Using ancestry-informative markers to identify fine structure across 15 populations of European origin

Regional european genetic ancestry predicts type I interferon level and risk of severe viral infection

BACKGROUND

Viral infection outcomes vary widely between individuals, ranging from mild symptoms to severe organ failure and death, and it is clear that host genetic factors play a role in this variability. Type I interferon (IFN) is a critical anti-viral cytokine, and we have previously noted differences in type I IFN levels between world populations.

METHODS

In this study, we investigate the interrelationship between regional European genetic ancestry, type I IFN levels, and severe viral infection outcomes.

RESULTS

In cohorts of European ancestry lupus patients living in Europe, we noted higher IFN in the Northwestern populations as compared to Southeastern populations. In an independent cohort of European ancestry lupus patients from the United States with varying proportional regional European genetic admixture, we observed the same Northwest vs. Southeast European ancestry IFN gradient. We developed a model to predict type I IFN level based on regional European ancestry (AUC = 0.73, p = 6.1e-6). Examining large databases containing serious viral outcomes data, we found that lower predicted IFN in the corresponding European country was significantly correlated with increased viral infection fatality rate, including COVID-19, viral hepatitis, and HIV [Correlation coefficients: -0.79 (p = 4e-2), -0.94 (p = 6e-3), and -0.96 (p = 8e-2) respectively].

CONCLUSIONS

This association between predicted type I IFN level and viral outcome severity suggests a potential causal relationship, as greater intrinsic type I IFN is beneficial in host defense against viruses. Genetic testing could provide insight into individual and population level risk of fatality due to viruses prior to infection, across a wide range of viral pathogens.

Optimal selection of genetic variants for adjustment of population stratification in European association studies

Population stratification is usually corrected relying on principal component analysis (PCA) of genome-wide genotype data, even in populations considered genetically homogeneous, such as Europeans. The need to genotype only a small number of genetic variants that show large differences in allele frequency among subpopulations-so-called ancestry-informative markers (AIMs)-instead of the whole genome for stratification adjustment could represent an advantage for replication studies and candidate gene/pathway studies. Here we compare the correction performance of classical and robust principal components (PCs) with the use of AIMs selected according to four different methods: the informativeness for assignment measure ($IN$-AIMs), the combination of PCA and F-statistics, PCA-correlated measurement and the PCA weighted loadings for each genetic variant. We used real genotype data from the Population Reference Sample and The Cancer Genome Atlas to simulate European genetic association studies and to quantify type I error rate and statistical power in different case-control settings. In studies with the same numbers of cases and controls per country and control-to-case ratios reflecting actual rates of disease prevalence, no adjustment for population stratification was required. The unnecessary inclusion of the country of origin, PCs or AIMs as covariates in the regression models translated into increasing type I error rates. In studies with cases and controls from separate countries, no investigated method was able to adequately correct for population stratification. The first classical and the first two robust PCs achieved the lowest (although inflated) type I error, followed at some distance by the first eight $IN$-AIMs.

Genotypes of informative loci from 1000 Genomes data allude evolution and mixing of human populations

Principal Component Analysis (PCA) projects high-dimensional genotype data into a few components that discern populations. Ancestry Informative Markers (AIMs) are a small subset of SNPs capable of distinguishing populations. We integrate these two approaches by proposing an algorithm to identify necessary informative loci whose removal from the data deteriorates the PCA structure. Unlike classical AIMs, necessary informative loci densely cover the genome, hence can illuminate the evolution and mixing history of populations. We conduct a comprehensive analysis to the genotype data of the 1000 Genomes Project using necessary informative loci. Projections along the top seven principal components demarcate populations at distinct geographic levels. Millions of necessary informative loci along each PC are identified. Population identities along each PC are approximately determined by weighted sums of minor (or major) alleles over the informative loci. Variations of allele frequencies are aligned with the history and direction of population evolution. The population distribution of projections along the top three PCs is recapitulated by a simple demographic model based on several waves of founder population separation and mixing. Informative loci possess locational concentration in the genome and functional enrichment. Genes at two hot spots encompassing dense PC 7 informative loci exhibit differential expressions among European populations. The mosaic of local ancestry in the genome of a mixed descendant from multiple populations can be inferred from partial PCA projections of informative loci. Finally, informative loci derived from the 1000 Genomes data well predict the projections of an independent genotype data of South Asians. These results demonstrate the utility and relevance of informative loci to investigate human evolution.

Fine scale human genetic structure in three regions of Cameroon reveals episodic diversifying selection

Inferences from genetic association studies rely largely on the definition and description of the underlying populations that highlight their genetic similarities and differences. The clustering of human populations into subgroups (population structure) can significantly confound disease associations. This study investigated the fine-scale genetic structure within Cameroon that may underlie disparities observed with Cameroonian ethnicities in malaria genome-wide association studies in sub-Saharan Africa. Genotype data of 1073 individuals from three regions and three ethnic groups in Cameroon were analyzed using measures of genetic proximity to ascertain fine-scale genetic structure. Model-based clustering revealed distinct ancestral proportions among the Bantu, Semi-Bantu and Foulbe ethnic groups, while haplotype-based coancestry estimation revealed possible longstanding and ongoing sympatric differentiation among individuals of the Foulbe ethnic group, and their Bantu and Semi-Bantu counterparts. A genome scan found strong selection signatures in the HLA gene region, confirming longstanding knowledge of natural selection on this genomic region in African populations following immense disease pressure. Signatures of selection were also observed in the HBB gene cluster, a genomic region known to be under strong balancing selection in sub-Saharan Africa due to its co-evolution with malaria. This study further supports the role of evolution in shaping genomes of Cameroonian populations and reveals fine-scale hierarchical structure among and within Cameroonian ethnicities that may impact genetic association studies in the country.

Ancestry-informative marker (AIM) SNP panel for the Malay population

Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise F_ST, informativeness for assignment (I_n), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise F_ST method, while a panel of 200 SNPs selected using I_n and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.

Using Ancestry Informative Markers (AIMs) to Detect Fine Structures Within Gorilla Populations

The knowledge of ancestral origin is monumental in conservation of endangered animals since it can aid in preservation of population level genetic integrity and prevent inbreeding among related individuals. Despite maintenance of studbook, the biogeographical affiliation of most captive gorillas is largely unknown, which has constrained management of captive gorillas aiming at maximizing genetic diversity at the population level. In recent years, ancestry informative markers (AIMs) has been successfully employed for the inference of genomic ancestry in a wide range of studies in evolutionary genetics, biomedical research, genetic stock identification, and introgression analysis and forensic analyses. In this study, we sought to derive the AIMs yielding the most cohesive and faithful understanding of biogeographical affiliation of query gorillas. To this end, we compared three commonly used AIMs-determining methods namely, Infocalc, F_ST, and Smart Principal Component Analysis (SmartPCA) with ADMIXTURE, using gorilla genome data available through Great Ape Genome Project database. Our findings suggest that the SNPs that were detected by at least three of the four AIMs-determining approaches (N = 1,531), is likely most suitable for delineation of gorilla AIMs. It recapitulated the finer structure within western lowland gorilla genomes with high degree of precision. We further have validated the robustness of our results using a randomized negative control containing the same number of SNPs. To the best of our knowledge, this is the first report of an AIMs panel for gorillas that may aid in developing cost-effective resources for large-scale demographic analyses, and greatly help in conservation of this charismatic mega-fauna.

Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians

The rapid accumulation of ancient human genomes from various areas and time periods potentially enables the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations, which are known to misrepresent the population structure. Past studies addressed these problems by using ancestry informative markers (AIMs). It is, thereby, unclear whether AIMs derived from contemporary human genomes can capture ancient population structures, and whether AIM-finding methods are applicable to aDNA, provided that the high missingness rates in ancient-and oftentimes haploid-DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperform all of the competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of single nucleotide polymorphism (SNP) microarrays and the interpretation of aDNA results, which enables a population-wide testing of primordialist theories.

Age at menarche and osteoporosis: A Mendelian randomization study

PURPOSE

Traditional epidemiological studies suggest that there is an association between age at menarche (years) (AAM) and bone mineral density (BMD) at the sites of the femoral neck and lumbar spine (FNK and LS BMD), indicating a potentially important relationship between AAM and the development of osteoporosis (OP). However, these findings may be influenced by unmeasured confounding factors that can obscure the true relationship between the phenotypic traits. Therefore, we performed Mendelian randomization (MR) analyses to determine whether there is a causal relationship between AAM and BMD (FNK and LS BMD), where late AAM may increase the risk of developing OP.

METHODS

Adopting a two-sample MR approach we incorporated genome-wide association (GWAS) summary statistics from the Reproductive Genetics (ReproGen) Consortium (n = 182,416) (females only) and the GEnetic Factors for OSteoporosis (GEFOS) Consortium (n = 53,236) (both males and females).

RESULTS

Using this MR approach we discovered that each additional year in AAM is associated with a modest reduction in FNK BMD (β = -0.072 se = 0.022, 95% CI (-0.115, -0.030), p = 0.001) and LS BMD ((β = -0.072, se = 0.025, 95% CI (-0.121, -0.023), p = 0.004), and therefore influences OP susceptibility.

CONCLUSIONS

This study demonstrates that AAM in females may play a causal role in OP etiology and provides novel insights into the pathophysiology of bone related diseases like osteoporosis, osteopenia and fracture.

SUMMARY

Our study demonstrates that AAM in females may play a causal role in OP etiology and provides novel insights into the pathophysiology of bone related diseases like osteoporosis, osteopenia and fracture. By adopting Mendelian Randomization approaches, our study was not susceptible to bias from unmeasured confounders or reverse causation.

Genetic modifiers of radon-induced lung cancer risk: a genome-wide interaction study in former uranium miners

PURPOSE

Radon is a risk factor for lung cancer and uranium miners are more exposed than the general population. A genome-wide interaction analysis was carried out to identify genomic loci, genes or gene sets that modify the susceptibility to lung cancer given occupational exposure to the radioactive gas radon.

METHODS

Samples from 28 studies provided by the International Lung Cancer Consortium were pooled with samples of former uranium miners collected by the German Federal Office of Radiation Protection. In total, 15,077 cases and 13,522 controls, all of European ancestries, comprising 463 uranium miners were compared. The DNA of all participants was genotyped with the OncoArray. We fitted single-marker and in multi-marker models and performed an exploratory gene-set analysis to detect cumulative enrichment of significance in sets of genes.

RESULTS

We discovered a genome-wide significant interaction of the marker rs12440014 within the gene CHRNB4 (OR = 0.26, 95% CI 0.11-0.60, p = 0.0386 corrected for multiple testing). At least suggestive significant interaction of linkage disequilibrium blocks was observed at the chromosomal regions 18q21.23 (p = 1.2 × 10-6), 5q23.2 (p = 2.5 × 10-6), 1q21.3 (p = 3.2 × 10-6), 10p13 (p = 1.3 × 10-5) and 12p12.1 (p = 7.1 × 10-5). Genes belonging to the Gene Ontology term "DNA dealkylation involved in DNA repair" (GO:0006307; p = 0.0139) or the gene family HGNC:476 "microRNAs" (p = 0.0159) were enriched with LD-blockwise significance.

CONCLUSION

The well-established association of the genomic region 15q25 to lung cancer might be influenced by exposure to radon among uranium miners. Furthermore, lung cancer susceptibility is related to the functional capability of DNA damage signaling via ubiquitination processes and repair of radiation-induced double-strand breaks by the single-strand annealing mechanism.

Off-target phenotypes in forensic DNA phenotyping and biogeographic ancestry inference: A resource

With recent advances in DNA sequencing technologies it has become feasible and cost effective to genotype larger marker sets for forensic purposes. Two technologies that make use of the larger marker sets have come into focus in forensic research and applications; inference of biogeographic ancestry (BGA) and forensic DNA phenotyping (FDP). These methods hold the promise to reveal information about a yet unknown perpetrator from a DNA sample. In contrast, DNA-profiling, that is a standard practice in case work, relies on matching DNA-profiles between crime scene material and suspects on a database of DNA-profiles. Markers for DNA-profiling were developed under the premise to reveal as little additional information about the human source of the profile as possible, the rationale being that personal privacy rights have to be balanced against the public interest in solving a crime. The same argument holds for markers used in BGA and FDP; these markers might also reveal information on off-target phenotypes (OTPs), that go beyond BGA and the phenotypes targeted in FDP. In particular, health related OTPs might shift the balance between privacy protection and public interest. However, to our knowledge, there is currently no convenient resource available to incorporate knowledge on OTPs in BGA and FDP assay design and application. In order to provide such a resource, we performed a systematic search for OTPs associated with a comprehensive set of markers (1766 SNPs) used or suggested to be used for BGA inference and FDP. In this set, we identified a relatively small number of 27 SNPs (1.53%) that convey information on diverse health related OTPs such as cancer risk, induced asthma, or risk of alcoholism. Some of these SNPs are commonly used for FDP and BGA across different marker sets. We conclude that the effects of SNP markers used in FDP and BGA on OTPs are currently limited, with few exceptions that should be considered in a balanced decision on assay design and application.

Genetically decreased vitamin D and risk of Alzheimer disease

OBJECTIVE

To test whether genetically decreased vitamin D levels are associated with Alzheimer disease (AD) using mendelian randomization (MR), a method that minimizes bias due to confounding or reverse causation.

METHODS

We selected single nucleotide polymorphisms (SNPs) that are strongly associated with 25-hydroxyvitamin D (25OHD) levels (p < 5 × 10-8) from the Study of Underlying Genetic Determinants of Vitamin D and Highly Related Traits (SUNLIGHT) Consortium (N = 33,996) to act as instrumental variables for the MR study. We measured the effect of each of these SNPs on 25OHD levels in the Canadian Multicentre Osteoporosis Study (CaMos; N = 2,347) and obtained the corresponding effect estimates for each SNP on AD risk from the International Genomics of Alzheimer's Project (N = 17,008 AD cases and 37,154 controls). To produce MR estimates, we weighted the effect of each SNP on AD by its effect on 25OHD and meta-analyzed these estimates using a fixed-effects model to provide a summary effect estimate.

RESULTS

The SUNLIGHT Consortium identified 4 SNPs to be genome-wide significant for 25OHD, which described 2.44% of the variance in 25OHD in CaMos. All 4 SNPs map to genes within the vitamin D metabolic pathway. MR analyses demonstrated that a 1-SD decrease in natural log-transformed 25OHD increased AD risk by 25% (odds ratio 1.25, 95% confidence interval 1.03-1.51, p = 0.021). After sensitivity analysis in which we removed SNPs possibly influenced by pleiotropy and population stratification, the results were largely unchanged.

CONCLUSIONS

Our results provide evidence supporting 25OHD as a causal risk factor for AD. These findings provide further rationale to understand the effect of vitamin D supplementation on cognition and AD risk in randomized controlled trials.

Obesity and Multiple Sclerosis: A Mendelian Randomization Study

BACKGROUND

Observational studies have reported an association between obesity, as measured by elevated body mass index (BMI), in early adulthood and risk of multiple sclerosis (MS). However, bias potentially introduced by confounding and reverse causation may have influenced these findings. Therefore, we elected to perform Mendelian randomization (MR) analyses to evaluate whether genetically increased BMI is associated with an increased risk of MS.

METHODS AND FINDINGS

Employing a two-sample MR approach, we used summary statistics from the Genetic Investigation of Anthropometric Traits (GIANT) consortium and the International MS Genetics Consortium (IMSGC), the largest genome-wide association studies for BMI and MS, respectively (GIANT: n = 322,105; IMSGC: n = 14,498 cases and 24,091 controls). Seventy single nucleotide polymorphisms (SNPs) were genome-wide significant (p < 5 x 10-8) for BMI in GIANT (n = 322,105) and were investigated for their association with MS risk in the IMSGC. The effect of each SNP on MS was weighted by its effect on BMI, and estimates were pooled to provide a summary measure for the effect of increased BMI upon risk of MS. Our results suggest that increased BMI influences MS susceptibility, where a 1 standard deviation increase in genetically determined BMI (kg/m2) increased odds of MS by 41% (odds ratio [OR]: 1.41, 95% CI 1.20-1.66, p = 2.7 x 10-5, I2 = 0%, 95% CI 0-29). Sensitivity analyses, including MR-Egger regression, and the weighted median approach provided no evidence of pleiotropic effects. The main study limitations are that, while these sensitivity analyses reduce the possibility that pleiotropy influenced our results, residual pleiotropy is difficult to exclude entirely.

CONCLUSION

Genetically elevated BMI is associated with risk of MS, providing evidence for a causal role for obesity in MS etiology. While obesity has been associated with many late-life outcomes, these findings suggest an important consequence of childhood and/or early adulthood obesity.

Overview of genetic research in anorexia nervosa: The past, the present and the future

BACKGROUND

Even though the evidence supporting the presence of a heritable component in the aetiology of anorexia nervosa (AN) is strong, the underlying genetic mechanisms remain poorly understood. The recent publication of a genome-wide association study (GWAS) of AN (Boraska, Mol Psychiatry, 2014) was an important step in genetic research in AN.

OBJECTIVE

To briefly sum up strengths and weaknesses of candidate-gene and genome-wide approaches, to discuss the genome-wide association studies of AN and to make predictions about the genetic architecture of AN by comparing it to that of schizophrenia (since the diseases share some similarities and genetic research in schizophrenia is more advanced).

METHOD

Descriptive literature review.

RESULTS

Despite remarkable efforts, the gene-association studies in AN did not advance our knowledge as much as had been hoped, although some results still await replication.

DISCUSSION

Continuous effort of participants, clinicians and researchers remains necessary to ensure that genetic research in AN follows a similarly successful path as in schizophrenia. Identification of genetic susceptibility loci provides a basis for follow-up studies.

Tracking crop varieties using genotyping-by-sequencing markers: a case study using cassava (Manihot esculenta Crantz)

Background

Accurate identification of crop cultivars is crucial in assessing the impact of crop improvement research outputs. Two commonly used identification approaches, elicitation of variety names from farmer interviews and morphological plant descriptors, have inherent uncertainty levels. Genotyping-by-sequencing (GBS) was used in a case study as an alternative method to track released varieties in farmers’ fields, using cassava, a clonally propagated root crop widely grown in the tropics, and often disseminated through extension services and informal seed systems. A total of 917 accessions collected from 495 farming households across Ghana were genotyped at 56,489 SNP loci along with a “reference library” of 64 accessions of released varieties and popular landraces.

Results

Accurate cultivar identification and ancestry estimation was accomplished through two complementary clustering methods: (i) distance-based hierarchical clustering; and (ii) model-based maximum likelihood admixture analysis. Subsequently, 30 % of the identified accessions from farmers’ fields were matched to specific released varieties represented in the reference library. ADMIXTURE analysis revealed that the optimum number of major varieties was 11 and matched the hierarchical clustering results. The majority of the accessions (69 %) belonged purely to one of the 11 groups, while the remaining accessions showed two or more ancestries. Further analysis using subsets of SNP markers reproduced results obtained from the full-set of markers, suggesting that GBS can be done at higher DNA multiplexing, thereby reducing the costs of variety fingerprinting. A large proportion of discrepancy between genetically unique cultivars as identified by markers and variety names as elicited from farmers were observed. Clustering results from ADMIXTURE analysis was validated using the assumption-free Discriminant Analysis of Principal Components (DAPC) method.

Conclusion

We show that genome-wide SNP markers from increasingly affordable GBS methods coupled with complementary cluster analysis is a powerful tool for fine-scale population structure analysis and variety identification. Moreover, the ancestry estimation provides a framework for quantifying the contribution of exotic germplasm or older improved varieties to the genetic background of contemporary improved cultivars.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0273-1) contains supplementary material, which is available to authorized users.

A common variant in the CLDN7/ELP5 locus predicts adiponectin change with lifestyle intervention and improved fitness in obese individuals with diabetes

Overweight/obese individuals with Type 2 diabetes have low adiponectin levels, which may improve with lifestyle changes. We investigated whether genetic variants associated with adiponectin levels in genome-wide association studies (GWAS) would also be related with adiponectin changes in response to an intensive lifestyle intervention (ILI), potentially through mechanisms altering the adipose microenvironment via weight loss and/or improved cardiorespiratory fitness. Look AHEAD was a randomized trial comparing the cardiovascular benefits of ILI-induced weight loss and physical activity compared with diabetes support and education among overweight/obese individuals with Type 2 diabetes. In a subsample of Look AHEAD with adiponectin data and genetic consent (n=1,351), we evaluated the effects of 24 genetic variants, demonstrated by GWAS to be cross-sectionally associated with adiponectin, on adiponectin change 1-yr postintervention. We explored via mediational analyses whether any differential effects by treatment arm were occurring through weight loss and/or improved fitness. A variant, rs222857, in the CLDN7 locus, potentially associated with epithelial barrier integrity and tight junction physiology, and a putative cis expression quantitative trail locus for elongator acetyltransferase complex subunit 5 (ELP5), predicted adiponectin increases within ILI (log-adiponectin in overall sample per copy: β±SE=0.05±0.02, P=0.008; in non-Hispanic whites: 0.06±0.02, P=0.009). The favorable effects of rs222857 (minor allele frequency 45.5%) appeared to be mediated by mechanisms associated with improved fitness, and not weight loss. This is the first study to identify a genetic variant that modifies adiponectin response to lifestyle intervention in overweight/obese diabetic individuals.

A genome-wide association study of anorexia nervosa

Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome-wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2907 cases with AN from 14 countries (15 sites) and 14 860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery data sets. Seventy-six (72 independent) single nucleotide polymorphisms were taken forward for in silico (two data sets) or de novo (13 data sets) replication genotyping in 2677 independent AN cases and 8629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication data sets comprised 5551 AN cases and 21 080 controls. AN subtype analyses (1606 AN restricting; 1445 AN binge-purge) were performed. No findings reached genome-wide significance. Two intronic variants were suggestively associated: rs9839776 (P=3.01 × 10(-7)) in SOX2OT and rs17030795 (P=5.84 × 10(-6)) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (P=5.76 × 10(-)(6)) between CUL3 and FAM124B and rs1886797 (P=8.05 × 10(-)(6)) near SPATA13. Comparing discovery with replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (P=4 × 10(-6)), strongly suggesting that true findings exist but our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field.

A genome-wide association study on common SNPs and rare CNVs in anorexia nervosa

Anorexia nervosa (AN) is a mental illness with high mortality that most commonly afflicts adolescent female individuals. Clinical symptoms include chronic food refusal, weight loss and body image distortions. We carried out a genome-wide association study on 1033 AN cases and 3733 pediatric control subjects, all of whom were of European ancestry and were genotyped on the Illumina HumanHap610 platform (Illumina, San Diego, CA, USA). We confirmed that common single-nucleotide polymorphisms (SNPs) within OPRD1 (rs533123, P=0.0015) confer risk for AN, and obtained suggestive evidence that common SNPs near HTR1D (rs7532266, P=0.04) confer risk for restricting-type AN specifically. However, no SNPs reached genome-wide significance in our data, whereas top association signals were detected near ZNF804B, CSRP2BP, NTNG1, AKAP6 and CDH9. In parallel, we performed genome-wide analysis on copy number variations (CNVs) using the signal intensity data from the SNP arrays. We did not find evidence that AN cases have more CNVs than control subjects, nor do they have over-representation of rare or large CNVs. However, we identified several regions with rare CNVs that were only observed in AN cases, including a recurrent 13q12 deletion (1.5 Mb) disrupting SCAS in two cases, and CNVs disrupting the CNTN6/CNTN4 region in several AN cases. In conclusion, our study suggests that both common SNPs and rare CNVs may confer genetic risk to AN. These results point to intriguing genes that await further validation in independent cohorts for confirmatory roles in AN.