• Aged, 80 and over
• Humans
• Longevity/genetics
• Aging/genetics
• Signal Transduction
• Centenarians
• Alleles

• R01 HG008153 NHGRI NIH HHS
• R01 AG061155 NIA NIH HHS
• R01 AG060747 NIA NIH HHS
• R01 AG057909 NIA NIH HHS
• P01 AG017242 NIA NIH HHS
• U19 AG056278 NIA NIH HHS

• Antidepressants
• Depression
• Drug response
• Hispanic
• Pharmacogenomics
• Whole-exome genotyping

• Antidepressive Agents/therapeutic use
• Depressive Disorder, Major/drug therapy
• Depressive Disorder, Major/genetics
• Double-Blind Method
• Humans
• Mexican Americans/genetics
• Pharmacogenetics
• Prospective Studies
• Treatment Outcome

• R01 MH106489 NIMH NIH HHS
• U01 GM061394 NIGMS NIH HHS

• big data
• spark
• whole-genome sequence
• rare-variants

The identification of novel genetic variants contributing to the widespread in the age of onset (AOO) of Alzheimer’s disease (AD) could aid in the prognosis and/or development of new therapeutic strategies focused on early interventions. We recruited 78 individuals with AD from the Paisa genetic isolate in Antioquia, Colombia. These individuals belong to the world largest multigenerational and extended pedigree segregating AD as a consequence of a dominant fully penetrant mutation in the PSEN1 gene and exhibit an AOO ranging from the early 1930s to the late 1970s. To shed light on the genetic underpinning that could explain the large spread of the age of onset (AOO) of AD, 64 single nucleotide polymorphisms (SNP) associated with neuroanatomical, cardiovascular, and cognitive measures in AD were genotyped. Standard quality control and filtering procedures were applied, and single- and multi-locus linear mixed-effects models were used to identify AOO-associated SNPs. A full two-locus interaction model was fitted to define how identified SNPs interact to modulate AOO. We identified two key epistatic interactions between the APOE*E2 allele and SNPs ASTN2-rs7852878 and SNTG1-rs16914781 that delay AOO by up to ~ 8 years (95% CI 3.2–12.7, P = 1.83 × 10⁻³) and ~ 7.6 years (95% CI 3.3–11.8, P = 8.69 × 10⁻⁴), respectively, and validated our previous finding indicating that APOE*E2 delays AOO of AD in PSEN1 E280 mutation carriers. This new evidence involving APOE*E2 as an AOO delayer could be used for developing precision medicine approaches and predictive genomics models to potentially determine AOO in individuals genetically predisposed to AD.

• APOE*E2
• ASTN2
• Age of onset
• Alzheimer’s disease
• Extreme phenotypes
• Genetic isolate
• PSEN1
• SNTG1

• Age of Onset
• Alleles
• Alzheimer Disease/genetics
• Cardiovascular System/pathology
• Cognition
• Epistasis, Genetic
• Female
• Genetic Predisposition to Disease
• Genome, Human
• Humans
• Male
• Neuronal Plasticity/genetics
• Polymorphism, Single Nucleotide/genetics

• COLCIENCIAS
• FOFICO
• Australian National University

• microrna
• whole genome sequencing
• 3’ untranslated regions
• single nucleotide variant
• african american
• cardiovascular disease

• 3' Untranslated Regions/genetics
• Adult
• Black or African American/genetics
• Cardiovascular Diseases/ethnology
• Cardiovascular Diseases/genetics
• Female
• Genetic Predisposition to Disease
• Genotyping Techniques
• Humans
• Male
• MicroRNAs/genetics
• Middle Aged
• Polymorphism, Single Nucleotide
• Risk Factors
• Whole Genome Sequencing

• HHSN268201700002C NHLBI NIH HHS
• HHSN268201700001I NHLBI NIH HHS
• HHSN268201700004I NHLBI NIH HHS
• HHSN268201000021C NHLBI NIH HHS
• HHSN268201700004C NHLBI NIH HHS
• HHSN268201700003I NHLBI NIH HHS
• U54HG003273 NHGRI NIH HHS
• RC2 HL102419 NHLBI NIH HHS
• 5RC2HL102419 NHLBI NIH HHS
• U54 HG003273 NHGRI NIH HHS
• HHSN268201700005C NHLBI NIH HHS
• HHSN268201700001C NHLBI NIH HHS
• HHSN268201700003C NHLBI NIH HHS
• HHSN268201700002I NHLBI NIH HHS
• HHSN268201700005I NHLBI NIH HHS
• UM1 HG008898 NHGRI NIH HHS
• HHSN268201700021C NHLBI NIH HHS
• UM1HG008898 NHGRI NIH HHS

A region-specific method, NTR (non-threshold rare) variant detection method, was developed—it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

• Genetic Association Studies
• Genetic Diseases, Inborn/genetics
• Humans
• Linkage Disequilibrium

• National Science Council
• China Medical University (TW)

• Platelets
• SNP
• exome
• genetic association
• platelet aggregation
• platelet reactivity

• Adult
• Case-Control Studies
• Depressive Disorder, Major/genetics
• Female
• Gene Expression
• Genetic Predisposition to Disease/genetics
• Genome-Wide Association Study
• Humans
• Los Angeles
• Male
• Mexican Americans/genetics
• Middle Aged
• Polymorphism, Single Nucleotide/genetics
• Risk Factors
• Stress, Psychological
• White People/genetics

• U01 GM061394 NIGMS NIH HHS

Despite thousands of genetic loci identified to date, a large proportion of genetic variation predisposing to complex disease and traits remains unaccounted for. Advances in sequencing technology enable focused explorations on the contribution of low-frequency and rare variants to human traits. Here we review experimental approaches and current knowledge on the contribution of these genetic variants in complex disease and discuss challenges and opportunities for personalised medicine.

• Alzheimer's disease
• E280A mutation
• G protein-coupled receptors
• PSEN1
• age of onset
• extreme phenotypes
• genetic isolates
• modifier genes
• whole exome analysis

• HPC
• MPI
• coalescence
• recombination
• sequential Markov coalescent

• Computational Biology/methods
• Computer Simulation
• Databases, Factual
• Exome
• Genetic Variation
• High-Throughput Nucleotide Sequencing
• Humans
• Liver/enzymology
• Oligonucleotide Array Sequence Analysis
• Phenotype

• U01 DK085501 NIDDK NIH HHS
• U01 DK085524 NIDDK NIH HHS
• U01 DK085545 NIDDK NIH HHS
• U01 DK085526 NIDDK NIH HHS
• U01 DK085584 NIDDK NIH HHS
• P30 DK020595 NIDDK NIH HHS

ADHD is the most common neuropsychiatric condition affecting individuals of all ages. Long‐term outcomes of affected individuals and association with severe comorbidities as SUD or conduct disorders are the main concern. Genetic associations have been extensively described. Multiple studies show that intronic variants harbored in the ADGRL3 (LPHN3) gene are associated with ADHD, especially associated with poor outcomes.

In this study, we evaluated this association in the Multimodal Treatment Study of children with ADHD (MTA), initiated as a 14‐month randomized clinical trial of 579 children diagnosed with DSM‐IV ADHD‐Combined Type (ADHD‐C), that transitioned to a 16‐year prospective observational follow‐up, and 289 classmates added at the 2‐year assessment to serve as a local normative comparison group (LNCG). Diagnostic evaluations at entry were based on the Diagnostic Interview Schedule for Children‐Parent (DISC‐P), which was repeated at several points over the years. For an add‐on genetic study, blood samples were collected from 232 in the MTA group and 139 in the LNCG.

For the 205 MTA participants, 14.6% retained the DISC‐P diagnosis of ADHD‐C in adolescence. For 127 LNCG participants, 88.2% remained undiagnosed by the DISC‐P. We genotyped 15 polymorphic SNP markers harbored in the ADGRL3 gene, and compared allele frequencies for the 30 cases with continued diagnosis of ADHD‐C in adolescence to the other participants. Replication of the association of rs2345039 ADGRL3 variant was observed (P value = 0.004, FDR corrected = 0.03; Odds ratio = 2.25, upper CI 1.28–3.97).

The detection of susceptibility conferred by ADGRL3 variants in the extreme phenotype of continued diagnosis of ADHD‐C from childhood to adolescence provides additional support that the association of ADGRL3 and ADHD is not spurious. Exploring genetic effects in longitudinal cohorts, in which refined, age‐dependent phenotypes are documented, is crucial to understand the natural history of ADHD.

• ADGRL3
• ADHD
• LPHN3
• MTA
• genetics

We previously reported age of onset (AOO) modifier genes in the world's largest pedigree segregating early-onset Alzheimer's disease (AD), caused by the p.Glu280Ala (E280A) mutation in the PSEN1 gene. Here we report the results of a targeted analysis of functional exonic variants in those AOO modifier genes in sixty individuals with PSEN1 E280A AD who were whole-exome genotyped for ~250,000 variants. Standard quality control, filtering, and annotation for functional variants were applied, and common functional variants located in those previously reported as AOO modifier loci were selected. Multiloci linear mixed-effects models were used to test the association between these variants and AOO. An exonic missense mutation in the G72 (DAOA) gene (rs2391191, P = 1.94 × 10⁻⁴, P_FDR = 9.34 × 10⁻³) was found to modify AOO in PSEN1 E280A AD. Nominal associations of missense mutations in the CLUAP1 (rs9790, P = 7.63 × 10⁻³, P_FDR = 0.1832) and EXOC2 (rs17136239, P = 0.0325, P_FDR = 0.391) genes were also found. Previous studies have linked polymorphisms in the DAOA gene with the occurrence of neuropsychiatric symptoms such as depression, apathy, aggression, delusions, hallucinations, and psychosis in AD. Our findings strongly suggest that this new conspicuous functional AOO modifier within the G72 (DAOA) gene could be pivotal for understanding the genetic basis of AD.

Genome-wide association studies (GWASs) have successfully uncovered thousands of robust associations between common variants and complex traits and diseases. Despite these successes, much of the heritability of these traits remains unexplained. Because low-frequency and rare variants are not tagged by conventional genome-wide genotyping arrays, they may represent an important and understudied component of complex trait genetics. In contrast to common variant GWASs, there are many different types of study designs, assays and analytic techniques that can be utilized for rare variant association studies (RVASs). In this review, we briefly present the different technologies available to identify rare genetic variants, including novel exome arrays. We also compare the different study designs for RVASs and argue that the best design will likely be phenotype-dependent. We discuss the main analytical issues relevant to RVASs, including the different statistical methods that can be used to test genetic associations with rare variants and the various bioinformatic approaches to predicting in silico biological functions for variants. Finally, we describe recent rare variant association findings, highlighting the unexpected conclusion that most rare variants have modest-to-small effect sizes on phenotypic variation. This observation has major implications for our understanding of the genetic architecture of complex traits in the context of the unexplained heritability challenge.

• Biofuel traits
• Domestication
• Forest biotechnology
• Natural variation
• Salicaceae

• Angiopoietin-Like Protein 4
• Angiopoietins/genetics
• Autistic Disorder/diagnosis
• Autistic Disorder/genetics
• Computational Biology
• Computer Simulation
• Developmental Disabilities/diagnosis
• Developmental Disabilities/genetics
• Evolution, Molecular
• Fingers/abnormalities
• Gene Frequency
• Genetic Variation
• Genome-Wide Association Study
• Humans
• Intellectual Disability/diagnosis
• Intellectual Disability/genetics
• Microcephaly/diagnosis
• Microcephaly/genetics
• Models, Genetic
• Muscle Hypotonia/diagnosis
• Muscle Hypotonia/genetics
• Myopia/diagnosis
• Myopia/genetics
• Obesity/diagnosis
• Obesity/genetics
• Retinal Degeneration
• Software
• Vesicular Transport Proteins/genetics

• R01 MH095797 NIMH NIH HHS
• U01 MH100233 NIMH NIH HHS
• MH100233 NIMH NIH HHS
• R01MH095797 NIMH NIH HHS

• Data Interpretation, Statistical
• Exome/genetics
• Genetic Association Studies/methods
• Genetic Variation
• Genetics, Population
• Genotype
• Humans
• Lipids/blood
• Lipids/genetics
• Meta-Analysis as Topic
• Models, Genetic
• Monte Carlo Method
• Research Design

• 282255 European Research Council
• N01WH42129-32 WHI NIH HHS
• R01HL117626 NHLBI NIH HHS
• R01HL107816 NHLBI NIH HHS
• N01WH32108-9 WHI NIH HHS
• N01WH42107-26 WHI NIH HHS
• R01EY022005 NEI NIH HHS
• MOP-82810 CIHR
• N01WH32122 WHI NIH HHS
• 090532 Wellcome Trust
• N01WH32105-6 WHI NIH HHS
• T32HL007208 NHLBI NIH HHS
• R01 HG007022 NHGRI NIH HHS
• R01 HL117626 NHLBI NIH HHS
• N01WH22110 WHI NIH HHS
• R01 HL107816 NHLBI NIH HHS
• R01 HL109946 NHLBI NIH HHS
• R21 DA040177 NIDA NIH HHS
• N01WH32100-2 WHI NIH HHS
• T32 HL007208 NHLBI NIH HHS
• R01 HG008983 NHGRI NIH HHS
• R01HG007022 NHGRI NIH HHS
• N01WH32111-13 WHI NIH HHS
• MOP-77682 CIHR
• N01WH32118-32119 WHI NIH HHS
• MOP-2380941 CIHR
• N01WH32115 WHI NIH HHS
• N01WH44221 WHI NIH HHS
• N01WH24152 WHI NIH HHS
• R01 EY022005 NEI NIH HHS

• Exome
• Genetics
• Inflammatory bowel disease
• Sequencing

• Animals
• Exome
• Genetic Predisposition to Disease
• Genetic Testing
• Genome-Wide Association Study
• Heredity
• Humans
• Inflammatory Bowel Diseases/diagnosis
• Inflammatory Bowel Diseases/genetics
• Inflammatory Bowel Diseases/therapy
• Pedigree
• Phenotype
• Predictive Value of Tests
• Prognosis
• Risk Factors
• Sequence Analysis, DNA

• U01 HG006830 NHGRI NIH HHS

The allelic architecture of complex traits is likely to be underpinned by a combination of multiple common frequency and rare variants. Targeted genotyping arrays and next-generation sequencing technologies at the whole-genome sequencing (WGS) and whole-exome scales (WES) are increasingly employed to access sequence variation across the full minor allele frequency (MAF) spectrum. Different study design strategies that make use of diverse technologies, imputation and sample selection approaches are an active target of development and evaluation efforts. Initial insights into the contribution of rare variants in common diseases and medically relevant quantitative traits point to low-frequency and rare alleles acting either independently or in aggregate and in several cases alongside common variants. Studies conducted in population isolates have been successful in detecting rare variant associations with complex phenotypes. Statistical methodologies that enable the joint analysis of rare variants across regions of the genome continue to evolve with current efforts focusing on incorporating information such as functional annotation, and on the meta-analysis of these burden tests. In addition, population stratification, defining genome-wide statistical significance thresholds and the design of appropriate replication experiments constitute important considerations for the powerful analysis and interpretation of rare variant association studies. Progress in addressing these emerging challenges and the accrual of sufficiently large data sets are poised to help the field of complex trait genetics enter a promising era of discovery.

• Exome
• Gene Frequency
• Genetic Variation
• Genome-Wide Association Study/methods
• Genotyping Techniques
• Humans
• Meta-Analysis as Topic
• Multifactorial Inheritance
• Quantitative Trait, Heritable

• 098051 Wellcome Trust
• 19542 Arthritis Research UK

In population genetics, simulation is a fundamental tool for analyzing how basic evolutionary forces such as natural selection, recombination, and mutation shape the genetic landscape of a population. Forward simulation represents the most powerful, but, at the same time, most compute-intensive approach for simulating the genetic material of a population.

We introduce AnA-FiTS, a highly optimized forward simulation software, that is up to two orders of magnitude faster than current state-of-the-art software. In addition, we present a novel algorithm that further improves runtimes by up to an additional order of magnitude, for simulations where a fraction of the mutations is neutral (e.g., only 10% of mutations have an effect on fitness). Apart from simulated sequences, our tool also generates a graph structure that depicts the complete observable history of neutral mutations.

The substantial performance improvements allow for conducting forward simulations at the chromosome and genome level. The graph structure generated by our algorithm can give rise to novel approaches for visualizing and analyzing the output of forward simulations.

In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing.

Inexpensive, high-throughput sequencing has transformed the field of case-control association studies. For the first time, it may be possible to identify the genetic underpinnings of complex diseases, by sequencing the DNA of hundreds (even thousands) of cases and controls and comparing patterns of DNA sequence variation. However, complex diseases are likely to be caused by many variants, some of which are very rare. Taken one at a time, the association between variant and disease phenotype may not be detectable by current statistical methods. One strategy is to identify regions where important variants occur by “collapsing” variants into groups. Here, we present a new collapsing approach, capable of detecting subtle genetic differences between cases and controls. We show, in extensive simulations and using a benchmark set of genes involved in human triglyceride levels, that the approach is potentially more powerful than existing methods. We apply the new method to an ongoing sequencing study of bipolar cases and controls and identify a set of genes found in neuronal synapses, which may be implicated in bipolar disorder.

• Case-Control Studies
• Computer Simulation
• Exome
• Genetic Association Studies
• Genome, Human
• Humans
• Likelihood Functions
• Mitogen-Activated Protein Kinase Kinases/genetics
• Mitogen-Activated Protein Kinase Kinases/metabolism
• Models, Genetic
• Models, Theoretical
• Polymorphism, Single Nucleotide
• Probability
• Signal Transduction/genetics

• R01MH087979 NIMH NIH HHS
• UL1 TR001105 NCATS NIH HHS
• K01 MH093809 NIMH NIH HHS
• R01 MH087979 NIMH NIH HHS
• UL1 TR000451 NCATS NIH HHS
• R01 MH087992 NIMH NIH HHS
• R01MH087992 NIMH NIH HHS
• R00 MH086049 NIMH NIH HHS
• K01MH093809 NIMH NIH HHS

Next-generation sequencing has made possible the detection of rare variant (RV) associations with quantitative traits (QT). Due to high sequencing cost, many studies can only sequence a modest number of selected samples with extreme QT. Therefore association testing in individual studies can be underpowered. Besides the primary trait, many clinically important secondary traits are often measured. It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits. However, analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled. Some methods exist for analyzing secondary traits in selected samples, where some burden tests can be implemented. However p-values can only be evaluated analytically via asymptotic approximations, which may not be accurate. Additionally, potentially more powerful sequence kernel association tests, variable selection-based methods, and burden tests that require permutations cannot be incorporated. To overcome these limitations, we developed a unified method for analyzing secondary trait associations with RVs (STAR) in selected samples, incorporating all RV tests. Statistical significance can be evaluated either through permutations or analytically. STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations. It also enables jointly analyzing multiple cohorts ascertained under different study designs, which greatly boosts power. The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies. STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced. A significant association between LDLR and systolic blood pressure was identified, which is supported by pharmacogenetic studies. In summary, for sequencing studies, STAR is an important tool for detecting secondary-trait RV associations.

Next-generation sequencing has greatly expanded our ability to identify missing heritability due to rare variants. In order to increase the power to detect associations, one desirable study design is to combine samples from multiple cohorts for mapping commonly measured traits. However, many current studies sequence selected samples (e.g. samples with extreme QT), which can bias the analysis of secondary traits, unless the sampling ascertainment mechanisms are properly adjusted. We developed a unified method for detecting secondary trait associations with rare variants (STAR) in selected and random samples, which can flexibly incorporate all rare variant association tests and allow joint analysis of multiple cohorts ascertained under different study designs. We demonstrate via simulations that STAR greatly boosts the power for detecting secondary trait associations. As an application of STAR, a dataset from the SardiNIA project was analyzed, where DNA samples from well-phenotyped individuals with extreme low-density lipoprotein levels were sequenced. LDLR was identified to be significantly associated with systolic blood pressure, which is supported by a previous pharmacogenetics study. In conclusion, STAR is an important tool for sequence-based association studies.

• Blood Pressure/genetics
• Computer Simulation
• Genetic Predisposition to Disease
• Genetic Variation
• Genome-Wide Association Study
• High-Throughput Nucleotide Sequencing
• Humans
• Italy
• Lipoproteins, LDL/genetics
• Models, Genetic
• Phenotype
• Quantitative Trait Loci/genetics
• Software

• RC4 MD005964 NIMHD NIH HHS
• HL084729 NHLBI NIH HHS
• Intramural NIH HHS
• UM1 HG006493 NHGRI NIH HHS
• MD005964 NIMHD NIH HHS
• HG005581 NHGRI NIH HHS
• RC2 HG005581 NHGRI NIH HHS
• U54 HG006493 NHGRI NIH HHS
• UC2 HL102926 NHLBI NIH HHS
• U01 HL084729 NHLBI NIH HHS
• RC2 HL102926 NHLBI NIH HHS
• N01-AG-1-2109 NIA NIH HHS
• R01 HG002651 NHGRI NIH HHS
• HG002651 NHGRI NIH HHS
• HG006493 NHGRI NIH HHS
• 263-MA-410953 PHS HHS
• HL102926 NHLBI NIH HHS

• Genotype calling
• Linkage disequilibrium (LD)
• Massively parallel sequencing
• Next-generation sequencing
• SNP detection

• Exome/genetics
• Genetic Diseases, Inborn/diagnosis
• Genetic Diseases, Inborn/genetics
• Genetic Predisposition to Disease/epidemiology
• Humans
• Models, Statistical
• Mutation
• Research Design
• Sample Size
• Sequence Analysis, DNA

• R00 RR024163 NCRR NIH HHS
• R01 EY018571 NEI NIH HHS
• R01EY018571 NEI NIH HHS

• 1-Alkyl-2-acetylglycerophosphocholine Esterase
• Computer Simulation
• Data Interpretation, Statistical
• Genetic Association Studies
• Genetic Predisposition to Disease
• Genetic Variation
• Genome, Human
• Humans
• Models, Statistical
• Phenotype
• Phospholipases A2/genetics
• Sample Size
• Sequence Analysis, DNA

• Canadian Institutes of Health Research

We have witnessed tremendous success in genome-wide association studies (GWAS) in recent years. Since the identification of variants in the complement factor H gene on the risk of age-related macular degeneration, GWAS have become ubiquitous in genetic studies and have led to the identification of genetic variants that are associated with a variety of complex human diseases and traits. These discoveries have changed our understanding of the biological architecture of common, complex diseases and have also provided new hypotheses to test. New tools, such as next-generation sequencing, will be an important part of the future of genetics research; however, GWAS studies will continue to play an important role in disease gene discovery. Many traits have yet to be explored by GWAS, especially in minority populations, and large collaborative studies are currently being conducted to maximize the return from existing GWAS data. In addition, GWAS technology continues to improve, increasing genomic coverage for major global populations and decreasing the cost of experiments. Although much of the variance attributable to genetic factors for many important traits is still unexplained, GWAS technology has been instrumental in mapping over a thousand genes to hundreds of traits. More discoveries are made each month and the scale, quality and quantity of current work has a steady trend upward. We briefly review the current key trends in GWAS, which can be summarized with three goals: increase power, increase collaborations and increase populations.

• Genome-wide association studies
• Single nucleotide polymorphisms
• Sequencing
• Genotype imputation
• Meta-analysis
• Genetic consortium

We propose a two-stage design for the analysis of sequence variants in which a proportion of genes that show some evidence of association are identified initially and then followed up in an independent data set. We compare two different approaches. In both approaches the same summary measure (total number of minor alleles) is used for each gene in the initial analysis. In the first (simple) approach the same summary measure is used in the analysis of the independent data set. In the second (alternative) approach a more specific hypothesis is formed for the second stage; the summary measure used is the count of minor alleles in only those variants that in the initial data showed the same direction of association as was seen overall. We applied the methods to the simulated quantitative traits of Genetic Analysis Workshop 17, blind to the simulation model, and then evaluated their performance once the underlying model was known. Performance was similar for most genes, but the simple strategy considerably out-performed the alternative strategy for one gene, where most of the effect was due to very rare variants; this suggests that the alternative approach would not be advisable when the effect is seen in very rare variants. Further simulations are needed to investigate the potential superior power of the alternative method when some variants within a gene have opposing effects. Overall, the power to detect associations was low; this was also true when using a more powerful joint analysis that combined the two stages of the study.

New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged.

• Alleles
• Genetic Predisposition to Disease/genetics
• Genotype
• High-Throughput Nucleotide Sequencing/methods
• Humans
• Phenotype

• R01 CA155767 NCI NIH HHS
• R01CA116167 NCI NIH HHS
• R01 CA116167 NCI NIH HHS
• CRN-87521 CIHR
• R01CA155767 NCI NIH HHS