Meta-analysis methods for genome-wide association studies and beyond

Association score testing for rare variants and binary traits in family data with shared controls

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2, WQLS, and SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

Relationship Between Alzheimer's Disease and the Immune System: A Meta-Analysis of Differentially Expressed Genes

Alzheimer's disease (AD), a neurodegenerative diseases (neuro-diseases) which is prevalent in the elderly and seriously affects the lives of individuals. Many studies have discussed the relationship between immune system and AD pathogenesis. Here, the meta-analysis of differentially expressed (DE) genes based on microarray data was conducted to study the association between AD and immune system. 9519 target genes of hippocampus in 146 subjects (73 AD cases and 73 controls) from 4 microarray data sets were compiled and DE genes with p < 1.00E - 04 were selected to conduct the pathway-analysis. The results indicated that the DE genes were significantly enriched in the neuro-diseases as well as the immune system pathways.

Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies

BACKGROUND

Random-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimer's gene expression datasets.

RESULTS

Sample quality adjusting within study variance (wP6) models provided an appropriate reduction of differentially expressed (DE) genes compared to other weighted functions in classical RE models. The BRE model with a uniform(0,1) prior was appropriate for detecting DE genes as compared to the models with other prior distributions. The precision of DE gene detection in the heterogeneous data was increased with the DSLR2wP6 weighted model compared to the DSLwP6 weighted model. Among the BRE weighted models, the wP6weighted- and unweighted-data models and both Gibbs- and MH-based models performed similarly. The wP6 weighted common-effect model performed similarly to the unweighted model in the homogeneous data, but performed worse in the heterogeneous data. The wP6weighted data were appropriate for detecting DE genes with high precision, while the wP6weighted between-study variance models were appropriate for detecting DE genes with high overall accuracy. Without the weight, when the number of genes in microarray increased, the DSLR2 performed stably, while the overall accuracy of the BRE model was reduced. When applying the weighted models in the Alzheimer's gene expression data, the number of DE genes decreased in all metadata sets with the DSLR2wP6weighted and the wP6weighted between study variance models. Four hundred and forty-six DE genes identified by the wP6weighted between study variance model could be potentially down-regulated genes that may contribute to good classification of Alzheimer's samples.

CONCLUSIONS

The application of sample quality weights can increase precision and accuracy of the classical RE and BRE models; however, the performance of the models varied depending on data features, levels of sample quality, and adjustment of parameter estimates.

A scientometric review of genome-wide association studies

This scientometric review of genome-wide association studies (GWAS) from 2005 to 2018 (3639 studies; 3508 traits) reveals extraordinary increases in sample sizes, rates of discovery and traits studied. A longitudinal examination shows fluctuating ancestral diversity, still predominantly European Ancestry (88% in 2017) with 72% of discoveries from participants recruited from three countries (US, UK, Iceland). US agencies, primarily NIH, fund 85% and women are less often senior authors. We generate a unique GWAS H-Index and reveal a tight social network of prominent authors and frequently used data sets. We conclude with 10 evidence-based policy recommendations for scientists, research bodies, funders, and editors.

Independent and Joint-GWAS for growth traits in Eucalyptus by assembling genome-wide data for 3373 individuals across four breeding populations

Genome-wide association studies (GWAS) in plants typically suffer from limited statistical power. An alternative to the logistical and cost challenge of increasing sample sizes is to gain power by meta-analysis using information from independent studies. We carried out GWAS for growth traits with six single-marker models and regional heritability mapping (RHM) in four Eucalyptus breeding populations independently and by Joint-GWAS, using gene and segment-based models, with data for 3373 individuals genotyped with a communal EUChip60KSNP platform. While single-single nucleotide polymorphism (SNP) GWAS hardly detected significant associations at high-stringency in each population, gene-based Joint-GWAS revealed nine genes significantly associated with tree height. Associations detected using single-SNP GWAS, RHM and Joint-GWAS set-based models explained on average 3-20% of the phenotypic variance. Whole-genome regression, conversely, captured 64-89% of the pedigree-based heritability in all populations. Several associations independently detected for the same SNPs in different populations provided unprecedented GWAS validation results in forest trees. Rare and common associations were discovered in eight genes involved in cell wall biosynthesis and lignification. With the increasing adoption of genomic prediction of complex phenotypes using shared SNPs and much larger tree breeding populations, Joint-GWAS approaches should provide increasing power to pinpoint discrete associations potentially useful toward tree breeding and molecular applications.

Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA

BACKGROUND

Genome-wide association studies have been extensively applied in identifying SNP associated with major psychiatric disorders. However, the SNPs identified by the prevailing univariate approach only explain a small percentage of the genetic variance of traits, and the extensive data have shown the major psychiatric disorders have common biological mechanisms and the overlapping pathophysiological pathways.

METHODS

We applied the genetic pleiotropy-informed metaCCA method on summary statistics data from the Psychiatric Genomics Consortium Cross-Disorder Group to examine the overlapping genetic relations between the five major psychiatric disorders. Furthermore, to refine all genes, we performed gene-based association analyses for the five disorders respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes.

RESULTS

After metaCCA analysis, 1147 SNPs reached the Bonferroni corrected threshold (p < 1.06 × 10-6) in the univariate SNP-multivariate phenotype analysis, and 246 genes with a significance threshold (p < 3.85 × 10-6) were identified as potentially pleiotropic genes in the multivariate SNP-multivariate phenotype analysis. By screening the results of gene-based p-values, we identified 37 putative pleiotropic genes which achieved significance threshold in metaCCA analyses and were also associated with at least one disorder in the VEGAS2 analyses.

LIMITATIONS

Alternative approaches and experimental studies may be applied to check whether novel genes could still be identified/substantiated with these methods.

CONCLUSIONS

The metaCCA method identified novel variants associated with psychiatric disorders by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of these five major psychiatric disorders based on the pleiotropic genes and common mechanisms identified.

Prognostic Value of CD133 and SOX2 in Advanced Cancer

BACKGROUND

The prognostic value of CD133 and SOX2 expression in advanced cancer remains unclear. This study was first conducted to investigate the association between CD133 or SOX2 positivity and clinical outcomes for advanced cancer patients.

METHODS

Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated to evaluate the correlation between CD133 or SOX2 positivity and overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), cancer-specific survival (CSS), or recurrence-free survival (RFS) from multivariable analysis. Trial sequential analysis (TSA) was also performed.

RESULTS

13 studies with 1358 cases (CD133) and five studies with 433 cases (SOX2) were identified. CD133 positivity was correlated with worse CSS and OS, but there was no correlation between CD133 positivity and DFS. SOX2 positivity was associated with poor DFS and RFS but was not linked to PFS. Stratified analysis by study source showed that only CD133 positivity can decrease OS for Chinese patients. Stratified analysis by treatment regimens indicated that CD133 positivity was linked to poor OS in patients treated with adjuvant therapy. TSA showed that additional studies were necessary.

CONCLUSIONS

CD133 and SOX2 might be associated with worse prognosis in advanced cancer. More prospective studies are strongly needed.

IMPACT

CD133 and SOX2 may be promising targeted molecular therapy for advanced cancer patients.

Meiotic Recombination: Genetics' Good Old Scalpel

In the era of genome engineering, a new study returns to classical genetics to decipher genotype-phenotype relationships in unprecedented throughput and with unprecedented accuracy. Capitalizing on natural variation in yeast strains and frequent meiotic recombination, She and Jarosz (2018) dissect and map to nucleotide resolution, simple and complex determinants of diverse phenotypic traits.

Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy experts.

Functionally distinct ERAP1 and ERAP2 are a hallmark of HLA-A29-(Birdshot) Uveitis

Birdshot Uveitis (Birdshot) is a rare eye condition that affects HLA-A29-positive individuals and could be considered a prototypic member of the recently proposed 'MHC-I (major histocompatibility complex class I)-opathy' family. Genetic studies have pinpointed the endoplasmic reticulum aminopeptidase (ERAP1) and (ERAP2) genes as shared associations across MHC-I-opathies, which suggests ERAP dysfunction may be a root cause for MHC-I-opathies. We mapped the ERAP1 and ERAP2 haplotypes in 84 Dutch cases and 890 controls. We identified association at variant rs10044354, which mediated a marked increase in ERAP2 expression. We also identified and cloned an independently associated ERAP1 haplotype (tagged by rs2287987) present in more than half of the cases; this ERAP1 haplotype is also the primary risk and protective haplotype for other MHC-I-opathies. We show that the risk ERAP1 haplotype conferred significantly altered expression of ERAP1 isoforms in transcriptomic data (n = 360), resulting in lowered protein expression and distinct enzymatic activity. Both the association for rs10044354 (meta-analysis: odds ratio (OR) [95% CI]=2.07[1.58-2.71], P = 1.24 × 10(-7)) and rs2287987 (OR[95% CI]: =2.01[1.51-2.67], P = 1.41 × 10(-6)) replicated and showed consistent direction of effect in an independent Spanish cohort of 46 cases and 2103 controls. In both cohorts, the combined rs2287987-rs10044354 haplotype associated with Birdshot more strongly than either variant alone [meta-analysis: P=3.9 × 10(-9)]. Finally, we observed that ERAP2 protein expression is dependent on the ERAP1 background across three European populations (n = 3353). In conclusion, a functionally distinct combination of ERAP1 and ERAP2 are a hallmark of Birdshot and provide rationale for strategies designed to correct ERAP function for treatment of Birdshot and MHC-I-opathies more broadly.

Gene-based GWAS analysis for consecutive studies of GEFOS

By integrating the multilevel biological evidence and bioinformatics analyses, the present study represents a systemic endeavor to identify BMD-associated genes and their roles in skeletal metabolism.

INTRODUCTION

Single-nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) have already identified about 100 loci associated with bone mineral density (BMD), but these loci only explain a small proportion of heritability to osteoporosis risk. In the present study, we performed a gene-based analysis of the largest GWASs in the bone field to identify additional BMD-associated genes.

METHODS

BMD-associated genes were identified by combining the summary statistic P values of SNPs across individual genes in the two consecutive meta-analyses of GWASs from the Genetic Factors for Osteoporosis (GEFOS) studies. The potential functionality of these genes to bone was partially assessed by differential gene expression analysis. Additionally, the consistency of the identification of potential bone mineral density (BMD)-associated variants were evaluated by estimating the correlation of the P values of the same single-nucleotide polymorphisms (SNPs)/genes between the two consecutive Genetic Factors for Osteoporosis Studies (GEFOS) with largely overlapping samples.

RESULTS

Compared to the SNP-based analysis, the gene-based strategy identified additional BMD-associated genes with genome-wide significance and increased their mutual replication between the two GEFOS datasets. Among these BMD-associated genes, three novel genes (UBTF, AAAS, and C11orf58) were partially validated at the gene expression level. The correlation analysis presented a moderately high between-study consistency of potential BMD-associated variants.

CONCLUSIONS

Gene-based analysis as a supplementary strategy to SNP-based genome-wide association studies, when applied here, is shown that it helped identify some novel BMD-associated genes. In addition to its empirically increased statistical power, gene-based analysis also provides a higher testing stability for identification of BMD genes.

Childhood asthma is associated with COPD and known asthma variants in COPDGene: a genome-wide association study

BACKGROUND

Childhood asthma is strongly influenced by genetics and is a risk factor for reduced lung function and chronic obstructive pulmonary disease (COPD) in adults. This study investigates self-reported childhood asthma in adult smokers from the COPDGene Study. We hypothesize that childhood asthma is associated with decreased lung function, increased risk for COPD, and that a genome-wide association study (GWAS) will show association with established asthma variants.

METHODS

We evaluated current and former smokers ages 45-80 of non-Hispanic white (NHW) or African American (AA) race. Childhood asthma was defined by self-report of asthma, diagnosed by a medical professional, with onset at < 16 years or during childhood. Subjects with a history of childhood asthma were compared to those who never had asthma based on lung function, development of COPD, and genetic variation. GWAS was performed in NHW and AA populations, and combined in meta-analysis. Two sets of established asthma SNPs from published literature were examined for association with childhood asthma.

RESULTS

Among 10,199 adult smokers, 730 (7%) reported childhood asthma and 7493 (73%) reported no history of asthma. Childhood asthmatics had reduced lung function and increased risk for COPD (OR 3.42, 95% CI 2.81-4.18). Genotype data was assessed for 8031 subjects. Among NHWs, 391(7%) had childhood asthma, and GWAS identified one genome-wide significant association in KIAA1958 (rs59289606, p = 4.82 × 10- 8). Among AAs, 339 (12%) had childhood asthma. No SNPs reached genome-wide significance in the AAs or in the meta-analysis combining NHW and AA subjects; however, potential regions of interest were identified. Established asthma SNPs were examined, seven from the NHGRI-EBI database and five with genome-wide significance in the largest pediatric asthma GWAS. Associations were found in the current childhood asthma GWAS with known asthma loci in IL1RL1, IL13, LINC01149, near GSDMB, and in the C11orf30-LRRC32 region (Bonferroni adjusted p < 0.05 for all comparisons).

CONCLUSIONS

Childhood asthmatics are at increased risk for COPD. Defining asthma by self-report is valid in populations at risk for COPD, identifying subjects with clinical and genetic characteristics known to associate with childhood asthma. This has potential to improve clinical understanding of asthma-COPD overlap (ACO) and enhance future research into ACO-specific treatment regimens.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT00608764 (Active since January 28, 2008).

Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs

Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).

Elevated expression of a minor isoform of ANK3 is a risk factor for bipolar disorder

Ankyrin-3 (ANK3) is one of the few genes that have been consistently identified as associated with bipolar disorder by multiple genome-wide association studies. However, the exact molecular basis of the association remains unknown. A rare loss-of-function splice-site SNP (rs41283526*G) in a minor isoform of ANK3 (incorporating exon ENSE00001786716) was recently identified as protective of bipolar disorder and schizophrenia. This suggests that an elevated expression of this isoform may be involved in the etiology of the disorders. In this study, we used novel approaches and data sets to test this hypothesis. First, we strengthen the statistical evidence supporting the allelic association by replicating the protective effect of the minor allele of rs41283526 in three additional large independent samples (meta-analysis p-values: 6.8E-05 for bipolar disorder and 8.2E-04 for schizophrenia). Second, we confirm the hypothesis that both bipolar and schizophrenia patients have a significantly higher expression of this isoform than controls (p-values: 3.3E-05 for schizophrenia and 9.8E-04 for bipolar type I). Third, we determine the transcription start site for this minor isoform by Pacific Biosciences sequencing of full-length cDNA and show that it is primarily expressed in the corpus callosum. Finally, we combine genotype and expression data from a large Norwegian sample of psychiatric patients and controls, and show that the risk alleles in ANK3 identified by bipolar disorder GWAS are located near the transcription start site of this isoform and are significantly associated with its elevated expression. Together, these results point to the likely molecular mechanism underlying ANK3´s association with bipolar disorder.

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

INFERNO: inferring the molecular mechanisms of noncoding genetic variants

The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, affecting regulatory elements including transcriptional enhancers. However, characterizing their effects requires the integration of GWAS results with context-specific regulatory activity and linkage disequilibrium annotations to identify causal variants underlying noncoding association signals and the regulatory elements, tissue contexts, and target genes they affect. We propose INFERNO, a novel method which integrates hundreds of functional genomics datasets spanning enhancer activity, transcription factor binding sites, and expression quantitative trait loci with GWAS summary statistics. INFERNO includes novel statistical methods to quantify empirical enrichments of tissue-specific enhancer overlap and to identify co-regulatory networks of dysregulated long noncoding RNAs (lncRNAs). We applied INFERNO to two large GWAS studies. For schizophrenia (36,989 cases, 113,075 controls), INFERNO identified putatively causal variants affecting brain enhancers for known schizophrenia-related genes. For inflammatory bowel disease (IBD) (12,882 cases, 21,770 controls), INFERNO found enrichments of immune and digestive enhancers and lncRNAs involved in regulation of the adaptive immune response. In summary, INFERNO comprehensively infers the molecular mechanisms of causal noncoding variants, providing a sensitive hypothesis generation method for post-GWAS analysis. The software is available as an open source pipeline and a web server.

Joint genome-wide association study of progressive supranuclear palsy identifies novel susceptibility loci and genetic correlation to neurodegenerative diseases

Background

Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease for which the genetic contribution is incompletely understood.

Methods

We conducted a joint analysis of 5,523,934 imputed SNPs in two newly-genotyped progressive supranuclear palsy cohorts, primarily derived from two clinical trials (Allon davunetide and NNIPPS riluzole trials in PSP) and a previously published genome-wide association study (GWAS), in total comprising 1646 cases and 10,662 controls of European ancestry.

Results

We identified 5 associated loci at a genome-wide significance threshold P < 5 × 10^− 8, including replication of 3 loci from previous studies and 2 novel loci at 6p21.1 and 12p12.1 (near RUNX2 and SLCO1A2, respectively). At the 17q21.31 locus, stepwise regression analysis confirmed the presence of multiple independent loci (localized near MAPT and KANSL1). An additional 4 loci were highly suggestive of association (P < 1 × 10^− 6). We analyzed the genetic correlation with multiple neurodegenerative diseases, and found that PSP had shared polygenic heritability with Parkinson’s disease and amyotrophic lateral sclerosis.

Conclusions

In total, we identified 6 additional significant or suggestive SNP associations with PSP, and discovered genetic overlap with other neurodegenerative diseases. These findings clarify the pathogenesis and genetic architecture of PSP.

Electronic supplementary material

The online version of this article (10.1186/s13024-018-0270-8) contains supplementary material, which is available to authorized users.

Genetik der Alzheimer-Krankheit

Die Alzheimer-Erkrankung („Alzheimer’s disease“, AD) ist die häufigste Ursache der neurodegenerativen Demenzen. Im Gegensatz zu monogenen und meist frühmanifesten Formen der AD, welche auf hochpenetrante Mutationen in den Genen APP, PSEN1 und PSEN2 zurückzuführen sind, wird die Suszeptibilität für die sporadische, oft spätmanifeste Form der AD durch eine komplexe Wechselwirkung zwischen genetischen und epigenetischen Faktoren wie auch umwelt- und lebensstilbedingten Faktoren bestimmt. Obgleich APOE ε4 der stärkste genetische Risikofaktor für die AD ist, macht der Effekt des APOE ε4 lediglich 27,3 % der geschätzten Heritabilität von 58–79 % aus. Durch den kontinuierlichen technischen Fortschritt von GWAS (genomweite Assoziationsstudien) und automatisierten Sequenziermethoden der nächsten Generation gelingt es Wissenschaftlern in groß angelegten Kollaborationen sukzessive die fehlende Heritabilität aufzudecken. Wichtige Erkenntnisse aus GWAS und Signalweganalysen suggerieren, dass Mikroglia, die residenten Immunzellen des ZNS, eine entscheidende Rolle bei der Pathogenese der AD spielen. Eine beachtliche Anzahl der in genetischen Studien identifizierten Risikogene weisen immunsystembezogene Funktionen auf und werden in höchstem Maße von Mikroglia exprimiert. Durch die Beschreibung von Risikovarianten in CR1, CLU, SPI1, CD33, MS4A, ABCA7, EPHA1, HLA-DRB5/1, INPP5D, TYROBP, TREM2, PLCG2 und ABI3 nimmt die Mikroglia vermittelte Immunantwort bei der Pathogenese der AD eine zentrale Rolle ein. Von besonderer Bedeutung könnte sein, dass die PLCγ2-Variante p.P522R einen protektiven Effekt auf die LOAD („late-onset“ AD; spätmanifeste Form der AD) ausübt und als Enzym ein klassisches Ziel für eine therapeutische Modulation von komplexen Formen der AD darstellt.

Evolution of heat-shock protein expression underlying adaptive responses to environmental stress

Heat-shock proteins (Hsps) and their cognates are primary mitigators of cell stress. With increasingly severe impacts of climate change and other human modifications of the biosphere, the ability of the heat-shock system to affect evolutionary fitness in environments outside the laboratory and to evolve in response is topic of growing importance. Since the last major reviews, several advances have occurred. First, demonstrations of the heat-shock response outside the laboratory now include many additional taxa and environments. Many of these demonstrations are only correlative, however. More importantly, technical advances in "omic" quantification of nucleic acids and proteins, genomewide association analysis, and manipulation of genes and their expression have enabled the field to move beyond correlation. Several consequent advances are already evident: The pathway from heat-shock gene expression to stress tolerance in nature can be extremely complex, mediated through multiple biological processes and systems, and even multiple species. The underlying genes are more numerous, diverse and variable than previously appreciated, especially with respect to their regulatory variation and epigenetic changes. The impacts and limitations (e.g., due to trade-offs) of natural selection on these genes have become more obvious and better established. At last, as evolutionary capacitors, Hsps may have distinctive impacts on the evolution of other genes and ecological consequences.

Rigor and reproducibility in genetic research on eating disorders

OBJECTIVE

We explored both within-method and between-method rigor and reproducibility in the field of eating disorders genetics.

METHOD

We present critical evaluation and commentary on component methods of genetic research (family studies, twin studies, molecular genetic studies) and discuss both successful and unsuccessful efforts in the field.

RESULTS

Eating disorders genetics has had a number of robust results that converge across component methodologies. Familial aggregation of eating disorders, twin-based heritability estimates of eating disorders, and genome-wide association studies (GWAS) all point toward a substantial role for genetics in eating disorders etiology and support the premise that genes do not act alone. Candidate gene and linkage studies have been less informative historically.

DISCUSSION

The eating disorders field has entered the GWAS era with studies of anorexia nervosa. Continued growth of sample sizes is essential for rigorous discovery of actionable variation. Molecular genetic studies of bulimia nervosa, binge-eating disorder, and other eating disorders are virtually nonexistent and lag seriously behind other major psychiatric disorders. Expanded efforts are necessary to reveal the fundamental biology of eating disorders, inform clinical practice, and deliver new therapeutic targets.

Transethnic, Genome-Wide Analysis Reveals Immune-Related Risk Alleles and Phenotypic Correlates in Pediatric Steroid-Sensitive Nephrotic Syndrome

Background Steroid-sensitive nephrotic syndrome (SSNS) is a childhood disease with unclear pathophysiology and genetic architecture. We investigated the genomic basis of SSNS in children recruited in Europe and the biopsy-based North American NEPTUNE cohort.Methods We performed three ancestry-matched, genome-wide association studies (GWAS) in 273 children with NS (Children Cohort Nephrosis and Virus [NEPHROVIR] cohort: 132 European, 56 African, and 85 Maghrebian) followed by independent replication in 112 European children, transethnic meta-analysis, and conditional analysis. GWAS alleles were used to perform glomerular cis-expression quantitative trait loci studies in 39 children in the NEPTUNE cohort and epidemiologic studies in GWAS and NEPTUNE (97 children) cohorts.Results Transethnic meta-analysis identified one SSNS-associated single-nucleotide polymorphism (SNP) rs1063348 in the 3' untranslated region of HLA-DQB1 (P=9.3×10-23). Conditional analysis identified two additional independent risk alleles upstream of HLA-DRB1 (rs28366266, P=3.7×10-11) and in the 3' untranslated region of BTNL2 (rs9348883, P=9.4×10-7) within introns of HCG23 and LOC101929163 These three risk alleles were independent of the risk haplotype DRB1*07:01-DQA1*02:01-DQB1*02:02 identified in European patients. Increased burden of risk alleles across independent loci was associated with higher odds of SSNS. Increased burden of risk alleles across independent loci was associated with higher odds of SSNS, with younger age of onset across all cohorts, and with increased odds of complete remission across histologies in NEPTUNE children. rs1063348 associated with decreased glomerular expression of HLA-DRB1, HLA-DRB5, and HLA-DQB1.Conclusions Transethnic GWAS empowered discovery of three independent risk SNPs for pediatric SSNS. Characterization of these SNPs provide an entry for understanding immune dysregulation in NS and introducing a genomically defined classification.

Statistical methods to detect pleiotropy in human complex traits

In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.

A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework

BACKGROUND

There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr).

RESULTS

We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power.

CONCLUSIONS

With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.

Machine Learning and Radiogenomics: Lessons Learned and Future Directions

Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Performance of epistasis detection methods in semi-simulated GWAS

BACKGROUND

Part of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.

RESULTS

GBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.

CONCLUSION

This study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.

Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry

In the 12 years since the publication of the first Consensus Paper of the WFSBP on biomarkers of neurodegenerative dementias, enormous advancement has taken place in the field, and the Task Force takes now the opportunity to extend and update the original paper. New concepts of Alzheimer's disease (AD) and the conceptual interactions between AD and dementia due to AD were developed, resulting in two sets for diagnostic/research criteria. Procedures for pre-analytical sample handling, biobanking, analyses and post-analytical interpretation of the results were intensively studied and optimised. A global quality control project was introduced to evaluate and monitor the inter-centre variability in measurements with the goal of harmonisation of results. Contexts of use and how to approach candidate biomarkers in biological specimens other than cerebrospinal fluid (CSF), e.g. blood, were precisely defined. Important development was achieved in neuroimaging techniques, including studies comparing amyloid-β positron emission tomography results to fluid-based modalities. Similarly, development in research laboratory technologies, such as ultra-sensitive methods, raises our hopes to further improve analytical and diagnostic accuracy of classic and novel candidate biomarkers. Synergistically, advancement in clinical trials of anti-dementia therapies energises and motivates the efforts to find and optimise the most reliable early diagnostic modalities. Finally, the first studies were published addressing the potential of cost-effectiveness of the biomarkers-based diagnosis of neurodegenerative disorders.

Long- and short-term outcomes in renal allografts with deceased donors: A large recipient and donor genome-wide association study

Improvements in immunosuppression have modified short-term survival of deceased-donor allografts, but not their rate of long-term failure. Mismatches between donor and recipient HLA play an important role in the acute and chronic allogeneic immune response against the graft. Perfect matching at clinically relevant HLA loci does not obviate the need for immunosuppression, suggesting that additional genetic variation plays a critical role in both short- and long-term graft outcomes. By combining patient data and samples from supranational cohorts across the United Kingdom and European Union, we performed the first large-scale genome-wide association study analyzing both donor and recipient DNA in 2094 complete renal transplant-pairs with replication in 5866 complete pairs. We studied deceased-donor grafts allocated on the basis of preferential HLA matching, which provided some control for HLA genetic effects. No strong donor or recipient genetic effects contributing to long- or short-term allograft survival were found outside the HLA region. We discuss the implications for future research and clinical application.

Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects

Motivation

Meta-analysis is essential to combine the results of genome-wide association studies (GWASs). Recent large-scale meta-analyses have combined studies of different ethnicities, environments and even studies of different related phenotypes. These differences between studies can manifest as effect size heterogeneity. We previously developed a modified random effects model (RE2) that can achieve higher power to detect heterogeneous effects than the commonly used fixed effects model (FE). However, RE2 cannot perform meta-analysis of correlated statistics, which are found in recent research designs, and the identified variants often overlap with those found by FE.

Results

Here, we propose RE2C, which increases the power of RE2 in two ways. First, we generalized the likelihood model to account for correlations of statistics to achieve optimal power, using an optimization technique based on spectral decomposition for efficient parameter estimation. Second, we designed a novel statistic to focus on the heterogeneous effects that FE cannot detect, thereby, increasing the power to identify new associations. We developed an efficient and accurate p-value approximation procedure using analytical decomposition of the statistic. In simulations, RE2C achieved a dramatic increase in power compared with the decoupling approach (71% vs. 21%) when the statistics were correlated. Even when the statistics are uncorrelated, RE2C achieves a modest increase in power. Applications to real genetic data supported the utility of RE2C. RE2C is highly efficient and can meta-analyze one hundred GWASs in one day.

Availability and implementation

The software is freely available at http://software.buhmhan.com/RE2C.

Supplementary information

Supplementary data are available at Bioinformatics online.

An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data

We introduce a strategy for creating virtual control groups-cases generated through computer algorithms that, when aggregated, may serve as experimental comparators where live controls are difficult to recruit, such as when programs are widely disseminated and randomization is not feasible. We integrated and harmonized data from eight archived longitudinal adolescent-focused data sets spanning the decades from 1980 to 2010. Collectively, these studies examined numerous psychosocial variables and assessed past 30-day alcohol, cigarette, and marijuana use. Additional treatment and control group data from two archived randomized control trials were used to test the virtual control algorithm. Both randomized controlled trials (RCTs) assessed intentions, normative beliefs, and values as well as past 30-day alcohol, cigarette, and marijuana use. We developed an algorithm that used percentile scores from the integrated data set to create age- and gender-specific latent psychosocial scores. The algorithm matched treatment case observed psychosocial scores at pretest to create a virtual control case that figuratively "matured" based on age-related changes, holding the virtual case's percentile constant. Virtual controls matched treatment case occurrence, eliminating differential attrition as a threat to validity. Virtual case substance use was estimated from the virtual case's latent psychosocial score using logistic regression coefficients derived from analyzing the treatment group. Averaging across virtual cases created group estimates of prevalence. Two criteria were established to evaluate the adequacy of virtual control cases: (1) virtual control group pretest drug prevalence rates should match those of the treatment group and (2) virtual control group patterns of drug prevalence over time should match live controls. The algorithm successfully matched pretest prevalence for both RCTs. Increases in prevalence were observed, although there were discrepancies between live and virtual control outcomes. This study provides an initial framework for creating virtual controls using a step-by-step procedure that can now be revised and validated using other prevention trial data.

XPAT: a toolkit to conduct cross-platform association studies with heterogeneous sequencing datasets

High-throughput sequencing data are increasingly being made available to the research community for secondary analyses, providing new opportunities for large-scale association studies. However, heterogeneity in target capture and sequencing technologies often introduce strong technological stratification biases that overwhelm subtle signals of association in studies of complex traits. Here, we introduce the Cross-Platform Association Toolkit, XPAT, which provides a suite of tools designed to support and conduct large-scale association studies with heterogeneous sequencing datasets. XPAT includes tools to support cross-platform aware variant calling, quality control filtering, gene-based association testing and rare variant effect size estimation. To evaluate the performance of XPAT, we conducted case-control association studies for three diseases, including 783 breast cancer cases, 272 ovarian cancer cases, 205 Crohn disease cases and 3507 shared controls (including 1722 females) using sequencing data from multiple sources. XPAT greatly reduced Type I error inflation in the case-control analyses, while replicating many previously identified disease-gene associations. We also show that association tests conducted with XPAT using cross-platform data have comparable performance to tests using matched platform data. XPAT enables new association studies that combine existing sequencing datasets to identify genetic loci associated with common diseases and other complex traits.

Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy

In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.

A Simple Test Identifies Selection on Complex Traits

Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome-wide association studies and selection-mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique uses additive-effects estimates from all available markers, and relates these estimates to allele-frequency change over time. Using this information, we generate a composite statistic, denoted [Formula: see text] which can be used to test for significant evidence of selection on a trait. Our test requires pre- and postselection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that [Formula: see text] is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

The Value of Biosamples in Smoking Cessation Trials: A Review of Genetic, Metabolomic, and Epigenetic Findings

Introduction

Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral.

Aims and Methods

To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment.

Results

These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior.

Conclusions

The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation.

Implications

Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials.

Association between angiotensin II receptor type 1 A1166C polymorphism and chronic kidney disease

Studies of the association between angiotensin II receptor type 1 A1166C (AGTR1 A1166C) polymorphism and chronic kidney disease (CKD) risk have yielded conflicting results. We conducted a combined case-control study and meta-analysis to better define this association. The case-control study included 634 end-stage renal disease (ESRD) patients and 739 healthy controls. AGTR1 A1166C genotype was determined using polymerase chain reaction and iPLEX Gold SNP genotyping methods. The meta-analysis included 24 studies found in the PubMed and Cochrane Library databases. Together, the case-control study and meta-analysis included 36 populations (7,918 cases and 6,905 controls). We found no association between the C allele and ESRD (case-control study: OR: 1.02, 95% CI: 0.77–1.37; meta-analysis: OR: 1.07; 95% CI: 0.97–1.18). Co-dominant, dominant, and recessive model results were also not significant. No known environmental factors moderated the effect of AGTR1 A1166C on CKD in our gene-environment interaction analysis. Sensitivity analysis showed an AGTR1 A1166C-CKD association in Indian populations (OR: 1.46, 95% CI: 1.26–1.69), but not in East Asian or Caucasian populations. Additional South Asian studies will be required to confirm the potential role of this polymorphism in CKD.

Systematic reviews and cancer research: a suggested stepwise approach

Systematic reviews, with or without meta-analysis, play an important role today in synthesizing cancer research and are frequently used to guide decision-making. However, there is now an increase in the number of systematic reviews on the same topic, thereby necessitating a systematic review of previous systematic reviews. With a focus on cancer, the purpose of this article is to provide a practical, stepwise approach for systematically reviewing the literature and publishing the results. This starts with the registration of a protocol for a systematic review of previous systematic reviews and ends with the publication of an original or updated systematic review, with or without meta-analysis, in a peer-reviewed journal. Future directions as well as potential limitations of the approach are also discussed. It is hoped that the stepwise approach presented in this article will be helpful to both producers and consumers of cancer-related systematic reviews and will contribute to the ultimate goal of preventing and treating cancer.

Statistical methods to detect novel genetic variants using publicly available GWAS summary data

We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study.

AQR is a novel type 2 diabetes-associated gene that regulates signaling pathways critical for glucose metabolism

Type 2 diabetes mellitus (T2DM) is a common metabolic disease influenced by both genetic and environmental factors. In this study, we performed an in-house genotyping and meta-analysis study using three independent GWAS datasets of T2DM and found that rs3743121, located 1 kb downstream of AQR, was a novel susceptibility SNP associated with T2DM. The risk allele C of rs3743121 was correlated with the increased expression of AQR in white blood cells, similar to that observed in T2DM models. The knockdown of AQR in HepG2 facilitated the glucose uptake, decreased the expression level of PCK2, increased the phosphorylation of GSK-3β, and restored the insulin sensitivity. Furthermore, the suppression of AQR inhibited the mTOR pathway and the protein ubiquitination process. Our study suggests that AQR is a novel type 2 diabetes-associated gene that regulates signaling pathways critical for glucose metabolism.

Use of meta-analyses and joint analyses to select variants in whole genome sequences for genomic evaluation: An application in milk production of French dairy cattle breeds

As a result of the 1000 Bull Genome Project, it has become possible to impute millions of variants, with many of these potentially causative for traits of interest, for thousands of animals that have been genotyped with medium-density chips. This enormous source of data opens up very interesting possibilities for the inclusion of these variants in genomic evaluations. However, for computational reasons, it is not possible to include all variants in genomic evaluation procedures. One potential approach could be to select the most relevant variants based on the results of genome-wide association studies (GWAS); however, the identification of causative mutations is still difficult with this method, partly because of weak imputation accuracy for rare variants. To address this problem, this study assesses the ability of different approaches based on multi-breed GWAS (joint and meta-analyses) to identify single-nucleotide polymorphisms (SNP) for use in genomic evaluation in the 3 main French dairy cattle breeds. A total of 6,262 Holstein bulls, 2,434 Montbéliarde bulls, and 2,175 Normande bulls with daughter yield deviations for 5 milk production traits were imputed for 27 million variants. Within-breed and joint (including all 3 breeds) GWAS were performed and 3 models of meta-analysis were tested: fixed effect, random effect, and Z-score. Comparison of the results of within- and multi-breed GWAS showed that most of the quantitative trait loci identified using within-breed approaches were also found with multi-breed methods. However, the most significant variants identified in each region differed depending on the method used. To determine which approach highlighted the most predictive SNP for each trait, we used a marker-assisted best unbiased linear prediction model to evaluate lists of SNP generated by the different GWAS methods; each list contained between 25 and 2,000 candidate variants per trait, which were identified using a single within- or multi-breed GWAS approach. Among all the multi-breed methods tested in this study, variant selection based on meta-analysis (fixed effect) resulted in the most-accurate genomic evaluation (+1 to +3 points compared with other multi-breed approaches). However, the accuracies of genomic evaluation were always better when variants were selected using the results of within-breed GWAS. As has generally been found in studies of quantitative trait loci, these results suggest that part of the genetic variance of milk production traits is breed specific in Holstein, Montbéliarde, and Normande cattle.

ANK3 gene polymorphisms and bipolar disorder: a meta-analysis

OBJECTIVE

Converging evidence has suggested ankyrin 3 (ANK3) as a risk gene for bipolar disorder (BD). However, association studies investigating its genetic variants and BD susceptibility have reported inconsistent results. In the present meta-analysis, we aimed to establish whether ANK3 single nucleotide polymorphisms (SNPs) confer increased risk for BD.

METHODS

PubMed, Medline, PsycINFO, Embase, and Scopus were searched for literature published up to January 2017. Fourteen case-control studies met our eligibility criteria. We targeted ANK3 SNPs that have been reported by three or more studies to be included in the current meta-analysis, resulting in a final list of four SNPs: rs10994336, rs9804190, rs10994397, and rs1938526. Odds ratios (ORs) for the allele model were calculated using a random effect model as a measure of association. Additional experimental characteristics and between-study heterogeneity were explored using sensitivity test, subgroup analysis, and meta-regression techniques. Publication bias was also assessed using Egger's test and rank correlation test.

RESULTS

Overall, a significant association was found between BD and rs10994336 (OR=1.18; 95% confidence interval: 1.06-1.31; P=0.0027) as well as rs1938526 (OR=1.16; 95% confidence interval: 1.06-1.28; P=0.0016). Subsequent sensitivity analysis and publication bias test reaffirmed the stability and consistency of these results.

CONCLUSION

The current meta-analysis provides corroborating evidence suggesting two ANK3 SNPs are associated with an increased susceptibility for developing BD. However, broader coverage is needed on less explored SNPs to further elucidate the genetic effect of other ANK3 variants that may harbor potential BD risk.

Meta-Analysis of Common and Rare Variants

Meta-analysis is a statistical technique that is widely used for improving the power to detect associations, by synthesizing data from independent studies, and is extensively used in the genomic analyses of complex traits. Estimates from different studies are combined and the results effectively provide the power of a much larger study. Meta-analysis also has the potential of discovering heterogeneity in the effects among the different studies. This chapter provides an overview of the methods used for meta-analysis of common and rare single variants and also for gene/region-based analyses; common variants are mainly identified via genome-wide association studies (GWAS) and rare variants through various types of sequencing experiments.

Introduction to Epidemiological Studies

The basic epidemiological study designs are cross-sectional, case-control, and cohort studies. Cross-sectional studies provide a snapshot of a population by determining both exposures and outcomes at one time point. Cohort studies identify the study groups based on the exposure and, then, the researchers follow up study participants to measure outcomes. Case-control studies identify the study groups based on the outcome, and the researchers retrospectively collect the exposure of interest. The present chapter discusses the basic concepts, the advantages, and disadvantages of epidemiological study designs and their systematic biases, including selection bias, information bias, and confounding.

Identification of Potential MicroRNA Biomarkers by Meta-analysis

Meta-analysis statistically assesses the results (e.g., effect sizes) across independent studies that are conducted in accordance with similar protocols and objectives. Current genomic meta-analysis studies do not perform extensive re-analysis on raw data because full data access would not be commonplace, although the best practice of open research for sharing well-formed data have been actively advocated. This chapter describes a simple and easy-to-follow method for conducting meta-analysis of multiple studies without using raw data. Examples for meta-analysis of microRNAs (miRNAs) are provided to illustrate the method. MiRNAs are potential biomarkers for early diagnosis and epigenetic monitoring of diseases. A number of miRNAs have been identified to be differentially expressed, i.e., overexpressed or underexpressed, under diseased states but only a small fraction would be highly effective biomarkers or therapeutic targets of diseases. The meta-analysis method as described in this chapter aims to identify the miRNAs that are consistently found dysregulated across independent studies as biomarkers.

Genome-wide association studies, meta-analyses and derived gene network for meat quality and carcass traits in pigs

A large number of quantitative trait loci (QTL) for meat quality and carcass traits has been reported in pigs over the past 20 years. However, few QTL have been validated and the biological meaning of the genes associated to these QTL has been underexploited. In this context, a meta-analysis was performed to compare the significant markers with meta-QTL previously reported in literature. Genome association studies were performed for 12 traits, from which 144 SNPs were found out to be significant (P < 0.05). They were validated in the meta-analysis and used to build the Association Weight Matrix, a matrix framework employed to investigate co-association of pairwise SNP across phenotypes enabling to derive a gene network. A total of 45 genes were selected from the Association Weight Matrix analysis, from which 25 significant transcription factors were identified and used to construct the networks associated to meat quality and carcass traits. These networks allowed the identification of key transcription factors, such as SOX5 and NKX2–5, gene–gene interactions (e.g. ATP5A1, JPH1, DPT and NEDD4) and pathways related to the regulation of adipose tissue metabolism and skeletal muscle development. Validated SNPs and knowledge of key genes driving these important industry traits might assist future strategies in pig breeding.

Methods and Tools in Genome-wide Association Studies

Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.