Dataset Information

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

ABSTRACT: A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

SUBMITTER: Jun G

PROVIDER: S-EPMC5777025 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

Jun Goo G Manning Alisa A Almeida Marcio M Zawistowski Matthew M Wood Andrew R AR Teslovich Tanya M TM Fuchsberger Christian C Feng Shuang S Cingolani Pablo P Gaulton Kyle J KJ Dyer Thomas T Blackwell Thomas W TW Chen Han H Chines Peter S PS Choi Sungkyoung S Churchhouse Claire C Fontanillas Pierre P King Ryan R Lee SungYoung S Lincoln Stephen E SE Trubetskoy Vasily V DePristo Mark M Fingerlin Tasha T Grossman Robert R Grundstad Jason J Heath Alison A Kim Jayoun J Kim Young Jin YJ Laramie Jason J Lee Jaehoon J Li Heng H Liu Xuanyao X Livne Oren O Locke Adam E AE Maller Julian J Mazur Alexander A Morris Andrew P AP Pollin Toni I TI Ragona Derek D Reich David D Rivas Manuel A MA Scott Laura J LJ Sim Xueling X Tearle Rick G RG Teo Yik Ying YY Williams Amy L AL Zöllner Sebastian S Curran Joanne E JE Peralta Juan J Akolkar Beena B Bell Graeme I GI Burtt Noël P NP Cox Nancy J NJ Florez Jose C JC Hanis Craig L CL McKeon Catherine C Mohlke Karen L KL Seielstad Mark M Wilson James G JG Atzmon Gil G Below Jennifer E JE Dupuis Josée J Nicolae Dan L DL Lehman Donna D Park Taesung T Won Sungho S Sladek Robert R Altshuler David D McCarthy Mark I MI Duggirala Ravindranath R Boehnke Michael M Frayling Timothy M TM Abecasis Gonçalo R GR Blangero John J

Proceedings of the National Academy of Sciences of the United States of America 20171226 2

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, ...[more]

PMID: 29279374

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Project description:BackgroundWhole-genome sequencing and imputation methodologies have enabled the study of the effects of genomic variants with low to very low minor allele frequency (MAF) on variation in complex traits. Our objective was to estimate the proportion of variance explained by imputed sequence variants classified according to their MAF compared with the variance explained by the pedigree-based additive genetic relationship matrix for 17 traits in Nordic Holstein dairy cattle.ResultsImputed sequence variants were grouped into seven classes according to their MAF (0.001-0.01, 0.01-0.05, 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4 and 0.4-0.5). The total contribution of all imputed sequence variants to variance in deregressed estimated breeding values or proofs (DRP) for different traits ranged from 0.41 [standard error (SE) = 0.026] for temperament to 0.87 (SE = 0.011) for milk yield. The contribution of rare variants (MAF < 0.01) to the total DRP variance explained by all imputed sequence variants was relatively small (a maximum of 12.5% for the health index). Rare and low-frequency variants (MAF < 0.05) contributed a larger proportion of the explained DRP variances (>13%) for health-related traits than for production traits (<11%). However, a substantial proportion of these variance estimates across different MAF classes had large SE, especially when the variance explained by a MAF class was small. The proportion of DRP variance that was explained by all imputed whole-genome sequence variants improved slightly compared with variance explained by the 50 k Illumina markers, which are routinely used in bovine genomic prediction. However, the proportion of DRP variance explained by imputed sequence variants was lower than that explained by pedigree relationships, ranging from 1.5% for milk yield to 37.9% for the health index.ConclusionsImputed sequence variants explained more of the variance in DRP than the 50 k markers for most traits, but explained less variance than that captured by pedigree-based relationships. Although in humans partitioning variants into groups based on MAF and linkage disequilibrium was used to estimate heritability without bias, many of our bovine estimates had a high SE. For a reliable estimate of the explained DRP variance for different MAF classes, larger sample sizes are needed.

Dataset Information

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

Publications

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

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