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Multi‐Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Karen C. Clark, Anne E. Kwitek

10.1002/cphy.c210010

Source: Volume 12, Issue 1, January 2022

Published online: December 2021

Full Article on Wiley Online Library



Abstract

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS.

As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome‐wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi‐omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1‐40, 2022.

Figure 1. Figure 1. Common diseases are most often caused by common variants. The “common disease, common variant” hypothesis is used to explain the prevalence of both disease and the causal genetic variants. Mendelian diseases are caused by rare or ultra‐rare single variants. Alternatively, common diseases such as Metabolic Syndrome (or its component features) are typically caused by common variants that may be found at frequencies higher than 1% in the population. Common variants usually have minimal effects on a phenotype and are insufficient to cause disease without other genetic or environmental interactions. Rare variants that cause rare diseases have high effects and are usually sufficient to cause disease alone. Worth mentioning is that the same common disease, such as obesity, can result from numerous mutations, both rare and common. Though most obese individuals have numerous individual risk alleles, where each may only contribute a few grams of excess body weight, rarer variants may also cause the same diseases. For example, individuals with complete loss of the leptin receptor always exhibit profound obesity, but these alleles are rare, as is this form of obesity. Variants in the MC4R and FTO loci exhibit modest effects on body weight and can be found at frequencies higher than 1% in most populations.
Figure 2. Figure 2. Statistical associations between genotypes and phenotypes on a genome‐wide scale. Genetic tests of association begin with a population comprised of a selection of cases with a trait, such as obesity, and a selection of controls that are ideally identical to the cases, apart from the trait that is being tested. In this example of outbred rats, a group of obese cases and lean controls are genotyped at some position for the alleles A and a, where the allele frequencies of each are shown. While allele a is present in both subgroups, it is significantly enriched among the obese cases. Compiling this individual test and other like it across the genome produces a genome‐wide plot, where each point represents an individual test. The P‐values of each association test are −log10 transformed and plotted with respect to their genomic position, with individual chromosome positions shown here in alternating colors. Most GWAS datasets contain tests of association for hundreds of thousands to millions of independent SNPs, and at that density of points, the plots begin to resemble city skylines and are called Manhattan plots. When multiple independent tests are conducted, it is important to adjust the threshold for P‐values significance to lower the chances of detecting a false association. By convention, a Bonferroni correction is applied. If you test 100 independent loci, the threshold for genome‐wide significance is equal to 0.05/100, or 5 × 10−4, to account for the 100 independent tests. The negative log‐transformation of this threshold puts the cutoff for significance roughly at 3.3, indicated with a solid red line. The hypothetical example shown here is the only SNP that exceeds the threshold, so allele a is significantly associated with obesity, although that does not necessarily mean it is causing obesity.
Figure 3. Figure 3. Backcrosses, intercrosses, and outbreeding with two parent genomes. When two distinct parental genomes (blue and red) are combined, the resulting progeny in the F1, or first filial generation are identical to each other and an equal mixture of both Parent A and Parent B. In a backcross scheme, the F1 progeny are subsequently crossed back to either parent, with successive phenotyping and/or genotyping at each stage. This type of approach is useful when the trait one is attempting to map has an autosomal dominant inheritance pattern, and after 10 generations, an inbred animal is achieved that has been selected to have the minimum portion of the donor genome necessary to produce a phenotype, while the rest of the genome is homozygous for the recipient parental genome. For traits where the inheritance pattern is unknown or recessive, an intercross breeding scheme must be used, where F1 progeny are sibling mated to produce F2s, the genomes of which are random mosaics, containing portions of chromosomes that are heterozygous (purple) or homozygous for one genome or the other (blue or red). Each additional intercross produces new recombinations, which increases genetic complexity and leads to finer mapping resolution, and this process can be theoretically continued indefinitely. Twenty intercrosses are required to produce a fully inbred animal for follow‐up functional studies with brother‐sister mating.
Figure 4. Figure 4. Multi‐parental rodent populations for QTL mapping. Eight founder genomes are represented in the collaborative cross (CC) mouse, diversity outbred (DO) mouse, and heterogeneous stock (HS) rat. In contrast to the other two, the CC mice are inbred and were developed as highly diverse series of lineages in a RI panel. From the CC resource populations, a subset of animals was outbred to reestablish genetic variation using at least 20 generations of circular or semi‐random mating designed to maximize outbreeding by ensuring close relatives were never bred together. This became the DO mouse. The founder genomes of the CC/DO populations are: C57BL/6J, 129S1/SvlmJ, A/J, NOD/ShiLtJ, NZO/HiLtJ, CAST/EiJ, PWK/PhJ, WSB/EiJ, and the inclusion of wild strains of Mus musculus generates the most diverse rodent population and is the closest to matching the spectrum of human genetic diversity. Aside from species differences, a major factor affecting the genetic diversity of the HS rat is that the HS rat was established using a one‐way breeding funnel, which allows alleles to become fixed within the population more easily, rather than retaining a high degree of variance. With balanced, reciprocal crosses, allele fixation or loss is less likely. Although the introduction of wild alleles increases the breadth of diversity in the CC/DO genomes; these alleles have proven somewhat deleterious as they have undergone negative selection in the unfavorable laboratory environment.
Figure 5. Figure 5. The abundance of an mRNA transcript is genetically regulated by eQTL and sQTL. Transcription factors recruit RNA polymerases when bound to transcription factor binding sites (TFBS). The strength of the TFBS is dependent on the sequence, and variation in sequences can alter the abundance of the transcribed gene. When the sequence variant is located near the gene‐often in a TFBS, it is said to be cis regulated or a cis‐eQTL. If the transcription factor itself has a variant that hinders its ability to bind to its TFBS, then the gene is trans‐regulated and the SNP is a trans‐eQTL, as the coding region for the transcription factor need not be near the regulated gene. When the nascent mRNA is transcribed, introns are spliced out to form the mature mRNA. Each exon is bounded by GT‐‐‐‐AC nucleotides that signal the boundaries of the intron to be removed. When these sites vary, the proportions of certain exons might change, or exons that are normally included in the transcript might be skipped entirely.
Figure 6. Figure 6. Genetic variants contribute to gene expression changes by altering chromatin organization. Stretched end to end, each cell contains nearly 2 m of DNA. The only way DNA can fit within the confines of the nucleus is by extensive compaction into chromosomes; however, DNA is not uniformly compacted. Areas containing genes that are undergoing active transcription (heterochromatin) are more loosely organized to allow access to various DNA‐binding proteins and RNA polymerases. These open areas are not protected by any histone proteins and are vulnerable to enzymatic digestion by DNases. As with most proteins, histones contain posttranslational modifications, which change their function to indicate a promotor or enhancer site, or to mark areas of closed chromatin. Long‐range chromatin interactions, such as topologically associated domains (TADs) are indicated by specific patterns of histone marks, allowing them to form a cluster of chromatin loops that bring linearly distant loci to close together in three‐dimensional space. All of these features display heterogeneity at the sequence level, causing quantitative changes to the transcriptome.


Figure 1. Common diseases are most often caused by common variants. The “common disease, common variant” hypothesis is used to explain the prevalence of both disease and the causal genetic variants. Mendelian diseases are caused by rare or ultra‐rare single variants. Alternatively, common diseases such as Metabolic Syndrome (or its component features) are typically caused by common variants that may be found at frequencies higher than 1% in the population. Common variants usually have minimal effects on a phenotype and are insufficient to cause disease without other genetic or environmental interactions. Rare variants that cause rare diseases have high effects and are usually sufficient to cause disease alone. Worth mentioning is that the same common disease, such as obesity, can result from numerous mutations, both rare and common. Though most obese individuals have numerous individual risk alleles, where each may only contribute a few grams of excess body weight, rarer variants may also cause the same diseases. For example, individuals with complete loss of the leptin receptor always exhibit profound obesity, but these alleles are rare, as is this form of obesity. Variants in the MC4R and FTO loci exhibit modest effects on body weight and can be found at frequencies higher than 1% in most populations.


Figure 2. Statistical associations between genotypes and phenotypes on a genome‐wide scale. Genetic tests of association begin with a population comprised of a selection of cases with a trait, such as obesity, and a selection of controls that are ideally identical to the cases, apart from the trait that is being tested. In this example of outbred rats, a group of obese cases and lean controls are genotyped at some position for the alleles A and a, where the allele frequencies of each are shown. While allele a is present in both subgroups, it is significantly enriched among the obese cases. Compiling this individual test and other like it across the genome produces a genome‐wide plot, where each point represents an individual test. The P‐values of each association test are −log10 transformed and plotted with respect to their genomic position, with individual chromosome positions shown here in alternating colors. Most GWAS datasets contain tests of association for hundreds of thousands to millions of independent SNPs, and at that density of points, the plots begin to resemble city skylines and are called Manhattan plots. When multiple independent tests are conducted, it is important to adjust the threshold for P‐values significance to lower the chances of detecting a false association. By convention, a Bonferroni correction is applied. If you test 100 independent loci, the threshold for genome‐wide significance is equal to 0.05/100, or 5 × 10−4, to account for the 100 independent tests. The negative log‐transformation of this threshold puts the cutoff for significance roughly at 3.3, indicated with a solid red line. The hypothetical example shown here is the only SNP that exceeds the threshold, so allele a is significantly associated with obesity, although that does not necessarily mean it is causing obesity.


Figure 3. Backcrosses, intercrosses, and outbreeding with two parent genomes. When two distinct parental genomes (blue and red) are combined, the resulting progeny in the F1, or first filial generation are identical to each other and an equal mixture of both Parent A and Parent B. In a backcross scheme, the F1 progeny are subsequently crossed back to either parent, with successive phenotyping and/or genotyping at each stage. This type of approach is useful when the trait one is attempting to map has an autosomal dominant inheritance pattern, and after 10 generations, an inbred animal is achieved that has been selected to have the minimum portion of the donor genome necessary to produce a phenotype, while the rest of the genome is homozygous for the recipient parental genome. For traits where the inheritance pattern is unknown or recessive, an intercross breeding scheme must be used, where F1 progeny are sibling mated to produce F2s, the genomes of which are random mosaics, containing portions of chromosomes that are heterozygous (purple) or homozygous for one genome or the other (blue or red). Each additional intercross produces new recombinations, which increases genetic complexity and leads to finer mapping resolution, and this process can be theoretically continued indefinitely. Twenty intercrosses are required to produce a fully inbred animal for follow‐up functional studies with brother‐sister mating.


Figure 4. Multi‐parental rodent populations for QTL mapping. Eight founder genomes are represented in the collaborative cross (CC) mouse, diversity outbred (DO) mouse, and heterogeneous stock (HS) rat. In contrast to the other two, the CC mice are inbred and were developed as highly diverse series of lineages in a RI panel. From the CC resource populations, a subset of animals was outbred to reestablish genetic variation using at least 20 generations of circular or semi‐random mating designed to maximize outbreeding by ensuring close relatives were never bred together. This became the DO mouse. The founder genomes of the CC/DO populations are: C57BL/6J, 129S1/SvlmJ, A/J, NOD/ShiLtJ, NZO/HiLtJ, CAST/EiJ, PWK/PhJ, WSB/EiJ, and the inclusion of wild strains of Mus musculus generates the most diverse rodent population and is the closest to matching the spectrum of human genetic diversity. Aside from species differences, a major factor affecting the genetic diversity of the HS rat is that the HS rat was established using a one‐way breeding funnel, which allows alleles to become fixed within the population more easily, rather than retaining a high degree of variance. With balanced, reciprocal crosses, allele fixation or loss is less likely. Although the introduction of wild alleles increases the breadth of diversity in the CC/DO genomes; these alleles have proven somewhat deleterious as they have undergone negative selection in the unfavorable laboratory environment.


Figure 5. The abundance of an mRNA transcript is genetically regulated by eQTL and sQTL. Transcription factors recruit RNA polymerases when bound to transcription factor binding sites (TFBS). The strength of the TFBS is dependent on the sequence, and variation in sequences can alter the abundance of the transcribed gene. When the sequence variant is located near the gene‐often in a TFBS, it is said to be cis regulated or a cis‐eQTL. If the transcription factor itself has a variant that hinders its ability to bind to its TFBS, then the gene is trans‐regulated and the SNP is a trans‐eQTL, as the coding region for the transcription factor need not be near the regulated gene. When the nascent mRNA is transcribed, introns are spliced out to form the mature mRNA. Each exon is bounded by GT‐‐‐‐AC nucleotides that signal the boundaries of the intron to be removed. When these sites vary, the proportions of certain exons might change, or exons that are normally included in the transcript might be skipped entirely.


Figure 6. Genetic variants contribute to gene expression changes by altering chromatin organization. Stretched end to end, each cell contains nearly 2 m of DNA. The only way DNA can fit within the confines of the nucleus is by extensive compaction into chromosomes; however, DNA is not uniformly compacted. Areas containing genes that are undergoing active transcription (heterochromatin) are more loosely organized to allow access to various DNA‐binding proteins and RNA polymerases. These open areas are not protected by any histone proteins and are vulnerable to enzymatic digestion by DNases. As with most proteins, histones contain posttranslational modifications, which change their function to indicate a promotor or enhancer site, or to mark areas of closed chromatin. Long‐range chromatin interactions, such as topologically associated domains (TADs) are indicated by specific patterns of histone marks, allowing them to form a cluster of chromatin loops that bring linearly distant loci to close together in three‐dimensional space. All of these features display heterogeneity at the sequence level, causing quantitative changes to the transcriptome.
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Article Title: Multi‐Omic Approaches to Identify Genetic Factors in Metabolic Syndrome
 

How to Cite

Karen C. Clark, Anne E. Kwitek. Multi‐Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021, 12: 3045-3084. doi: 10.1002/cphy.c210010