Genome-Wide Association Studies and Liver Disease

Sequencing of the human genome has opened up many opportunities to learn about our own genetic susceptibilities to disease. In this Foreword to this issue of Seminars in Liver Disease, I provide some required background to understanding genome-wide association analyses in general, including a list of terms ([Table 1]) often used in such studies. Five areas of particular significance are then reviewed in detail in the articles that follow.

Table 1 Genetic terms often used in genome-wide association study analyses

Term	Definition
Genetic	Relating to DNA
Phenotype	A measured trait or disease
Genetic code	The series of nucleotides (A,C,G,T) that make up DNA
Genetic variant	Nucleotide or structural differences in parts of the genome that differ between individuals
Nucleotide	The adenine, cytosine, guanine, and thymine components that compose DNA
Single nucleotide polymorphism (SNP)	Variation in nucleotide sequence at a particular spot in the genetic code between individuals
Allele	An alternative form of a nucleotide at a spot in the genome
Variance	The diversity in the measurement of a particular trait
Heritability	The proportion of the variation in a trait that is genetically influenced
Genotyping	Determining the nucleotide present in one particular point in the genome
Sequencing	Determining all of the nucleotides present in regions of the genome
HapMap	Characterized variation in millions of genetic variants across the genome in diverse ancestries
Linkage disequilibrium (LD)	The nonrandom association of particular alleles in the genome
Haplotype	A group of nearby alleles that are inherited together
CEU	Individuals of Northern European ancestry (from Utah specifically) in HapMap
YRI	Individuals of African ancestry from Yoruba (Nigeria specifically) in HapMap
GWAS	Genome-wide association study; where many thousands of variants across the genome are tested for association with a trait of interest
Association	The presence of a particular genotype with a phenotype above and beyond statistical chance
Stratification	The presence of individuals of different ancestries in a sample which when not accounted for can lead to spurious genetic associations
Multiple hypothesis testing	When more than one hypothesis is being tested for statistical association (i.e., more than 1 SNP)
Clustering	Individuals with particular genotypes will give off more similar detection patterns and thus cluster together as they are being detected
Hardy-Weinberg equilibrium	A measure of the frequencies of each genotype should be in a population that has reached equilibrium—if SNPs do not genotype properly they often are out of equilibrium and are thus eliminated from analyses
Impute	To use known SNP genotypes and LD patterns from reference populations to predict the genotype at nearby SNPs
Power	The chance of detecting an effect of a certain size in a sample of particular size—range 0 to 100%
Arrays	Products that contain probes to assess multiple SNPs simultaneously
Lead SNP	The SNP initially found to best associate with a trait of interest statistically
Causal SNP	The SNP that actually causes variation in the trait of interest
Prevalence	Total number of cases of a disease at a particular time
Incidence	Total number of new cases of a disease per a certain number of people surveyed over a particular period
Nonsynonymous variant	A variant that changes the amino acid structure of a protein
Synonymous variant	A variant that does not change the amino acid structure of a protein
Missense variant	A variant that changes the amino acid structure of a protein
Gain-of-function mutation	When a variant leads to a protein having a new function that the normal protein does not have
eQTL	Expression QTL studies where genetic variants are tested for whether they are associated with changes in gene expression
Functional variant	A variant that leads to a noticeable phenotype
Locus	A region of the genome
Gene	A part of the genome that make a protein of particular function
Odds ratio	A measure of effect size, describing the strength of association or nonindependence between two binary data values
Attributable risk	Difference in the rate of a condition between an exposed population and an unexposed population
Positive predictive value	Proportion of individuals with positive test results who truly have the disease/response
Negative predictive value	Proportion of individuals with negative test results who truly do not have the disease/response

• References

• 1 Cargill M, Altshuler D, Ireland J , et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet 1999; 22 (3) 231-238
• 2 Halushka MK, Fan JB, Bentley K , et al. Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat Genet 1999; 22 (3) 239-247
• 3 Li WH, Sadler LA. Low nucleotide diversity in man. Genetics 1991; 129 (2) 513-523
• 4 Wang DG, Fan JB, Siao CJ , et al. Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 1998; 280 (5366) 1077-1082
• 5 Kruglyak L, Nickerson DA. Variation is the spice of life. Nat Genet 2001; 27 (3) 234-236
• 6 Reich DE, Gabriel SB, Altshuler D. Quality and completeness of SNP databases. Nat Genet 2003; 33 (4) 457-458
• 7 Gibbs RA, Belmont JW, Hardenbol P , et al; International HapMap Consortium. The International HapMap Project. Nature 2003; 426 (6968) 789-796
• 8 International HapMap Consortium. A haplotype map of the human genome. Nature 2005; 437 (7063) 1299-1320
• 9 Welter D, MacArthur J, Morales J , et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 2014; 42 (Database issue): D1001-D1006
• 10 McCarthy MI, Abecasis GR, Cardon LR , et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008; 9 (5) 356-369
• 11 Chanock SJ, Manolio T, Boehnke M , et al; NCI-NHGRI Working Group on Replication in Association Studies. Replicating genotype-phenotype associations. Nature 2007; 447 (7145) 655-660
• 12 Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38 (8) 904-909
• 13 Rice TK, Schork NJ, Rao DC. Methods for handling multiple testing. Adv Genet 2008; 60: 293-308
• 14 Pe'er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 2008; 32 (4) 381-385
• 15 Park J-H, Wacholder S, Gail MH , et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet 2010; 42 (7) 570-575
• 16 Speliotes EK, Willer CJ, Berndt SI , et al; MAGIC; Procardis Consortium. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42 (11) 937-948
• 17 Darabi H, Humphreys K. Single- and multi-locus association tests incorporating phenotype heterogeneity. Hum Hered 2011; 71 (1) 11-22
• 18 Anderson CA, Pettersson FH, Barrett JC , et al. Evaluating the effects of imputation on the power, coverage, and cost efficiency of genome-wide SNP platforms. Am J Hum Genet 2008; 83 (1) 112-119
• 19 Pe'er I, de Bakker PI, Maller J, Yelensky R, Altshuler D, Daly MJ. Evaluating and improving power in whole-genome association studies using fixed marker sets. Nat Genet 2006; 38 (6) 663-667
• 20 Manning AK, LaValley M, Liu CT , et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol 2011; 35 (1) 11-18
• 21 Zhang X, Huang S, Zou F, Wang W. Tools for efficient epistasis detection in genome-wide association study. Source Code Biol Med 2011; 6 (1) 1
• 22 Willer CJ, Speliotes EK, Loos RJ , et al; Wellcome Trust Case Control Consortium; Genetic Investigation of ANthropometric Traits Consortium. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009; 41 (1) 25-34
• 23 Lango Allen H, Estrada K, Lettre G , et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010; 467 (7317) 832-838