this paper.All anonymized data was placed in a secure research environment accessible only by 23andMe scientists. These scientists had no access to individually identifying information that was held separately at the company. The scientists also had no interaction with study participants.To obtain formal recognition of this strict partitioning of the research, following consultation with the editors at PLoS, we sought and were granted an independent determination by a commercial Institutional Review Board (IRB) that this research did not involve human subjects under the Department of Health and Human Services definition, 45 CFR 46.102(f). This definition states that research performed on anonymized data with no contact between investigators and participants does not constitute research on human subjects. It is 23andMe policy that every scientist who obtains access to the restricted research environment first undergo online human subject assurance training made available by the Office for Human Research Protections (OHRP).23andMe is committed to an ongoing evaluation of our process of Institutional Review and oversight of Consent as part of our commitment to protection of participant rights. There are significant novel challenges in this process relating to the evolution of policies concerning privacy of genetic data, the ongoing nature of our study, and the return of data to participants. Our study participants arguably have greater opportunity to review and consent to their involvement than most participants in genetic studies. However, they also have greater access to their own genetic data and hence to personal results that may impact their self-perception. We expect the definition of human subjects research to evolve along with standards for protecting individual identity and providing public access to GWAS results. We are continually evaluating our protocols with an external, AAHRPP accredited IRB in an effort to set the standard for web-based genetic studies.DNA extraction and genotyping were performed on saliva samples by National Genetics Institute (NGI), a CLIA licensed clinical laboratory and a subsidiary of Laboratory Corporation of America. Samples were genotyped on the Illumina HumanHap550+ BeadChip platform, which included SNPs from the standard HumanHap550 panel in addition to a custom set of about 25,000 SNPs selected by the 23andMe staff. Every sample that failed to reach 98.5% call rate was re-analyzed. Individuals whose analyses failed repeatedly were re-contacted by 23andMe customer service to provide additional samples, as is done for all 23andMe customers.Two slightly different versions of the genotyping platform were used in this study. See SNPs with a call rate under 98% were excluded from analysis, as were those with minor allele frequency under All p-values were calculated using linear or logistic regressions as appropriate . For linear regressions with covariates, we used a simple multilevel model: first we performed the regression of the phenotype solely on the covariates and then regressed the residuals against individual SNPs. This multilevel model is similar to the model in EIGENSTRAT Bayes factors  We measured identity by descent (IBD) for all pairs of participants using a novel algorithm that acts on unphased data by comparing homozygous calls in a window . A set oExtensive population structure exists in the customer base as a whole, which includes individuals from around the world. This structure might yield spurious associations if not taken into account Only unrelated (in the aforementioned sense) individuals from this subset were considered during the present association studies. Although the sample included only individuals of northern European ancestry, mild population structure still existed. Inspection of the eigenvalues showed that the first five principal components captured the majority of this structure , rs12913832 (eye color), rs1805007 (red hair), rs1667394 (hair color), rs4778138 and rs1805007 (freckling). For each SNP, we tested whether seeing their data influenced their responses, controlling for their genotype at that SNP, sex, age, and five principal components. Unadjusted p-values (using a logistic regression) were all over 0.1 except for rs1667394 and hair color, which had a p-value of 0.02. After adjusting for the seven tests performed, we fail to reject the hypothesis that seeing the data did not influence people's responses.Participants were asked “Is your hair naturally straight or curly?” Answer choices were presented as a series of six pictures with accompanying descriptive text. Analysis used the codings  in a linear regression. The pictures are shown in Supplementary The question asked was “Have you ever noticed a peculiar odor when you pee after eating asparagus?” This phenotype was scored as cases and controls where those who could not smell the odor after eating asparagus were considered cases. Participants who answered that they did not know or did not eat asparagus were excluded.Participants were asked to compare the amount of freckling on their face, arms, and shoulders to three series of images  for hair color on the ordinal coding for genotype  as well as five principal components and sex.For red versus non-red hair color, participants were asked to “describe the amount of red in my hair ” with available choices “No red at all,” “A tinge of red,” “Some red” and “A lot of red” which were coded from 0 to 3 in a linear regression.We performed two analyses of eye color: blue to brown and blue versus green. Eye color was assessed by asking participants to match their eye color to a set of 7 pictures (without accompanying text) ranging from blue to dark brown. These were coded as 0 = blue through 6 = dark brown for the main analysis. See Supplementary For blue versus green eye color, blue eyes were treated as controls and greenish-blue or green eyes were treated as cases.Participants were asked one question for this trait: “Do you have a tendency to sneeze when exposed to bright sunlight?” Available answers were “Yes” and “No, what are you talking about?” People who did sneeze were treated as cases, those who did not were controls.Participants were asked “When you're in the mood for a snack, what kind of snack do you usually reach for?” Available answers were “Sweet” “Salty or savory” “Both” or “Neither.” People answering either both or neither were disregarded. People reaching for sweet snacks were treated as cases.Handedness was scored on an eight-point scale (where 1 was “pure right” and 8 was “pure left”) using the questions and scoring from Table S1−6 for the 22 studies.All significant and marginal SNPs for the 22 studies. All SNPs with p-values under 10(0.04 MB XLS)Click here for additional data file.Text S1Selection of study individuals by ancestry.(1.17 MB PDF)Click here for additional data file.Text S2Relatedness.(0.06 MB PDF)Click here for additional data file.Text S3Genotyping and SNP quality control.(0.04 MB PDF)Click here for additional data file.Text S4Phenotypes.(0.06 MB PDF)Click here for additional data file.Text S5Questions for individual phenotypes.(0.18 MB PDF)Click here for additional data file.Text S6Details for specific associations.(6.54 MB PDF)Click here for additional data file.


LSR) method to estimate a region-specific p-value to provide an index for the most likely candidate region.Association testing is a powerful tool for identifying disease susceptibility genes underlying complex diseases. Technological advances have yielded a dramatic increase in the density of available genetic markers, necessitating an increase in the number of association tests required for the analysis of disease susceptibility genes. As such, multiple-tests corrections have become a critical issue. However the conventional statistical corrections on locus-specific multiple tests usually result in lower power as the number of markers increases. Alternatively, we propose here the application of the longest significant run (LSR method relative to procedures based on genotypic data is that only p-value data are needed and hence can be applied extensively to different study designs. In this study the proposed LSR method was compared with commonly used methods such as Bonferroni's method and FDR controlling method. We found that while all methods provide good control over false positive rate, LSR has much better power and false discovery rate. In the authentic analysis on psoriasis and asthma disease data, the LSR method successfully identified important candidate regions and replicated the results of previous association studies.An advantage of the LSR method provides an efficient exploratory tool for the analysis of sequences of dense genetic markers. Our results show that the LSR method has better power and lower false discovery rate comparing with the locus-specific multiple tests.The proposed  Recently whole genome association studies (WGA) with high density SNP data are becoming popular due to new technology in genotyping  -4. OptimIn order to improve power, many samples have been used in WGA studies; however the cost for such studies is still expensive even though the expense of genotyping has dropped significantly. One problem common of WGA studies is a high false positive rate if the direct method, based on the simple chi-square test for association, is performed without any correction for multiple testing. By far, the most commonly used remedy is the Bonferroni approach. However, its overly conservative correction might result in power reduction. There have been several methods proposed to circumvent the problem of the over correction of multiple testing procedures, e.g. false discovery rate (FDR) methods -9 that cMost direct association tests are locus-specific and therefore seldom account for the association of different markers. Different markers usually have heterogeneous genetic backgrounds, such as allele frequency and marker characteristics. Single-point inference sometimes yields a misleading conclusion for an overall phenomenon. Methods considered multiple markers simultaneously and included logistic regression analysis, haplotype analysis ,13, globInstead of testing the significance of a single marker, our novel method tests the significance of all markers within a defined region, and therefore can be regarded as a simultaneous test for multiple markers that account for the dependence of close genetic markers.LSR) method is a two-stage procedure. The first stage conducts conventional association tests, such as the chi-square test for the case-control design or the transmission disequilibrium test .LSR method, we estimated the transition probabilities η00 and η11 to be 0.647 and 0.7, respectively, and identified L1  to a binary sequence with the value '1' representing significance and '0' otherwise, and determines the LSR to identify the location of the target disease susceptibility gene. The statistical significance of LSR method can be accessed by imbedding the sequence onto a Markov chain. A sequential goodness-of- fit-test  where I [A] = 1 if event A holds, and I [A] = 0 otherwise.Association tests are applied to examine the significance of associations between genetic markers and a disease susceptibility gene, and the corresponding p-values (significance probabilities) of the tests are denoted by {n = 15), the corresponding p-value vector of 15 association tests is α1 = 0.05, we obtainIn the following hypothetical example of 15 genotyped genetic markers  in a run. We denote the more flexible run as a "nearly perfect run" in contrast to a perfect run that does not contain 0s. A unifying notation of the length of an LSR is Lk, where the subscript k denotes the number of interrupting 0s. When k = 0, the case reduces to a perfect run. In the example in Equation (1), L0 = 3, L1 = 7, L2 = 8 and L3 = 12.An inordinately long 0: "there is no disease susceptibility gene in the sequence of markers", versusH1:"there is at least one disease susceptibility gene in the sequence".HLSR (i.e. large Lk) indicates a non-random cluster of significant associations with nearby markers, suggesting that disease susceptibility genes may exist in the candidate region. For a pre-chosen k, an intuitive rule to reject H0 is Lk > m*, where the constant m* is the minimum integer of m satisfying the following inequalityAccording to the argument above, an inordinately long α2. To determine the critical region, we need to calculate the tail probability of testing statistic Lk.for a test size L0 of LSR is analogous to the tail probability of their longest head-run. Fu and Koutras  and the exact form of the imbedded transition matrix Λi is a function of η1 and ηsti s in Equation (3). By considering a homogeneous Markov chain, the subscript i can be suppressed and Λi and ηsti can be reduced to Λ and ηst. We estimated the initial probability by the proportion of the occurrence of "1" in {Xj, j = 1,Λ,n}, i.e., H0 of independency, {Xj is assumed to be of no special pattern and the transition probabilities ηt 1and ηt 0can be replaced by where u = , X1...Xn}, we used the following sequential goodness-of-fit tests proposed by Anderson and Goodman [To examine the Markov chain assumption on sequence { Goodman :0: independent sequence vs. H1: first order Markov chain(i) H0: first order Markov chain vs. H1: second order Markov chain(ii) HIf the first order Markov chain hypothesis is accepted, we suggest using the following approach to estimate the dependency structure and calculate the p-value.w and sliding distance d, d <w <n, under proper choices of d and w, the windows {X1...Xw}, {Xd1+...Xw+d}, ..., {X1+sd...Xw+sd} will cover nearly the whole sequence, where s is the largest integer smaller than or equal to (n-w)/d. For the subsequence in the i-th window, the transition probabilities ηst (i) = P(Xi+1 = t | Xi = s), where s, t = 0,1, are estimated by its maximal likelihood estimator, namely, i) =  pairs)/(number of 1s), and i = 1 to d+1. For a homogeneous first-order Markov chain, we estimate i)'s and i)'s, respectively, and Consider a set of sliding windows of fixed size w can be used to estimate the transition probabilities. After i)'s are estimated from each window, the robustness of median can diminish the influence from those few windows which might contain disease markers. With the estimated ηst's, tail probabilities of LSR statistics can be calculated using (5), and then the null hypothesis of no disease gene is rejected if Lk ≥ mα, where mα is a threshold such that Pr{Lk ≥ m α|H0} = α.The validity of the sliding window approach is based on the assumption that most of the widows contain no disease markers, and therefore the sequences within the set of sliding windows of fixed size LSR method, and we compared the results with those from Bonferroni method.To summarize, we performed simulation studies to investigate the false-positive rate, the power, and the false discovery rate of the proposed I–BL developed method and directed simulations. Y–HL and Y–CL conducted simulations and data analyses. H–CY drafted the manuscript. C–JC proposed the original idea and directed simulations. CSJF directed simulations, drafted manuscript and supervised the project. All authors have read and approved the final manuscript.R code for LSR method. The LSR program in R code.The program in Click here for fileThe details of the markers in the 0–1 sequence. The marker position and marker name of the 0–1 sequence.Click here for file


Results from whole-genome association studies of many common diseases are now available. Increasingly, these are being incorporated into meta-analyses to increase the power to detect weak associations with measured single-nucleotide polymorphisms (SNPs). Imputation of genotypes at unmeasured loci has been widely applied using patterns of linkage disequilibrium (LD) observed in the HapMap panels, but there is a need for alternative methods that can utilize the pooled effect estimates from meta-analyses and explore possible associations with SNPs and haplotypes that are not included in HapMap.p(R-1)/[1 + p(R-1)], where R is the relative risk associated with a measured or unmeasured allele of frequency p, is appropriately scaled by 1/D' and weighted in proportion to r2, both common measures of LD being derived from the reference data set.By a weighted average technique, we show that association results for common SNPs in an observed data set can be scaled and combined to infer the effect of a genetic variant that has been measured only in an independent reference data set. We show that the ratio We illustrate this computationally simple method by combining the results of a genome-wide association screen from the North American Rheumatoid Arthritis Consortium with LD measures from the British 1958 Birth Cohort, and explore the validity of underlying assumptions about the generalizability of LD from one population to another, and from healthy subjects to subjects with clinical disease. DRB1*04 has been shown to be more strongly associated with rheumatoid arthritis than nearby tagging single-nucleotide polymorphisms (SNPs) , where Robs is the relative risk of disease associated with each copy of a measured allele of frequency q. The expression for Φ is similar to that for the population-attributable risk fraction (PARF) in epidemiological studies. However, in this genetic application, because each individual has two chromosomes, the PARF of a variant is Φ(2-Φ). In the Appendix we show that when the tagSNP is more common than the target variant, Φobs/D' is an unbiased estimator of Φtrue, where Φtrue is the equivalent parameter at the unmeasured target locus and D' is the conventional measure of LD between the measured tagSNP and the target variant in an undiseased population. We also show (see Appendix) that, for any given target variant and study design, the variance of Φobs for each tagSNP under the null hypothesis is inversely proportional to the LD measure r2, relating the measured tagSNP to the unmeasured target variant in the undiseased population.We define Φinfer = Φobs/D' across all tagSNPs in the selected 600-kb region, and derived an inferred relative risk from this pooled estimate of Φ. In this paper we explore the effect of different assumptions about the generalizability of LD measures and selection of tagSNPs upon this point estimate. An empirical variance of this pooled estimate under the null hypothesis was derived from multiple random permutations of the B58C data set.We calculated a weighted average of the values of ΦHLA-DRB1 diplotypes and Illumina HumanHap550 genotypes were available for 1217 members of B58C and for 1187 controls and 799 cases from NARAC. The frequency of DRB1*04 was 21.1% in the B58C reference set, 16.5% in the NARAC controls and 52.2% in the NARAC cases. Counting chromosomes among cases and controls, the observed odds ratio of rheumatoid arthritis per copy of the DRB1*04 variant was 5.54 (95%CI 4.78-6.41) corresponding to a Φ of 0.4280.Individually linked DRB1*04, also less than 44% in the reference data set and |D| > 0.01, where D is the covariance measure of LD. These restrictions are imposed to remove SNPs where the effect allele could be inconsistent between the B58C data and the NARAC controls data.All subsequent data relates to 156 SNPs common to both data sets where the minor allele frequency (MAF) of the tag is greater than that of s measure of LD (see Appendix for definition) is constant between cases and controls. In this example, even where there is a strong association between the target variant and disease, this assumption appears valid, as shown in Figure D' and r2 from the B58C data, the pooled estimate of Φ is 0.4530, which is very close to the value of 0.4280 derived directly in the NARAC case-control data.For general application, the method needs to apply measures of LD derived from a reference data set to the observed data set where the target variant has not been measured. Figure r2 < 0.3. Indeed, in this example, from a region of high LD (D' > 0.5), useful information could be derived even from tagSNPs with r2 < 0.1.The variance of the pooled estimate of Φ is greater than the sum of the weights, because many of the neighboring tagSNPs are intercorrelated. The precision of this pooled estimate was estimated by performing 1000 random permutations of the B58C data set to derive the variance of the pooled estimate under the null hypothesis. These results are summarized in Table r2 and t in the region of interest were markedly different in cases and controls, reflecting the strong association between the target variant (DRB1*04) and rheumatoid arthritis. When compared with imputation approaches, the inference method has the advantage of working only with the LD pattern among controls, although it does assume generalizability of the sensitivity parameter s from controls to cases. This latter assumption was supported in the NARAC data. The inference approach that we describe can, with modification, be applied to continuous outcomes and offers an alternative to imputation methods that will be particularly attractive for target variants that are not part of standard panels such as HapMap, and for exploring further genome-wide sets of association measures that have been derived from meta-analysis of a particular disease or quantitative trait.Comparing B58C to the NARAC controls, there was close correspondence in LD patterns. Within the NARAC data, however, the LD measures B58C: British 1958 Birth Cohort; LD: Linkage disequilibrium; NARAC: North American Rheumatoid Arthritis Consortium; PARF: Population-attributable risk fraction; SNPs: Single-nucleotide polymorphisms.s represents the proportion of the target variant that also has the tagSNP variant (akin to sensitivity in screening terminology). Another measure, equivalent to the positive predictive value, is t (= sp/q), the proportion of chromosomes with the tagSNP allele that also have the target variant. We seek to infer a value for the true relative risk (R) from the measured relative risk (Robs) associated with each tagSNP.Within a total population, the LD between each tagSNP and an unmeasured target variant may be represented by Table R, as follows:We define a transformation of obs/D' is an unbiased estimator of Φtrue.We show below that ΦIb = Io [R (1-s)p+(1-q)-(1-s)p]/[1-q],In Table Itotal = Io [1+p(R-1)] = Ib [1+q (Rob -1)]Also in Table infer = Φobs/D' for each tagSNP and derive a weighted average, with weights inversely proportional to their variance under the null hypothesis. An approximate variance for Φinfer can be derived by the Delta method by considering Φobs as a function of p, q, D' and β = ln(Robs), as follows, noting that:We infer values of ΦR = Robs = 1, and c is the proportion of N chromosomes that come from cases:Also, if r2 are therefore appropriate because p  is the same for all tag SNPs and c and N are fixed by the study design.Inverse-variance weights proportional to the LD measure The authors declare that they have no competing interests.DH carried out the data analysis. DPS conceived the method. DH and DPS drafted, read, and approved the manuscript.


Our results show that this difference in the variance of gestational age can largely be attributed to environmental sources; which were 3.1 times greater in African Americans. Specifically, environmental factors that change between pregnancies, versus exposures that influence all pregnancies within a family, are largely responsible for the increased reproductive heterogeneity observed in African American mothers. Although the contribution of both fetal and maternal genetic factors differed between race categories, genetic studies may best be directed to understanding the differences in the socio-cultural sources of this heterogeneity, and their possible interaction with genetic differences within and between races. This study provides a comprehensive description of the relative genetic and environmental contributions to racial differences in gestational age.Preterm birth is more prevalent in African Americans than European Americans and contributes to 3.4 times more African American infant deaths. Models of social inequity do not appreciably account for this marked disparity and molecular genetic studies have yet to characterize whether allelic differences that exist between races contribute to this gap. In this study, biometrical genetic models are applied to a large mixed-race sample consisting of 733,339 births to measure the extent that heritable factors and environmental exposures predict the timing of birth and explain differences between racial groups. Although we expected significant differences in mean gestational age between racial groups, we did not anticipate the variance of gestational age in African Americans (σ Preterm birth, defined as delivery before 37 weeks of complete gestation, is a major cause of perinatal mortality and morbidity. Prematurity is also associated with long term complications including developmental delay, and central nervous system disorders While salient risk factors for preterm birth have been indentified, ambiguity about the overall contribution of genetic and environmental sources remains. Previous preterm birth associated with an increased odds of 5.9  for a subsequent preterm birth per se, is how these factors differ between racial groups. Although many environmental risk factors for preterm birth, such as low socioeconomic status A further complication in understanding whether the genetic and environmental risks that track with these variables contribute to prematurity between racial groups and those responsible for variation within groups, knowledge of the latter may inform the former, especially if there are known racial disparities in environmental covariates within groups. Quantitative genetic methods can be used separate phenotypic variation into fetal genetic, maternal genetic, familial environmental and pregnancy-specific environmental components Although there is no obligatory connection between the causes of differences in preterm birth rates p-value <0.001; refer to model 3 in 2 = 3.764). Although the difference in mean values of gestational age between racial groups was expected the difference in variance was not.The prevalence of gestational ages less than or equal to 37 weeks was 14.7% in European Americans and 20.6% in African Americans. At a threshold of less than 37 weeks the percentages were 7.8% and 11.9% for European American and African Americans respectively. The average adjusted gestational age of African Americans of 38.91 weeks was significantly less than the average value for European Americans of 39.39 weeks (f2), maternal genetic (m2), shared environment (c2) and unique environment (e2) to take unique values in each race. This model also included a parameter, h, to allow for differences in the contribution of the shared  environment between full and half-siblings. Compared to model 1, a nested model (model 2) with h removed resulted in a non-significant degradation in model fit and indicated that this parameter could be omitted. All subsequent nested models (with fewer parameters) were compared to model 2. Models 4 to 8 indicated that the variance components could not be equated across racial groups and provided evidence for both genetic and environmental heterogeneity. The sequential omission of variance components in models 9 to 14 showed that dropping the f2 contribution for African Americans provided the most parsimonious fit to the data. In summary, tests indicated the presence of race-specific effects of the fetal genotype, f2, the maternal genotype, m2, non-genetic effects shared by successive pregnancies of the same mother, c2, and random environmental effects specific to individual pregnancies, e2. m2AA = 1.040 versus m2EA = 0.503); ii) environment factors shared across siblings (c2AA = 1.281 versus c2EA = 0.264) and; iii) environmental factors unique to each pregnancy (e2AA = 4.777 versus e2EA = 1.674).Our results show that the significant racial difference in the variance of gestational age can largely be attributed to non-genetic sources that contribute to differences between successive pregnancies of the same mother and between sibships. Taken together, these contributions were 3.1 times greater in African Americans versus European Americans. For both racial groups the magnitude of unique environmental influences was approximately twice as large as the combined effect of maternal genetic and shared environmental factors, which operate to create stability in the uterine environment within sibships. This suggests that the observed racial difference in variance of gestational age was due in large part to the effect of greater environmental heterogeneity in African Americans. This greater environmental variance generates larger differences among successive births to the same mother, as opposed to stable differences which would affect all pregnancies of the same mother.Substantial heterogeneity in the effect of environmental exposures were detected between racial groups even after accounting for differences that can be ascribed to fetal and maternal genetic influences. These results also persist over and above the effects of multiple covariates known to correlate with prematurity. With the exception of maternal education, which changes to a small degree over successive pregnancies, the remaining covariates were pregnancy-specific and are expected to diminish the large effect we observe for the unique environment. The non-overlapping 95% confidence intervals  for bothWe corroborated the contribution of fetal and maternal genetic factors to variation in gestational age The rate of births before 37 weeks gestation has increased by 21% from the period of 1989 to 2006 with consistent differences observed between races over this period In summary, we report quantitative genetic analyses in a large sample of Virginia families to describe how genetic and environmental factors contribute to differences in variability of gestational age between Americans of European and African ancestry. Environmental factors, particularly environmental exposures that differ across pregnancies, were largely responsible for the increased variability in the timing of African American births compared with European Americans. This greater environmental variation of African American births could be, for instance, a reflection of the greater unpredictability in accessing prenatal care or to their greater vulnerability to the effects of random non-genetic influences via genetic and/or social mechanisms. Future genetic studies may best be directed to understanding the racial differences in the socio-cultural sources of this heterogeneity, and their possible interaction with genetic differences within and between races. Otherwise, in order to further our understanding of the observed racial disparity in preterm birth, these results argue for greater resources to be invested in the identification and measurement of environmental influences that are less stable over successive pregnancies in African Americans versus European Americans.Pregnancy histories were obtained by combining the results of two separate requests for birth records from the Virginia Department of Health Office of Vital Records. A data-merge identified full and half-sibships by combining birth records that shared parental social security numbers (SSN) from Virginia births between 1989 and 2008. Individuals in full-sibships were required to share both the maternal and paternal SSN, while individuals in maternal half-sibships shared only the maternal SSN and those sharing only the paternal SSN were identified as paternal half-sibships. Records with either parental SSN missing were excluded. The result of this match was combined with a second set of birth records obtained from a previous study f2) and maternal (m2) genetic and shared (c2) and unique (e2) environmental latent variables. The proportion of genetic influences shared between related individuals  is inferred by the laws of segregation assuming random mating. By this model, the covariance between sibling births can be explained by the one-half of genes they share (½f2), the maternal genetic factors from a common mother (m2) and aspects of the familial environment that they share (c2). Unique environmental factors (e2) are not shared and account for pregnancy specific environmental exposures in addition to measurement error. The covariance between maternal half-siblings would differ in that they share one-fourth of their genes in common (¼f2+m2+c2). Using these expectations a rough estimate of fetal genetic influences (f2) on phenotypic variance can be derived by subtracting four times the difference of the full-sibling and maternal half-sibling correlation (f2 = 4((½f2+m2+c2) − (¼f2+m2+c2))).Expectations for genetic and environmental contributions to variances and covariances of relatives are derived from biometrical genetic theory m2) can be derived by subtracting the correlation of paternal half-siblings from maternal half-siblings since both relationships share one-fourth of their fetal genes but only maternal half-siblings share mothers in common (m2 = (¼f2+m2+c2) − (¼f2+c2)).All individuals who share maternal genetic influences also share the contribution of fetal genes, yet the converse is not true if one considers paternal half-siblings. Thus, an estimate of maternal genetic influences (f2) in common, while the offspring of dizygotic (DZ) twins, like other first cousins, share one-eighth their genetic load. Cousins related through MZ female twins would also share all of their maternal genetic influence but not the effects of the shared environment since they are members of different sibships. Accordingly, an estimate of the shared environment can be calculated by subtracting the correlation of the offspring of MZ female twins from the maternal half-sibship correlation (c2 = (¼f2+m2+c2) − (¼f2+m2)). An estimate of unique environmental sources can be obtained by subtracting both the genetic and common environment from the total phenotypic variance (vt2), e2 = vt2− f2− m2− c2. The importance of a factor accounting for within group variance can be calculated as the proportion of variance explained relative to total variance; thus the proportion of fetal genetic variance is calculated as f2/(f2 + m2 + c2 + e2).Additional relationships beyond full and half-sibships need to be considered to distinguish the influence of shared and unique environmental from genetic sources . The offAlthough it is instructive to derive estimates of genetic and environmental effects using correlations between relatives, in practice, structural equation modeling is preferred to make a simultaneous decomposition of the covariance matrix using widely available software implementing maximum likelihood et al. i.e., genetic effects are autosomal and neither X-linked nor sex-limited); (4) genetic and environmental variables do not interact and; (5) environmental effects were pregnancy specific apart from the effects of maternal genotype, shared environmental effects, measured covariates and other aspects of the parental phenotype .A convenient feature of structural equation methods is the ease in which families composed of different relationships and sizes can be incorporated To balance computation time with gains in information sibships were limited to the first four reported births, which corresponded to 96.7% of available births. Measured covariates were included based on prior evidence of association with preterm birth risk or mean levels differed between race, namely: birth order, maternal age, maternal education, source of care (private physician or other), fetal sex and number of reported cigarettes smoked daily while pregnant. Maximum likelihood estimates of the means and expected covariance matrices were obtained using the structural equation modeling program Mx


Drosophila melanogaster to quantify genetic variation in body weight, the content of three major metabolites  associated with obesity, and metabolic rate in young flies. We chose these lines because they were previously screened for variation in whole-genome transcript abundance and in several adult life-history traits, including longevity, resistance to starvation stress, chill-coma recovery, mating behavior, and competitive fitness. This enabled us not only to identify candidate genes and transcriptional networks that might explain variation for energy metabolism traits, but also to investigate the genetic interrelationships among energy metabolism, behavioral, and life-history traits that have evolved in natural populations.Obesity and phenotypic traits associated with this condition exhibit significant heritability in natural populations of most organisms. While a number of genes and genetic pathways have been implicated to play a role in obesity associated traits, the genetic architecture that underlies the natural variation in these traits is largely unknown. Here, we used 40 wild-derived inbred lines of We found significant genetically based variation in all traits. Using a genome-wide association screen for single feature polymorphisms and quantitative trait transcripts, we identified 337, 211, 237, 553, and 152 novel candidate genes associated with body weight, glycogen content, triacylglycerol storage, glycerol levels, and metabolic rate, respectively. Weighted gene co-expression analyses grouped transcripts associated with each trait in significant modules of co-expressed genes and we interpreted these modules in terms of their gene enrichment based on Gene Ontology analysis. Comparison of gene co-expression modules for traits in this study with previously determined modules for life-history traits identified significant modular pleiotropy between glycogen content, body weight, competitive fitness, and starvation resistance.Combining a large phenotypic dataset with information on variation in genome wide transcriptional profiles has provided insight into the complex genetic architecture underlying natural variation in traits that have been associated with obesity. Our findings suggest that understanding the maintenance of genetic variation in metabolic traits in natural populations may require that we understand more fully the degree to which these traits are genetically correlated with other traits, especially those directly affecting fitness. Obesity is a condition characterized by an excess of adipose tissue that adversely affects human health . The cliIn humans, large twin, adoption, and family studies have firmly established that obesity-related traits, such as body mass index and measures of body composition , are highly heritable -6. SegreD. melanogaster. We chose D. melanogaster as a model system because many of the genetic mechanisms controlling lipid metabolism and energy homeostasis are evolutionarily conserved between invertebrates and mammals , the content of three metabolites , and metabolic rate (MR) in 40 wild-derived lines of iewed in -22). Thuariation ,23-25. Tbundance . This pr2H, ranging from 25% to 65% in the combined sex analyses. These estimates are comparable to those found by several studies in humans [MF r(± SEM), were very high for BW , TAG , and GLYC  indicating that the same loci affect these traits in the two sexes. In contrast, moderate cross sex correlations were observed for GLY  and MR , suggesting that some of the variation in these traits is due to loci with sex-specific effects.We found significant genetic variation among the lines for all traits analyzed  and a total of 10,096 quantitative trait transcripts (QTTs) [P-value of 0.01, we found 93, 98, 131, 213, and 71 SFPs  . To idens (QTTs) . At a P-P-value of 0.01 , Calbindin 53E (Cbp53E), transferrin 1 (Tsf1), sevenless (sev), and junctophilin (jp). We then tested for phenotypic differences between homozygous mutants of these genes and their controls. After Bonferroni correction for multiple tests, we found that four of the mutant alleles showed a significant difference in GLY compared to the control . Flies with mutations in all four genes have more GLY than the control strain , dead-box-1 (Ddx1), sugarless (sgl), Sirt7, RhoGAP71E, and GXIVsPLA. We found that four of the mutant alleles showed a significant difference in TAG compared to the control . While flies with a mutation in rut have more TAG than the control strain, flies with mutations in sgl, Sirt7, and GXIVsPLA have all less TAG than the controls  histone deacetylase enzyme family, which has been shown to play a role in energy homeostasis and lifespan [GXIVsPLA2 encodes an enzyme involved in phospholipid metabolism [We selected six candidate genes for TAG: s Figure . rut encd memory  and alsod memory . Sgl encynthesis . P-elemey stores . Sirt7 ilifespan . Finallytabolism .b4GalNAcTA, CG5946, CG8920, Gliotactin (gli), Glutamate dehydrogenase (Gdh), and tweety (tty). We found that four of the mutant alleles showed a significant difference in GLYC compared to the control . Flies with mutations in all four genes have more GLYC than the control strain , module 2 (P = 2.3E-7), and module 13 (P = 1.2E-4) were enriched for genes involved in defense response categories. These results add to previous findings from systems genetics studies in mice showing that genes involved in immune function are involved in body weight regulation [et al. [D. melanogaster also suppresses insulin/IGF signaling leading to a decrease in both nutrient stores and growth. A major difference between flies and mammals is that while the effects on the insulin signaling in the latter are mediated by the c-Jun NH2 terminal kinase (JNK) branch of the mitogen-activated protein (MAPK) kinase signaling [Drosophila and mammals [Drosophila [We identified 13 modules of correlated transcripts associated with BW, ranging from 2 to 97 probe sets  and signal transduction (P = 2.5E-6) and two of its hub genes, g protein γ 1 (Ggamma1) and klingon (klg)  gene as a candidate regulating variation in BW among the Drosophila Raleigh lines. We also found that hug transcript abundance is highly correlated with Activating transcription factor-2 (Atf-2), which encodes a member of the ATF/cAMP response element-binding protein family of transcription factors and has been shown to regulate fat metabolism in the fat body [The second pattern observed in our analysis is that module 10 was enriched in genes involved in cell communication  Figure , are kno) Figure . Collectn humans  and singn humans ,52, whicn humans . Furthern humans . Our resesponses ,55-57. Fesponses . Consistfat body , the flyP = 1.1E-3), and phospholipase A1 activity (P = 1.4E-3), respectively  and one of its major hub genes is Cbp53E  gene [KCTD12 gene was associated with T2DM in a French population [KCTD12 as a strong candidate for T2DM in humans.Module 7 was enriched in genes mediating transmission of nerve impulse  gene . A recenpulation . Taken tpuckered (puc) and hemipterous (hep), encode a MAP kinase phosphatase and a MAP kinase, respectively, that regulate the JNK signaling pathway [Drosophila insulin-like peptide transcription in the neurosecretory cells of the brain that produce them [puc and hep modulates GLY by regulating insulin-like peptide secretion via the JNK pathway. This hypothesis however needs to be tested in future studies.Finally, it is worth mentioning that two of the genes in GLY module 6,  pathway . The mecuce them . Thus, adeath executioner Bcl-2 homologue (debcl), sex combs extra (Sce), viral iap-associated factor (viaf), Sirt7, CG7516, GXIVsPLA2, and Srp54, have human homologs, BOK, RING1, Pdcl3, SIRT7, NOL10, PLA2G12A, and SFRS12, respectively, whose transcript abundance has been associated with obesity in mice using a systems genetics analysis [Sirt7 and debcl, are hubs  and anti-apoptotic (e. g. bcl-2 and bcl-xL) proteins [We identified 5 modules of correlated transcripts associated with TAG, ranging from 1 to 71 probe sets   that plays a major role in the metabolism of carbohydrates for insect flight [We identified 13 modules associated with GLYC, ranging from 2 to 101 probe sets  Figure  that is t flight .P = 2.6E-3) and alpha-glucosidase  activities  in females. Additionally, we observed correlations between BW and GLY  and BW and MR  in males. These correlations however are not significant after correction for multiple tests based on sequential Bonferroni [Drosophila. For example, several laboratory selection studies in D. melanogaster have shown that both female and male adult flies selected for resistance to desiccation and starvation are significantly heavier and have higher GLY than unselected controls [et al. [Next, we asked whether there were significant genetic correlations between the BW and the metabolic traits. While we did not find any significant correlation using all data pooled across sexes, when we analyzed the data stratified by sex we observed a correlation significant at nferroni . Althougcontrols . Based ocontrols . Thus, wcontrols . A possi [et al. , who useAttacin-C (AttC), Cecropin C (CecC), and PGRP-SB1, were associated with variation in all three traits. Large energy investments are necessary for an adequate immune system to fight infections [Drosophila, have been extensively reported [D. melanogaster may reflect differences in allocation of resources between traits associated with survival in a way that maximizes fitness. This view is consistent with evidence of an evolutionarily conserved link between immune function and the insulin/IGF signaling discussed above. These Drosophila lines are currently being assessed for variation in the efficacy of their immune response to infection which will allow us to test this hypothesis.To gain insight into the molecular basis of the observed genetic correlations, we tested whether there was significant overlap of common transcripts between modules for the energy metabolism traits and life-history traits. We found substantial modular pleiotropy between BW, competitive fitness, and starvation resistance , Diuretic hormone 31 receptor 1 (Dh31-R1), and cAMP-dependent protein kinase 3 (Pka-C3) were associated with variation in all the traits. nAcRβ-64B and Dh31-R1 encode predicted G-protein coupled receptors that bind to the neurotransmitter acetylcholine and a diuretic hormone, respectively [Pka-C3 encodes a cAMP-dependent protein kinase [rhodopsin 4 (Rh4) and rhodopsin 6 (Rh6), were associated with variation in GLY and competitive fitness. In insects, the transcriptional coordination of circadian clocks has been implicated in affecting life-history traits by regulating physiological and behavioral rhythms [Drosophila circadian photoreception is mediated by cryptochrome in clock neurons and by rhodopsins in photic organs [Our analysis also showed significant modular pleiotropy between BW, GLY, and competitive fitness  and inbred to near-homozygosity by 20 generations of full-sib inbreeding [Drosophila Stock Center http://www.flybase.org.The 40 unrelated wild-type inbred lines of breeding . Mutantset al. [i.e. standard cornmeal, agar, molasses, and yeast medium, 25°C, 60-75% humidity, and 12 hr/12 hr light/dark cycle.We maintained each stock at constant parental density for at least two generations to minimize environmental effects. To control for larval density, we allowed the parents of the experimental flies to mate for 3 hours to generate egg collections on apple juice/agar medium in laying plates. After 24 hours, we picked groups of 100 first-instar larvae from the surface of the medium and put into replicate vials. For all assays, we used ten replicate vials per line, with each vial containing a group of 10 single-sexed individuals aged 3-5 days. We reared flies under the same experimental conditions described in Ayroles et al. , i.e. stWe first starved the flies for one hour under non-dehydrating conditions to reduce the food-derived TAG and GLY present in the gut . We thenet al. [540 was measured. Concentration of GLY was determined from glucose and glycogen standards run with each replicate. Each sample was assayed twice and the mean used in the analysis. Previous studies have shown that this protocol accurately reflects glycogen concentration and that endogenous glucose present in the flies contributes only negligibly to the results [GLY was measured from the same homogenates using a modification of the protocol described in Clark et al. . Briefly results .2 production using a flow-through respirometry system  and a modification of the method described in Van Voorhies et al. [2 scrubber therefore providing CO2-free air to the system. The airstream is saturated with H2O by passing through a series of gas syringes filled with sterile H2O and cotton wool. Pressure in the line is controlled by a precision pressure regulator that sets the input pressure to the 4-channel mass flow meter/controller where the flow is divided into 4 gas streams and provided to the sample chambers. The flow rate entering the chamber was 30 ml/min. After leaving the sample chambers, air enriched in CO2 enters into the 4-channel gas switcher that directs the flow to either the analysis system or to waste (vented). For the determination of CO2, sample air was pulled through a drying column to remove H2O, a mass flow meter, and then the CO2 analyzer that has a range of 0-2000 ppm CO2 with a resolution of better than 1 ppm. We measured CO2 for 10 minutes/chamber with a 30 second flush period between measurements. The amount of CO2 produced by each group of flies was calculated using C950 Data Acquisition software .We measured MR as COs et al. . BrieflyY = μ + L + S + L × S + E, where μ is the overall mean; L and S are the main effects of Line (Random) and Sex (Fixed); LxS is the random effect of sex-by-line interaction; and E is the within-vial error variance. Reduced models by sex were also run. Broad-sense heritabilities (2H) were computed as H2 = (Lσ2 + LSσ2)/(Lσ2 + LSσ2 + Eσ2) for the analyses pooled across sexes, where L2, σLS2σ, and E2 σare the among line, sex-by-line and within line variance components, respectively. Cross-sex genetic correlations (MFr) were also estimated as MF = cov♀♂/(σ♀σ♂)r, where ♀♂ covis the covariance of line means between females and males, and ♀ σand ♂ σare the square roots of the among line variance components for females and males, respectively. Genetic correlations between traits were calculated as GT r= G12 cov/(G1σG2σ), where G12 covis the covariance between traits among line means from the joint analysis, and G1 σand Gσ2 are the square roots of the variances among lines from the analyses of each trait separately. We used sequential Bonferroni procedure to correct for multiple tests of significance of correlation coefficients among traits [GCV) and environmental (ECV) variances were calculated as G CV= 100Gσ/μ and E CV= 100Eσ/μ, respectively, where G σand E σare the square roots of the line and within line variance components, respectively.All statistical analyses were performed using SAS version 9.1. We used a mixed model ANOVA to partition variation in each trait among the inbred lines according to the model, g traits . The coeP < 0.01) with variation in each trait we performed a regression analysis as previously described [Y = μ + S + T + S × T + ε, where S is sex, T is the trait, and ε is the error term were computed for each probe set. Similarly, SFPs associated (P < 0.01) with each trait were identified using the ANOVA model Y = μ + M + S + S × M + ε, where M is the presence or absence of the SFP, S is sex, and ε is the error term. Reduced models by sex were also run.To identify transcripts associated  and extracting the residuals to compute pair-wise transcript correlations for module construction [The genetic correlations between all transcripts significantly associated with each trait were computed after removing the correlation between these transcripts and the trait. This was achieved by fitting the model truction . Modulestruction  by transtruction  were ideMD and TFCM conceived the study and participated in its design and coordination. PJL and MMC performed research. JFA, KWJ, PJL, and JL analyzed the data. MD wrote the paper. All authors read, critically revised the manuscript, and approved the final manuscript.We are grateful to Dr Brett McKinney for contributing with analytic tools. We thank two anonymous reviewers for their valuable suggestions. We also thank Dr Barbara Gower for her technical assistance in the metabolite measurements. Dr Gower's laboratory is supported, in part, by NIH Grants P30-DK56336 (NORC), P60-DK079626 (DRTC), and UL1RR025777 (CCTS). This study was supported by NIH Grants R01 DK084219 to MDL and JL and R01 GM 45146 to TFCM.Quantitative genetic analyses of body weight and energy metabolism traits. This file includes estimates of variance components among 40 wild-derived inbred lines of D. melanogaster for the combined sex analyses.Click here for fileList of SFPs significantly correlated with body weight and energy metabolism traits in the analyses averaged across sexes (panel A) and stratified by sex (panel B). This file includes Affymetrix Probe identification number, FlyBase accession number and name of the gene tagged by the SFP, probe containing SFP, P-value from the ANOVA of the difference in trait means between the two SFP classes, minor allele frequency of the SFP, trait mean for lines with the common and minor alleles, and gene ontology information. SFPs included were significant at uncorrected P < 0.01. Genes detected by the two analyses are highlighted in gray in panel B.Click here for fileList of transcripts significantly correlated with body weight and energy metabolism traits. This file includes Affymetrix Probe identification number, FlyBase accession number and name of the gene transcript, P-value from the regression analyses, mean expression level, broad-sense heritability, and gene ontology information. Transcripts included were significant at uncorrected P < 0.01.Click here for fileP-element insert lines for alterations in energy metabolitesResults of the screen of . This file shows the genes tagged by the P-elements, the P-element insertion sites, and P-values from analysis of covariance  comparing the mutant and control data across the sexes and stratified by sex.Click here for fileModules of correlated transcripts associated with body weight and energy metabolism traits. This file shows the average correlation of a transcript with all other transcripts in its module (degree) and the average correlation of all transcripts in the module (average degree).Click here for fileOver-representation of Gene Ontology Categories, KEGG Pathways, and Keywords for transcripts associated with body weight and energy metabolism traits. This file shows the results of the over-representation analysis of transcript modules performed with EASE. Included are the number of genes in the annotation category (count), the number of genes in the annotation category/total number of significant genes (%), and the P value from a modified Fisher exact test for enrichment of genes in an annotation category. The file reports only results that survived Benjamini correction P-values of 0.05 or less.Click here for fileModular pleiotropy between energy metabolism and life history traits. This file includes a list of modules of transcripts significantly overlapping between traits in this study and traits listed in [P value from a Fisher exact test, and pleiotropic genes.isted in , P valueClick here for file


HLA*DRB1 to a less than 6 cM, 99% confidence interval with the two asymptotic approaches.We proposed a confidence interval method for disease gene localization by testing every position on each chromosome of interest for its possibility of being a disease locus and including those not rejected into the interval. Three test statistics were proposed to perform the tests, including one based on LOD and two generalized likelihood ratio tests with or without model averaging (GLRT/MA and GLRT). For the statistic based on LOD, an integrated procedure was proposed with an adaptive and an importance sampling component. We also proposed asymptotic approaches based on GLRT and GLRT/MA as alternatives that are much more efficient computationally but depends on the reliability of the limiting distributions. Besides its efficiency, the asymptotic procedure based on GLRT/MA also takes model uncertainty into consideration. Applications of these methods to the Genetic Analysis Workshop 15 (GAW15) rheumatoid arthritis data from the French population gave results that successfully captured the well recognized susceptibility gene  With the advances in molecular biology, more and more genome-scan data are available for linkage studies. Even in a preliminary genome scan, there is a need to localize a disease gene to as small a chromosomal region as possible without missing the signal of a true disease locus. The LOD support interval approach tends to undercover disease loci unless the linkage signal is extremely strong, and may be further complicated by the difficulty of choosing an appropriate threshold to account for multiplicity adjustment. Lin et al.  proposedIn this study, instead of deducing a confidence interval, by efficiently testing every position on the chromosome, we obtained a confidence interval of a disease locus with a couple of strategies based on three different statistics that are applicable to general pedigree data. Investigation of the performance of the new approaches by simulation showed that they worked well even when there were only moderate linkage signals. Because initial genome scan for the rheumatoid arthritis data provided by Genetic Analysis Workshop 15 (GAW15) showed moderate to strong linkage signals on chromosome 6, we applied our methods to the three data sets with microsatellite (MS) markers on that chromosome. We compared our results with the traditional 1-LOD and 3-LOD support intervals.C with at most one disease gene in it. We want to localize the gene, if it exists, by constructing a confidence set of its locus with coverage probability p such that the exclusion of the true disease locus on the map from the confidence set is controlled at level α = 1 - p. Because we can only make a type I error at exactly one of the excluded positions, if there is a disease gene on the map, no multiplicity adjustment is needed  as well as when d is off the map (d = ∞). The model averaging LOD score MALOD is defined aswhere LOD compatible with the data. More specifically, the set of disease models considered are those that are consistent with the identical-by-descent (IBD) probabilities estimated at the hypothesized trait locus or their perturbations. Such a setup not only accounts for model uncertainty associated with the estimated IBDs but also the uncertainty associated with the estimation of the IBDs. The weights assigned to the models, P(Mi), are bimodal, with those obtained from the IBD estimates getting a larger weight than those from the perturbations. More details can be found in Wan [which is an average of LOD scores over a set of d in Wan .H0 cannot be found analytically, we used simulation or asymptotic distribution to approximate this null distribution. For the simulation-based approach, data from multiple markers are simulated simultaneously conditional on the affection status and pedigree structure at each hypothesized disease position d0. Based on the simulated marker data, the null distribution at that hypothesized position is constructed by a Monte Carlo estimate. The test statistic λd0 is then compared to the null distribution to determine whether d0 should be included in the confidence set. It is worth emphasizing that at each hypothesized disease position, all marker data (multipoint) are simulated, regardless of the marker interval in which the hypothesized disease locus lies. Because there are an infinite number of putative disease loci to be tested, a practical strategy is needed to discretize the chromosome so that only a finite number of positions need to be tested without compromising the level of coverage. To further improve the computational efficiency of this simulation-based procedure, an importance sampling (IS) component was also proposed. In the following we describe the integrated procedure and the asymptotic approaches.A confidence set of the disease locus is then constructed by including all of the positions not rejected. Because the distribution of any of the three test statistics under We begin with a broad search of chromosomal regions to be included in or excluded from the confidence set. This broad search strategy is being referred to as our adaptive component of the integrated procedure. Specifically, the chromosome of interest is divided by the genetic markers, and each interval is considered in turn. For each such chromosomal segment, we divide it into two equal halves. For each half, we test the two end points (L and R) and the mid-point (M), and make inference about whether L-M, and/or M-R should be included in/excluded from the confidence set based on properties of the LOD scores, such as unimodality between two markers ,3. If ind0 is an interior point of one of such segments with the left end point being dL. We would like to test the hypotheses in Eq. (1) to determine whether d0 should be included in the confidence set. LetOne could have set the threshold to be sufficiently small so that interpolation based on the two end points of any remaining undecided segment would lead to a coverage probability close to the nominal. However, this would be a computationally inefficient procedure due to the need of constructing a large number of simulated null distributions. Instead, we used a relatively coarse grid (leading to a threshold of 1 cM) in the adaptive step and adopted an importance sampling strategy to further refine the remaining segments  without any additional simulation. Specifically, suppose d0, where G is the collection of genotypes of all the individuals at all the marker loci. Then the c.d.f. of X can be written as:denote the random variable corresponding to the LOD score hypothesizing the disease at position c.d.f. of the LOD score at d0 can be estimated byThus, the N sets of simulated marker data  with the disease locus hypothesized to be at dL. Note that these simulated marker data are available from the adaptive component step, and thus no additional simulations are needed. The importance sampling weight, P(Gi|D = d0)/P(Gi|D = dL), can be shown to equal to d0 based on this estimated null distribution. Our simulation study [which makes use of the on study  indicateon study .When the sample size is moderate or large and/or when the family structure is not extremely heterogeneous, we can approximate the null distribution of the GLRT by a HLA*DRB1 on chromosome 6. Thus, we focused on chromosome 6 and analyzed those three sets of data individually both at 95% and 99% confidence levels. For each population, we performed the analysis using two disease models inferred from each of the data sets. We also applied the two disease models inferred from the NARAC data to the FR data , North American Rheumatoid Arthritis Consortium (NARAC), and United Kingdom (UK), where the NARAC data consist of general pedigrees and the other two sets are of nuclear families. Prior information showed that there was a well recognized susceptibility gene HLA*DRB1 locus with at least one of the disease models. All 99% model averaging methods gave intervals containing the HLA*DRB1 locus. Specifically, when there are strong linkage signals as in the NARAC data (maximum LOD scores around 13), at 99% confidence level, our asymptotic methods gave results with shorter length compared to those from the 3-LOD method. Even when there are only moderate signals as in the FR data (maximum LOD around 2.8), at 99% confidence level, the integrated method and the asymptotic methods with or without model averaging all yielded confidence intervals (from 5 to 20 cM) containing the disease locus compared to the null set from the 3-LOD method  declare that they have no competing interests.


Genome-wide association studies (GWAS) based on single nucleotide polymorphisms (SNPs) revolutionized our perception of the genetic regulation of complex traits and diseases. Copy number variations (CNVs) promise to shed additional light on the genetic basis of monogenic as well as complex diseases and phenotypes. Indeed, the number of detected associations between CNVs and certain phenotypes are constantly increasing. However, while several software packages support the determination of CNVs from SNP chip data, the downstream statistical inference of CNV-phenotype associations is still subject to complicated and inefficient in-house solutions, thus strongly limiting the performance of GWAS based on CNVs.CONAN is a freely available client-server software solution which provides an intuitive graphical user interface for categorizing, analyzing and associating CNVs with phenotypes. Moreover, CONAN assists the evaluation process by visualizing detected associations via Manhattan plots in order to enable a rapid identification of genome-wide significant CNV regions. Various file formats including the information on CNVs in population samples are supported as input data.http://genepi-conan.i-med.ac.at.CONAN facilitates the performance of GWAS based on CNVs and the visual analysis of calculated results. CONAN provides a rapid, valid and straightforward software solution to identify genetic variation underlying the 'missing' heritability for complex traits that remains unexplained by recent GWAS. The freely available software can be downloaded at  Genome-wide association studies (GWAS) have identified associations between various phenotypes and common sequence polymorphisms, which might play a role for disease development (for a comprehensive overview see ). For moAvailable computer programs like Birdsuite , QuantiSCopy Number Variation Analysis Tool), a freely available software package to support scientists by GWAS based on CNVs. It was developed with the goal of creating a user-friendly, intuitive and fast software tool which covers the whole analysis process of association studies based on CNVs. To use it in real-life scenarios, a variety of de facto standard data formats are supported (Affymetrix Genotyping Console and Microsoft Excel) and all implemented algorithms are scalable and fast enough for typical problem sizes. Moreover, visual analytical methods assist the user to get a fast overview of the results.We present CONAN . For wizards and progress monitor dialogs we used JFace http://wiki.eclipse.org/index.php/JFace. The complete application was programmed in a strictly object-oriented way using JFace's Action Framework and is based on the Model-View-Controller Pattern. Libraries such as JExcelApi and opencsv were used to enable the import of phenotypes and CNVs from a variety of different data formats. All needed Java libraries are included in the software package and need no additional installation.The CONAN software package consists of a client application and several database packages. The client application was implemented in Java The users can upload their CNVs, phenotypes and genotypes directly through the client application to the server. All imported and calculated data are stored in a relational database (Oracle Database 10gR2). In order to avoid unnecessary data transfer between the client workstation and the database server, all time and data intensive analysis methods used by CONAN are executed on the database server itself. This leads to a markedly faster generation of results compared to traditional approaches where the application requests data, processes it locally and uploads the results  are defined as the union of all overlapping CNVs across subjects. As these regions are very long and therefore inadequate for the analysis, we divided them (based on the rules defined in ) into seMore precisely, our CNVR algorithm performs the following steps to detect sub-CNVRs with a frequency greater than the threshold:1. A list is created that contains only SNPs from all study individuals on a specific chromosome that define the borders of individual CNVs; upstream SNPs are designated as "starting" SNPs "S", downstream SNPs are designated as "ending" SNPs "E"2. The list is sorted by the physical position of those SNPs 3. A counter is initiated which increments on each CNV-starting SNP and decrements on each CNV-ending SNP.4. When two consecutive SNPs within this sorted list have different (ascending) physical positions, a next sub-CNVR could begin or previous sub-CNVR would end. The frequency of this potential sub-CNVR is determined with the help of the counter, and only if the frequency is greater than the user-specified threshold, the specific sub-CNVR is actually designated as CNVR.5. When two consecutive SNPs within this sorted list have exactly the same physical position, the counter actualizes to the frequency of the respective CNVR as defined under step 3.Note: the boundaries of each sub-CNVR are only approximated by the physical positions of its bracketing SNPs.If the number of subjects is very huge and their CNVs are highly interlaced with each other, the algorithm will detect many regions with almost all of the calculated CNVRs having a length of only two SNPs. Therefore we implemented a second algorithm which extends the former one by merging consecutive sub-CNVRs with a frequency greater than the threshold and building a single one on their basis. This leads to regions with greater length, but has the consequence that the state of a subject (e.g. deletion or amplification) in a region is no longer unique. Thus we have introduced a second threshold which is used to define the state of a subject: if the CNV is the longest in the given region and its physical length is greater than this threshold, then the state of this CNV is used for the association analysis  of each region using Student's t-test. A sub-CNVR is genome-wide significant if the calculated p-value is below the Bonferroni-threshold.CONAN is a cross-platform analysis software tool developed to provide several methods for genome-wide association studies based on copy number variations. An intuitive graphical user interface (GUI) enables the determination of CNV regions and carrying out association analysis through multiple regressions. In addition, the explorative process of results is supported by several interactive visualizations.CONAN implements a simple but effective workflow to enable CNV analysis Figure : in a fiCONAN has a very clear and simple interface Figure : on the http://genepi-conan.i-med.ac.at).CONAN supports copy number variations which are determined using Affymetrix 500K SNP Arrays. Additionally, our solution supports the import of the "Copy Number Segment Summary" and the "Copy Number Segment Data" file format which can be exported from the frequently used Affymetrix Genotype Console software. There also exists a generic importer for CNVs that were detected from any other platform with any other software tool; however, then the CNVs need to be stored in a comma separated values file format  in which each row represents a certain person and contains its related phenotypic information. In addition to spreadsheet and statistic software, the efficient phenotype management software eCOMPAGT  can alsoFor further analysis with statistical software such as R  and SPSSOnce all data are stored in the database, the analysis process starts with the determination of CNV regions. For this purpose we have implemented the procedure described in ,23 for tIf the number of subjects is huge (>1000) and their CNVs are highly interlaced with each other, the algorithm will detect many regions with almost all of the calculated CNVRs having a length of only two SNPs. Therefore, we developed a second algorithm which extends the former procedure by merging consecutive regions and building a single one on their basis.Table After CNV regions were calculated, the user is provided with the ability to perform GWA analysis on their basis. For this task we provide a multiple linear regression model (assuming an additive genetic model) which enables to discover associations between the detected regions and the imported phenotypes. A second association analysis method combines the genotyping data from SNPs with the states of detected CNVRs in order to discover associations between cumulative effect of SNPs and CNVS and phenotypes. In both cases the user can select the dependent phenotype (e.g. blood sugar level) and a list of phenotypes which should be used for adjustment . The software automatically calculates the corresponding p-values for all selected regions and checks for genome-wide significance after Bonferroni-correction for multiple testing . In some cases it is necessary to perform the analysis only on subjects with particular properties . Therefore, it is possible to build user defined filters in order to perform the association analysis only on a subset of all available data.In addition, to save CPU time, already detected CNV regions can be reused for several studies. These can be compared quickly to see their difference and to identify the impact of each changed parameter.10-transformation and each chromosome has a different color; genome-wide significant hits can be found above the Bonferroni-threshold line which is automatically drawn considering the number of tests performed . GWAS are performed through a user-friendly GUI and different graphical representations enable a rapid interpretation. However, most of those approaches are client-oriented and perform their calculations locally; this leads to poor scalability and all results are stored on different workstations and not on a central machine. This is an important aspect because the amount of the genotyping data for GWAS is increasing continuously and in a non-linear manner; thus high-performance data retrieval is an important issue. CONAN solved this problem by outsourcing all tasks to a central database server and by using the client workstation only for the presentation of the results.The open source command line tool irdsuite  enables irdsuite . VisualiSCIMMkit  is also Our software has several strengths: (1) Extensive tests with real data demonstrated that the analysis of a study with about 1,600 subjects and hundreds of thousands of CNVs can be performed with CONAN without any problems and in reasonable time frames. 2) Due to an intuitive user interface and a detailed user manual, no knowledge in computer science and statistics is required to perform the association analysis. (3) With the help of the Manhattan Plot it is possible to spot within seconds which regions are genome-wide significant. In addition, various export functions enable the further usage of the newly-detected information in other software packages such as R or SPSS : Windows and LinuxProgramming language: JavaOther requirements: Java 1.5+, relational database (Oracle)LF was responsible for programming and designing CONAN and drafted the manuscript. TK and FH provided CNV data from KORA. SS and HW helped with database issues. GG and HEW were responsible for the KORA study. FK and GS helped drafting the manuscript. AK-B initialized the project, supervised it and drafted the manuscript. All authors read and approved the final manuscript.