Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Hundreds of variants clustered in genomic loci and biological pathways affect human height

This Review discusses the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.

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Statistical power and significance testing in large-scale genetic studies

Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.

Meta-analysis Methods for Genome-Wide Association Studies and Beyond

Background: Over the past decade genome-wide association studies (GWAS) have been applied to aid in the
understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried
by the true risk variants and the corresponding statistical power to observe such effects given the study design and
sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however,
these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15).
Methods: We conducted a large-scale coordinated international collaboration to combine independent genotyping
data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide
genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary
statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls).
Results: We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription
factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social
skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia
which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P=9 ×10−6). We further
combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to
identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12
novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a
‘neurodevelopmental hub’ on chromosome 8p11.23.
Conclusions: This study is an important step in the ongoing endeavour to identify the loci which underpin the common
variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia
and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4.

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Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia

Clinicians, when trying to apply trial results to patient care, need to individualize patient care and, potentially, manage patients based on results of subgroup analyses. Apparently compelling subgroup effects often prove spurious, and guidance is needed to differentiate credible from less credible subgroup claims. We therefore provide 5 criteria to use when assessing the validity of subgroup analyses: (1) Can chance explain the apparent subgroup effect; (2) Is the effect consistent across studies; (3) Was the subgroup hypothesis one of a small number of hypotheses developed a priori with direction specified; (4) Is there strong preexisting biological support; and (5) Is the evidence supporting the effect based on within- or between-study comparisons. The first 4 criteria are applicable to individual studies or systematic reviews, the last only to systematic reviews of multiple studies. These criteria will help clinicians deciding whether to use subgroup analyses to guide their patient care.

How to Use a Subgroup Analysis: Users’ Guide to the Medical Literature

Background Robust replication is a sine qua non for the rigorous documentation of proposed associations in the genome-wide association (GWA) setting. Currently, associations of common variants reaching P ≤ 5 × 10(-8) are considered replicated. However, there is some ambiguity about the most suitable threshold for claiming genome-wide significance. Methods We defined as 'borderline' associations those with P > 5 × 10(-8) and P ≤ 1 × 10(-7). The eligible associations were retrieved using the 'Catalog of Published Genome-Wide Association Studies'. For each association we assessed whether it reached P ≤ 5 × 10(-8) with inclusion of additional data from subsequent GWA studies. Results Thirty-four eligible genotype-phenotype associations were evaluated with data and clarifications contributed from diverse investigators. Replication data from subsequent GWA studies could be obtained for 26 of them. Of those, 19 associations (73%) reached P ≤ 5 × 10(-8) for the same or a related trait implicating either the exact same allele or one in very high linkage disequilibrium and 17 reached P 10(-6) [corresponding false-discovery rate 19% (95% CI 7-39%)]. Conclusion A substantial proportion, but not all, of the associations with borderline genome-wide significance represent replicable, possibly genuine associations. Our empirical evaluation suggests a possible relaxation in the current GWS threshold.

Yu Sun Chang

Papers

What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations