Genotype imputation

Multiple apical plasma membrane constituents are associated with susceptibility to meconium ileus in individuals with cystic fibrosis

Variants associated with meconium ileus in cystic fibrosis were identified in 3,763 affected individuals by genome-wide association study (GWAS). Five SNPs at two loci near SLC6A14 at Xq23-24 (minimum P = 1.28 × 10(-12) at rs3788766) and SLC26A9 at 1q32.1 (minimum P = 9.88 × 10(-9) at rs4077468) accounted for ~5% of phenotypic variability and were replicated in an independent sample of affected individuals (n = 2,372; P = 0.001 and 0.0001, respectively). By incorporating the knowledge that disease-causing mutations in CFTR alter electrolyte and fluid flux across surface epithelium into a hypothesis-driven GWAS (GWAS-HD), we identified associations with the same SNPs in SLC6A14 and SLC26A9 and established evidence for the involvement of SNPs in a third solute carrier gene, SLC9A3. In addition, GWAS-HD provided evidence of association between meconium ileus and multiple genes encoding constituents of the apical plasma membrane where CFTR resides (P = 0.0002; testing of 155 apical membrane genes jointly and in replication, P = 0.022). These findings suggest that modulating activities of apical membrane constituents could complement current therapeutic paradigms for cystic fibrosis.

Genome-wide meta-analyses of smoking behaviors in African Americans

The identification and exploration of genetic loci that influence smoking behaviors have been conducted primarily in populations of the European ancestry. Here we report results of the first genome-wide association study meta-analysis of smoking behavior in African Americans in the Study of Tobacco in Minority Populations Genetics Consortium (n = 32,389). We identified one non-coding single-nucleotide polymorphism (SNP; rs2036527[A]) on chromosome 15q25.1 associated with smoking quantity (cigarettes per day), which exceeded genome-wide significance (β = 0.040, s.e. = 0.007, P = 1.84 × 10(-8)). This variant is present in the 5'-distal enhancer region of the CHRNA5 gene and defines the primary index signal reported in studies of the European ancestry. No other SNP reached genome-wide significance for smoking initiation (SI, ever vs never smoking), age of SI, or smoking cessation (SC, former vs current smoking). Informative associations that approached genome-wide significance included three modestly correlated variants, at 15q25.1 within PSMA4, CHRNA5 and CHRNA3 for smoking quantity, which are associated with a second signal previously reported in studies in European ancestry populations, and a signal represented by three SNPs in the SPOCK2 gene on chr10q22.1. The association at 15q25.1 confirms this region as an important susceptibility locus for smoking quantity in men and women of African ancestry. Larger studies will be needed to validate the suggestive loci that did not reach genome-wide significance and further elucidate the contribution of genetic variation to disparities in cigarette consumption, SC and smoking-attributable disease between African Americans and European Americans.

Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs

BACKGROUND

We explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.

METHODS

The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n=1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy.

RESULTS

The single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers.

CONCLUSIONS

The program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.

Genetic Loci implicated in erythroid differentiation and cell cycle regulation are associated with red blood cell traits

OBJECTIVE

To identify common genetic variants influencing red blood cell (RBC) traits.

PATIENTS AND METHODS

We performed a genomewide association study from June 2008 through July 2011 of hemoglobin, hematocrit, RBC count, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration in 12,486 patients of European ancestry from the electronic MEdical Records and Genomics (eMERGE) network. We developed an electronic medical record-based algorithm that included individuals who had RBC measurements obtained for clinical care and excluded values measured in the setting of hematopoietic disorders, comorbid conditions, or medications known to affect RBC production or a recent history of blood loss.

RESULTS

We identified 4 new genetic loci and replicated 11 loci previously reported to be associated with one or more RBC traits in individuals of European ancestry. Notably, genes present in 3 of the 4 newly identified loci (THRB, PTPLAD1, CDT1) and in 6 of the 11 replicated loci (KLF1, ALDH8A1, CCND3, SPTA1, FBXO7, TFR2/EPO) are implicated in erythroid differentiation and regulation of cell cycle in hematopoietic stem cells.

CONCLUSION

Genes in the erythroid differentiation and cell cycle regulation pathways influence interindividual variation in RBC indices. Our results provide insights into the molecular basis underlying variation in RBC traits.

Massively parallel haplotyping on microscopic beads for the high-throughput phase analysis of single molecules

In spite of the many advances in haplotyping methods, it is still very difficult to characterize rare haplotypes in tissues and different environmental samples or to accurately assess the haplotype diversity in large mixtures. This would require a haplotyping method capable of analyzing the phase of single molecules with an unprecedented throughput. Here we describe such a haplotyping method capable of analyzing in parallel hundreds of thousands single molecules in one experiment. In this method, multiple PCR reactions amplify different polymorphic regions of a single DNA molecule on a magnetic bead compartmentalized in an emulsion drop. The allelic states of the amplified polymorphisms are identified with fluorescently labeled probes that are then decoded from images taken of the arrayed beads by a microscope. This method can evaluate the phase of up to 3 polymorphisms separated by up to 5 kilobases in hundreds of thousands single molecules. We tested the sensitivity of the method by measuring the number of mutant haplotypes synthesized by four different commercially available enzymes: Phusion, Platinum Taq, Titanium Taq, and Phire. The digital nature of the method makes it highly sensitive to detecting haplotype ratios of less than 1:10,000. We also accurately quantified chimera formation during the exponential phase of PCR by different DNA polymerases.

A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data

OBJECTIVES

There is great interest to sequence unrelated or pedigree samples for detecting rare variant quantitative trait associations. In order to reduce the cost of sequencing and improve power, many studies sequence selected samples with extreme traits. Existing methods for detecting rare variant associations were developed for unrelated samples. Methods are needed to analyze (selected or randomly ascertained) pedigree samples.

METHODS

We propose a unified framework of modeling extreme trait genetic associations (MEGA) with rare variants. Using MEGA and appropriate permutation algorithms, many rare variant tests can be extended to family data. As an application, we compared study designs using both sib-pairs and unrelated individuals. Extensive simulations were carried out using realistic population genetic and complex trait models.

RESULTS

It is demonstrated that when extreme sampling is implemented within equal-sized cohorts of unrelated individuals or sib-pairs, analyzing unrelated individuals is consistently more powerful than studying sib-pairs. A higher portion of rare variants can be identified through sequencing unrelated samples compared to sibs. Alternatively, if samples are ascertained using fixed thresholds from an infinite-sized population, sequencing one sib with the most extreme trait from each extreme concordant sib-pair is consistently the most powerful design.

CONCLUSIONS

MEGA will play an important role in the analysis of sequence-based genetic association studies.

Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma

Optic nerve degeneration caused by glaucoma is a leading cause of blindness worldwide. Patients affected by the normal-pressure form of glaucoma are more likely to harbor risk alleles for glaucoma-related optic nerve disease. We have performed a meta-analysis of two independent genome-wide association studies for primary open angle glaucoma (POAG) followed by a normal-pressure glaucoma (NPG, defined by intraocular pressure (IOP) less than 22 mmHg) subgroup analysis. The single-nucleotide polymorphisms that showed the most significant associations were tested for association with a second form of glaucoma, exfoliation-syndrome glaucoma. The overall meta-analysis of the GLAUGEN and NEIGHBOR dataset results (3,146 cases and 3,487 controls) identified significant associations between two loci and POAG: the CDKN2BAS region on 9p21 (rs2157719 [G], OR = 0.69 [95%CI 0.63-0.75], p = 1.86×10⁻¹⁸), and the SIX1/SIX6 region on chromosome 14q23 (rs10483727 [A], OR = 1.32 [95%CI 1.21-1.43], p = 3.87×10⁻¹¹). In sub-group analysis two loci were significantly associated with NPG: 9p21 containing the CDKN2BAS gene (rs2157719 [G], OR = 0.58 [95% CI 0.50-0.67], p = 1.17×10⁻¹²) and a probable regulatory region on 8q22 (rs284489 [G], OR = 0.62 [95% CI 0.53-0.72], p = 8.88×10⁻¹⁰). Both NPG loci were also nominally associated with a second type of glaucoma, exfoliation syndrome glaucoma (rs2157719 [G], OR = 0.59 [95% CI 0.41-0.87], p = 0.004 and rs284489 [G], OR = 0.76 [95% CI 0.54-1.06], p = 0.021), suggesting that these loci might contribute more generally to optic nerve degeneration in glaucoma. Because both loci influence transforming growth factor beta (TGF-beta) signaling, we performed a genomic pathway analysis that showed an association between the TGF-beta pathway and NPG (permuted p = 0.009). These results suggest that neuro-protective therapies targeting TGF-beta signaling could be effective for multiple forms of glaucoma.

Identification of a novel percent mammographic density locus at 12q24

Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.

Evidence of differential allelic effects between adolescents and adults for plasma high-density lipoprotein

A recent meta-analysis of genome-wide association (GWA) studies identified 95 loci that influence lipid traits in the adult population and found that collectively these explained about 25–30% of heritability for each trait. Little is known about how these loci affect lipid levels in early life, but there is evidence that genetic effects on HDL- and LDL-cholesterol (HDL-C, LDL-C) and triglycerides vary with age. We studied Australian adults (N = 10,151) and adolescents (N = 2,363) who participated in twin and family studies and for whom we have lipid phenotypes and genotype information for 91 of the 95 genetic variants. Heterogeneity tests between effect sizes in adult and adolescent cohorts showed an excess of heterogeneity for HDL-C (p_Het<0.05 at 5 out of 37 loci), but no more than expected by chance for LDL-C (1 out of 14 loci), or trigycerides (0 out 24). There were 2 (out of 5) with opposite direction of effect in adolescents compared to adults for HDL-C, but none for LDL-C. The biggest difference in effect size was for LDL-C at rs6511720 near LDLR, adolescents (0.021±0.033 mmol/L) and adults (0.157±0.023 mmol/L), p_Het = 0.013; followed by ZNF664 (p_Het = 0.018) and PABPC4 (p_Het = 0.034) for HDL-C. Our findings suggest that some of the previously identified variants associate differently with lipid traits in adolescents compared to adults, either because of developmental changes or because of greater interactions with environmental differences in adults.

A two-platform design for next generation genome-wide association studies

Genome-wide association studies (GWAS) have been successful in their search for common genetic variants associated with complex traits and diseases. With new advances in array technologies together with available genetic reference sets, the next generation of GWAS will extend the search for associations with uncommon SNPs (1% ≤ MAF ≤ 10%). Two possible approaches are genotyping all participants, a prohibitively expensive option for large GWAS, or using a combination of genotyping and imputation. Here, we consider a two platform method that genotypes all participants on a standard genotyping array, designed to identify common variants, and then supplements that data by genotyping only a small proportion of the participants on a platform that has higher coverage for uncommon SNPs. This subset of the study population is then included as part of the imputation reference set. To demonstrate the use of this two-platform design, we evaluate its potential efficiency using a newly available dataset containing 756 individuals genotyped on both the Illumina Human OmniExpress and Omni2.5 Quad. Although genotyping all individuals on the denser array would be ideal, we find that genotyping only 100 individuals on this array, in combination with imputation, leads to only a modest loss of power for detecting associations. However, the loss of power due to imputation can be more substantial if the relative risks for rare variants are significantly larger than those previously observed for common variants.

Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture

Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10(-4), Bonferroni corrected), of which six reached P < 5 × 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

Methods for meta-analyses of genome-wide association studies: critical assessment of empirical evidence

There has been a steep increase in the number of meta-analyses of genome-wide association (GWA) studies aimed at identifying genetic variants with increasingly smaller effects, but pressure to publish findings of new genetic associations has limited the time available for careful consideration of all of their methodological aspects. The authors surveyed the literature (2007-2010) to provide empirical evidence on the methods used in GWA meta-analyses, including their organization, requirements about the uniformity of methods used in primary studies, methods for data pooling, investigation of between-study heterogeneity, and quality of reporting. This review showed that a great variety of methods are being used, but the rationale for their choice is often unclear. It also highlights how important methodological aspects have received insufficient attention, potentially leading to missed opportunities for improving gene discovery and characterization. Evaluation of power to replicate findings was inadequate, and the number of variants selected for replication was not associated with replication sample size. A low proportion of GWA meta-analyses investigated the presence and magnitude of heterogeneity, even when there was little uniformity in methods used in primary studies. More methodological work is required before clear guidance can be offered as to optimal methods or tradeoffs between alternative methods. However, there is a clear need for guidelines for reporting the results of GWA meta-analyses.

A genome-wide association study identifies UGT1A1 as a regulator of serum cell-free DNA in young adults: The Cardiovascular Risk in Young Finns Study

Introduction

Circulating cell-free DNA (cf-DNA) is a useful indicator of cell death, and it can also be used to predict outcomes in various clinical disorders. Several innate immune mechanisms are known to be involved in eliminating DNA and chromatin-related material as part of the inhibition of potentially harmful autoimmune responses. However, the exact molecular mechanism underlying the clearance of circulating cf-DNA is currently unclear.

Methods

To examine the mechanisms controlling serum levels of cf-DNA, we carried out a genome-wide association analysis (GWA) in a cohort of young adults (aged 24–39 years; n = 1841; 1018 women and 823 men) participating in the Cardiovascular Risk in Young Finns Study. Genotyping was performed with a custom-built Illumina Human 670 k BeadChip. The Quant-iT^TM high sensitivity DNA assay was used to measure cf-DNA directly from serum.

Results

The results revealed that 110 single nucleotide polymorphisms (SNPs) were associated with serum cf-DNA with genome-wide significance (p<5×10⁻⁸). All of these significant SNPs were localised to chromosome 2q37, near the UDP-glucuronosyltransferase 1 (UGT1) family locus, and the most significant SNPs localised within the UGT1 polypeptide A1 (UGT1A1) gene region.

Conclusion

The UGT1A1 enzyme catalyses the detoxification of several drugs and the turnover of many xenobiotic and endogenous compounds by glucuronidating its substrates. These data indicate that UGT1A1-associated processes are also involved in the regulation of serum cf-DNA concentrations.

Vitamin D-related genetic variation, plasma vitamin D, and risk of lethal prostate cancer: a prospective nested case-control study

BACKGROUND

The association of vitamin D status with prostate cancer is controversial; no association has been observed for overall incidence, but there is a potential link with lethal disease.

METHODS

We assessed prediagnostic 25-hydroxyvitamin D [25(OH)D] levels in plasma, variation in vitamin D-related genes, and risk of lethal prostate cancer using a prospective case-control study nested within the Health Professionals Follow-up Study. We included 1260 men who were diagnosed with prostate cancer after providing a blood sample in 1993-1995 and 1331 control subjects. Men with prostate cancer were followed through March 2011 for lethal outcomes (n = 114). We selected 97 single-nucleotide polymorphisms (SNPs) in genomic regions with high linkage disequilibrium (tagSNPs) to represent common genetic variation among seven vitamin D-related genes (CYP27A1, CYP2R1, CYP27B1, GC, CYP24A1, RXRA, and VDR). We used a logistic kernel machine test to assess whether multimarker SNP sets in seven vitamin D pathway-related genes were collectively associated with prostate cancer. Tests for statistical significance were two-sided.

RESULTS

Higher 25(OH)D levels were associated with a 57% reduction in the risk of lethal prostate cancer (highest vs lowest quartile: odds ratio = 0.43, 95% confidence interval = 0.24 to 0.76). This finding did not vary by time from blood collection to diagnosis. We found no statistically significant association of plasma 25(OH)D levels with overall prostate cancer. Pathway analyses found that the set of SNPs that included all seven genes (P = .008) as well as sets of SNPs that included VDR (P = .01) and CYP27A1 (P = .02) were associated with risk of lethal prostate cancer.

CONCLUSION

In this prospective study, plasma 25(OH)D levels and common variation among several vitamin D-related genes were associated with lethal prostate cancer risk, suggesting that vitamin D is relevant for lethal prostate cancer.

A genome-wide association study of attempted suicide

The heritable component to attempted and completed suicide is partly related to psychiatric disorders and also partly independent of them. Although attempted suicide linkage regions have been identified on 2p11-12 and 6q25-26, there are likely many more such loci, the discovery of which will require a much higher resolution approach, such as the genome-wide association study (GWAS). With this in mind, we conducted an attempted suicide GWAS that compared the single-nucleotide polymorphism (SNP) genotypes of 1201 bipolar (BP) subjects with a history of suicide attempts to the genotypes of 1497 BP subjects without a history of suicide attempts. In all, 2507 SNPs with evidence for association at P<0.001 were identified. These associated SNPs were subsequently tested for association in a large and independent BP sample set. None of these SNPs were significantly associated in the replication sample after correcting for multiple testing, but the combined analysis of the two sample sets produced an association signal on 2p25 (rs300774) at the threshold of genome-wide significance (P=5.07 × 10(-8)). The associated SNPs on 2p25 fall in a large linkage disequilibrium block containing the ACP1 (acid phosphatase 1) gene, a gene whose expression is significantly elevated in BP subjects who have completed suicide. Furthermore, the ACP1 protein is a tyrosine phosphatase that influences Wnt signaling, a pathway regulated by lithium, making ACP1 a functional candidate for involvement in the phenotype. Larger GWAS sample sets will be required to confirm the signal on 2p25 and to identify additional genetic risk factors increasing susceptibility for attempted suicide.

Finding genes and variants for lipid levels after genome-wide association analysis

PURPOSE OF REVIEW

We review the main findings from genome-wide association studies (GWAS) for levels of HDL-cholesterol, LDL-cholesterol and triglycerides, including approaches to identify the functional variant(s) or gene(s). We discuss study design and challenges related to whole genome or exome sequencing to identify novel genes and variants.

RECENT FINDINGS

GWAS have detected approximately 100 loci associated with one or more lipid trait. Fine mapping of several loci for LDL-cholesterol demonstrated that the trait variance explained may double when the functional variants responsible for the association signals are identified. Experimental follow-up of three loci identified by GWAS has identified functional genes GALNT2, TRIB1, and SORT1, and a functional variant at SORT1.

SUMMARY

The goal of genetic studies for lipid levels is to improve treatment and ultimately reduce the prevalence of heart disease. Many signals identified by GWAS have modest effect sizes, useful for identifying novel biologically relevant genes, but less useful for personalized medicine. Whole genome or exome sequencing studies may fill this gap by identifying rare variants of larger effect associated with lipid levels and heart disease.

Integrating pathway analysis and genetics of gene expression for genome-wide association study of basal cell carcinoma

Genome-wide association studies (GWASs) have primarily focused on marginal effects for individual markers and have incorporated external functional information only after identifying robust statistical associations. We applied a new approach combining the genetics of gene expression and functional classification of genes to the GWAS of basal cell carcinoma (BCC) to identify potential biological pathways associated with BCC. We first identified 322,324 expression-associated single-nucleotide polymorphisms (eSNPs) from two existing GWASs of global gene expression in lymphoblastoid cell lines (n = 955), and evaluated the association of these functionally annotated SNPs with BCC among 2,045 BCC cases and 6,013 controls in Caucasians. We then grouped them into 99 KEGG pathways for pathway analysis and identified two pathways associated with BCC with p value <0.05 and false discovery rate (FDR) <0.5: the autoimmune thyroid disease pathway (mainly HLA class I and II antigens, p < 0.001, FDR = 0.24) and Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway (p = 0.02, FDR = 0.49). Seventy-nine (25.7%) out of 307 significant eSNPs in the JAK-STAT pathway were associated with BCC risk (p < 0.05) in an independent replication set of 278 BCC cases and 1,262 controls. In addition, the association of JAK-STAT signaling pathway was marginally validated using 16,691 eSNPs identified from 110 normal skin samples (p = 0.08). Based on the evidence of biological functions of the JAK-STAT pathway on oncogenesis, it is plausible that this pathway is involved in BCC pathogenesis.

Accurate imputation of rare and common variants in a founder population from a small number of sequenced individuals

Advances in DNA sequencing technologies have greatly facilitated the discovery of rare genetic variants in the human genome, many of which may contribute to common disease risk. However, evaluating their individual or even collective effects on disease risk requires very large sample sizes, which involves study designs that are often prohibitively expensive. We present an alternative approach for determining genotypes in large numbers of individuals for all variants discovered in the sequence of relatively few individuals. Specifically, we developed a new imputation algorithm that utilizes whole-exome sequencing data from 25 members of the South Dakota Hutterite population, and genome-wide single nucleotide polymorphism (SNP) genotypes from >1,400 individuals from the same founder population. The algorithm relies on identity-by-descent sharing of phased haplotypes, a different strategy than the linkage disequilibrium methods found in most imputation algorithms. We imputed genotypes discovered in the sequence data to on average ∼77% of chromosomes among the 1,400 individuals. Median R(2) between imputed and directly genotyped data was >0.99. As expected, many variants that are vanishingly rare in European populations have risen to larger frequencies in the founder population and would be amenable to single-SNP analyses.

A genome-wide association study identifies a breast cancer risk variant in ERBB4 at 2q34: results from the Seoul Breast Cancer Study

Introduction

Although approximately 25 common genetic susceptibility loci have been identified to be independently associated with breast cancer risk through genome-wide association studies (GWAS), the genetic risk variants reported to date only explain a small fraction of the heritability of breast cancer. Furthermore, GWAS-identified loci were primarily identified in women of European descent.

Methods

To evaluate previously identified loci in Korean women and to identify additional novel breast cancer susceptibility variants, we conducted a three-stage GWAS that included 6,322 cases and 5,897 controls.

Results

In the validation study using Stage I of the 2,273 cases and 2,052 controls, seven GWAS-identified loci [5q11.2/MAP3K1 (rs889312 and rs16886165), 5p15.2/ROPN1L (rs1092913), 5q12/MRPS30 (rs7716600), 6q25.1/ESR1 (rs2046210 and rs3734802), 8q24.21 (rs1562430), 10q26.13/FGFR2 (rs10736303), and 16q12.1/TOX3 (rs4784227 and rs3803662)] were significantly associated with breast cancer risk in Korean women (P_trend < 0.05). To identify additional genetic risk variants, we selected the most promising 17 SNPs in Stage I and replicated these SNPs in 2,052 cases and 2,169 controls (Stage II). Four SNPs were further evaluated in 1,997 cases and 1,676 controls (Stage III). SNP rs13393577 at chromosome 2q34, located in the Epidermal Growth Factor Receptor 4 (ERBB4) gene, showed a consistent association with breast cancer risk with combined odds ratios (95% CI) of 1.53 (1.37-1.70) (combined P for trend = 8.8 × 10^-14).

Conclusions

This study shows that seven breast cancer susceptibility loci, which were previously identified in European and/or Chinese populations, could be directly replicated in Korean women. Furthermore, this study provides strong evidence implicating rs13393577 at 2q34 as a new risk variant for breast cancer.

Regular Multivitamin Supplement Use, Single Nucleotide Polymorphisms in ATIC, SHMT2, and SLC46A1, and Risk of Ovarian Carcinoma

ATIC, SHMT2, and SLC46A1 have essential roles in one-carbon (1-C) transfer. The authors examined whether associations between ovarian carcinoma and 15 variants in these genes are modified by regular multivitamin use, a source of 1-C donors, among Caucasian participants from two US case–control studies. Using a phased study design, variant-by-multivitamin interactions were tested, and associations between variants and ovarian carcinoma were reported stratified by multivitamin supplement use. Per-allele risk associations were modified by multivitamin use at six variants among 655 cases and 920 controls (Phase 1). In a larger sample of 968 cases and 1,265 controls (Phases 1 and 2), interactions were significant (P ≤ 0.03) for two variants, particularly among regular multivitamin users: ATIC rs7586969 [odds ratio (OR) = 0.7, 95% confidence interval (CI) = 0.6–0.9] and ATIC rs16853834 (OR = 1.5, 95% CI = 1.1–2.0). The two ATIC single nucleotide polymorphisms (SNPs) did not share the same haplotype; however, the haplotypes they comprised mirrored their SNP risk associations among regular multivitamin supplement users. A multi-variant analysis was also performed by comparing the observed likelihood ratio test statistic from adjusted models with and without the two ATIC variant-by-multivitamin interaction terms with a null distribution of test statistics generated by permuting case status 10,000 times. The corresponding observed P value of 0.001 was more extreme than the permutation-derived P value of 0.009, suggesting rejection of the null hypothesis of no association. In summary, there is little statistical evidence that the 15 variants are independently associated with risk of ovarian carcinoma. However, the statistical interaction of ATIC variants with regular multivitamin intake, when evaluated at both the SNP and gene level, may support these findings as relevant to ovarian health and disease processes.

Mammographic breast density and breast cancer: evidence of a shared genetic basis

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.

Genome-wide screen for metabolic syndrome susceptibility Loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits

BACKGROUND

Genome-wide association (GWA) studies have identified several susceptibility loci for metabolic syndrome (MetS) component traits, but have had variable success in identifying susceptibility loci to the syndrome as an entity. We conducted a GWA study on MetS and its component traits in 4 Finnish cohorts consisting of 2637 MetS cases and 7927 controls, both free of diabetes, and followed the top loci in an independent sample with transcriptome and nuclear magnetic resonance-based metabonomics data. Furthermore, we tested for loci associated with multiple MetS component traits using factor analysis, and built a genetic risk score for MetS.

METHODS AND RESULTS

A previously known lipid locus, APOA1/C3/A4/A5 gene cluster region (SNP rs964184), was associated with MetS in all 4 study samples (P=7.23×10(-9) in meta-analysis). The association was further supported by serum metabolite analysis, where rs964184 was associated with various very low density lipoprotein, triglyceride, and high-density lipoprotein metabolites (P=0.024-1.88×10(-5)). Twenty-two previously identified susceptibility loci for individual MetS component traits were replicated in our GWA and factor analysis. Most of these were associated with lipid phenotypes, and none with 2 or more uncorrelated MetS components. A genetic risk score, calculated as the number of risk alleles in loci associated with individual MetS traits, was strongly associated with MetS status.

CONCLUSIONS

Our findings suggest that genes from lipid metabolism pathways have the key role in the genetic background of MetS. We found little evidence for pleiotropy linking dyslipidemia and obesity to the other MetS component traits, such as hypertension and glucose intolerance.

Evolutionary conserved longevity genes and human cognitive abilities in elderly cohorts

Genetic influences have an important role in the ageing process. The genetic factors that influence success in bodily ageing may also contribute to the successful ageing of cognitive abilities. A comparative genomics approach found longevity genes conserved between yeast Saccharomyces cerevisiae and nematode Caenorhabditis elegans. We hypothesised that these longevity genes influence variance in cognitive ability and age-related cognitive decline in humans. Here, we investigated six of these genes that have human orthologs and show expression in the brain. We tested AFG3L2 (MIM: 604581, AFG3 ATPase family gene 3-like 2 (yeast)), FRAP1 (MIM: 601231, a FK506 binding protein 12-rapamycin associated protein), MAT1A, MAT2A (MIM: 610550 and 601468, methionine adenosyltransferases I alpha and II alpha, respectively), SYNJ1 and SYNJ2 (MIM: 604297 and 609410, synaptojanin-1 and synaptojanin-2, respectively) in approximately 1000 healthy older Scots: the Lothian Birth Cohort 1936 (LBC1936). They were tested on general cognitive ability at age 11 years. At a mean age of 70 years, they re-sat the same general cognitive ability test and underwent an additional battery of diverse cognitive tests. In all, 70 tag and functional SNPs in the six longevity genes were genotyped and tested for association with cognition and cognitive ageing in LBC1936. Suggestive associations were detected between SNPs in SYNJ2, MAT1A, AFG3L2 and SYNJ1 and a general memory factor and general cognitive ability at age 11 and 70 years. Replication studies for cognitive ability associations were performed in 2506 samples from the Cognitive Ageing Genetics in England and Scotland consortium. A meta-analysis replicated the SYNJ2 association with cognitive abilities (lowest P=0.00077). SYNJ2 is a novel gene in which variation is potentially associated with cognitive abilities.

OMERO: flexible, model-driven data management for experimental biology

Data-intensive research depends on tools that manage multidimensional, heterogeneous datasets. We built OME Remote Objects (OMERO), a software platform that enables access to and use of a wide range of biological data. OMERO uses a server-based middleware application to provide a unified interface for images, matrices and tables. OMERO's design and flexibility have enabled its use for light-microscopy, high-content-screening, electron-microscopy and even non-image-genotype data. OMERO is open-source software, available at http://openmicroscopy.org/.

Genome-wide association study of genetic determinants of LDL-c response to atorvastatin therapy: importance of Lp(a)

We carried out a genome-wide association study (GWAS) of LDL-c response to statin using data from participants in the Collaborative Atorvastatin Diabetes Study (CARDS; n = 1,156), the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT; n = 895), and the observational phase of ASCOT (n = 651), all of whom were prescribed atorvastatin 10 mg. Following genome-wide imputation, we combined data from the three studies in a meta-analysis. We found associations of LDL-c response to atorvastatin that reached genome-wide significance at rs10455872 (P = 6.13 × 10(-9)) within the LPA gene and at two single nucleotide polymorphisms (SNP) within the APOE region (rs445925; P = 2.22 × 10(-16) and rs4420638; P = 1.01 × 10(-11)) that are proxies for the ε2 and ε4 variants, respectively, in APOE. The novel association with the LPA SNP was replicated in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial (P = 0.009). Using CARDS data, we further showed that atorvastatin therapy did not alter lipoprotein(a) [Lp(a)] and that Lp(a) levels accounted for all of the associations of SNPs in the LPA gene and the apparent LDL-c response levels. However, statin therapy had a similar effect in reducing cardiovascular disease (CVD) in patients in the top quartile for serum Lp(a) levels (HR = 0.60) compared with those in the lower three quartiles (HR = 0.66; P = 0.8 for interaction). The data emphasize that high Lp(a) levels affect the measurement of LDL-c and the clinical estimation of LDL-c response. Therefore, an apparently lower LDL-c response to statin therapy may indicate a need for measurement of Lp(a). However, statin therapy seems beneficial even in those with high Lp(a).

Transgenic expression and genetic variation of Lmf1 affect LPL activity in mice and humans

OBJECTIVE

Lipoprotein lipase (LPL) is a principal enzyme in lipoprotein metabolism, tissue lipid utilization, and energy metabolism. LPL is synthesized by parenchymal cells in adipose, heart, and muscle tissues followed by secretion to extracellular sites, where lipolyic function is exerted. The catalytic activity of LPL is attained during posttranslational maturation, which involves glycosylation, folding, and subunit assembly within the endoplasmic reticulum. A lipase-chaperone, lipase maturation factor 1 (Lmf1), has recently emerged as a critical factor in this process. Previous studies demonstrated that loss-of-function mutations of Lmf1 result in diminished lipase activity and severe hypertriglyceridemia in mice and human subjects. The objective of this study is to investigate whether, beyond its role as a required factor in lipase maturation, variation in Lmf1 expression is sufficient to modulate LPL activity in vivo.

METHODS AND RESULTS

To assess the effects of Lmf1 overexpression in adipose and muscle tissues, we generated aP2-Lmf1 and Mck-Lmf1 transgenic mice. Characterization of relevant tissues revealed increased LPL activity in both mouse strains. In the omental and subcutaneous adipose depots, Lmf1 overexpression was associated with increased LPL specific activity without changes in LPL mass. In contrast, increased LPL activity was due to elevated LPL protein level in heart and gonadal adipose tissue. To extend these studies to humans, we detected association between LMF1 gene variants and postheparin LPL activity in a dyslipidemic cohort.

CONCLUSIONS

Our results suggest that variation in Lmf1 expression is a posttranslational determinant of LPL activity.

A genome-wide association study of gestational diabetes mellitus in Korean women

Knowledge regarding the genetic risk loci for gestational diabetes mellitus (GDM) is still limited. In this study, we performed a two-stage genome-wide association analysis in Korean women. In the stage 1 genome scan, 468 women with GDM and 1,242 nondiabetic control women were compared using 2.19 million genotyped or imputed markers. We selected 11 loci for further genotyping in stage 2 samples of 931 case and 783 control subjects. The joint effect of stage 1 plus stage 2 studies was analyzed by meta-analysis. We also investigated the effect of known type 2 diabetes variants in GDM. Two loci known to be associated with type 2 diabetes had a genome-wide significant association with GDM in the joint analysis. rs7754840, a variant in CDKAL1, had the strongest association with GDM (odds ratio 1.518; P=6.65×10(-16)). A variant near MTNR1B, rs10830962, was also significantly associated with the risk of GDM (1.454; P=2.49×10(-13)). We found that there is an excess of association between known type 2 diabetes variants and GDM above what is expected under the null hypothesis. In conclusion, we have confirmed that genetic variants in CDKAL1 and near MTNR1B are strongly associated with GDM in Korean women. There seems to be a shared genetic basis between GDM and type 2 diabetes.

1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data

We hypothesize that imputation based on data from the 1000 Genomes Project can identify novel association signals on a genome-wide scale due to the dense marker map and the large number of haplotypes. To test the hypothesis, the Wellcome Trust Case Control Consortium (WTCCC) Phase I genotype data were imputed using 1000 genomes as reference (20100804 EUR), and seven case/control association studies were performed using imputed dosages. We observed two 'missed' disease-associated variants that were undetectable by the original WTCCC analysis, but were reported by later studies after the 2007 WTCCC publication. One is within the IL2RA gene for association with type 1 diabetes and the other in proximity with the CDKN2B gene for association with type 2 diabetes. We also identified two refined associations. One is SNP rs11209026 in exon 9 of IL23R for association with Crohn's disease, which is predicted to be probably damaging by PolyPhen2. The other refined variant is in the CUX2 gene region for association with type 1 diabetes, where the newly identified top SNP rs1265564 has an association P-value of 1.68 × 10(-16). The new lead SNP for the two refined loci provides a more plausible explanation for the disease association. We demonstrated that 1000 Genomes-based imputation could indeed identify both novel (in our case, 'missed' because they were detected and replicated by studies after 2007) and refined signals. We anticipate the findings derived from this study to provide timely information when individual groups and consortia are beginning to engage in 1000 genomes-based imputation.

Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the Women's Health Initiative

Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005-0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies (GWAS) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into 6,459 WHI SNP Health Association Resource (SHARe) study subjects with only GWAS genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r(2) , specific to each SNP) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r(2) (squared Pearson correlation between imputed and experimental genotypes). With a desired dosage r(2) of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03-0.05, 0.01-0.03, 0.005-0.01, and 0.001-0.005) passed the post-imputation filter. The average dosage r(2) for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from GWAS data, including low frequency SNPs with MAF 0.005-0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.

Next-generation sequencing approaches for genetic mapping of complex diseases

The advent of next generation sequencing technologies has opened new possibilities in the analysis of human disease. In this review we present the main next-generation sequencing technologies, with their major contributions and possible applications to the study of the genetic etiology of complex diseases.

A genome-wide association study identified AFF1 as a susceptibility locus for systemic lupus eyrthematosus in Japanese

Systemic lupus erythematosus (SLE) is an autoimmune disease that causes multiple organ damage. Although recent genome-wide association studies (GWAS) have contributed to discovery of SLE susceptibility genes, few studies has been performed in Asian populations. Here, we report a GWAS for SLE examining 891 SLE cases and 3,384 controls and multi-stage replication studies examining 1,387 SLE cases and 28,564 controls in Japanese subjects. Considering that expression quantitative trait loci (eQTLs) have been implicated in genetic risks for autoimmune diseases, we integrated an eQTL study into the results of the GWAS. We observed enrichments of cis-eQTL positive loci among the known SLE susceptibility loci (30.8%) compared to the genome-wide SNPs (6.9%). In addition, we identified a novel association of a variant in the AF4/FMR2 family, member 1 (AFF1) gene at 4q21 with SLE susceptibility (rs340630; P = 8.3×10(-9), odds ratio = 1.21). The risk A allele of rs340630 demonstrated a cis-eQTL effect on the AFF1 transcript with enhanced expression levels (P<0.05). As AFF1 transcripts were prominently expressed in CD4(+) and CD19(+) peripheral blood lymphocytes, up-regulation of AFF1 may cause the abnormality in these lymphocytes, leading to disease onset.

Rare and common variants: twenty arguments

Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.

Genome-wide association of serum uric acid concentration: replication of sequence variants in an island population of the Adriatic coast of Croatia

A genome-wide association study of serum uric acid (SUA) laevels was performed in a relatively isolated population of European descent from an island of the Adriatic coast of Croatia. The study sample included 532 unrelated and 768 related individuals from 235 pedigrees. Inflation due to relatedness was controlled by using genomic control. Genetic association was assessed with 2,241,249 single nucleotide polymorphisms (SNPs) in 1300 samples after adjusting for age and gender. Our study replicated four previously reported SUA loci (SLC2A9, ABCG2, RREB1, and SLC22A12). The strongest association was found with a SNP in SLC2A9 (rs13129697, P=2.33×10(-19)), which exhibited significant gender-specific effects, 35.76 μmol/L (P=2.11×10(-19)) in females and 19.58 μmol/L (P=5.40×10(-5)) in males. Within this region of high linkage disequilibrium, we also detected a strong association with a nonsynonymous SNP, rs16890979 (P=2.24×10(-17)), a putative causal variant for SUA variation. In addition, we identified several novel loci suggestive of association with uric acid levels (SEMA5A, TMEM18, SLC28A2, and ODZ2), although the P-values (P<5×10(-6)) did not reach the threshold of genome-wide significance. Together, these findings provide further confirmation of previously reported uric-acid-related genetic variants and highlight suggestive new loci for additional investigation.

A family-based probabilistic method for capturing de novo mutations from high-throughput short-read sequencing data

Recent advances in high-throughput DNA sequencing technologies and associated statistical analyses have enabled in-depth analysis of whole-genome sequences. As this technology is applied to a growing number of individual human genomes, entire families are now being sequenced. Information contained within the pedigree of a sequenced family can be leveraged when inferring the donors' genotypes. The presence of a de novo mutation within the pedigree is indicated by a violation of Mendelian inheritance laws. Here, we present a method for probabilistically inferring genotypes across a pedigree using high-throughput sequencing data and producing the posterior probability of de novo mutation at each genomic site examined. This framework can be used to disentangle the effects of germline and somatic mutational processes and to simultaneously estimate the effect of sequencing error and the initial genetic variation in the population from which the founders of the pedigree arise. This approach is examined in detail through simulations and areas for method improvement are noted. By applying this method to data from members of a well-defined nuclear family with accurate pedigree information, the stage is set to make the most direct estimates of the human mutation rate to date.

Comparison of requirements and capabilities of major multipurpose software packages

The aim of this chapter is to introduce the reader to commonly used software packages and illustrate their input requirements, analysis options, strengths, and limitations. We focus on packages that perform more than one function and include a program for quality control, linkage, and association analyses. Additional inclusion criteria were (1) programs that are free to academic users and (2) currently supported, maintained, and developed. Using those criteria, we chose to review three programs: Statistical Analysis for Genetic Epidemiology (S.A.G.E.), PLINK, and Merlin. We will describe the required input format and analysis options. We will not go into detail about every possible program in the packages, but we will give an overview of the packages requirements and capabilities.

Replication of a genome-wide association study of birth weight in preterm neonates

OBJECTIVE

To examine associations between rs9883204 in ADCY5 and rs900400 near LEKR1 and CCNL1 with birth weight in a preterm population. Both markers were associated with birth weight in a term population in a recent genome-wide association study of Freathy et al.

STUDY DESIGN

A meta-analysis of mother and infant samples was performed for associations of rs900400 and rs9883204 with birth weight in 393 families from the US, 265 families from Argentina, and 735 mother-infant pairs from Denmark. Z-scores adjusted for infant sex and gestational age were generated for each population separately and regressed on allele counts. Association evidence was combined across sites by inverse-variance weighted meta-analysis.

RESULTS

Each additional C allele of rs900400 (LEKR1/CCNL1) in infants was marginally associated with a 0.069 SD lower birth weight (95% CI, -0.159 to 0.022; P = .068). This result was slightly more pronounced after adjusting for smoking (P = .036). No significant associations were identified with rs9883204 or in maternal samples.

CONCLUSIONS

These results indicate the potential importance of this marker on birth weight regardless of gestational age.

Extent of height variability explained by known height-associated genetic variants in an isolated population of the Adriatic coast of Croatia

BACKGROUND

Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia.

METHODOLOGY/PRINCIPAL FINDINGS

In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20~50 SNPs reported by the remaining individual GWA studies explained 3~5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent.

CONCLUSIONS/SIGNIFICANCE

We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.

How to deal with the early GWAS data when imputing and combining different arrays is necessary

Genotype imputation has become an essential tool in the analysis of genome-wide association scans. This technique allows investigators to test association at ungenotyped genetic markers, and to combine results across studies that rely on different genotyping platforms. In addition, imputation is used within long-running studies to reuse genotypes produced across generations of platforms. Typically, genotypes of controls are reused and cases are genotyped on more novel platforms yielding a case-control study that is not matched for genotyping platforms. In this study, we scrutinize such a situation and validate GWAS results by actually retyping top-ranking SNPs with the Sequenom MassArray platform. We discuss the needed quality controls (QCs). In doing so, we report a considerable discrepancy between the results from imputed and retyped data when applying recommended QCs from the literature. These discrepancies appear to be caused by extrapolating differences between arrays by the process of imputation. To avoid false positive results, we recommend that more stringent QCs should be applied. We also advocate reporting the imputation quality measure (R(T)(2)) for the post-imputation QCs in publications.

An assessment of the individual and collective effects of variants on height using twins and a developmentally informative study design

In a sample of 3,187 twins and 3,294 of their parents, we sought to investigate association of both individual variants and a genotype-based height score involving 176 of the 180 common genetic variants with adult height identified recently by the GIANT consortium. First, longitudinal observations on height spanning pre-adolescence through adulthood in the twin sample allowed us to investigate the separate effects of the previously identified SNPs on pre-pubertal height and pubertal growth spurt. We show that the effect of SNPs identified by the GIANT consortium is primarily on prepubertal height. Only one SNP, rs7759938 in LIN28B, approached a significant association with pubertal growth. Second, we show how using the twin data to control statistically for environmental variance can provide insight into the ultimate magnitude of SNP effects and consequently the genetic architecture of a phenotype. Specifically, we computed a genetic score by weighting SNPs according to their effects as assessed via meta-analysis. This weighted score accounted for 9.2% of the phenotypic variance in height, but 14.3% of the corresponding genetic variance. Longitudinal samples will be needed to understand the developmental context of common genetic variants identified through GWAS, while genetically informative designs will be helpful in accurately characterizing the extent to which these variants account for genetic, and not just phenotypic, variance.

We evaluated the developmental specificity of 176 SNPs known to affect adult height based on meta-analysis from the GIANT consortium. First, longitudinal observations on height spanning pre-adolescence through adulthood in a twin sample allowed us to investigate the individual effects of the previously identified SNPs on both pre-pubertal height and pubertal growth spurt. We show that the effect of the SNPs identified by the GIANT consortium is primarily on prepubertal height. Only one SNP, rs7759938 in LIN28B, approached a significant association with pubertal growth. Second, using standard twin heritability models, we investigated the extent to which the collective effect of these SNPs explained genetic variance in height—as opposed to phenotypic variance, as other studies have done. We computed a genetic score by weighting SNPs according to their effects as assessed via meta-analysis. We show that, while the score accounts for ∼9% of the phenotypic variance in height (i.e., the overall variance), it accounts for ∼14% of the corresponding genetic variance. Longitudinal samples are necessary to understand the developmental context of common genetic variants identified through GWAS, while twin samples will be helpful in accurately characterizing the extent to which these variants account for genetic, and not just phenotypic, variance.

Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies

Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc "fixes." To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted P-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses.

Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci

OBJECTIVE

To perform a 1-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci.

METHODS

We synthesized 7 MS GWAS. Each data set was imputed using HapMap phase II, and a per single nucleotide polymorphism (SNP) meta-analysis was performed across the 7 data sets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease.

RESULTS

We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified 3 novel susceptibility alleles: rs170934(T) at 3p24.1 (odds ratio [OR], 1.17; p = 1.6 × 10(-8)) near EOMES, rs2150702(G) in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p = 3.3 × 10(-8)), and rs6718520(A) in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p = 3.4 × 10(-8)). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 × 10(-6) , some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1).

INTERPRETATION

We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.

Breast cancer susceptibility polymorphisms and endometrial cancer risk: a Collaborative Endometrial Cancer Study

Recent large--scale association studies, both of genome-wide and candidate gene design, have revealed several single-nucleotide polymorphisms (SNPs) which are significantly associated with risk of developing breast cancer. As both breast and endometrial cancers are considered to be hormonally driven and share multiple risk factors, we investigated whether breast cancer risk alleles are also associated with endometrial cancer risk. We genotyped nine breast cancer risk SNPs in up to 4188 endometrial cases and 11,928 controls, from between three and seven Caucasian populations. None of the tested SNPs showed significant evidence of association with risk of endometrial cancer.

Low-pass genome-wide sequencing and variant inference using identity-by-descent in an isolated human population

Whole-genome sequencing in an isolated population with few founders directly ascertains variants from the population bottleneck that may be rare elsewhere. In such populations, shared haplotypes allow imputation of variants in unsequenced samples without resorting to complex statistical methods as in studies of outbred cohorts. We focus on an isolated population cohort from the Pacific Island of Kosrae, Micronesia, where we previously collected SNP array and rich phenotype data for the majority of the population. We report identification of long regions with haplotypes co-inherited between pairs of individuals and methodology to leverage such shared genetic content for imputation. Our estimates show that sequencing as few as 40 personal genomes allows for inference in up to 60% of the 3000-person cohort at the average locus. We ascertained a pilot data set of whole-genome sequences from seven Kosraean individuals, with average 5× coverage. This assay identified 5,735,306 unique sites of which 1,212,831 were previously unknown. Additionally, these variants are unusually enriched for alleles that are rare in other populations when compared to geographic neighbors (published Korean genome SJK). We used the presence of shared haplotypes between the seven Kosraen individuals to estimate expected imputation accuracy of known and novel homozygous variants at 99.6% and 97.3%, respectively. This study presents whole-genome analysis of a homogenous isolate population with emphasis on optimal rare variant inference.

Common variants in FTO are not significantly associated with obesity-related phenotypes among Samoans of Polynesia

The association between obesity and the fat mass and obesity-associated (FTO) gene has been widely replicated among Caucasian populations. The limited number of studies assessing its significance in Asian populations has been somewhat conflicting. We performed a genetic association study of 51 tagging, genome-wide association studies, and imputed single nucleotide polymorphisms with 12 measures of adiposity and skeletal robustness in two Samoan populations of Polynesia. We included 465 and 624 unrelated American Samoan and Samoan individuals, respectively; these populations derive from a single genetic background traced to Southeast Asia and represent one sociocultural unit, although they are economically disparate with distinct environmental exposures. American Samoans were significantly larger than Samoans in all measures of obesity and most measures of skeletal robustness. In separate analyses of American Samoa and Samoa, we found a total of 36 nominal associations between FTO variants and skeletal and obesity measures. The preponderance of these nominal associations (32 of 36) was observed in the Samoan population, and predominantly with skeletal rather than fat mass measures (28 of 36). All significance disappeared, however, following corrections for multiple testing. Based on these findings, it could be surmised that FTO is not likely a major obesity locus in Polynesian populations.

Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record

Background

Susceptibility variants identified by genome-wide association studies (GWAS) have modest effect sizes. Whether such variants provide incremental information in assessing risk for common 'complex' diseases is unclear. We investigated whether measured and imputed genotypes from a GWAS dataset linked to the electronic medical record alter estimates of coronary heart disease (CHD) risk.

Methods

Study participants (n = 1243) had no known cardiovascular disease and were considered to be at high, intermediate, or low 10-year risk of CHD based on the Framingham risk score (FRS) which includes age, sex, total and HDL cholesterol, blood pressure, diabetes, and smoking status. Of twelve SNPs identified in prior GWAS to be associated with CHD, four were genotyped in the participants as part of a GWAS. Genotypes for seven SNPs were imputed from HapMap CEU population using the program MACH. We calculated a multiplex genetic risk score for each patient based on the odds ratios of the susceptibility SNPs and incorporated this into the FRS.

Results

The mean (SD) number of risk alleles was 12.31 (1.95), range 6-18. The mean (SD) of the weighted genetic risk score was 12.64 (2.05), range 5.75-18.20. The CHD genetic risk score was not correlated with the FRS (P = 0.78). After incorporating the genetic risk score into the FRS, a total of 380 individuals (30.6%) were reclassified into higher-(188) or lower-risk groups (192).

Conclusion

A genetic risk score based on measured/imputed genotypes at 11 susceptibility SNPs, led to significant reclassification in the 10-y CHD risk categories. Additional prospective studies are needed to assess accuracy and clinical utility of such reclassification.

A genome-wide association study of aging

Human longevity and healthy aging show moderate heritability (20%-50%). We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death. No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p < 5 × 10(-8)). We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p < 10(-5)). These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease. In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings. These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.

Replication of genetic association studies in aortic stenosis in adults

Only a handful of studies have attempted to unravel the genetic architecture of calcific aortic valve stenosis (AS). The goal of this study was to validate genes previously associated with AS. Seven genes were assessed: APOB, APOE, CTGF, IL10, PTH, TGFB1, and VDR. Each gene was tested for a comprehensive set of single-nucleotide polymorphisms (SNPs). SNPs were genotyped in 457 patients who underwent surgical aortic valve replacement, and allele frequencies were compared to 3,294 controls. A missense mutation in the APOB gene was significantly associated with AS (rs1042031, E4181K, p = 0.00001). A second SNP located 5.6 kilobases downstream of the APOB stop codon was also associated with the disease (rs6725189, p = 0.000013). Six SNPs surrounding the IL10 locus were strongly associated with AS (0.02 > p > 6.2 × 10⁻¹¹). The most compelling association for IL10 was found with a promoter polymorphism (rs1800872) well known to regulate the production of the encoded anti-inflammatory cytokine. The frequency of the low-producing allele was greater in cases compared to controls (30% vs 20%, p = 6.2 × 10⁻¹¹). SNPs in PTH, TGFB1, and VDR had nominal p values <0.05 but did not resist Bonferroni correction. In conclusion, this study suggests that subjects carrying specific polymorphisms in the IL10 and APOB genes are at higher risk for developing AS.

Genotype imputation with thousands of genomes

Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework. Our approach is built around a new approximation that uses local sequence similarity to choose a custom reference panel for each study haplotype in each region of the genome. This approximation makes it computationally efficient to use all available reference haplotypes, which allows us to bypass the panel selection step and to improve accuracy at low-frequency variants by capturing unexpected allele sharing among populations. Using data from HapMap 3, we show that our framework produces accurate results in a wide range of human populations. We also use data from the Malaria Genetic Epidemiology Network (MalariaGEN) to provide recommendations for imputation-based studies in Africa. We demonstrate that our approximation improves efficiency in large, sequence-based reference panels, and we discuss general computational strategies for modern reference datasets. Genome-wide association studies will soon be able to harness the power of thousands of reference genomes, and our work provides a practical way for investigators to use this rich information. New methodology from this study is implemented in the IMPUTE2 software package.