JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.

Recently, large scale genome-wide association study (GWAS) meta-analyses have boosted the number of known signals for some traits into the tens and hundreds. Typically, however, variants are only analysed one-at-a-time. This complicates the ability of fine-mapping to identify a small set of SNPs for further functional follow-up. We describe a new and scalable algorithm, joint analysis of marginal summary statistics (JAM), for the re-analysis of published marginal summary statistics under joint multi-SNP models. The correlation is accounted for according to estimates from a reference dataset, and models and SNPs that best explain the complete joint pattern of marginal effects are highlighted via an integrated Bayesian penalized regression framework. We provide both enumerated and Reversible Jump MCMC implementations of JAM and present some comparisons of performance. In a series of realistic simulation studies, JAM demonstrated identical performance to various alternatives designed for single region settings. In multi-region settings, where the only multivariate alternative involves stepwise selection, JAM offered greater power and specificity. We also present an application to real published results from MAGIC (meta-analysis of glucose and insulin related traits consortium) - a GWAS meta-analysis of more than 15,000 people. We re-analysed several genomic regions that produced multiple significant signals with glucose levels 2 hr after oral stimulation. Through joint multivariate modelling, JAM was able to formally rule out many SNPs, and for one gene, ADCY5, suggests that an additional SNP, which transpired to be more biologically plausible, should be followed up with equal priority to the reported index

Methodological Issues in Multistage Genome-Wide Association Studies

Because of the high cost of commercial genotyping chip technologies, many investigations have used a two-stage design for genome-wide association studies, using part of the sample for an initial discovery of ``promising'' SNPs at a less stringent significance level and the remainder in a joint analysis of just these SNPs using custom genotyping. Typical cost savings of about 50% are possible with this design to obtain comparable levels of overall type I error and power by using about half the sample for stage I and carrying about 0.1% of SNPs forward to the second stage, the optimal design depending primarily upon the ratio of costs per genotype for stages I and II. However, with the rapidly declining costs of the commercial panels, the generally low observed ORs of current studies, and many studies aiming to test multiple hypotheses and multiple endpoints, many investigators are abandoning the two-stage design in favor of simply genotyping all available subjects using a standard high-density panel. Concern is sometimes raised about the absence of a ``replication'' panel in this approach, as required by some high-profile journals, but it must be appreciated that the two-stage design is not a discovery/replication design but simply a more efficient design for discovery using a joint analysis of the data from both stages. Once a subset of highly-significant associations has been discovered, a truly independent ``exact replication'' study is needed in a similar population of the same promising SNPs using similar methods.Comment: Published in at http://dx.doi.org/10.1214/09-STS288 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Identifying susceptibility genes by using joint tests of association and linkage and accounting for epistasis

Simulated Genetic Analysis Workshop14 data were analyzed by jointly testing linkage and association and by accounting for epistasis using a candidate gene approach. Our group was unblinded to the "answers." The 48 single-nucleotide polymorphisms (SNPs) within the six disease loci were analyzed in addition to five SNPs from each of two non-disease-related loci. Affected sib-parent data was extracted from the first 10 replicates for populations Aipotu, Kaarangar, and Danacaa, and analyzed separately for each replicate. We developed a likelihood for testing association and/or linkage using data from affected sib pairs and their parents. Identical-by-descent (IBD) allele sharing between sibs was explicitly modeled using a conditional logistic regression approach and incorporating a covariate that represents expected IBD allele sharing given the genotypes of the sibs and their parents. Interactions were accounted for by performing likelihood ratio tests in stages determined by the highest order interaction term in the model. In the first stage, main effects were tested independently, and in subsequent stages, multilocus effects were tested conditional on significant marginal effects. A reduction in the number of tests performed was achieved by prescreening gene combinations with a goodness-of-fit chi square statistic that depended on mating-type frequencies. SNP-specific joint effects of linkage and association were identified for loci D1, D2, D3, and D4 in multiple replicates. The strongest effect was for SNP B03T3056, which had a median p-value of 1.98 × 10(-34). No two- or three-locus effects were found in more than one replicate

Comparison of missing data approaches in linkage analysis

BACKGROUND: Observational cohort studies have been little used in linkage analyses due to their general lack of large, disease-specific pedigrees. Nevertheless, the longitudinal nature of such studies makes them potentially valuable for assessing the linkage between genotypes and temporal trends in phenotypes. The repeated phenotype measures in cohort studies (i.e., across time), however, can have extensive missing information. Existing methods for handling missing data in observational studies may decrease efficiency, introduce biases, and give spurious results. The impact of such methods when undertaking linkage analysis of cohort studies is unclear. Therefore, we compare here six methods of imputing missing repeated phenotypes on results from genome-wide linkage analyses of four quantitative traits from the Framingham Heart Study cohort. RESULTS: We found that simply deleting observations with missing values gave many more nominally statistically significant linkages than the other five approaches. Among the latter, those with similar underlying methodology (i.e., imputation- versus model-based) gave the most consistent results, although some discrepancies remained. CONCLUSION: Different methods for addressing missing values in linkage analyses of cohort studies can give substantially diverse results, and must be carefully considered to protect against biases and spurious findings

Snagger: A user-friendly program for incorporating additional information for tagSNP selection

<p>Abstract</p> <p>Background</p> <p>There has been considerable effort focused on developing efficient programs for tagging single-nucleotide polymorphisms (SNPs). Many of these programs do not account for potential reduced genomic coverage resulting from genotyping failures nor do they preferentially select SNPs based on functionality, which may be more likely to be biologically important.</p> <p>Results</p> <p>We have developed a user-friendly and efficient software program, Snagger, as an extension to the existing open-source software, Haploview, which uses pairwise <it>r</it>²linkage disequilibrium between single nucleotide polymorphisms (SNPs) to select tagSNPs. Snagger distinguishes itself from existing SNP selection algorithms, including Tagger, by providing user options that allow for: (1) prioritization of tagSNPs based on certain characteristics, including platform-specific design scores, functionality (i.e., coding status), and chromosomal position, (2) efficient selection of SNPs across multiple populations, (3) selection of tagSNPs outside defined genomic regions to improve coverage and genotyping success, and (4) picking of surrogate tagSNPs that serve as backups for tagSNPs whose failure would result in a significant loss of data. Using HapMap genotype data from ten ENCODE regions and design scores for the Illumina platform, we show similar coverage and design score distribution and fewer total tagSNPs selected by Snagger compared to the web server Tagger.</p> <p>Conclusion</p> <p>Snagger improves upon current available tagSNP software packages by providing a means for researchers to select tagSNPs that reliably capture genetic variation across multiple populations while accounting for significant genotyping failure risk and prioritizing on SNP-specific characteristics.</p

Prevalence of common disease-associated variants in Asian Indians

<p>Abstract</p> <p>Background</p> <p>Asian Indians display a high prevalence of diseases linked to changes in diet and environment that have arisen as their lifestyle has become more westernized. Using 1200 genome-wide polymorphisms in 432 individuals from 15 Indian language groups, we have recently shown that: (i) Indians constitute a distinct population-genetic cluster, and (ii) despite the geographic and linguistic diversity of the groups they exhibit a relatively low level of genetic heterogeneity.</p> <p>Results</p> <p>We investigated the prevalence of common polymorphisms that have been associated with diseases, such as atherosclerosis (<it>ALOX5</it>), hypertension (<it>CYP3A5</it>, <it>AGT</it>, <it>GNB3</it>), diabetes (<it>CAPN10</it>, <it>TCF7L2</it>, <it>PTPN22</it>), prostate cancer (DG8S737, rs1447295), Hirschsprung disease (<it>RET</it>), and age-related macular degeneration (<it>CFH</it>, <it>LOC387715</it>). In addition, we examined polymorphisms associated with skin pigmentation (<it>SLC24A5</it>) and with the ability to taste phenylthiocarbamide (<it>TAS2R38</it>). All polymorphisms were studied in a cohort of 576 India-born Asian Indians sampled in the United States. This sample consisted of individuals whose mother tongue is one of 14 of the 22 "official" languages recognized in India as well as individuals whose mother tongue is Parsi, a cultural group that has resided in India for over 1000 years. Analysis of the data revealed that allele frequency differences between the different Indian language groups were small, and interestingly the variant alleles of <it>ALOX5 </it>g.8322G>A and g.50778G>A, and <it>PTPN22 </it>g.36677C>T were present only in a subset of the Indian language groups. Furthermore, a latitudinal cline was identified both for the allele frequencies of the SNPs associated with hypertension (<it>CYP3A5</it>, <it>AGT</it>, <it>GNB3</it>), as well as for those associated with the ability to taste phenylthiocarbamide (<it>TAS2R38</it>).</p> <p>Conclusion</p> <p>Although caution is warranted due to the fact that this US-sampled Indian cohort may not represent a random sample from India, our results will hopefully assist in the design of future studies that investigate the genetic causes of these diseases in India. Our results also support the inclusion of the Indian population in disease-related genetic studies, as it exhibits unique genotype as well as phenotype characteristics that may yield new insights into the underlying causes of common diseases that are not available in other populations.</p

Chester supersolid of spatially indirect excitons in double-layer semiconductor heterostructures

A supersolid, a counter-intuitive quantum state in which a rigid lattice of particles flows without resistance, has to date not been unambiguously realised. Here we reveal a supersolid ground state of excitons in a double-layer semiconductor heterostructure over a wide range of layer separations outside the focus of recent experiments. This supersolid conforms to the original Chester supersolid with one exciton per supersolid site, as distinct from the alternative version reported in cold-atom systems of a periodic modulation of the superfluid density. We provide the phase diagram augmented by the supersolid. This new phase appears at layer separations much smaller than the predicted exciton normal solid, and it persists up to a solid--solid transition where the quantum phase coherence collapses. The ranges of layer separations and exciton densities in our phase diagram are well within reach of the current experimental capabilities

Author Correction: Elevated numbers of PD-L1 expressing B cells are associated with the development of AIDS-NHL.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Flattening conduction and valence bands for interlayer excitons in a moir\'e MoS2_2/WSe2_2 heterobilayer

We explore the flatness of conduction and valence bands of interlayer excitons in MoS$_2$/WSe$_2$ van der Waals heterobilayers, tuned by interlayer twist angle, pressure, and external electric field. We employ an efficient continuum model where the moir\'e pattern from lattice mismatch and/or twisting is represented by an equivalent mesoscopic periodic potential. We demonstrate that the mismatch moir\'e potential is too weak to produce significant flattening. Moreover, we draw attention to the fact that the quasi-particle effective masses around the $\Gamma$-point and the band flattening are \textit{reduced} with twisting. As an alternative approach, we show (i) that reducing the interlayer distance by uniform vertical pressure can significantly increase the effective mass of the moir\'e hole, and (ii) that the moir\'e depth and its band flattening effects are strongly enhanced by accessible electric gating fields perpendicular to the heterobilayer, with resulting electron and hole effective masses increased by more than an order of magnitude leading to record-flat bands. These findings impose boundaries on the commonly generalized benefits of moir\'e twistronics, while also revealing alternate feasible routes to achieve truly flat electron and hole bands to carry us to strongly correlated excitonic phenomena on demand