Robust relationship inference in genome-wide association studies

Manichaikul, Ani; Mychaleckyj, Josyf C.; Rich, Stephen S.; Daly, Kathleen; Sale, Michèle M.; Chen, Weimin

doi:10.1093/bioinformatics/btq559

Cited by 2,615 publications

(2,653 citation statements)

References 19 publications

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“…There is also the possibility that two families recruited from the same population could be related to one another in some unrecognized fashion. We investigated the extent to which sequenced subjects were related to each other (beyond their reported familial relationships) by estimating kinship coefficients between affected subjects from genome-wide markers using an estimator robust to population stratification as implemented in the King package (Manichaikul et al 2010). The family segregating for the novel CDH1 variant listed in Table 2 was Bengali.…”

Section: Discussionmentioning

confidence: 99%

Whole Exome Sequencing of Distant Relatives in Multiplex Families Implicates Rare Variants in Candidate Genes for Oral Clefts

et al. 2014

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A dozen genes/regions have been confirmed as genetic risk factors for oral clefts in human association and linkage studies, and animal models argue even more genes may be involved. Genomic sequencing studies should identify specific causal variants and may reveal additional genes as influencing risk to oral clefts, which have a complex and heterogeneous etiology. We conducted a whole exome sequencing (WES) study to search for potentially causal variants using affected relatives drawn from multiplex cleft families. Two or three affected second, third, and higher degree relatives from 55 multiplex families were sequenced. We examined rare single nucleotide variants (SNVs) shared by affected relatives in 348 recognized candidate genes. Exact probabilities that affected relatives would share these rare variants were calculated, given pedigree structures, and corrected for the number of variants tested. Five novel and potentially damaging SNVs shared by affected distant relatives were found and confirmed by Sanger sequencing. One damaging SNV in CDH1, shared by three affected second cousins from a single family, attained statistical significance (P = 0.02 after correcting for multiple tests). Family-based designs such as the one used in this WES study offer important advantages for identifying genes likely to be causing complex and heterogeneous disorders.

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Section: Discussionmentioning

confidence: 99%

Whole Exome Sequencing of Distant Relatives in Multiplex Families Implicates Rare Variants in Candidate Genes for Oral Clefts

et al. 2014

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“…To detect any errors in these pedigrees, we ran KING (20) on each cohort and also used the results to identify any cryptic first-degree relationships that had not been mapped. We further removed pedigrees where age information was inconsistent with the pedigree specified.…”

Section: Methodsmentioning

confidence: 99%

Linear mixed model for heritability estimation that explicitly addresses environmental variation

Heckerman

Gurdasani

Kadie

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce this inflation, we used a more general LMM with two random effects-one based on genomic variants and one based on easily measured spatial location as a proxy for environmental effects. We investigated this approach with simulated data and with data from a Uganda cohort of 4,778 individuals for 34 phenotypes including anthropometric indices, blood factors, glycemic control, blood pressure, lipid tests, and liver function tests. For the genomic random effect, we used identity-by-descent estimates from accurately phased genomewide data. For the environmental random effect, we constructed a covariance matrix based on a Gaussian radial basis function. Across the simulated and Ugandan data, narrow-sense heritability estimates were lower using the more general model. Thus, our approach addresses, in part, the issue of "missing heritability" in the sense that much of the heritability previously thought to be missing was fictional. Software is available at https://github.com/MicrosoftGenomics/ FaST-LMM. A n important causal question comes from the age-old debate about nature versus nurture. For any phenotype such as height or intelligence quotient, how much of the phenotype is inherited and how much is determined by environment? This question was made precise by Fisher (1) and Wright (2) almost a century ago: Given observations of a phenotype from a population of individuals, what is the fraction of variance of the phenotype that is caused by inherited factors relative to the total variance of the phenotype due to both inherited and environmental factors? This fraction, termed "heritability," has been the subject of intense study across various phenotypes and populations since it was defined. Note that, in contrast to how some interpret the informal question around the nature-versus-nurture debate, heritability is not an absolute quantity but rather a quantity relative to a given population. For example, a phenotype in a population where environmental factors have large variation will have a smaller heritability than in an otherwise similar population where environmental factors have a small variation.Over the years, many approaches have been developed to estimate heritability from data (3, 4). Here, we concentrate on an approach made possible by the recent ability to sequence genomes at a modest cost (5, 6). The approach uses a linear mixed model (LMM), a form of multivariate regression of the genomic and environmental factors on the phenotype, which we examine in detail in the next section.In the standard LMM approach, the effects of environmental factors on the phenotype are modeled as noise. Specifically, the phenotype of each individual is assumed to be the sum of two random effects, one based on genomic factors and one based on environmental factors, where the latter is assumed to be mutually independent across indivi...

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“…MESA participants were stratifi ed by race/ethnic group using an unrelated subset of individuals from MESA and from MESAFS, by selecting, at most, one individual from each pedigree, based on inferred relationships in KING ( 25 ). Linear regression of quantitative phenotypes or logistic regression of dichotomous phenotypes was performed under an additive 1 d.f .…”

Section: Analysis Of Acsl1 Snps In Mesamentioning

confidence: 99%

Genetic association of long-chain acyl-CoA synthetase 1 variants with fasting glucose, diabetes, and subclinical atherosclerosis

Manichaikul

Wang

Zhao

et al. 2016

Journal of Lipid Research

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The enzyme long-chain acyl-CoA synthetase 1 (ACSL1), which catalyzes conversion of free long-chain fatty acids into their acyl-CoA derivatives, has emerged as a metabolic rheostat in mouse skeletal muscle, heart, and adipose tissue ( 1-3 ). Thus, mice defi cient in ACSL1 in skeletal muscle exhibit a marked reduction in fatty acid utilization through ␤ -oxidation and a concomitant increase in glucose utilization during fasting, and they are hypoglycemic during endurance training ( 4 ). In the heart, which relies heavily on fatty acids as an energy source, ACSL1 deficiency results in reduced fatty acid oxidation and increased glucose utilization ( 1, 2 ). Mice with adipose tissue-selective ACSL1 defi ciency have reduced blood glucose levels during cold exposure ( 3 ). Furthermore, an inducible wholebody ACSL1-defi cient mouse model exhibits lower blood glucose levels than matched controls ( 1 ). Thus, mouse studies have revealed a clear relationship between reduced blood glucose levels and reduced ACSL1 activity in several tissues due to metabolic fl exibility in these tissues.ACSL1 is ubiquitously expressed, with high levels in typical insulin target tissues, such as skeletal muscle, liver, and adipose tissue ( 5 ). ACSL1 is also expressed in myeloid cells, Abstract Long-chain acyl-CoA synthetase 1 (ACSL1) converts free fatty acids into acyl-CoAs. Mouse studies have revealed that ACSL1 channels acyl-CoAs to ␤ -oxidation, thereby reducing glucose utilization, and is required for diabetesaccelerated atherosclerosis. The role of ACSL1 in humans is unknown. We therefore examined common variants in the human ACSL1 locus by genetic association studies for fasting glucose, diabetes status, and preclinical atherosclerosis by using the MAGIC and DIAGRAM consortia; followed by analyses in participants from the Multi-Ethnic Study of Atherosclerosis, the Penn-T2D consortium, and a meta-analysis of subclinical atherosclerosis in African Americans; and finally, expression quantitative trait locus analysis and identifi cation of DNase I hypersensitive sites (DHS). The results show that three SNPs in ACSL1 (rs7681334, rs735949, and rs4862423) are associated with fasting glucose or diabetes status in these large (>200,000 subjects) data sets. Furthermore, rs4862423 is associated with subclinical atherosclerosis and coincides with a DHS highly accessible in human heart. SNP rs735949 is in strong linkage disequilibrium with rs745805, signifi cantly associated with ACSL1 levels in skin, suggesting tissue-specifi c regulatory mechanisms. This study provides evidence in humans of ACSL1 SNPs associated with fasting glucose, diabetes, and subclinical atherosclerosis and suggests links among these traits and acyl-CoA synthesis.

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Robust relationship inference in genome-wide association studies

Abstract: Our robust relationship inference algorithm is implemented in a freely available software package, KING, available for download at http://people.virginia.edu/∼wc9c/KING.

Cited by 2,615 publications

References 19 publications

Whole Exome Sequencing of Distant Relatives in Multiplex Families Implicates Rare Variants in Candidate Genes for Oral Clefts

Whole Exome Sequencing of Distant Relatives in Multiplex Families Implicates Rare Variants in Candidate Genes for Oral Clefts

Linear mixed model for heritability estimation that explicitly addresses environmental variation

Genetic association of long-chain acyl-CoA synthetase 1 variants with fasting glucose, diabetes, and subclinical atherosclerosis

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