A complex trait is defined to be a non-Mendelian trait, which means that the trait does not obey the rules and assumptions of Mendelian genetics such as 1) a single gene determines the trait, 2) there are only dominant and recessive alleles for each gene, and 3) the phenotype is discrete and not continuous.
Complex traits are multifactorial and quantitative, violating Mendel’s assumptions. In a multifactorial trait, multiple genetic and environmental factors contribute to the phenotype. A quantitative trait possesses not two or three possible phenotypes (e.g. round or wrinkled) but an infinite number along a continuous scale (e.g. slightly wrinkled).
In an earlier post, I stated that the fundamental law of genetics is that Phenotype = Genotype + Environment, and that heritability is the portion of a trait (phenotype) that can be attributed to genotype (genetics) and not environment. A classic Mendelian trait possesses heritability = 1 (gene determines the trait). In the real world, human complex traits are multifactorial and hence have heritability much less than 1 and often close to 0 (i.e. environmental factors contribute more to trait than genetic factors).
How can we identify the genetic factors (i.e. genes) that influence a given trait of interest? For example, blood pressure is a complex trait possessing multiple genetic and environmental factors. The estimated heritability is approximately 0.25 (SNP) to 0.5 (twin studies). What genetic variants (i.e. gene alleles) contribute to high blood pressure? Identifying the relevant genes can help predict risk of hypertension from one’s genome sequence (even before the phenotype manifests itself), and can serve as targets for therapeutic treatments.
One approach is linkage analysis which follows the segregation of genetic markers (i.e. SNPs) and the phenotype in family trees. Co-segregation indicates that the genetic factor contributing to the trait and the marker are close together (linked) on the same chromosome (i.e. not likely to recombine and become separated). This method works best for Mendelian traits in which a single gene “determines” the trait with little to no influence from other genetic and environmental factors so that the genetic signal is strong.
As a reminder, a SNP (single nucleotide polymorphism) is defined as a variation at a single position in a DNA sequence among individuals of a species. SNPs play a central role in human genetics because they can serve as genetic markers. These markers are “mile posts” of chromosomal (genome) locations (loci) analogous to a map. Certain SNPs are close to or within certain genes.
The second, more commonly-used approach of identifying phenotype-influencing genetic factors is a Genome-Wide Association Study (GWAS), which is a powerful observational study design to pinpoint genetic variants that are statistically associated with a particular trait or disease. GWAS examines a large set of genetic markers (i.e. SNPs) spanning the entire genome so that it isn't restricted to a single gene or a small set of candidate genes. Importantly, association does not prove causation; it simply means that a particular variant is found more often in individuals with the trait than in those without. In this manner, one can identify regions of the human genome that associate with the trait and focus in on genes residing in that region.
For example, the "T" allele of a certain SNP (rs16998073) is associated with the trait of high blood pressure, and so people containing the T nucleotide at a particular location in the human genome (in the vicinity of 4q21: chromosome 4, long arm (q), band 21) are more likely to have high blood pressure than people with a different nucleotide at that position in the genome (link). This SNP marker happens to be close to some gene(s) presumably involved in blood pressure control. Further work is needed to demonstrate a causal link with a particular gene and a particular variant (allele) of the gene.
Association between genetic marker and trait arises because of linkage disequilibrium (LD), which refers to the non-random association of alleles at two or more different loci (locations on a chromosome) in a given population. In simple terms, an allele (variant) at one location is disproportionately likely to be found together with a certain allele at another location on the same chromosome. Not surprisingly, the main cause of LD is linkage (physical proximity). Alleles at loci that are physically close together on the same chromosome tend to be inherited together because recombination events (crossing over during meiosis that separate the alleles on to different chromosomes) are less likely to occur between them. Other causes of linkage disequilibrium include epistasis, population admixture, genetic drift and recent mutation.
A hypothetical causal mutation that influences a trait can be in linkage disequilibrium with a nearby SNP genetic marker so that the marker associates with the trait. We need to genotype (determine alleles) of many SNP markers in a large population to find out which ones associate with the trait, and thus localize the hypothetical causal mutations to particular genes near the SNP "hits". Remember there are only about 20,000 genes in the human genome.
The basic steps for performing a GWAS are as follows:
1. Recruit study population. Decide on a (disease) trait to investigate and gather hundreds of thousands, even millions of unrelated individuals. They are divided into cases (individuals with the trait) and controls (without the trait). Why so many? Most common diseases are influenced by many genes (polygenic), each with a very small effect. A huge sample size is required to have enough statistical power to detect these subtle effects.
2. Genotyping. DNA samples (usually from blood or saliva) are collected from participants, and analyzed by SNP arrays or more recently by whole genome sequencing (WGS). This provides information on the particular allele (nucleotide) at each SNP being assessed in the GWAS for every person. Typically hundreds of thousands to millions of SNPs scattered across the human genome are genotyped for every subject.
3. Statistical analysis. Each SNP is tested for association with the trait over the whole study population. For illustrative purposes let’s assume that the trait is discrete and binary, e.g. disease or no disease. Then for each SNP (assuming 2 alleles, which are referred to as genotype 1 and genotype 2), one can construct the following 2x2 contingency table with $a, b, c, d$ representing the numbers of people in each category i.e. genotype 1 with disease.
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| Genotype 1 would be one allele at a particular SNP (e.g. "A" at SNP rs16998073), and Genotype 2 would be another allele at that SNP (e.g. "T"). The variables $a$, $b$, $c$, and $d$ are the number of people with a particular genotype and phenotype that sums up to total study population. |
From this table one can calculate the odds ratio (OR) which is a measure of effect size (i.e. how much the particular SNP will affect the trait). It is the ratio of the odds of an event occurring in one group relative to the odds of it occurring in another group (odds is the probability of disease divided by probability of no disease). Thus, the odds-ratio for the contingency table above is $OR = \frac{a}{b} / \frac{c}{d} = \frac{ad}{bc}$. If $OR = 1$, no association between genotype and outcome; if
$OR > 1$, SNP genotype (allele) is associated with higher odds of the outcome; and if $OR < 1$, genotype is associated with lower odds of the outcome.
One can then perform the chi-square significance test to assess the statistical significance of the OR deviating from 1, which is summarized as a p-value for the particular genotype (SNP allele). Thus, one obtains a p-value for every SNP that is tested in the GWAS for association with the trait of interest.
An alternative (and more common) approach to GWAS statistical analysis is logistic (for binary traits) or linear (for continuous traits) regression in which a linear model describes the contribution of each SNP to the trait. A big advantage of using regression is that adjustments can be made for potential confounders (e.g., age, sex, ancestry differences).
4. Interpreting and Displaying Results. The final results of the statistical analysis (SNP p-values) are visualized in a Manhattan plot, which is a scatter plot displaying SNP associations across chromosomes. Each data point represents one SNP with its chromosome position along the x-axis (i.e. in which chromosome is it located), and the y-axis is the p-value for the association with the trait from the statistical analysis. SNPs that surpass a stringent significance threshold (typically a p-value of <5 × 10⁻⁸ which accounts for multiple testing of hundreds of thousands of SNPs at the same time) indicate genomic regions associated with the trait. Usually a group of SNPs in the same region, presumably near a causal mutation/variant, will rise above the background. It is called a Manhattan plot because of the visual appearance of the Manhattan skyline with high-scoring hits appearing as skyscrapers.
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| Each dot represent one of numerous (hundreds of thousands of SNPs). They are color-coded according to their chromosomal location on the x-axis. The y-axis plots $-\log_{10}(\text{p-value})$ of the SNP association with the trait as measured by chi-square or linear/logistic regression. The red line indicates the threshold above which a SNP p-value is considered statistically significant ("hit"). In this example, a cluster of SNPs near the gene TCF7L2 clear the threshold implicating this gene for the trait (type 2 diabetes). |
It is important to acknowledge the limitations of GWAS. First, it usually identifies common genetic variants with modest effects. Rare variants with larger effects may not be detected because they are so rare, and variants with very small effects may also be missed. Second, population stratification (genetic ancestry differences) can lead to false positives if the confounding is not properly accounted for. Certain variants may be prevalent in certain subpopulations, and so may associate with traits that are enriched in the subpopulations for environmental (e.g. cultural) reasons (e.g. chopstick gene). Third, GWAS requires very large sample sizes to achieve adequate statistical power. Finally as mentioned above, association does not imply causality. There may be confounding factors so that the SNP association with the trait is spurious and not the result of tightly-linked (nearby) causal genetic factors.
Despite these limitations, there has been a flood of new information over the last 20 years. It is no exaggeration to say that GWAS has ushered in a revolution in human genetics identifying numerous genetic factors associated with countless traits ranging from coronary artery disease to Alzheimer’s to type 2 diabetes. One example among many is how GWAS pinpointed CFH and ARMS2/HTRA1 as major risk genes for age-related macular degeneration (AMD). These findings have led to new biological understanding as GWAS hits revealed unexpected genes and pathways such as PCSK9, which later became the target of cholesterol-lowering drugs. Indeed, the genetic associations can aid in the discovery and validation of drug targets. Last but not least, GWAS is a necessary step for constructing Polygenic Risk Scores (PRS, also known as polygenic score or PGS) which uses SNPs to estimate the genetic risk for a disease trait. This is sort of the reverse of GWAS which starts with the trait and then identifies genetic factors that contribute to the trait. These associated SNPs can be used in a PRS to predict the likelihood of the trait in new individuals from a different population from their genotypes. PRS have already entered the clinic, and they have provided significant additional information beyond standard clinical risk scores which are derived from lab tests.
TL;DR: GWAS is a powerful tool for identifying genomic regions that associate with a particular trait. This has allowed researchers to pinpoint specific genes that influence the trait.
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