TFAP2B  and MSRA . A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6×10−8). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies  informative for adult waist circumference (WC) and waist–hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near  TFAP2B and MSRA) associated with WC, and a further locus, near LYPLAL1, which shows gender-specific relationships with WHR . These loci vary in the strength of their associations with overall adiposity, and LYPLAL1 in particular appears to have a specific effect on patterns of fat distribution. All in all, these three loci provide novel insights into human physiology and the development of obesity.Here, we describe a meta-analysis of genome-wide association data from 38,580 individuals, followed by large-scale replication  designed to uncover variants influencing anthropometric measures of central obesity and fat distribution, namely waist circumference (WC) and waist–hip ratio (WHR). This work complements parallel efforts that have been successful in defining variants impacting overall adiposity and focuses on the visceral fat accumulation which has particularly strong relationships to metabolic and cardiovascular disease. Our analyses have identified two loci ( The accumulation of abnormal amounts of intra-abdominal fat  is associated with serious adverse metabolic and cardiovascular outcomes, including type 2 diabetes (T2D) and atherosclerotic heart disease 2∼0.9 with waist circumference [WC] and ∼0.6 with waist-hip ratio [WHR], 2∼0.6 and 0.5, respectively) Measures of central and overall adiposity are highly correlated  regulation of overall adiposity With this in mind, we set out to identify genetic loci influencing anthropometric measures of central obesity and fat distribution, namely, WC and WHR. Our meta-analysis of 16 genome wide association studies (GWAS), followed by large-scale replication testing, generating a combined sample of up to 118,691 individuals of European origin, has identified three loci associated with these critical biomedical traits.Our strategy for identifying common variants influencing central adiposity is summarized in The stage 1 meta-analysis combined data from 16 GWAS scans  informative for anthropometric phenotypes . We seleGC) in the mixed-gender analysis were λGC_WC = 1.09 (λGC_WC_1000 = 1.003 [standardised to a sample size of 1000]) and λGC_WHR = 1.07 (λGC_WC_1000 = 1.002)  that showed evidence of association to one or both of the traits  and MC4R . These two markers are highly correlated (r2>0.8) with markers that represent two of the strongest signals for overall adiposity  see  [19]. Fre traits . As mighdiposity  [9]–[10]−5) associations with WC and/or WHR while displaying only weak evidence of an association with overall adiposity  or adult height (P≥0.005) in the stage 1 GWAS meta-analysis data , given an additive model and MAF of 20%.From this initial set of 76 WC- and/or WHR- associated signals, we sought to enrich for variants with specific impacts on central adiposity, by identifying a subset of 23 SNPs for which there was greatest evidence for a disproportionate effect on central adiposity, as opposed to overall adiposity or height. These 23 variants all had strong , and performed de novo genotyping in subjects from 20 additional studies   . Follow-ncestry)  and S6.−8, −9) maps near TFAP2B, which encodes transcription factor activating enhancer-binding protein 2 beta. The second  resides near MSRA, encoding methionine sulfoxide reductase A, whilst the third  is located within the TBCE (tubulin folding cofactor E) gene region.After combining gender- and study-specific measures of association across all studies .TFAP2B, CHARGE analyses revealed directionally-consistent association with WC  resulting in a combined P-value of 1.9×10−11 . At MSRA, genotypes for rs7826222 could only be imputed in a subset  of CHARGE samples  (At  see −9) .TBCE showed no evidence of association with WC in the full CHARGE data set . Since analysis of the combined data set no longer reached genome-wide significance (P = 2.9×10−7), further studies will be required to establish the status of this signal. For the TFAP2B and MRSA loci, there was no evidence of heterogeneity of effect size across the various sample sets, and no evidence that the inclusion of diabetes or coronary artery disease case samples had any impact on the associations  had shown modest evidence for association with WHR in our primary (both genders included) analysis (P = 3.6×10−6) . When stage 1 and stage 2 data were combined, the female-only signal remained highly-significant (P = 2.6×10−8) (−3). As the CHARGE GWAS analyses were restricted to WC, we were unable to follow-up the LYPLAL1 signal in these data.Given the clear gender dimorphism of central obesity, and evidence that some genetic effects on fat distribution may be gender-specific .6×10−6) . However.6×10−8)  with eviWe had designed this study to be complementary to equivalent analyses of overall adiposity (as measured by BMI) conducted on many of the same samples TFAP2B, MSRA and LYPLAL1 were indeed specific for central fat accumulation as opposed to being driven by other highly-correlated anthropometric traits . To evaluate this, we used data from the stage 2 replication samples, from which we can expect to obtain less biased estimates of the relative effects across anthropometric phenotypes.We were interested therefore in establishing the extent to which the confirmed, genome-wide associations identified at/near TFAP2B, these stage 2 data indicated that, notwithstanding the evidence for discordant effects in the stage 1 data (which led to its selection for follow up), rs987237 showed strong associations with overall adiposity . The association with WC remained only nominally significant in stage 2 (P = 0.02) after adjustment for BMI. The TFAP2B rs987237 G allele was weakly associated with overall fat mass  measures: In the case of TFAP2B, which is the most obvious candidate gene in the locus. TFAP2B encodes a transcription factor preferentially expressed in adipose tissue, and over-expression of the transcript in 3T3L1-adipocytes leads to insulin sensitivity via enhanced glucose transport and increased lipid accumulation TFAP2B also down-regulates expression of the insulin-sensitizing hormone adiponectin by direct transcriptional repression TFAP2B have recently been reported to correlate positively with TFAP2B transcript levels in adipose tissue TFAP2B can be added to the growing list of loci influencing overall adiposity TFAP2B may be involved in global adipocyte response to positive energy balance.These data suggest that the chromosome 6p12 signal exerts its predominant effect on fat accumulation at multiple sites, a finding consistent with the known biology of LYPLAL1) showed relatively modest associations with overall obesity  and WC (P = 0.01). Crucially, the strength of the association with WHR was substantially greater after adjustment for BMI . In the limited subset of women  for whom direct measures of hip circumference (HC) could be retrieved, and in whom there was a proportionate signal for WHR (P = 5.2×10−4), we found no association with HC (P = 0.7) and a directionally consistent trend of association to WC (P = 0.06). Whilst these data would suggest that the LYPLAL1 signal does indeed have a specific effect on fat distribution, our own DXA data on regional fat distribution are non-contributory   , and larMSRA locus and adiposity phenotypes are unclear at this stage. The signal near MSRA showed only weak association with overall adiposity (P = 2.2×10−3 for BMI in stage 2), but the strong association with WC in stage 2 samples became non-significant after BMI-adjustment (P = 0.11). The main proposed function of MSRA is to repair oxidative damage to proteins by enzymatic reduction of methionine sulfoxide. An alternative candidate in the vicinity is TNKS, which encodes a TRF1-interacting ankyrin-related ADP-ribose polymerase (tankyrase). Tankyrase is a peripheral membrane protein known to interact with insulin-responsive aminopeptidase (IRAP) in GLUT4 vesicles in adipocytes TNKS has a putative role in insulin-regulated glucose disposal into fat and other tissues.Biological connections between the TFAP2B) and 0.04% for rs7826222 (MSRA). This corresponds to absolute WC effect sizes of 0.49 and 0.43 cm respectively . The SNP near LYPLAL1 (rs2605100) explains 0.02% of the WHR variance in women (absolute effect size on WHR of 0.0014).We estimated the variance in these traits attributable to the loci discovered using data from the KORA-S4 sample (the largest population-based sample within stage 2). The explained variance of WC was estimated to be 0.05% for rs987237 (LYPLAL1) and increased fasting triglycerides  and rs987237, r2 = 0.67). Thus, we see some evidence that the variants identified have anticipated effects on downstream phenotypes, although, as recently demonstrated for FTO , analyses of this type have only limited power even in extremely large data sets The accumulation of central adiposity has serious adverse health consequences including hyperlipidemia and increased risks of T2D. We examined the relationships between adiposity-related SNPs and these clinical phenotypes using available GWAS meta-analysis data . We founIn summary, by focusing on anthropometric measures of central obesity, we have identified three loci strongly implicated in the regulation of human adiposity and fat distribution. The extent of phenotypic variation explained by these variants is small. However, the variant or variants at each locus which are directly involved in influencing these traits are yet to be identified, and these may have more substantial effects. Even if this is not the case, effect size has very little bearing on the biological pertinence of these findings nor the potential impact of perturbing these pathways through therapeutic modification. Although determination of the influence of these signals on the development and maintenance of specific fat depots will require analyses that relate genetic variation to detailed imaging data in large numbers of subjects, the loci identified appear to highlight a variety of novel mechanisms involved in the regulation of adiposity. At this stage, it is unclear to what extent these same loci influence fat distribution in other ethnic groups, such as South Asians, in which patterns of fat distribution, and the relationships between fat distribution and metabolic disturbance, differ from those in Europeans. The data are consistent with a model whereby fat mass and distribution are determined through the concerted action of processes acting at the level of both the hypothalamus and peripheral fat depots.in-silico” GWA results  and (b) “de novo” genotyping  giving a total of 70,689 (WC) or 61,612 (WHR) follow-up samples (collectively referred to as stage 2). In addition, genome wide signals for WC identified after stage 2 were confirmed using data with The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, whose meta-analysis included eight studies totaling 31,375 individuals. All samples included in these analyses were of European ancestry. We also undertook gender specific analysis of the stage 1 GWAS. An overview of the study design and results is given in Our study began with a genome-wide screen for discovery of loci potentially associated with two different anthropometric measures of central adiposity: waist circumference (WC) and waist-hip-ratio (WHR) All samples in the discovery cohorts were of European ancestry and detailed information on each of these studies is provided in 2∼0.9 with WC and ∼0.6 WHR, 2∼0.6, and 0.5, respectively) as are measures involving DXA (r2∼0.6) The most appropriate adiposity measurements for assessing various fat compartments and the risk of adverse health outcomes is debated. Despite the close correlation between WC and BMI , WC has WC and WHR were measured in the individual cohorts using standard protocols. Recently a multi-centre comparison of WC measurements at different sites showed that the measurement at different centres have no substantial influence on the association to various adverse health outcomes http://www.sph.umich.edu/csg/abecasis/MACH/index.html) and IMPUTE http://www.stats.ox.ac.uk/~marchini/software).Operational details of each of the 16 GWAS  can be found in CETP and HDL cholesterol) Quality control (QC) criteria for SNPs to be included in the meta-analysis were minor allele frequency (MAF)≥1% and for imputed SNPs good imputation quality, which was defined as proper_info≥0.4  or rsq-hat≥0.3 . The rsq_hat measure allows us to assess imputation accuracy for markers with many different allele frequencies. In comparison to filters based on the accuracy of individual genotype calls, it generally translates into a more stringent standard for rare SNPs and a more lenient standard for common SNPs. For intuition on how the measure performs consider the simple example of region where a particular SNP always occurs in a specific haplotype background. Further, assume that the SNP has a frequency of 10% and that the haplotype has a frequency of 20%. In this example, whenever we observed this particular haplotype we expect the SNP will be present ∼50% of the time so that >50% imputation accuracy cannot be achieved for this SNP. On the other hand, knowledge of the haplotype does provide useful information about the SNP, and the rsq_hat statistic takes a value of about 0.44 in this setting. There are examples of association between imputed SNPs with similar rsq_hat statistics and complex traits that have been confirmed in follow-up genotyping  means (approximately) that the amount of statistical information (about the parameter of interest in the model) at the imputed SNP is equivalent to perfect genotype data in a sample 100×% as big as the used sample size.in press; Empirical assessments show that genotype imputation using either MACH or IMPUTE provides an effective and accurate means of evaluating evidence for association at untyped markers ).We combined the association results for WC from the GIANT and CHARGE samples to derive a combined meta-analysis of all studies . This anMSRA locus, genotypes for rs7826222 were only available for a subset of the CHARGE samples . This is due to the fact that this SNP has been renamed to rs545854 in NCBI build 36 and was consequently one of the SNPs omitted from HapMap release 22 and therefore is not present in build 36 imputations based on that release of HapMap. Nonetheless, the effect of rs7826222 in CHARGE was directionally-consistent (P = 0.28), and CHARGE data available in larger sample size  for two moderately-good proxies  and both show some support  with the other findings.For the Table S1Phenotypic correlation between anthropometric values in men and women.(0.02 MB XLS)Click here for additional data file.Table S2The loci that were followed up in GIANT Stage 2.(0.04 MB XLS)Click here for additional data file.Table S3Description of samples included in the GWA meta-analysis (Stage 1) and follow-up studies (stage 2).(0.03 MB XLS)Click here for additional data file.Table S4Descriptive characteristics of genome-wide association study cohorts (Stage 1) and follow-up study cohorts (Stage 2).(0.04 MB XLS)Click here for additional data file.Table S5Information on genotyping methods, quality control of SNPs, imputation, and statistical analysis for Stage 1 and 2 study cohorts.(0.04 MB XLS)Click here for additional data file.Table S6Lead markers and proxies used in stage 2 replication.(0.03 MB XLS)Click here for additional data file.Table S7Association analysis of fat phenotypes using raw estimates for the significant loci.(0.02 MB XLS)Click here for additional data file.Table S8The effect of the genome wide significant adiposity loci on lipids within ENGAGE cohorts.(0.02 MB XLS)Click here for additional data file.Table S9The effect of the genome wide significant adiposity loci on type 2 diabetes risk.(0.02 MB XLS)Click here for additional data file.Table S10The association results for waist-circumference and waist-hip-ratio to the recently reported 17 BMI loci.(0.03 MB XLS)Click here for additional data file.Text S1Supporting text and acknowledgments.(0.16 MB DOC)Click here for additional data file.


GDF8 locus and determined its contribution to variation in muscling and fatness in sheep.The current investigation surveyed genetic polymorphism at the ovine g+6723G>A, is known to increase muscularity within the Belgian Texel. A genetic survey of 326 animals revealed that the mutation is near fixation within Australian Texels and present in additional breeds including White Suffolk, Poll Dorset and Lincoln. Using a resource population comprising 15 sires and 1191 half-sib progeny with genotypic data, the effect of this and other SNP was tested against a set of 50 traits describing growth, muscling, fatness, yield, meat and eating quality. The loss of function allele (g+6723A) showed significant effects on slaughter measurements of muscling and fatness. No effect was detected on objectively assessed meat quality however evidence was found for an association between g+6723G>A, decreased intramuscular fat and reduced eating quality. Haplotype analysis using flanking microsatellites was performed to search for evidence of currently unidentified mutations which might affect production traits. Four haplotypes were identified that do not carry g+6723A but which showed significant associations with muscling and fatness.Re-sequencing 2988 bp from a panel of 15 sires revealed a total of six SNP, none of which were located within exons of the gene. One of the identified SNP, g+6723G>A is present within Australian sheep facilitated an independent evaluation into its phenotypic consequence. Testing was conducted using a separate genetic background and animals raised in different environments to the Belgian Texel in which it was first identified. The observation that the direction and size of effects for g+6723A is approximately consistent represented a robust validation of the effects of the mutation. Based on observed allele frequencies within breeds, selection for g+6723A will have the largest impact within the White Suffolk. GDF8 may harbour additional mutations which serve to influence economically important traits in sheep.The finding that  GDF8 or myostatin) is known to directly influence muscular hypertrophy and carcass conformation. The gene is a member of the transforming growth factor-β superfamily of growth and differentiation factors and its role was first identified following gene targeting in the mouse [GDF8's role as a negative regulator of muscle growth. Naturally occurring GDF8 mutations were subsequently identified in two hyperplastic or 'double muscled' cattle breeds [Growth and differentiation factor 8  creates an octamer recognition motif for the microRNAs, Mir1 and Mir206, which act to suppress GDF8 translation. This confirmed that removal of GDF8's inhibitory role in sheep confers an increase in muscularity consistent with findings in other mammalian species.Investigation of  fatness . Examina fatness . Analysi fatness . Analysi fatness . The preGDF8 which contribute to economically important trait variation. The recent identification of g+6723A prompted testing to determine its frequency within 12 breeds and a set of Australian terminal sires. The final aim was to estimate the phenotypic effect of g+6723A on a range of carcass and meat quality traits measured under Australian conditions.Discovery of quantitative trait nucleotide (QTN) opens the possibility of using marker assisted selection to enhance genetic gain. As a result, the first aim of this study was to search for previously unidentified QTN at ovine GDF8 exons. No indels, synonymous or non-synonymous substitutions were detected, consistent with a single previous report for ovine GDF8 [g-41C>A), intron 1 (g+391G>T), intron 2 (g+4036A>C) and the 3'UTR (g+6723G>A) [g+2154C>T) and the 3'UTR (g+6871G>A). The genotypic status, using IUB convention, at each polymorphism  within the 15 sires (S1 – S15) was as follows: S1 CGCAGG, S2 CGCARG, S3 MGCAGG, S4 CGCAGG, S5 MGCAGG, S6 MGCAGG, S7 CGCAGG, S8 CGCAGG, S9 MGCAGG, S10 MKCMGG, S11 CGCARG, S12 MKCMGG, S13 MKCAGR, S14 CGCARG, S15 MKYMGG.A set of 15 sires (Table ine GDF8 . A furth6723G>A) . The remg-41C>A, g+4036A>C and g+6723G>A). The remaining SNP  were not tested either due to low informativeness within sires 1 – 15 or unsuitability of assay design. Genotyping 326 animals from 12 breeds revealed that two SNP were polymorphic within every breed tested  [g+6723A p = 0.946, Table g+6723A in the heterozygous form.Three SNP were selected for analysis within a larger set of animals 6723G>A) . Consist3G>A . CoGDF8 (BM81124 and BULGE20) [g-41C>A, g+4036A>C and g+6723G>A) were used to assess haplotype diversity. Genotyping was performed using 15 Poll Dorset, White Suffolk or mixed breed sires  and ten (BM81124) alleles observed within the 30 sire chromosomes tested. Marker segregation within family was used to assign allelic phase and construct five-marker haplotypes across the GDF8 locus . Inspection of the surrounding phase-known alleles at the other four markers revealed that in each case, g+6723A was observed on a single marker haplotype  was determined from five generation pedigrees. Poll Dorset, Texel, Suffolk and White Suffolk ancestors were identified and the number of each is recorded in Table  – S15 waGDF8 SNP  highly significant effects on fatness (P < 0.01 for cfatc and imf) and significant effects on subjective measures of eating quality . The mutant allele (g+6723A) was associated with increased muscling, decreased fatness and a decrease in perceived eating quality (Table g-41C>A on forequarter weight (p < 0.05), meat tenderness (P < 0.05 for two shear measurements avkgf0 and diff072) and for +4036A>C on muscling (P < 0.05 for emwc).Eleven of the 15 resource sires were heterozygous for at least one of the three g+6723A, microsatellites BM81124 and BULGE20 were genotyped across all 15 half-sib families. Haplotype effects were estimated for a set of five widely measured live-animal and carcass traits using both across-family and within-family analysis , one on slaughter measurements of fat and muscle  and one on eye muscle area  [g+6723A in Australian flocks. The allele appeared near fixation in a sample of 116 Australian Texels   promptedGDF8 alleles and variation in phenotypic traits. Regression analysis was performed using data collected from three SNP and 50 phenotypic traits measuring growth, muscling, fatness, meat quality and eating quality. Strikingly, segregation at g+6723G>A accounted for 12 of the 16 significant marker by trait associations detected  and gives an expected false discovery rate of only 20% [A major objective of the study was to search for association between only 20% .g+6723A) was associated with increased muscularity and decreased fatness measured on the carcass. It is worth noting that the half-sib progeny used were developed from Merino ewes and raised under Australian environmental conditions. Previous work using both F2 and backcross progeny was performed under European conditions using Romanov ewes [g+6723A was approximately consistent for carcass traits common to both studies. This conclusion represents a robust and independent validation of g+6723G>A effect on sheep carcass traits. In addition to carcass traits, ultrasound derived measures of muscling and fat depth were recorded when progeny were approximately seven and nine months of age. Testing revealed none were significantly associated, indicating that the production level impact of g+6723G>A on muscling and fatness was restricted to heavier animals. This differs with reported QTL located near Myostatin in both New Zealand and UK Texels where significant effects were detected for ultrasound measures in some but not all sires [g+6723G>A does not have a large impact on growth or live weight under Australian conditions. The final trait categories measured meat quality, tenderness and eating quality as it is critical to determine if favourable alleles which promote muscling have an associated negative impact on other traits. Four biochemical measures of meat quality  showed no association. In addition, three time point recordings of Warner-Bratzler shear force indicate that allelic variation at g+6723G>A imposed no significant effect on meat tenderness. This result is entirely consistent with a detailed examination of meat quality conducted using New Zealand Texels segregating divergent haplotypes at GDF8 [g+6723A was associated with elevated creatinine concentration which has been used as an indicator of total muscle mass and is elevated in sheep which carry a separate hypertrophy phenotype  [g+6723A on the percentage of intramuscular fat  . Testingat Table . This haat Table . In eachg+6723G>A) with an impact on the traits of interest. This was addressed using two approaches. Firstly, each protein coding exon and splice junction were re-sequenced using DNA from each of the 15 sires which comprise the resource population. This revealed a complete absence of sequence differences and indicates that the Poll Dorset, Suffolk and White Suffolk are unlikely to carry exonic mutations at or near fixation. Investigation of sires from additional breeds is required before a firm conclusion can be drawn, however this appears to distinguish the ovine homolog of GDF8 from its bovine equivalent where a series of loss of functions alleles have been reported within the three coding exons [BM81124 and BULGE20. The intention was to exploit the superior polymorphic information content resident at the microsatellite markers to search for haplotypes in phase with currently unidentified mutations. Two analytical methods were used. Firstly, haplotype effects in different families were correlated as the covariance was determined by haplotype similarity . Amplified products were directly sequenced in both forward and reverse orientation using ABI Prism™ Big Dye Terminator 3.2 chemistry and the ABI 3130xl Genetic Analyser . Sequence was trimmed and aligned using Sequencher version 4.2 . SNP were named in relation to the first base in the ATG translation start site, as given in GenBank accession DQ530260[Six primer sets were designed from available ovine sequences [GenBank:  DQ530260.μl containing 2 ng of genomic DNA, 2.25 μM of each PCR primer, 0.8 pM of each TaqMan probe and 5 μl of TaqMan Universal Master Mix (ABI). Cycling was performed as follows: 2 min at 50°C and 10 min at 95°C; 45 cycles of 95°C for 30 sec and 60°C for 60 sec. End point allele discrimination analysis was performed using the Sequence Detection System (SDS) software version 2.2.2 (ABI). Primers for amplification of microsatellite markers BM81124 and BULGE20  7900HT 'TaqMan' sequence detection system  which provides a national genetic information and evaluation service to the sheep industry . Each peFifteen half sib families were generated using Poll Dorset, White Suffolk or mixed breed sires  for the marker allele that animal j received from sire i for the kth marker, and b is the regression coefficient (estimated marker effect) of marker k. Phenotypes were pre-adjusted for relevant fixed effects  using an additive linear model. Slaughter traits were also corrected for effect of batch (9 slaughter batches over 2 years) and eating quality traits were corrected for test night (15 levels), birth-year and sex. Marker regression analysis was performed using the R-Program [BM81124 and BULGE20. In both models, regression was used where each sire haplotype was fitted under the hypothesis it carried a QTN. Identity by Descent (IBD) probabilities were calculated for each putative QTL at the midpoint between BM81124 and BULGE20. In the first model, haplotype contrasts were calculated independently within each of the 15 half-sib families. The covariance between identical haplotypes in different families was set to zero, implying that the QTL effects were allowed to vary between sire families. The resulting solutions for haplotype effects are referred to as the 'within-family' analysis (Table where y-Program . In subsis Table . In the is Table . For botJWK conceived and guided the study, carried out the exon scanning and drafted the manuscript. RM performed DNA extractions the genotyping of all the animals. JEHE coordinated construction of the half-sib resource flocks and collection of phenotypic trait data. VHO participated in study design, data coordination and manuscript preparation. SHL carried out the association and haplotype analysis. JVDW contributed to study design, manuscript preparation and coordination of the statistical components of the work. All authors read and approved the manuscript.


The role of race in human genetics and biomedical research is among the most contested issues in science. Much debate centers on the relative importance of genetic versus sociocultural factors in explaining racial inequalities in health. However, few studies integrate genetic and sociocultural data to test competing explanations directly.color, an aspect of social classification based on the culturally defined meaning of race in Puerto Rico, better predicts blood pressure than does a genetic-based estimate of continental ancestry. We also find that incorporating sociocultural variables reveals a new and significant association between a candidate gene polymorphism for hypertension (α2C adrenergic receptor deletion) and blood pressure.We draw on ethnographic, epidemiologic, and genetic data collected in southeastern Puerto Rico to isolate two distinct variables for which race is often used as a proxy: genetic ancestry versus social classification. We show that This study addresses the recognized need to measure both genetic and sociocultural factors in research on racial inequalities in health. Our preliminary results provide the most direct evidence to date that previously reported associations between genetic ancestry and health may be attributable to sociocultural factors related to race and racism, rather than to functional genetic differences between racially defined groups. Our results also imply that including sociocultural variables in future research may improve our ability to detect significant allele-phenotype associations. Thus, measuring sociocultural factors related to race may both empower future genetic association studies and help to clarify the biological consequences of social inequalities. The current, interdisciplinary debate over racial inequalities in health has revived controversy about the role of race in health-related sciences Two common problems contribute to this limitation. First, race is often used uncritically as a proxy for unspecified genetic, sociocultural, or behavioral risk factors To address these limitations, we introduce a measurement strategy designed to isolate the presumed genetic and sociocultural dimensions of race. Our premise is that health researchers often implicitly use “race” in reference to two distinct concepts: genetic ancestry versus social classification. This conceptual distinction is widely recognized We focus on the relationship between African ancestry and blood pressure Second, the relationship between African ancestry and blood pressure is significant because it has long been a focus of debate over the relative importance of genetic versus environmental influences on racial inequalities in health. Some researchers have interpreted African ancestry—initially estimated by skin pigmentation, later by genetic markers—as a proxy for racial-genetic predisposition to disease color—an analog to the concept of race in the United States color is shaped not only by skin color by also by other physical features and non-biological markers of social status We draw on preliminary data collected in southeastern Puerto Rico. This research setting is apt because the history of the sugar economy and slavery in this region led us to expect substantial variation in levels of African ancestry color associated with individual genetic ancestry? (2) Does genetic ancestry or color better predict variation in arterial blood pressure? (3) Does the inclusion of sociocultural data alter the association between blood pressure and candidate gene polymorphisms for hypertension? Our results show, for the first time, that (a) social classification better predicts blood pressure variation than does genetic ancestry and (b) accounting for sociocultural aspects of race reveals a significant association between a genetic variant (α2C adrenergic receptor deletion) and blood pressure. These preliminary results have important implications for future research on racial inequalities in health.We take advantage of this setting to ask three questions: (1) How is social classification based on color estimates how people are perceived by other Puerto Ricans in everyday social interaction. This estimate improves on standard observer ratings color: blanco, or white (n = 37); trigueño, the major intermediate category (n = 31), and negro, or black (n = 19). These categories correspond to statistically significant differences in both genetic ancestry and education, but not in other study variables , and use of antihypertensive medication (p = 0.037) but not DBP (p = 0.326) when adjusted only for standard covariates.Using multiple linear regression, we first tested the association between individual African genetic ancestry and blood pressure, controlling for age, sex, body mass index , as well as the interaction between color and SES (color (negro versus trigueño/blanco) and SES was significantly associated with SBP (p = 0.003), while evidence for an association between genetic ancestry and SBP disappeared (p = 0.176). The interaction and simple main effects of SES and color account for an additional 11.7% of variance in SBP and 7.7% of variance in DBP, as compared to models without those variables. In contrast, we found no evidence of an interaction between SES and genetic ancestry. This pattern suggests that whatever exposures related to blood pressure were initially captured by the estimate of genetic ancestry are better modeled by color and SES.Next we added  and SES , Model Bcolor in relation to SBP and DBP, adjusting for covariates. The figure facilitates interpretation because it reveals that the direction of the association between SES and blood pressure varies between categories of color. For respondents who are culturally defined as blanco or trigueño, SBP and DBP decrease as SES increases. In contrast, for respondents culturally defined as negro, SBP and DBP increase along with SES. Mean blood pressure differences between categories of color are greatest at high levels of SES.2CDel322-325). However, when color and SES were added to the model, the Del322-325 polymorphism in the α2C adrenergic receptor gene showed a significant association with SBP. Specifically, α2CDel322-325 homozygotes showed a negative association with SBP .Last, we tested the association between blood pressure and six candidate gene polymorphisms. Without the sociocultural data, there was no evidence of a statistically significant association between any of the candidate gene alleles and SBP or DBP . Specifically, mean SBP for α2CDel322-325 homozygotes versus non-deletion homozygotes was 136.0 versus 152.0 mmHg for the combination negro/high-SES, 111.5 versus 129.9 mmHg for negro/low-SES, and 117.6 versus 125.5 mmHg for trigueño + blanco/low-SES. Thus, color and SES were needed to partition the sociocultural heterogeneity within α2CDel322-325 homozygotes and reveal a genetic association that was otherwise hidden.The composition of αThe role of race in health-related research is among the most controversial issues in science. Our study is significant because it answers repeated calls to measure both genetic and sociocultural factors in research on racial inequalities in health The relationship between African ancestry and blood pressure is a valuable test case because it has been taken as evidence of both genetic and sociocultural mechanisms. Boyle Other researchers argue that the apparent association between African ancestry and high blood pressure could be mediated by sociocultural factors color is associated with blood pressure through an interaction with SES, but genetic ancestry is not. The form of the interaction between SES and color mirrors results from an earlier analysis of this sample using skin pigmentation as a proxy for genetic ancestry r = 0.63). However, the use of a genetic-based estimate of ancestry in our current analysis provides a more direct test of competing explanations for excess hypertension in the African Diaspora. Furthermore, the analysis in our current study shows that the interaction between SES and color is robust to the addition of hypertension candidate gene variants.Our study is the most direct test of these alternative explanations to date. We find that social classification based on negro may seem unexpected; in industrialized societies, increased household income and education are often associated with better health. However, the pattern we observe is consistent with the hypothesis that social classification based on color entails differential exposure to social stressors related to blood pressure. In particular, there is ethnographic evidence that Puerto Ricans perceived as negro, as compared to trigueño or blanco, may encounter more frequent frustrating interactions in high-SES settings due to institutional and interpersonal discrimination The positive association between SES and blood pressure for respondents classified as Our findings have implications for the role of genetic ancestry in health disparities research, beyond the specific debate over racial inequalities in blood pressure. Recent advances in genomic technology have led to the escalating use of AIMs in health-related research. The primary purpose of estimating ancestry using AIMs is to control for population structure, which, if neglected, can cause spurious associations in genetic association studies The critical limitation is that most previous studies of the relation between genetic ancestry and health neglect the sociocultural implications of genetic ancestry. If African ancestry is associated with socially defined race how race is culturally constructed. Our measurement strategy is innovative, because it explicitly links survey measurement to ethnographic data on how race is culturally constructed in a particular context. This strategy is consistent with other measurement approaches, notably observer ratings A related implication concerns the need to refine the measurement of racial classification as a sociocultural variable. Social scientists almost uniformly espouse the view that race is a cultural construct, but it is often expressed only to reject racial-genetic determinism, not to stimulate empirical research on 2CDel322-325 variant and SBP only when color and SES were included in the analysis. The inclusion of color and SES may also explain why our results differ from previous studies of this polymorphism, which have produced conflicting results. The most recent, largest study (n = 3398) found no association with the α2CDel322-325 polymorphism and hypertension Although our findings support a sociocultural explanation for the link between African ancestry and blood pressure, they do not deny the importance of genetic influences on complex disease. On the contrary, our study provides empirical support for the theoretical expectation that improved measurement of environmental factors improves the ability to detect associations between candidate gene polymorphisms and complex phenotypes color in everyday social interaction. Future research could build on our measurement approach by testing whether non-biological markers of social status  influence social classification Our conclusions warrant additional research in several directions. First, in comparison to related studies, our sample size is small. It is noteworthy, however, that we detected statistically significant associations despite the small sample size, and that the form of the interaction effect is consistent with larger studies The research protocol was approved by the University of Florida Institutional Review Board. Informed consent was obtained from all participants prior to data collection.color. Within each area, we drew a simple random sample of 25 households and then randomly selected one adult, aged 25–55 years, from each household (N = 100). Ninety-six participants donated buccal swabs and gave informed consent for DNA analyses. After removing samples and markers with >25% missing data, our dataset included 87 individuals. The combination of probability and non-probability sampling methods limits the generalizability of our results but improves the efficiency of our attempt to isolate contrasts related to socioeconomic status and color.Data were collected in Guayama, Puerto Rico, a town of approximately 44,000 people on the southeastern coast of the island 1AR, Gly16Arg, Gln27Glu, and Arg523Arg in β2AR, and Del322-325 in α2CAR.Samples were genotyped for 100 biallelelic autosomal SNP markers at Prevention Genetics . These ancestry informative markers (AIMs) were selected for large frequency differences between West African, European, and Native American populations, derived from a GeneMapping 10K Affymetrix array. Eighty-seven individuals were typed for 78 AIMs. Eighty-four individuals were also assayed via pyrosequencing at the University of Florida Center for Pharmacogenomics for six hypertension candidate genes within three different genes of the adrenergic receptor family: Ser49Gly and Arg389Gly in βStructure 2.2 Structure and ADMIXMAP/MLE, respectively) were provided by Mark Shriver. Ancestral proportions for K = 3 were selected for all three programs because of the putative ancestral contribution from three distinct populations in Puerto Rico Three independent methods were used to estimate individual genetic ancestry: two Bayesian approaches using a Markov chain Monte Carlo algorithm, implemented in color estimates how people are perceived by other Puerto Ricans in mundane social interaction. In an earlier ethnographic study in the same community where the survey was conducted, Gravlee color. He asked ethnographic informants to identify the color of 72 standardized facial portraits that varied systematically in skin tone, hair texture, and facial features. He then used cultural consensus analysis color classification and to determine the culturally appropriate categorization of each portrait. In the current study, two observers determined which of the same 72 portraits most closely resembled each respondent, as described elsewhere color. This method produces an ethnographically grounded estimate of how survey respondents would be perceived by others in everyday social interaction.Our measure of Blood pressure was measured using an automatic oscillometric blood pressure monitor  that has been validated for population-based studies 2), and current use of antihypertensive medication . Weight (0.1 kg) was measured with a digital scale; height (0.1 cm) was measured with a portable stadiometer. SES was estimated as a combination of self-reported education (years) and household income . We tested multiple ways of modeling SES Standard covariates included sex , age (years), socioeconomic status (SES), body mass index  and blanco (white); the second for differences between negro (black) and the mean of trigueño and blanco. This coding scheme reflects both the natural ordering of categories and the expectation from ethnography that the stigmatized category negro would differ from the mean of trigueño and blanco.Regression analyses were performed separately for SBP and DBP. Ascribed color. Continuous predictors were mean-centered to reduce multicollinearity and to ease interpretation of interaction terms. We examined variance inflation factors for multicollinearity and case diagnostics for evidence of influential observations. In models including α2CAR genotype, three participants with missing genotype data for this polymorphism were excluded from the analysis. We replicated all other regression models without these three participants and observed no significant differences in any coefficients.Based on previous research


The advent of high-throughput and cost-effective genotyping platforms made genome-wide association (GWA) studies a reality. While the primary focus has been invested upon the improvement of reducing genotyping error, the problems associated with missing calls are largely overlooked.To probe into the effect of missing calls on GWAs, we demonstrated experimentally the prevalence and severity of the problem of missing call bias (MCB) in four genotyping technologies . Subsequently, we showed theoretically that MCB leads to biased conclusions in the subsequent analyses, including estimation of allele/genotype frequencies, the measurement of HWE and association tests under various modes of inheritance relationships. We showed that MCB usually leads to power loss in association tests, and such power change is greater than what could be achieved by equivalent reduction of sample size unbiasedly. We also compared the bias in allele frequency estimation and in association tests introduced by MCB with those by genotyping errors. Our results illustrated that in most cases, the bias can be greatly reduced by increasing the call-rate at the cost of genotyping error rate.The commonly used 'no-call' procedure for the observations of borderline quality should be modified. If the objective is to minimize the bias, the cut-off for call-rate and that for genotyping error rate should be properly coupled in GWA. We suggested that the ongoing QC cut-off for call-rate should be increased, while the cut-off for genotyping error rate can be reduced properly. Severalr2 ≥ 0.8 and Gener2 ≥ 0.8  to ensure the accuracy of allele call becomes a critical issue. When genotyping errors occur, its effects on linkage analysis -19, LD mStrong emphasis on the accuracy of allele calls and technical success to achieve that has made the effect of missing call largely overlooked. It was suggested that 'no-call' procedure should be taken where observations of borderline quality be removed from allele calls in order to keep the genotyping error rate as low as possible . This 'nThere are two major causes for missing calls. One is due to poor quality of DNA samples, which often fails to be amplified and to generate strong enough intensity of fluorescence signals over the background. The other arises when an observation, i.e., a read out of fluorescence signals, cannot be assigned unequivocally to any of the clusters of genotype, therefore, is subject to 'no-call' procedure. In this report, we mainly focus on the missing calls due to the failure of being assigned to any clusters of genotype.® SNP Genotyping Assays  and GeneChip® Human Mapping 500 K Array Set . Eight SNPs were selected and subjected to regenotyping of equivocal observations  through sequencing. The criteria for the selection of SNPs and samples for sequencing were presented in Methods.To evaluate the nature of no-calls in reality, four different widely-used high-throughput genotyping platforms were included in this study, and they are GenomeLab™ SNPstream Genotyping System , BeadLab SNP Genotyping System , TaqManobserved data at each locus which were produced by the respective genotyping technology was compared with that of no-calls which were obtained by sequencing  were calculated, most of which were above 0.95, but it could be as low as 0.75 for GeneChip 500 K.The genotype distribution of the ng Table . Statistc (0 ≤ c ≤ 1).In the subsequent sections, in order to explore the effects introduced by MCB, we proposed a model to investigate the nature of no-calls. Typically an equivocal observation occurs as follows Fig. . For theHardy-Weinberg Equilibrium has been repeatedly recommended as a measure for QC in the context of genetic association studies . In the c , excessive homozygotes result in the departure from HWE. The inflation is more pronounced for MCB in heterozygotes than that in homozygotes. For example, the type-I error rate is 0.055 ~0.228 in Scenario I and Scenario IV, while it can be 0.076 ~0.654 in Scenario II & III under different MAFs in the presence of MCB (c = 0.80) for a population (N = 500) under HWE in the significant level of 0.05. However, HWE still holds, as expected, when missing equivalently but unbiasedly across different genotypes .The type-I error rate for departure from HWE disturbed by MCB is inflated with the increasing of c Fig. . When MCc = 0.80, 0.068 with c = 0.50, and even to 0.184 with c = 0.20, while the real value of MAF is only 0.10.The accuracy of allele/genotype frequency estimation is of special importance since many analyses such as association studies, inference of haplotype, and inference of population structure rely on it. UBM does not affect allele/genotype frequency estimation although it reduces the sample size. However, MCB does. When missing bias in Scenario I and Scenario II & III, MAF is underestimated; and when in Scenario IV, MAF is overestimated and this change is larger than the former two Fig. . It shouThe development of high-throughput genotyping technologies make association study widely conducted for identification of disease loci underlying complex traits. We now examine the effect of MCB on association using various disease models and statistical tests.Power issue is of special importance in association studies ,32. To iχ2 test) when c = 0.80, while the change can be around and even more than 10% when disturbed by MCB  , the power change in Scenario IV (missing in aa) is most; for the disease model with recessive relationship (hAA ≠ hAa = haa), the power change in Scenario I (missing in AA) is the largest; for the disease model with overdominant relationship (hAa ≠ hAA = haa), the power change in Scenario II & III (missing in Aa) is the most; and for the disease model with additive relationship (hAA>hAa>haa), the power change in Scenario I is similar to that in Scenario IV, especially when c is small, while the decrease of sample size in quantity differs greatly  is similar to that with additive relationship , the conclusions drawn above can also be extended when A is a protective one (data not shown). Moreover, in the disease model , the type-I error rate in MCB remains to be 0.05, indicating that MCB does not inflate the false positive rate for the association studies, under the assumption that the extent of missing is identical in both case and control.In addition, though the minor allele of In the previous section, we showed that MCB is common in the current genotyping technologies, and it could affect the subsequent analyses seriously and lead to false conclusions. The key issue is how to deal with those equivocal observations which apparently are responsible for MCB. Two alternative options are available. The first option is to discard the observations of borderline quality using the 'no-call' procedure which may lead to MCB. The second option is to assign these observations to one of the genotypes at the cost of increasing genotyping errors. Here, we compare the overall outcome  of these two options and try to offer guidelines for different scenarios to minimize the biases caused by the equivocal observations. In addition, we evaluate the overall call-rate and genotyping error rate in the two options respectively and intend to re-examine the current QC standards.c was set to 0.80, a moderate MCB as shown previously. For the second option, we assumed all of the equivocal observations are called and the proportion of accurate calls among these equivocal ones is denoted by conf (0 ≤ conf ≤ 1). The genotyping error rate increases with decreasing conf. For instance, if conf = 1, it means all of the equivocal observations are called accurately; whereas, if conf = 0, all of the equivocal ones are misclassified.To facilitate the presentation, the genotype-specific call-rate c. When the equivocal observations are called, the bias in allele frequency estimation depends on conf in addition to MAF and c. In particular, the bias in allele frequency estimation reflected by the changes of MAF estimation increases with the decreasing conf . Therefore, it is suggested that 'no-call' principle should not be taken in Scenario I if the objective is to minimize the biased estimation of MAF  and genotyping errors , are assumed to be identical for case and control groups. MCB and genotyping errors were introduced to the disease models with various modes of inheritance relationship ; otherwise, it may cause power loss. But for χ2 test based on allele frequencies, it may occasionally cause power gain because of the biased estimation of allele frequency. Though the power affected by genotyping errors is complicated in different scenarios and disease models, the power either does not change or changes monotonically with the conf given the scenarios and disease models , the overall call-rate can still achieve at 97.6%. The power is 87.7% in MCB compared with 92.1% of the null in the multiplicative disease model for allelic χ2 test. However, if these equivocal observations are called even though they are completely misclassified (conf = 0), the power with genotyping errors can be 88.1% at least. It indicates that in order to reduce the power loss caused by the equivocal observations, it would be more beneficial to call the equivocal observations with a genotyping error rate 2.5% than 'no-call' with an overall call-rate 97.5% , the power loss is affected more by the genotyping error and it may be better to invoke 'no-call' procedure. In addition, with different disease models, different decisions for dealing with these equivocal observations may be made. A program called QC-Tradeoff is available online to suggest whether 'no-call' procedure could be conducted  to minimize the biases caused by the equivocal observations.As shown above, the commonly-used 'no-call' principal for the observations of borderline quality is not always the best choice. By weighing the influences on the performance of association study and allele frequency estimation, it is therefore preferable to force the calling of, even though they can be erroneous, these equivocal observations when they lie between the cluster of homozygotes of minor alleles and that of heterozygotes (i.e. in Scenario I & Scenario II). When the equivocal observations lie between the cluster of homozygotes of major alleles and the cluster of heterozygotes , the loss of power introduced by MCB is more pronounced than that by genotyping error when these equivocal observations can be accurately called; but when the calling accuracy cannot be granted (the α)% of equivocal observations (0 ≤ α ≤ 1) were called with accuracy still denoted by conf. The genotype frequencies in this joint model were illustrated in Additional file α = 0, this model is equivalent to the model of MCB; and when α = 1, this model is equivalent to the model of genotyping errors. We introduced this joint model  to the disease models denoted in Table α from 0 (the model of MCB) to 1(the model of genotyping error). It indicates in order to minimize the biases caused by the equivocal observations on association studies, the validate procedure is either no-call resulted in MCB or call all of the equivocal observations with genotyping errors, which we had discussed above.In the above analyses, for the models of genotyping errors, we assumed all of equivocal observations were called to facilitate the discussion. Here, we extended a general model to explore the joint effects caused by MCB and genotyping errors. We assumed only .The advent of high-throughput genotyping technologies led to an exciting era of genome-wide associations. Genotype data with good quality are imperative in ensuring the creditability of a study. Missing calls in high-throughput genotyping has long been ignored in genetic studies. In this study, we demonstrated experimentally the prevalence and severity of the problem of missing calls, especially MCB, in the current genotyping technologies.χ2test, genotypic χ2 test is more robust to MCB. Various modes of inheritance relationship  were considered in our study. We also showed that when missing bias happens in the genotype whose contribution to the disease differs most, regardless whether it is susceptible or protective to the disease, it affects the power of association studies most.We also showed theoretically how MCB could lead to biased conclusions in the subsequent analyses including estimation of allele/genotype frequencies and association tests. MCB leads to power loss in most cases, and such loss may lead to false negative conclusions. Compared with allelic In this study, we investigated the bias of association in the presence of both MCB and genotyping errors, and demonstrated that they contributed to the bias differently. This result is of special importance in determining the cut-offs used for QC in the current practice of GWA. The question is whether the current QC standards are optimal. If the objective is to minimize the bias in allele/genotype frequency estimation and in association tests, the cut-off for call-rate and that for genotyping error rate should be properly coupled in GWA. This leads to a re-examination of the existing QC standards for both call rate and genotyping error rate that are widely used in various association studies.A commonly used QC standard for call rate is 80% and 95% or above for the first screening and fine mapping, respectively, in GWAs , it allows either higher call rates or greater genotyping rate. For example, we adjusted quality value of TaqMan from 0.95 (default) to 0.80 to illustrate the change of calling in the two loci rs10109984 and rs11226. After a change of the quality value, the overall call-rate increased substantially. In particular, when the quality value is 0.95, 30 and 29 calls were not called for the loci rs10109984 and rs11226, respectively. When 0.8 was chosen as the quality value, only 5 were not called at rs10109984 and 7 at rs11226. Subsequently, the number of genotyping discordance between the genotyping results and sequencing results was 8 (corresponding to conf = 0.73) and 0 (corresponding to conf = 1.0) for the locus rs10109984 and rs11226, respectively.A program called Furthermore, our results also suggested that MCB does not inflate type-I error rate for association studies. But it should be noted that the conclusion made here is under the assumption that the extent of missing is same to case and control. Sometimes differential bias between case and control is unavoidable, i.e., for the different sourcing of samples. In this case, effects of MCB and genotyping errors could be more complicated. Clayton et al.  showed cMissing calls in high-throughput genotyping has long been ignored in genetic studies. However, it had been illustrated that the problem of missing call bias does exist widely and sometimes seriously in prevalent high-throughput genotyping technologies. Missing call bias could lead to biased conclusions in subsequent analyses, including allele/genotype frequency estimation and association studies. The commonly used 'no-call' procedure does not always a best option for observations of borderline quality. Our results indicated that in most cases, the biased conclusion can be greatly reduced by increasing the call-rate at the cost of genotyping error rate. Therefore, the existing QC standards should be modified that the cut-off for call-rate and that for genotyping error rate should be properly coupled in GWA. A program called QC-Tradeoff is available online to suggest call or no-call the equivocal observations according to the case the user faced to minimize the power influences in association studies, and illustrate the acceptable QC standard in the corresponding case.Two SNPs were selected for each platform  from a large number of loci which were genotyped by the respective technology in our laboratory. The SNPs were selected using the following criteria: 1) the genotype calls were made using the software provided by the venders of the respective technology; 2) the call-rate at each locus is around or above the average call-rate from the same platform; 3) the minor allele frequency (MAF) of the observed data is above 0.15.As for SNPstream, Illumina and TaqMan, their calling algorithms are based on various methods, including GetGenos/QCReview program for SNPstream, GeneCall software for Illumina and SDS software for TaqMan. But generally, a baseline is set to distinguish the background signals and the informative ones, and then clustering and calling procedures conduct in the informative ones. The samples that show signals above the baseline but cannot be called unequivocally were selected for further analysis in the respective genotyping platforms. As for GeneChip 500 K, genotype data was called by the software GTYPE. It introduces a dynamic model-based algorithm  to suit rs6743724  and rs699512 (98.6%) for SNPstream, rs2277632 (98.9%) and rs1457043 (98.9%) for Illumina, rs10109984 (95.8%) and rs11226 (96.6%) for TaqMan, rs1192885 (94.2%) and rs6855202 (95.1%) for GeneChip 500 K were selected. The genotypes for the missing data were generated by sequencing the DNA segment containing the polymorphism locus. In the 2 × 3 contingency table of genotype in the 'observed' data produced by the genotyping platforms (Obs.) and the 'missing' data produced by sequencing (Seq.), Fisher Exact Test was used to examine whether there is difference in the genotypic distribution between them . The genotype-specific call-rate was calculated as well, AA, Aa and aa, respectively.Overall, Gi  in the population and ci be the call-rate of the genotypes where 0 ≤ ci ≤ 1. The observed genotype frequencies can be presented as cAA = cAa = caa, there is no bias in missing data, we called it Unbiased Missing (UBM). A violation to any of the equality would lead to MCB. In the presence of MCB, Scenario II is equivalent to Scenario III. To facilitate discussion, we assumed no-calls only happen in a specific genotype with 0 ≤ c ≤ 1, i.e., in AA (Scenario I), in Aa (Scenario II & III), and in aa (Scenario IV).In the presence of no-calls, let conf denote the proportion of genotypes that could be accurately called among these equivocal ones. The genotyping error rate is (1 - conf)(1 - c)pG, where G denotes the genotype of equivocal data points.On the other hand, an equivocal data point can be assigned to a genotype, which could lead to a genotyping error. Assume all the equivocal data points are called and let The observed genotype frequencies A in a population, where 0 ≤ π ≤ 0.5. Suppose HWE holds in the population, the genotype frequencies are pAA = π2, pAa = 2π(1 - π) and paa = (1 - π)2, respectively.Let π denote the frequency for minor allele obs-π) to reflect the changes in the allele frequency estimation, where πobs is the estimated frequency of minor allele A in the existence of MCB or genotyping errors according to Table conf threshold in the given c and MAF, where if the equivocal observations are called above the conf threshold, the changes of allele frequency estimation (|πobs-π|) caused by genotyping errors are smaller than those by MCB, and vice versa.However, in the presence of MCB or genotyping errors, the estimation of π will be affected. Here, we present the results by the difference (πχ2 distribution with one degree of freedom (df = 1), when the null hypothesis holds [χ2 distribution with df = 1 and the non-centrality parameter is χ2 distribution in the significant level of 0.05 and the sample size was assumed to 500, which was conducted in the package of R[MCB and genotyping errors will also cause the violation of HWE. For biallelic locus, is holds . Howeverkage of R.hAA, hAa, haa be the penetrances of a disease for the genotypes AA, Aa and aa, respectively, and D be the prevalence of the disease,Let Following the Bayesian formulation, the genotype frequencies in the case population areSimilarly, the genotype frequencies in the control population are,χ2 test and genotypic χ2 test are the most two widely used tests. Here we examined the influences affected by MCB, UBM or genotyping errors on the power in association studies. The genotype frequencies in case and control can be calculated respectively according to Table χ2 distribution following Gordon et al. [conf threshold, where if the equivocal observations are called above the conf threshold, the power loss caused by genotyping errors are smaller than that by MCB, and vice versa.The aim of case-control association studies is to search for disease-susceptible polymorphisms by testing whether allele/genotype frequencies of the case and control differ significantly. Allelic n et al. . The powχ2 test, when MAF is 0.25 and the sample size of case and control is 500 respectively.Here, the same extent of MCB, UBM or genotyping errors was assumed for both case and control. Various modes of inheritance relationships were considered, including dominant, recessive, overdominant, additive and multiplicative relationship Table . The powMCB: missing call bias; UBM: unbiased missing; SNP: single nucleotide polymorphism; QC: quality control; HWE: Hardy-Weinberg Equilibrium.WF, YiW and LJ conceived and designed the study. WF, YingW, RuiL and RongL carried out sequencing and genotyping experiments. WF analyzed the data and drafted the manuscript. LJ revised the manuscript. All authors read and approved the final manuscript.χ2 test was used.The power comparison among the null (printed in green), UBM (printed in yellow) and MCB (printed in red) under various disease models  in the significant level of 0.05 when allelic  The figures correspond to Scenario I, Scenario II & III and Scenario IV from the left to right.Click here for fileχ2 test was used.The power comparison among the null (printed in green), UBM (printed in yellow) and MCB (printed in red) under various disease models  in the significant level of 0.05 when genotypic  The figures correspond to Scenario I, Scenario II & III and Scenario IV from the left to right.Click here for fileχ2 test (printed in green) and genotypic χ2 test (printed in yellow) were used.Effects of genotyping errors on allele frequency estimation and on power of association studies under various disease models in the significant level of 0.05 when allelic  When conf = 1, the changes of MAF estimation is 0 and the power corresponds to that in the null. The figures correspond to Scenario I, Scenario II, Scenario III and Scenario IV from the left to right.Click here for fileχ2 test was used.The power comparison for association studies between MCB and genotyping errors under various disease models  when allelic A) illustrates the overall call-rate for the loci with different MAFs in the presence of MCB (c = 0.8). B) illustrates the threshold of conf by a solid line. If the equivocal observations can be called accurately in a confidence above the conf threshold, it prefers to call those equivocal ones at the cost of genotyping errors to minimize the power loss (grey area above the line). Otherwise, 'no-call' procedure is beneficial, which results in MCB (area below the line). C) illustrates the genotyping error rate, when the equivocal observations are called in the conf threshold mentioned above. The figures correspond to Scenario I, Scenario II, Scenario III and Scenario IV from the left to right.Click here for fileχ2 test was used.The power comparison for association studies between MCB and genotyping errors under various disease models  when genotypic A) illustrates the overall call-rate for the loci with different MAFs in the presence of MCB (c = 0.8). B) illustrates the threshold of conf by a solid line. If the equivocal observations can be called accurately in a confidence above the conf threshold, it prefers to call those equivocal ones at the cost of genotyping errors to minimize the power loss (grey area above the line). Otherwise, 'no-call' procedure is beneficial, which results in MCB (area below the line). C) illustrates the genotyping error rate, when the equivocal observations are called in the conf threshold mentioned above. The figures correspond to Scenario I, Scenario II, Scenario III and Scenario IV from the left to right.Click here for fileJoint effects of MCB and genotyping errors on association studies under various disease models .A) illustrates the overall call-rate for the loci with different values of α in the joint models of MCB and genotyping errors . B) illustrates the genotyping error rate with different values of α in the corresponding joint models. C) illustrates the power based on allelic χ2 test with different values of α. D) illustrates the power based on genotypic χ2 test with different values of α. The figures correspond to Scenario I, Scenario II, Scenario III and Scenario IV from the left to right.Click here for fileThe non-centrality parameter of HWE test in the presence of MCB or genotyping errors; the joint model of MCB and genotyping errors.Click here for file


Family-based association approaches such as the transmission-disequilibrium test (TDT) are used extensively in the study of genetic traits because they are generally robust to the presence of population structure. However, these approaches necessarily involve recruitment of families, which is more costly and time-consuming than sampling unrelated individuals in the population-based approaches. Therefore, a family-based approach, which has high power, would be appealing because of the gain in time and cost due to the reduced sample size that is required to attain adequate power. Here we introduce a new family-based transmission test using the joint transmission status from affected sib pairs. We show that by including the transmission status of both siblings, our method gives higher power than the TDT design, while maintaining the correct type I error rate. We use the simulated data from affected sib-pair families with rheumatoid arthritis provided by Genetic Analysis Workshop 15 to illustrate our approach. Genetic association studies have contributed significantly in recent years to our understanding of the genetic basis of complex diseases. Association studies are roughly categorized into either population-based or family-based association approaches. Population-based approaches have the advantage that samples are easy to ascertain. However, it has been shown that population-based approaches, such as case-control studies, can produce spurious associations in the presence of population substructure, especially in large-scale studies at the genomic level ,2. In thEach of the 1500 simulated ASP families consists of four members: the father, the mother, and the ASP. The genotypes of all four members in each family are available. We used the simulated genotype data at both the genome-wide single-nucleotide polymorphism (SNP) markers and the chromosome 6 dense SNP markers from simulation Replicate 1.O and E represent the observed and expected frequencies of each type, respectively. The test statistic has a chi-square distribution with 2 degrees of freedom under the null hypothesis of no association.All the SNP markers are biallelic, denoted as 1 or 2. Suppose allele 2 is associated with rheumatoid arthritis affection status. We used all the families with a homozygous by heterozygous mating at the marker locus. For illustration, consider the transmission status of allele 2 from a pair of homozygous by heterozygous parents. We could classify the ASPs into one of three categories, , , and , where Y means that allele 2 is transmitted from the heterozygous parent to the offspring, and N means it is not transmitted. Under the null hypothesis of no association between allele 2 at the marker locus and the rheumatoid arthritis affection status, the transmission probability of allele 1 and 2 should be equal. Figure We applied our method to both the simulated data set of 9187 genome-wide SNPs from Replicate 1 and the data set of 17,820 dense SNPs on chromosome 6. For comparison, we also applied the standard TDT to the same set of families with homozygote by heterozygote matings. In performing the TDT analysis, we treated the transmission/non-transmission of the alleles to the two siblings in an ASP as two independent observations. This assumption may not necessarily be valid, and could result in inflated power.The type I error rate is estimated empirically by performing the association tests on markers that are not associated with RA status and calculating the proportion of times the null hypothesis is rejected. We excluded the markers on the chromosomes that have trait loci associated with RA status and combined the test results of all available markers from all other chromosomes. Specifically, trait loci DR, C, and D on chromosome 6, locus F on chromosome 11, and locus E on chromosome 18 are associated with RA status, and, therefore, we used markers from all chromosomes excluding 6, 11 and 18, resulting in 7718 SNP markers for type I error analysis. Out of the 7718 tests performed using our approach and the standard TDT, our test rejected the null hypothesis 328 times, and the standard TDT rejected the null hypothesis 367 times. The corresponding estimated type I error rates of our method and TDT are 0.043 and 0.048, respectively, both of which are well under the nominal level of 0.05.p-values). However, as the distance between the trait loci and the marker increases, our approach consistently performs better than the TDT in detecting true signals. The pattern of test results on chromosome 6 for the two methods is given in Figure p-values, and correspondingly higher power, than the TDT throughout the entire region. Our method can also detect signals in an extended area, while the TDT detects signals only in the immediate vicinity of the trait loci. A similar pattern was also observed in the analysis of dense markers on chromosome 6. This suggests that in practice, if the makers are not very close the trait loci, we may fail to detect the signal using the TDT, while we can detect the signal with our method.The power estimates of the two procedures are also obtained empirically by performing the test on markers that are known to be associated with the RA status. We used markers that are physically close to trait loci DR, C, and E on chromosome 6 for the power analysis. We performed the tests using our approach and the standard TDT on markers, and tabulated the number of times the null hypothesis of no association was rejected under various significance levels. These results are tabulated separately for the genome-wide SNPs and the dense SNPs on chromosome 6 in Tables Because of the extra cost of recruiting family members, it is desirable to develop a family-based association method with relatively high power so that a smaller sample size is needed to achieve the same power. In this paper, we developed a new family-based transmission test using the joint transmission status of ASPs instead of the transmission status of individual offspring. The method gives the correct type I error rate and is more powerful than the TDT. In order to compare our method with the standard TDT, we treated the transmissions to the two siblings as two independent observations, which may not be a valid assumption for testing association . The TDTIt should be noted that the proposed approach, as well as the standard TDT, can easily be extended to include homozygous offspring from double heterozygous parents . Also, bWe proposed a new family based transmission test using the simulated ASP family data from GAW15. Our method gives the correct type I error rate. By considering the transmission status of the two siblings simultaneously, our method has higher power than the standard TDT.The author(s) declare that they have no competing interests.


Individuals with 1st degree relatives harboring an intracranial aneurysm (IA) are at an increased risk of IA, suggesting genetic variation is an important risk factor.Families with multiple members having ruptured or unruptured IA were recruited and all available medical records and imaging data were reviewed to classify possible IA subjects as definite, probable or possible IA or not a case. A 6 K SNP genome screen was performed in 333 families, representing the largest linkage study of IA reported to date. A 'narrow' (n = 705 definite IA cases) and 'broad' (n = 866 definite or probable IA) disease definition were used in multipoint model-free linkage analysis and parametric linkage analysis, maximizing disease parameters. Ordered subset analysis (OSA) was used to detect gene × smoking interaction.Model-free linkage analyses detected modest evidence of possible linkage . Parametric analyses yielded an unadjusted LOD score of 2.6 on chromosome 4q (162 cM) and 3.1 on chromosome 12p (50 cM). Significant evidence for a gene × smoking interaction was detected using both disease models on chromosome 7p . Our study provides modest evidence of possible linkage to several chromosomes.These data suggest it is unlikely that there is a single common variant with a strong effect in the majority of the IA families. Rather, it is likely that multiple genetic and environmental risk factors contribute to the susceptibility for intracranial aneurysms. Subarachnoid hemorrhage due to the rupture of an intracranial aneurysm (IA) occurs in 16,000 to 17,000 persons in the U.S. annually and nearly half of affected persons are dead within the first 30 days. There are several important factors which modulate the risk of SAH. The incidence of SAH increases moderately with advancing age and it is the only stroke subtype in which women have a higher age-adjusted risk of SAH as compared to men  with an In addition to the importance of environmental risk factors, studies have also consistently demonstrated a genetic component to the risk for SAH and IA. The risk for an SAH in first degree relatives of an SAH patient has been reported to range from 1.8 to as high as 6.6 times that of an age matched control ,4-7. StuLinkage analysis has been performed using both large families with evidence of a Mendelian form of IA -14 as weWe recently completed a mid-study genetic analysis of a sample of 192 multiplex IA pedigrees . The greProbands with an intracranial aneurysm (IA) were identified by 26 clinical centers (with 41 recruitment sites) located throughout North America, New Zealand and Australia . The FamQuestionnaire data regarding demographics, environmental risk factors, and family history of IA was obtained from all family members. Blood was obtained for the isolation of DNA. The first degree relatives of affected family members who met study criteria for a higher risk of IA were offered a free, study MRA with time-of-flight sequences. The majority of MRAs were performed using a 1.5T (72.4%) MR imaging systems. The remainder were diagnosed with 3.0T (21.9%), 4.0T (0.9%) or were identified with CTA (4.8%). Unfortunately, we can not address the specificity and sensitivity of our MRA results because our patients did not proceed to DSA for validation of the non-invasive imaging tests. However, we note that MRA screening for IAs is standard medical practice and studies have reported MRA sensitivities and specificities between 90 and 97% for the detection of IAs ,23. HighA Verification Committee reviewed all medical records and the phone screen of the proband and family members with a reported history of IA, SAH or intracerebral hemorrhage (ICH). Two neurologists who were members of the Verification Committee independently reviewed the subject's records and determined whether the subject met all the study inclusion and exclusion criteria. When the two members disagreed, a third neurologist reviewed the data. Each potential affected family member was ranked as definite, probable, possible, or not a case Table .Genotyping was performed by the Center for Inherited Disease Research (CIDR) using the 6 K Illumina array. A total of 2,317 individuals from 394 families were genotyped by CIDR, with the samples sent at two intervals, with results from the first sample set previously reported . All sumExtensive quality assessment of the genotypic data was performed prior to initiating any linkage analysis. The reported family structures were verified using the genotypic data  and, wheOnce the family structures were finalized, the remaining marker data were reviewed in detail as a further step in data validation. Marker allele frequencies were estimated using PEDSTATS , which s2 statistics) and in all cases where D' exceeded 0.70, we retained the SNP with the highest minor allele frequency (i.e. the most informative marker). Analyses were also performed to assess the effect of employing r2 as the measure of LD. In this instance, a threshold of r2 > 0.40 was employed to identify SNPs with high LD. Results were similar using both thresholds and final SNP selection was based on results from the D' statistic [Including markers in high linkage disequilibrium (LD) can inflate the evidence of linkage ; therefotatistic .The final analytic sample consisted of 333 families with 1,895 genotyped individuals. Of the 5,935 markers genotyped in common on the 6 K Illumina array by CIDR in the two independent samples, genotypic data from 4,839 markers were included in the final genome screen. Two models of disease were employed: 1) narrower disease definition, which classified as affected only those individuals who met the definite criteria Table  broader We employed three complementary analytic methods to evaluate the evidence of linkage. First, we performed multipoint, model independent affected-relative pair linkage analysis as implemented in Merlin . Using aSecond, we performed ordered subset analysis (OSA) to test for gene × smoking interactions ,30. A quGiven the extensive pedigree structures available for analysis, we proceeded to also perform parametric modeling. Similar to the previous analyses, no family members were classified as unaffected; all individuals not meeting disease criteria were coded as unknown. We avoided specifying disease parameters; rather we utilized an approach ,33, whicThe narrower disease model included in the analysis 290 families consisting of 1,647 genotyped samples. There were 705 family members with definite IA. The broader disease model included 333 families with 1,895 genotyped samples, with 866 members meeting criteria for definite or probable IA , and was influenced by the number of living members who met the high risk criteria.Genome-wide model independent linkage analysis employing both the narrower and broader disease model did not detect any chromosomal region with a LOD score greater than 1.5  that increased the likelihood that an unruptured aneurysm might be identified. Therefore, it is possible that aneurysms detected by study MRA may be more likely to be the result of a critical environmental exposure (smoking) rather than genetic factors. However, given the small number of positive MRA which met criteria for Probable or Definite criteria (< 9%), it is unlikely that the statistical results would be significantly biased by the inclusion of a small number of potentially environmentally influenced cases.The use of dense SNP marker maps, such as those currently available for genome wide association studies, may be an important tool needed to identify the genetic risk factors influencing IA susceptibility. We are currently performing a case control study I the FIA sample with a dense set of SNP markers to improve our power to detect common risk alleles with moderate effect.These data suggest it is unlikely that there is a single common variant with a strong effect in the majority of the IA families. Rather, it is likely that multiple genetic and environmental risk factors contribute to the susceptibility for intracranial aneurysms.The authors declare that they have no competing interests.TF contributed to the conception and design of the study and the statistical analysis and interpretation of data. LS contributed to the conception and design of the study and data acquisition. RB contributed to the conception and design of the study and the interpretation of the imaging data. CA contributed to the conception and design of the study and data acquisition. DW contributed to the conception and design of the study and phenotype verification. DK contributed to the conception and design of the study and phenotype verification. MLF contributed to the conception and design of the study and phenotype verification. RD contributed to the conception and design of the study and the interpretation of statistical analyses. RH contributed to the conception and design of the study. IM contributed to the conception and design of the study and interpretation of imaging data. JEB-W contributed to the conception and design of the study and interpretation of statistical analyses. CL contributed to the conception and design of the study and interpretation of statistical analyses. GR contributed to the conception and design of the study and data acquisition. ESC contributed to the conception and design of the study and data acquisition. DL contributed to the statistical analysis of the data. DLK contributed to the conception and design of the study and statistical analyses. JH contributed to the conception and design of the study and interpretation of imaging data. JPB contributed to the conception and design of the study, phenotype verification and data acquisition. All authors helped to draft the manuscript and read and approved the final manuscript.Clinical Centers – University of Alabama at Birmingham: W. Fisher, (PI), H. Forson, coordinator; Clinical Trials Research Unit, University of Auckland and Auckland City Hospital, New Zealand: C. Anderson, (PI), E. Mee, (PI), C. Howe, coordinator, S. Vos, coordinator; Royal Perth Hospital, Sir Charles Gairdner Hospital, Royal Adelaide Hospital, Royal Melbourne Hospital, Alfred Hospital, Westmead Hospital, Royal North Shore Hospital, Royal Prince Alfred Hospital, Australia: C. Anderson, (PI), G. Hankey, (PI), N. Knuckey, (PI), J. Laidlaw, (PI), P. Reilly, (PI), N. Dorsch, (PI), M. Morgan, (PI), M. Besser, (PI), J. Rosenfeld, (PI), K. Athanasiadis, coordinator, A Claxton, coordinator, V. Dunne, coordinator, J. Griffith, coordinator, J Davidson, coordinator, S. Pope, coordinator, Amanda Froelich, coordinator; Brigham & Women's Hospital: A. Day, (PI), R. Brach, coordinator; University of Cincinnati: D. Woo, co-(PI), M. Zuccarello, co-(PI), A. Ringer, co-(PI), H. Yeh, co-(PI), K. Franklin, coordinator; Cleveland Clinic Foundation: P. Ramussen, (PI), D. Andrews-Hinders, coordinator, T. Wheeler, coordinator; Columbia University: E. S. Connolly, (PI), R. Sacco, co-(PI), D. LaMonica, coordinator; University of Florida: S. B. Lewis, (PI), A. Royster, coordinator; Indianapolis Neurosurgical Group: T. Payner, (PI), N. Miracle, coordinator; Johns Hopkins: K. Murphy (PI), B. Kohler, coordinator; Massachusettes General Hospital: C. Ogilvy, (PI), D. Buckley, coordinator, J. Manansala, coordinator; London Health Science Center Research Inc.: G. Ferguson, (PI), C. Mayer, coordinator, J. Peacock, coordinator; Notre Dame Hospital: G. Rouleau, (PI), A. Desjarlais, coordinator; University of Maryland: E. F. Aldrich, (PI), C. Aldrich, coordinator, C. Byard, coordinator; Mayo Clinic: R. D. Brown (PI), L. Jaeger, coordinator; University of Michigan: L. Morgenstern, (PI), M. Concannon, Coordinator; New Jersey Medical School: A. I. Qureshi, (PI), P. Harris-Lane, coordinator; Northwestern University: H. Batjer, (PI), G. Joven, S. Thompson, coordinator; University of Ottawa: M. T. Richard, (PI), A. Hopper, (PI); University of Pittsburgh: A. B, Kassam, (PI), K. Lee, coordinator; University of California, SF, C. Johnston, (PI), K. Katsura, coordinator; University of Southern California: S. Giannotta, (PI), D. Fishback, coordinator; Stanford University Medical Center: G. Steinberg, (PI), D. Luu, coordinator, M. Coburn, coordinator; University of Texas at Houston: M. Malkoff, (PI), A. Wojner, coordinator; University of Virginia: N. Kassel, (PI), B. Worrall, co-(PI), G. Radakovic, coordinator; University of Washington: D. Tirschwell, (PI), P. Tanzi, coordinator; Washington University: C. Derdeyn, (PI), M. Catanzaro, coordinator; University of Manitoba (Winnipeg), A. Kaufmann, (PI), D. Gladish, coordinator.The pre-publication history for this paper can be accessed here: