You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls}
Article:
Abstract:
We report on results from whole-exome sequencing( WES) of 1, 039 subjects diagnosed with autism spectrum disorders( ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers( meta-analysis) or should data be combined and then analyzed( mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD.
Introduction:
Common and rare variants are important constituents of the genetic architecture of Autism Spectrum Disorders( ASD)[1]–[12]. Nonetheless analysis of rare variants has produced the vast majority of findings that implicate certain genes as playing a role in liability for ASD( i. e., ASD genes). Because of the promise of identifying novel ASD genes via rare variants, and the potential downstream implications regarding treatment, an ambitious exome sequencing study has been implemented including nearly 2000 case and control subjects sequenced at two genomic centers. Exome sequencing studies of complex traits have shown success in candidate gene studies[13]–[18]; however, most published candidate gene studies have not reported a p-value small enough to attain exome-wide significance[19]. For rare variants, even if effects are strong, single variant tests typically have little power. Rare variants have to be combined in some way, such as within a gene or across genes, for an association test to reach sufficient power. Hence statistical tests examine the cumulative effects over the observed rare variants in the target set. A number of statistical methods to test for association with rare variants are now available. Several of these tests fall into the category of burden tests in that they assess association with a “super-variant”[20]–[24]. Each of these burden methods assumes variants impact the phenotype in a common direction. Rather than aggregating variants, another class of methods, including C-alpha[25] and SKAT[26], look for an unusual distribution of rare variation among cases and controls. Power of the test is determined by the number of causal variants in the gene, the size of the corresponding effects, and the sample size. Assuming that the rarest variants are likely to have the largest effects, it is challenging to amass substantial evidence for association without a large sample size. Based on extrapolation of effect sizes and frequencies from published studies[19], the results indicate that thousands of individuals are required to obtain genome wide significance. In this ARRA autism sequencing consortium( AASC) study, data have been produced by two sequencing centers( Baylor College of Medicine and Broad Institute) and by different exome capture methods, different sequencing platforms and different pre-processing alignment and variant calling methods. Therefore the coverage and quality of these data sets varies. Nonetheless, as we show in the sequel, these data can be harmonized using standard filtering criteria. Given the distinct data sources, the most effective way of testing for association is unclear. Following in the tradition of association studies, meta-analysis is a natural option[27]. With this approach we can perform the analysis on each data set separately and then combine p-values using the weighted Z-score method. Alternatively, after filtering to homogenize data, we can combine the two data sets directly and perform mega-analysis. Meta-analysis has the advantage of permitting and adjusting for heterogeneity between samples[28]. All other things being equal, this is the preferred choice. On the other hand, if the power of mega-analysis is better, then this option is worth pursuing. In this report we show that mega-analysis is the more powerful procedure for gene-based tests, such as SKAT[26], a result that might be counter-intuitive given the well-known efficiency of meta-analysis for tests of linear form such as logistic regression. For these data we also find that population structure appears to be corrected for by using principal components analysis[29]. After quality control and controlling for ancestry, analysis of AASC data reveals no clear-cut associations, including associations in known ASD genes. We conclude that rare variants affecting risk are not clustering in a small number of genes, supporting recent results from de novo single nucleotide and copy number studies showing that hundreds of genes in the genome affect risk for ASD[4]–[6],[8].
Discussion:
Studies of the distribution of de novo copy number and sequence variants in ASD and control subjects invariably find elevated rates of damaging de novo events in ASD subjects[1]–[8]. These studies also invariably find relatively little convergence of de novo events on particular loci in the human genome. These results are consistent with only one conclusion about the genetic architecture of ASD, namely that there are hundreds of genes in the genome that can affect liability, possibly more. Indeed various statistical analyses of the data support this conclusion[5],[8]. Another common theme of ASD studies is that while de novo events are rare, they can successfully identify ASD liability genes, and in general the distribution of rare variation has been a key tool for gene discovery[37]. By contrast common variation has not yet proven an effective tool for discovering replicable ASD genes, although there are tantalizing findings[10]. With these observations in mind the AASC has implemented a study of rare variation in ASD based on WES[38]. Here we report on data from almost 2000 ASD subjects and controls. We find the distribution of rare variation between cases and controls is remarkably similar, showing that ASD risk genes cannot be identified in a case-control sample of this size. Indeed, even known ASD genes showed little association in this study. This finding is in keeping with other studies of rare variants, but with quite different phenotypes, supporting the conjecture that rare variant association studies require large samples[19],[39],[40]. With respect to the genetics of ASD, the results are also consistent with the inference from de novo studies that there must be hundreds genes affecting liability to ASD[3]–[6],[8]. These results underscore the scale of the challenges ahead in our effort to discover ASD genes. Large samples must be amassed and assessed and effective study designs implemented[41]. To gain insight into the limited power of this study, consider three scenarios:( A) the gene has 15 variants, each with MAF, for which all have odds ratio of 4;( B) the gene has 20 variants, each with MAF, for which 10 have odds ratio of 3; and( C) the gene has 40 variants, each with MAF, for which 30 have odds ratio of 2. We list the required samples size of each scenario in Table S6 to achieve a power of 50% and 80% per gene( with a p-value threshold of). Even though the power of mega-analysis is only 0. 31, 0. 11 and 0. 06 for our study, assuming these scenarios were realistic, power would have been sufficient to discover a fraction of the large number of ASD genes present in the genome. We conclude that these scenarios do not describe likely models for risk genes in ASD. As with GWAS, to assimilate large samples and gain power, multiple studies must be combined. In the analysis of samples from multiple studies, meta-analysis, based on Z-scores, has become the norm for most genetic investigations. This form of meta-analysis has power equal to mega-analysis for single variant tests[42], hence it is reasonable to assume that meta-analysis is generally superior to mega-analysis because the former more easily accommodates heterogeneity across studies. A notable result from our study is that these results do not carry over to gene-based tests such as SKAT. In that setting mega-analysis has considerably more power than meta-analysis because mega-analysis assesses the concordance of association for a variant across all sites and then combines information across all variants within a gene. In this way, the method separates true signals from false ones and attains a greater signal to noise ratio. In contrast, meta-analysis combines information across studies at the gene level and hence can not assess the pattern of signals at the variant level across sites. A drawback of mega-analysis is that we encounter challenges when combining datasets collected across multiple studies, which can differ in many respects due to the use of different sequencing platforms and protocols. For instance, these differences lead to differential coverage by exon and different alignment errors. Even the best laboratory process has measurement error and these errors are exacerbated when they differ across batches of samples, particularly if they differ between cases and controls. For these reasons caution must be exercised if one is to reap the benefits of mega-analysis. Indeed, even after careful filtering, heterogeneity between sites could account for the modest inflation in the associate test statistics and the genomic control factor after combining sites via meta- and mega-analysis. In this study we construct extra filters to ensure that the distribution of rare variation of the WES data is similar for the two centers. We find good results filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Ideally a filter is tuned by measuring some individuals on multiple platforms. We tune our filters using subjects measured twice. If such data are unavailable, however, we find that another promising approach is to compare minor allele counts( based on rare variants) per gene. A good filter is one that aims to equilibrate these quantities. Even with the most minimal filtering we observe no excess of positive signals for association within the individual data sets, but for mega-analysis we observe a great number of positive associations. These false discoveries are diminished, however, after filtering. Likewise mega-analysis is more susceptible than meta-analysis to the impact of differential missingness across platforms and across case/control status. Indeed, without filtering, mega-analysis has many false discoveries but meta-analysis did not. However, using filtered data we find that mega-analysis is quite robust to differences in missingness rates across platforms and case/control status, although we recognize that this robustness could fail for more extreme heterogeneity of missingness. Still our study has some differences in missingness and yet does not produce detectable false discoveries. From our analyses we conjecture that filtering that removes variants with missingness( per data set) is largely effective. When combining data sets the effects of population substructure on association is also a concern due to clustering of rare variants in ancestry space[40],[43]. Even though our case-control samples are approximately pair-matched by ancestry in the study design, we find weak evidence of population structure confounding the test of association. In our data these effects could be mitigated by regressing out principal components of ancestry using common variants or low frequency variants. This result supports findings of[44], but is contrary to other predictions[43]. Thus, although rare variants tend to be younger, and therefore distinctly clustered in populations, in our sample estimates of ancestry derived from common variants capture the major features of the distribution of rare variants in ancestry space. In conclusion we find that WES data on nearly 2000 samples collected for a case-control study are insufficient to discover novel liability genes for ASD, even after applying efficient methods like mega-analysis and controlling for ancestry effectively. These results demonstrate that much larger samples will be required for effective gene discovery and lend further support to the prediction that there are hundreds of genes that impact ASD liability in the human genome.
This study evaluates association of rare variants and autism spectrum disorders ( ASD ) in case and control samples sequenced by two centers . Before doing association analyses , we studied how to combine information across studies . We first harmonized the whole-exome sequence ( WES ) data , across centers , in terms of the distribution of rare variation . Key features included filtering called variants by fraction of missing data , read depth , and balance of alternative to reference reads . After filtering , the vast majority of variants calls from seven samples sequenced at both centers matched . We also evaluated whether one should combine summary statistics from data from each center ( meta-analysis ) or combine data and analyze it together ( mega-analysis ) . For many gene-based tests , we showed that mega-analysis yields more power . After quality control of data from 1 , 039 ASD cases and 870 controls and a range of analyses , no gene showed exome-wide evidence of significant association . Our results comport with recent results demonstrating that hundreds of genes affect risk for ASD; they suggest that rare risk variants are scattered across these many genes , and thus larger samples will be required to identify those genes .