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Vilhjálmsson, Bjarni J

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Vilhjálmsson

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Bjarni J

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Vilhjálmsson, Bjarni J
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    Effect of Genetic Variation in a Drosophila Model of Diabetes-Associated Misfolded Human Proinsulin
    (Genetics Society of America, 2014) He, Bin Z.; Ludwig, Michael Z.; Dickerson, Desiree A.; Barse, Levi; Arun, Bharath; Vilhjálmsson, Bjarni J; Jiang, Pengyao; Park, Soo-Young; Tamarina, Natalia A.; Selleck, Scott B.; Wittkopp, Patricia J.; Bell, Graeme I.; Kreitman, Martin
    The identification and validation of gene–gene interactions is a major challenge in human studies. Here, we explore an approach for studying epistasis in humans using a Drosophila melanogaster model of neonatal diabetes mellitus. Expression of the mutant preproinsulin (hINSC96Y) in the eye imaginal disc mimics the human disease: it activates conserved stress-response pathways and leads to cell death (reduction in eye area). Dominant-acting variants in wild-derived inbred lines from the Drosophila Genetics Reference Panel produce a continuous, highly heritable distribution of eye-degeneration phenotypes in a hINSC96Y background. A genome-wide association study (GWAS) in 154 sequenced lines identified a sharp peak on chromosome 3L, which mapped to a 400-bp linkage block within an intron of the gene sulfateless (sfl). RNAi knockdown of sfl enhanced the eye-degeneration phenotype in a mutant-hINS-dependent manner. RNAi against two additional genes in the heparan sulfate (HS) biosynthetic pathway (ttv and botv), in which sfl acts, also modified the eye phenotype in a hINSC96Y-dependent manner, strongly suggesting a novel link between HS-modified proteins and cellular responses to misfolded proteins. Finally, we evaluated allele-specific expression difference between the two major sfl-intronic haplotypes in heterozygtes. The results showed significant heterogeneity in marker-associated gene expression, thereby leaving the causal mutation(s) and its mechanism unidentified. In conclusion, the ability to create a model of human genetic disease, map a QTL by GWAS to a specific gene, and validate its contribution to disease with available genetic resources and the potential to experimentally link the variant to a molecular mechanism demonstrate the many advantages Drosophila holds in determining the genetic underpinnings of human disease.
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    Quantifying Missing Heritability at Known GWAS Loci
    (Public Library of Science, 2013) Gusev, Alexander; Bhatia, Gaurav; Zaitlen, Noah; Vilhjálmsson, Bjarni J; Diogo, Dorothee; Stahl, Eli A.; Gregersen, Peter K.; Worthington, Jane; Klareskog, Lars; Raychaudhuri, Soumya; Plenge, Robert M.; Pasaniuc, Bogdan; Price, Alkes
    Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.
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    Leveraging population admixture to explain missing heritability of complex traits
    (2014) Zaitlen, Noah; Pasaniuc, Bogdan; Sankararaman, Sriram; Bhatia, Gaurav; Zhang, Jianqi; Gusev, Alexander; Young, Taylor; Tandon, Arti; Pollack, Samuela; Vilhjálmsson, Bjarni J; Assimes, Themistocles L.; Berndt, Sonja I.; Blot, William J.; Chanock, Stephen; Franceschini, Nora; Goodman, Phyllis G.; He, Jing; Hennis, Anselm JM; Hsing, Ann; Ingles, Sue A.; Isaacs, William; Kittles, Rick A.; Klein, Eric A.; Lange, Leslie A.; Nemesure, Barbara; Patterson, Nick; Reich, David; Rybicki, Benjamin A.; Stanford, Janet L.; Stevens, Victoria L; Strom, Sara S.; Whitsel, Eric A; Witte, John S.; Xu, Jianfeng; Haiman, Christopher; Wilson, James G.; Kooperberg, Charles; Stram, Daniel; Reiner, Alex P.; Tang, Hua; Price, Alkes
    Despite recent progress on estimating the heritability explained by genotyped SNPs (hg2), a large gap between hg2 and estimates of total narrow-sense heritability (h2) remains. Explanations for this gap include rare variants, or upward bias in family-based estimates of h2 due to shared environment or epistasis. We estimate h2 from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (hγ2). We show that hγ2 = 2FSTCθ(1−θ)h2, where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We examined 21,497 African Americans from three cohorts, analyzing 13 phenotypes. For height and BMI, we obtained h2 estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of hg2 in these and other data, but smaller than family-based estimates of h2.
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    Efficient Bayesian mixed model analysis increases association power in large cohorts
    (2014) Loh, Po-Ru; Tucker, George; Bulik-Sullivan, Brendan K; Vilhjálmsson, Bjarni J; Finucane, Hilary K; Salem, Rany M; Chasman, Daniel; Ridker, Paul; Neale, Benjamin; Berger, Bonnie; Patterson, Nick; Price, Alkes
    Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts, and may not optimize power. All existing methods require time cost O(MN2) (where N = #samples and M = #SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here, we present a far more efficient mixed model association method, BOLT-LMM, which requires only a small number of O(MN)-time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to nine quantitative traits in 23,294 samples from the Women’s Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for GWAS in large cohorts.