Fine Mapping and Identification of BMI Loci in African Americans

Gong, Jian; Schumacher, Fredrick; Lim, Unhee; Hindorff, Lucia A.; Haessler, Jeff; Buyske, Steven; Carlson, Christopher S.; Rosse, Stephanie A.; Bůžková, Petra; Fornage, Myriam; Gross, Myron D.; Pankratz, Nathan; Pankow, James S.; Schreiner, Pamela J.; Cooper, Richard S.; Ehret, Georg; Gu, C. Charles; Houston, Denise K.; Irvin, Marguerite R.; Jackson, Rebecca D.; Kuller, Lew; Henderson, Brian E.; Cheng, Iona; Wilkens, Lynne R.; Leppert, M.; Lewis, Cora E.; Li, Rongling; Nguyen, Khanh‐Dung H.; Goodloe, Robert; Farber‐Eger, Eric; Boston, J.R.; Dilks, Holli H.; Ritchie, Marylyn D.; Fowke, Jay H.; Pooler, Loreall; Graff, Misa; Fernández-Rhodes, Lindsay; Cochrane, Barbara B.; Boerwinkle, Eric; Kooperberg, Charles; Matise, Tara C.; Marchand, Loı̈c Le; Crawford, Dana C.; Haiman, Christopher A.; North, Kari E.; Peters, Ulrike

doi:10.1016/j.ajhg.2013.08.012

Cited by 77 publications

(67 citation statements)

References 31 publications

(38 reference statements)

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“…Cohorts of individuals of African descent have been examined by others in an effort to reduce the size of the FTO interval implicated in adiposity (23)(24)(25). In these studies, rs8050136 (minor allele frequency = 0.34), and the landmark SNP rs9939609 first reported by McCarthy (1), were associated weakly or not at all with BMI in subjects of African descent.…”

Section: Resultsmentioning

confidence: 99%

Hypomorphism of Fto and Rpgrip1l causes obesity in mice

Stratigopoulos¹,

Burnett²,

Rausch³

et al. 2016

Journal of Clinical Investigation

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Section: Resultsmentioning

confidence: 99%

Hypomorphism of Fto and Rpgrip1l causes obesity in mice

Stratigopoulos¹,

Burnett²,

Rausch³

et al. 2016

Journal of Clinical Investigation

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“…Over the past few years, GWAS have identified hundreds of genetic loci harboring genetic variation affecting disease risk for hundreds of common diseases (Bauer et al 2013;Coram et al 2013;Diogo et al 2013;Gong et al 2013;Marigorta and Navarro 2013;Peters et al 2013;Wu et al 2013). Identifying the causal genetic variants affecting disease risk at these loci has the potential of providing clues to the mechanism of the disease, which can lead to identification of better targets for drug terrapins.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, LD hinders the identification of causal variants at risk loci in fine-mapping studies as at each locus, there are often tens to hundreds of variants tightly linked to the reported associated single-nucleotide polymorphism (SNP) (Malo et al 2008;Maller et al 2012;Yang et al 2012). In a continued effort to identify causal variants, many finemapping studies that assess genetic variation at known GWAS risk loci are currently underway (Bauer et al 2013;Coram et al 2013;Diogo et al 2013;Gong et al 2013;Marigorta and Navarro 2013;Peters et al 2013;Wu et al 2013).Fine-mapping studies typically follow a two-step procedure. First, a statistical analysis of the association signal is performed to identify a minimum set of SNPs that can explain the signal.…”

mentioning

confidence: 99%

Identifying causal variants at loci with multiple signals of association

Hormozdiari

Kostem

Kang

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

149

303

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Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/.A LTHOUGH genome-wide association studies (GWAS) reproducibly identified thousands of risk loci (Hakonarson et al. 2007;Sladek et al. 2007;Zeggini et al. 2007; Yang et al. 2011a,b;Kottgen et al. 2013;Lu et al. 2013;Ripke et al. 2013), only a handful of causal genetic variants (i.e., variants that biologically alter disease risk) have been found (Altshuler et al. 2008;Manolio et al. 2008;McCarthy et al. 2008), thus prohibiting the mechanistic understanding of the genetic basis of common diseases. The linkage disequilibrium (LD) (Pritchard and Przeworski 2001;Reich et al. 2001) structure of the human genome has greatly benefited GWAS in interrogating only a subset of all variants to assay common variation across the genome. Unfortunately, LD hinders the identification of causal variants at risk loci in fine-mapping studies as at each locus, there are often tens to hundreds of variants tightly linked to the reported associated single-nucleotide polymorphism (SNP) (Malo et al. 2008;Maller et al. 2012;Yang et al. 2012). In a continued effort to identify causal variants, many finemapping studies that assess genetic variation at known GWAS risk loci are currently underway (Bauer et al. 2013;Coram et al. 2013;Diogo et al. 2013;Gong et al. 2013;Marigorta and Navarro 2013;Peters et al. 2013;Wu et al. 2013).Fine-mapping studies typically follow a two-step procedure. First, a statistical analysis of the association signal is performed to identify a minimum set of SNPs that can explain the signal. Second, the SNPs that ar...

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“…However, the utility of this platform in non-Europeans remains to be addressed. In addition, consistent with their longer evolutionary history, populations of African origin are known to have, on average, characteristically smaller blocks of linkage disequilibrium compared to populations with European ancestry (Tishkoff and Kidd, 2004), implying that the study of populations of African origin could help to narrow the regions of interest and assist in identifying causative variants (Buyske et al, 2012;Gong et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

An ImmunoChip study of multiple sclerosis risk in African Americans

Isobe

Madireddy

Khankhanian

et al. 2015

Brain

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*These authors contributed equally to this work.The aims of this study were: (i) to determine to what degree multiple sclerosis-associated loci discovered in European populations also influence susceptibility in African Americans; (ii) to assess the extent to which the unique linkage disequilibrium patterns in African Americans can contribute to localizing the functionally relevant regions or genes; and (iii) to search for novel African American multiple sclerosis-associated loci. Using the ImmunoChip custom array we genotyped 803 African American cases with multiple sclerosis and 1516 African American control subjects at 130 135 autosomal single nucleotide polymorphisms. We conducted association analysis with rigorous adjustments for population stratification and admixture. Of the 110 non-major histocompatibility complex multiple sclerosis-associated variants identified in Europeans, 96 passed stringent quality control in our African American data set and of these, 470% (69) showed over-representation of the same allele amongst cases, including 21 with nominally significant evidence for association (one-tailed test P 5 0.05). At a further eight loci we found nominally significant association with an alternate correlated risk-tagging single nucleotide polymorphism from the same region. Outside the regions known to be associated in Europeans, we found seven potentially associated novel candidate multiple sclerosis variants (P 5 10 À 4 ), one of which (rs2702180) also showed nominally significant evidence for association (one-tailed test P = 0.034) in an independent second cohort of 620 African American cases and 1565 control subjects. However, none of these novel associations reached genome-wide significance (combined P = 6.3 Â 10 À 5 ). Our data demonstrate substantial overlap between African American and European multiple sclerosis variants, indicating common genetic contributions to multiple sclerosis risk.

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Fine Mapping and Identification of BMI Loci in African Americans

Cited by 77 publications

References 31 publications

Hypomorphism of Fto and Rpgrip1l causes obesity in mice

Hypomorphism of Fto and Rpgrip1l causes obesity in mice

Identifying causal variants at loci with multiple signals of association

An ImmunoChip study of multiple sclerosis risk in African Americans

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