Polygenes Are Involved in Determining Continuously Varying or Multiplefactor Traits

Polygene

Polygenes are situated in heterochromatin and are joined together in blocks.

From: Sex Chromosomes , 1967

Genetics of Gallstone Disease

Shih-Chang Chuang , ... King-Teh Lee , in Advances in Clinical Chemistry, 2013

1.4 Polygenic predisposition to gallstones and polymorphism studies

Human and mouse model studies have demonstrated that polygenes underlie the predisposition for most cholesterol gallstone development [14,23]. In the majority of cases, gallstones might develop as a result of lithogenic polymorphisms in several genes and their interactions with multiple environmental factors. Buch et al. performed a genome-wide association (GWA) study of >   500,000 SNP in 280 individuals with gallstones and 360 controls. They identified a coding variant rs11887534 (D19H) of the sterol transporters ABCG5/G8 on the canalicular membrane of hepatocytes as a risk factor for cholesterol gallstone development [24]. The D19H variant of cholesterol transporter ABCG5/G8 was also identified as a genetic determinant of gallstone formation [25]. This study showed that the ABCG5/G8 gene confers an odds ratio (OR) of 2:3 in heterozygotes and 1:7 in homozygous carriers based on GWA [24] and linkage [25] studies.

Certain polymorphisms of the apolipoprotein (APO)-E genes, ApoB genes, and the cholesteryl ester transfer protein (CETP) are associated with gallstone development. ApoE is a major and minor protein component of VLDL (very-low-density lipoprotein) and HDL, respectively. Defects in ApoE result in familial type III hyperlipoproteinemia (HLP III) [26]. Impaired clearance of chylomicron, VLDL, and low-density lipoprotein (LDL) remnants results in increased plasma cholesterol and triglycerides. Certain polymorphisms of the ApoE genes correlate with cholesterol gallstone formation. ApoE is inherited as three allelic variants, ɛ2, ɛ3, and ɛ4. The ɛ2 genotype protects against gallstone formation in women [27]. The ɛ4 genotype is a genetic risk factor for cholelithiasis [28]. However, the association of ApoE ɛ4 alleles and gallstones was not confirmed in subsequent studies [29]. Miettinen et al. demonstrated that intestinal cholesterol absorption was dependent on ApoE isoforms with ɛ4   >   ɛ3   >   ɛ2 [30]. Fecal excretion of cholesterol was higher in the ApoE ɛ2 phenotype versus the ɛ3 or ɛ4 phenotype.

Many genetic polymorphisms result in susceptibility to GSD. These include the cholesterol transporter [24,25,31–34], plasma transport [27,28,35–39], CETP [40] and uptake [41,42], bile acid synthesis [38,43,44], transporter [45–47] and bilirubin excretion [48,49], mucin [50,51], gallbladder motility [52,53], and hormone receptor [54] (Table 5.1).

Table 5.1. Single nucleotide polymorphisms in gallstone disease

	Gene	Gene describe	SNP	Results	References
Cholesterol transporter	ABCG8	ATP-binding cassette, subfamily G, member 8. Transporter that appears to play an indispensable role in the selective transport of the dietary cholesterol in and out of the enterocytes and in the selective sterol excretion by the liver into bile	rs11887534 (D19H)	– P  =   0.017, odds ratio (OR)   =   2.274 in Indian population – OR for D19H carriership is 2.2 in German – The risk of gallstones in carriers of the 19H allele was significantly increased in randomly selected cases from the ASP cohort compared to the stone-free controls (OR   =   3.018; P  =   0.017)	[24,25,31–33]
			T400K	Male carriers of the less frequent K allele of ABCG8 T400K had a 2.31-fold elevated risk (P  =   0.023) for gallstone disease compared to males with the common genotype after the adjustment for age, body mass index.	[55]
	ABCG5	ATP-binding cassette, subfamily G, member 5	604Q	Increased risk of gallstone disease, adjusted OR   =   4.7	[33]
Plasmatic transport	ApoA1	Apolipoprotein A-1. Major protein of plasma HDL, also found in chylomicrons. Synthesized in the liver and small intestine	−   75G   >   A	Patients with the GG genotype (P  =   0.015) and G allele carriers (P  =   0.004) had a significantly higher risk of gallstone disease (1.087-fold and 1.561-fold, respectively)	[36]
	ApoB	Apolipoprotein B (including Ag(x) antigen). ApoB-100 functions as a recognition signal for the cellular binding and internalization of LDL particles by the ApoB/E receptor	XbaI c.2488C   >   T, c.4154G   >   A	– The frequency of the X   +/− genotype (20.63% vs. 7.94%) and X   + allele (10.79% vs. 3.97%) was significantly higher in the patient group than in the control group – Percentages of X   + allele 8.57% in GSD and 4.01% in healthy controls (P  <   0.01)	[38,39]
			4154G   >   A (EcoRI)	A risk of gallstone formation was reduced in 4154AA homozygotes (OR   =   0.25, P  =   0.009) and heterozygous individuals (OR   =   0.63, P  =   0.03) as compared to 4154GG homozygotes	[35]
	ApoC1	Apolipoprotein C-1. Appears to modulate the interaction of ApoE with beta-migrating VLDL and inhibit binding of beta-VLDL to the LDL receptor-related protein	HpaI	Frequency of H2H2 was significantly higher (P  =   0.017) in patients than in controls, and it was imposing a very high risk (OR   =   9.416, 95% (confidence interval) CI   =   1.125–78.786) for gallstone disease	[37]
	ApoE	Apolipoprotein E. Mediates the binding, internalization, and catabolism of lipoprotein particles. It can serve as a ligand for the LDL (ApoB/E) receptor and for the specific ApoE receptor (chylomicron remnant) of hepatic tissues	E2/3/4	– The E4/3 phenotype was enriched in both patients with gallstones and those who underwent cholecystectomy, with significantly (P  <   0.006) higher epsilon 4 allele frequencies than in gallstone-free subjects (OR   =   2.67 and 3.62, respectively) – In women with apolipoprotein E2 (phenotypes E2/2, 2/3, and 2/4) compared with women without E2 (E3/3, 4/3, and 4/4), the OR for GSD was 0.28	[27,28]
Cholesteryl ester transfer protein	CETP	Cholesteryl ester transfer protein. Involved in the transfer of insoluble cholesteryl esters in the reverse transport of cholesterol	rs693, rs708272	– The nonancestral T/T genotype of apolipoprotein B rs693 polymorphism was associated with a decreased risk of GBC (OR: 0.14, 95% CI   =   0.03–0.63). The T/T genotype of cholesteryl ester transfer protein (CETP) rs708272 polymorphism was associated with an increased risk of GBC (OR: 5.04, 95% CI   =   1.43–17.8)	[40]
Cholesterol uptake	LRPAP1	Low-density lipoprotein receptor-related protein associated protein 1, which plays a key role in cholesterol metabolism	Intron 5 insertion/deletion	– Frequency of Ins allele was significantly higher in the patient group than in the control group (P  =   0.003). Frequencies of Del and Ins allele were 65.77% and 34.23% in patients, 76.24% and 23.76% in controls – The D allele of LRPAP1 was significantly higher in GBC patients as compared to gallstone patients (P  =   0.013; OR   =   1.6, 95% CI   =   1.1–2.4)	[41,42]
Bile salt synthesis	CYP7A1	Cytochrome P450, family 7, subfamily A, polypeptide 1. Involved in lipid metabolism, and bile acid biosynthesis pathways	−   204A   >   C	Percentages of A allele in patients and controls were 62.86% and 54.38% (P  <   0.05)	[38,44]
	FGFR4	Fibroblast growth factor receptor 4 (FGFR4) plays an important role in maintaining bile acid homeostasis by regulating the expression of cholesterol 7 alpha-hydroxylase (CYP7A1), a rate-limiting enzyme for bile acid biosynthesis	Gly388Arg (G-388R)	The ratio of gallstone patients with acute cholecystitis in the FGFR4 RR genotype (42%) was significantly higher than that in other genotypes of FGFR4 (P  =   0.019)	[43]
Bile acid transporter	SLC10A2	Solute carrier family 10 (sodium/bile acid cotransporter family), member 2. Plays a critical role in the sodium-dependent reabsorption of bile acids from the lumen of the small intestine. Plays a key role in cholesterol metabolism	rs9514089	Male nonobese rs9514089 was highly significantly linked to cholelithiasis (P  =   0.00767, OR   =   2.04)	[46]
			rs9514089	Association with low plasma cholesterol levels (P  =   0.05).
	SLCO1B1	Solute carrier organic anion transporter family, member 1B1. Mediates the Na(+)-independent transport of organic anions Organic anion transport protein 1B1 is a major transporter protein for bile salt uptake in enterohepatic circulation of bile salts	rs11045819 (Exon4 C   >   A, Pro155 Thr); rs4149056 (Ex6   +   40T   >   C, Val174Ala)	The frequency of CA genotype and A allele of Exon4 C   >   A polymorphism was higher in gallstone patients (12.4% and 6.2%) as compared to controls (5.2% and 2.6%), which was statistically significant [(P  =   0.029; OR   =   2.31; 95% CI   =   1.1–5.0); (P  =   0.034; OR   =   2.22; 95% CI   =   1.1-4.8), respectively]	[45]
	NR1H4 (FXR)	Nuclear receptor subfamily 1, group H, member 4. Receptor for bile acids such as chenodeoxycholic acid, lithocholic acid, and deoxycholic acid. Represses the transcription of the cholesterol 7 alpha-hydroxylase gene (CYP7A1) and activates the intestinal bile acid-binding protein (IBABP). Activates the transcription of bile salt export pump ABCB11 by directly recruiting histone methyltransferase CARM1 within its gene locus	NR1H4_1 [T-G-A]	OR   =   2.09 (1.13–3.86); P  =   0.02	[47]
Bilirubin excretion	UGT1A1	UDP glucuronosyltransferase 1 family, polypeptide A1 gene affects steady-state bilirubin levels and the incidence of gallstones in children with SCA Transforms bilirubin into water-soluble, excretable metabolites	Promoter	– UGT1A promoter polymorphisms may influence the ability of hydroxyurea to prevent gallstone formation in patients with SCA – Children with SCA had a lower frequency of the normal (TA)6 UGT1A promoter allele (0.413) than the abnormal (TA)7 allele (0.461)	[48,49]
Mucin	MUC1	Membrane-bound mucin	rs4072037	SNP rs4072037 at MUC1 was significant (P  =   0.035) in males	[50]
	MUC2	Gel-forming mucins	rs7396030	For males, the additive interaction model based on rs4072037 at MUC1 and rs7396030 at MUC2 yielded an age- and BMI-adjusted OR of 4.68 (P  =   0.0008)	[50]
	MUPCDH	Membrane-bound mucin	rs3758650	GSD association with an odds ratio (OR) of 1.59 (adjusted P  =   0.013) for the AG genotype and 5.82 (adjusted P  =   0.007) for the AA genotype	[51]
Gallbladder motility	CCK1R	Cholecystokinin receptor A mediates signals resulting in gallbladder contraction	CCK 27–33 sulf.	The frequency of the A1A1 genotype of CCK-AR was significantly higher in gallstone patients than in healthy individuals (P  =   0.008 odds ratio OR   =   2.25, and 95% CI   =   1.2–4.1)	[52]
	ADRB3	Adrenergic, beta-3-, receptor. Beta-adrenergic receptors mediate the catecholamine-induced activation of adenylate cyclase through the action of G proteins. Beta-3 is involved in the regulation of lipolysis and thermogenesis	p.R64W (rs4944)	Genotyping for ADRB3 revealed an Arg64 allele frequency of 5.9 versus 0.7% (HR   =   11.9, P  <   0.05) compared with controls	[53]
Hormone receptor	AR	Androgen receptor. Steroid hormone receptors are ligand-activated transcription factors that regulate eukaryotic gene expression and affect cellular proliferation and differentiation in target tissues	c.172(CAG)n	A significantly decreased OR for cholelithiasis risk was observed in individuals having the SL and LL genotype (OR   =   0.622; 95% CI   =   0.345–1.121; P  =   0.114 and OR   =   0.287; 95% CI   =   0.151–0.543, P  <   0.0001, respectively)	[54]
	ESR2	Estrogen receptor 2 (ER beta). Nuclear hormone receptor. Binds estrogens with an affinity similar to that of ESR1 and activates expression of reporter genes containing estrogen response elements (ERE) in an estrogen-dependent manner	c.1092þ3607(CA)n	A significantly decreased OR for cholelithiasis risk was observed in individuals having the SL and LL genotype for ER beta gene compared with SS genotype (OR   =   0.212; 95% CI   =   0.105–0.426; P  <   0.0001 and OR   =   0.042; 95% CI   =   0.018–0.097, respectively)	[54]

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Primary Hypertension

Richard J. Johnson , ... George L. Bakris , in Comprehensive Clinical Nephrology (Fourth Edition), 2010

Genetic (Polygene) Hypothesis

Pickering and later Lifton proposed that hypertension results from the expression of multiple genetic polymorphisms (polygene hypothesis) that favor sodium retention by the kidney in a westernized society in which there is often excessive intake (>10 g/day) of salt. The observation that numerous monogenic forms of both hypertension and hypotension are mediated by specific mutations involving sodium transport, especially involving the epithelial sodium channel (see Chapter 47), supports this hypothesis (Fig. 33.4). ⁴ Indeed, more than 20 genes have been identified in which mutations or polymorphisms can strongly influence BP. ⁴ Many of these involve sodium transport in the distal tubule or the collecting duct. Interestingly, some heterozygous mutations (such as the Na-K-2Cl cotransporter SLC12A1 or the inward rectifier K⁺ channel KCNJ1) that are carrier states for Gitelman's syndrome and the heterozygous mutation of the Na-Cl cotransporter SLC12A3 that is a carrier state for Bartter syndrome actually confer protection from hypertension. ⁵ Whereas genetic polymorphisms clearly have an important influence on BP, many studies suggest that genetic mechanisms can account for only 20% to 30% of cases of primary hypertension, indicating that other mechanisms are also likely to be operative.

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Genetics of Resistance to Pests and Disease

Ivan Simko , ... David Spooner , in Potato Biology and Biotechnology, 2007

7.2.2.2 Horizontal resistance

There is considerable interest in the development of potato cultivars with durable late-blight resistance due to polygenes. The so-called field resistance is complexly inherited and may involve the production of phytoalexins, phenolics, and glycoalkaloids ( Andreu et al., 2001). Breeding progress has been slow because the genetic basis of resistance is not yet understood. Horizontal resistance to late blight, presumably due to minor genes, has been reported in wild Solanum species (Rivera-Peña, 1990; Colon et al., 1995b), in cultivated relatives of potato (Cañizares and Forbes, 1995; Haynes and Christ, 1999; Trognitz et al., 2001; Zlesak and Thill, 2004), and in Tuberosum Group cultivars (Colon et al., 1995b; Grünwald et al., 2002; Porter et al., 2004). Late-blight resistance is present in some heirloom cultivars and appears to be durable, because the resistance levels are similar to those when the cultivars were released more than 40 years ago. However, the most resistant cultivars are also late-maturing, and it is not known whether durable resistance can be combined with early maturity.

Resistance to early blight (A. solani) appears to be quantitatively inherited. Good sources of resistance are rare, and the combination of resistance and early maturity is even more difficult to find (Boiteux et al., 1995). Christ and Haynes (2001) reported a relatively high heritability estimate (0.61) for early-blight resistance in a Phureja– Stenotomum Group population, indicating that additive genetic variance is important. Similarly, Herriott et al. (1990) and Ortiz et al. (1993) found that additive genetic variance is important for early-blight resistance. According to Brandolini et al. (1992) and Gopal (1988), non-additive gene action contributes to resistance as well. In most populations developed for early-blight resistance, the most resistant clones are late to mature. However, Boiteaux et al. (1995) surveyed a large number of clones (934) and found some with both early maturity and early-blight resistance.

A genetic study using diploid interspecific hybrids determined that broad-and narrow-sense heritability values for soft rot resistance are high (0.92 and 0.89, respectively) (Lebecka and Zimnoch-Guzowska, 2004). Consequently, additive genetic variance is more important than non-additive variance. Although individual resistance genes were not identified in this study, the high heritability estimates may indicate that only a few genes control resistance.

Several studies have identified quantitative resistance for Verticillium wilt. Tsror and Nachmias (1995) suggest that minor genes are responsible for resistance in some cultivars. Pavek and Corsini (1994) also favor a horizontal resistance model, with additive genetic variance contributing significantly to resistance. Recently, Simko et al. (2004b) identified four quantitative trait loci that contribute to Verticillium wilt resistance in diploid populations. It is interesting to note that different types of Verticillium wilt resistance may be controlled by different genetic systems. Lynch et al. (1997) believe that, in diploid S. chacoense, tolerance is a polygenic trait, whereas resistance to infection and colonization is due to a major gene.

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Selection Pressure

M. Tracey , K. Balamurugan , in Brenner's Encyclopedia of Genetics (Second Edition), 2013

Multiple Loci

For traits such as size, the underlying genetic variation is usually found at a number of loci called polygenes. Acting in concert, these loci produce the continuously distributed phenotypes. Although many scientists are currently attempting to identify specific loci contributing to continuously distributed characters, the quantification of these traits is still primarily a statistical exercise directed to the estimation of the degree to which the variation in a trait is determined by genetic variation, environmental variation, or interactions between the two sources of variation. This is known as heritability analysis and falls into the discipline known as quantitative genetics.

Continuing with the size example, imagine that four loci contribute additively to size with the same arbitrary units used in our single-locus example. The smallest genotype will be an eight j1j1k1k1m1m1n1n1, and the largest will be a 16 j2j2k2k2m2m2n2n2. Interestingly, the same phenotypes will be produced by different underlying genotypes. A cross between a 12 j1j1k1k1m2m2n2n2 and a 12 j2j2k2k2m1m1n1n1 will produce j1j2k1k2m1m2n1n2 progeny, all of whom are 12s just like their parents. The difference is that the parents come from monomorphic 12 populations where there is no genetic variation for size. Selection in either the j1j1k1k1m2m2n2n2 or j2j2k2k2m1m1n1n1 will produce no response, because there is no genetic variation for selection to act upon. The j1j2k1k2m1m2n1n2 progeny of this cross are an entirely different story. Intercrossing these progeny will produce size classes ranging from 8 to 16 with the most frequent class being the 12 class. Note that different genotypes are capable of producing the 12 phenotype. Different genotypes frequently produce the same phenotype and the same genotype may produce very different phenotypes as the result of environmental variation.

How does selection pressure apply to traits that vary continuously? As for the single-locus example, selection pressure is a measure of the degree of differential reproduction. For example, the progeny population, produced by the intercross of the 12s heterozygous at all four loci, has phenotypes ranging from 8 to 16. If only 16s are allowed to reproduce, it is possible that only j2j2k2k2m2m2n2n2 genotypes will reproduce and all of the genetic variation for size will be gone in a single generation. If environmental variation produces 16s from genotypes like j1j2k2k2m2m2n2n2 or j2j2k1k2m2m2n2n2, which are expected to be 15s in the absence of environmental variation, some genetic variation will be preserved.

Selection pressure is not so severe when a range of phenotypic classes, say 14s, 15s, and 16s, are all part of the breeding pool producing the next generation. Two results are expected. The response to selection will not be as quick, because there will be some j1, k1, m1, and n1 alleles in the breeding population and in their offspring. In addition, the genetic variation will not be exhausted as rapidly.

Selection pressure is the fraction of a population contributing to the next generation; the larger the fraction contributing to the next generation, the lower the selection pressure. Obviously, there is no selection or selection pressure when all genotypes reproduce at the same rate. Selection pressure is most extreme when only one genotype reproduces ( Figure 1 ).

Figure 1. Histograms for single locus (a), (b), and (c). (a) The size phenotypes are 2, 3, and 4, and the relative fitness for each phenotype is plotted next to the phenotype. Note that j2j2 has a fitness equal to 1.0 while the other two genotypes have fitnesses of 0.5; j2j2 will reproduce twice as much as j1j1 or j1j2. (b) The sizes of the phenotypes are 2, 3, and 4, and the relative fitness for each phenotype is plotted next to the phenotype. Note that j2j2 has a fitness equal to 1.0, while the other two genotypes have fitnesses of 0.5; j2j2 will reproduce twice as much as j1j1 or j1j2. (c) Selection pressure is dependent on the presence of genetic variation, and for a two-allele locus, genetic variation is at its maximum when the alleles are equal in frequency. (a) p  =   0.9 and q  =   0.1, here it will be difficult to apply a lot of selection pressure unless N is large, because there will be very few j2j2 homozygotes. (b) p  =   0.5 and q  =   0.5, here 25% of the population is j2j2 and the next generation could be produced solely from j2j2 parents.

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Phenotype, A Historical Perspective on

R.J. Berry , in Encyclopedia of Biodiversity, 2001

III. Formal Analysis

In the past, it was suggested that the genes which control major or qualitative traits are different from those which affect quantitative ones (oligogenes and polygenes, respectively). This distinction is now rarely made. In other words, it is assumed that genes affecting quantitative traits (such as weight or size or physiological properties such as metabolic rate) follow Mendelian patterns of inheritance, may have multiple alleles, can mutate, change in gene frequency, show dominance, etc. Quantitative inheritance is merely a general case of the interaction of genes in which the interacting components are little or wholly known.

The number of genes which affect a trait can be estimated by the amount and speed of response in a selection experiment or by the mean and variance of the character as measured in a population. Using these techniques it has been calculated that human skin color may be determined by only 5 or 6 loci, whereas the number of genes affecting oil and protein production in maize may be as high as 54 and 122, respectively. However, such estimates are very dependent on the nature of interactions between the loci concerned and should be regarded as no more than suggestive of a large or small number.

The simplest assumption in multigenic trait determination is that all the loci affect the trait equally and therefore additively. However, detailed analysis has shown that in many (perhaps most) cases, a few genes have a major effect and many genes have a minor effect. For example, in the well-studied case of variation in sternopleural bristle number in D. melanogaster, approximately 10 loci account for 75% of the genetic variation in number. The situation is further complicated by pleiotropy: A gene may have a major effect on one character but minor effects on others. For example, phenylketonuria is an inborn error of metabolism producing severe mental retardation in humans and is produced by the nonfunctioning of phenylalanine hydroxylase, which is controlled as a recessive trait by a single gene on chromosome 12. The same enzyme is involved in melanin synthesis, and phenylketonurics have slightly paler hair and complexion than their normal sibs. The gene can therefore be regarded as having a major effect on intelligence but a minor effect on pigmentation.

However, a quantitatively inherited trait will be more likely than a qualitative one to be affected by environment. A group of individuals having identical genes for growth (i.e., a pure line or a clone) may show considerable variation in size due to differences in available nutrients. Although the same potential for size is present in the initial gene products, the manifestation of the phenotype will be limited by such factors as food availability. Conversely, a population of genetically heterogeneous individuals may grow to the same size if no gene–environment interaction is limiting (or if different interactions compensate for each other). Generalizing, we can express the phenotypic value P for individual i in environment j as

$P_{ij}=G_i+E_j$

where G_i is the genetic contribution of the jth genotype and E_j is the environmental deviation resulting from the jth environment.

A particular genotype may do well in a particular environment, implying a specific interaction between the two. In this case,

$P_{ij}=G_i+E_j+G{E}_i$

In practice, there will be variance of these components so that

$V_{\text{p}}=V_G+V_E+2{\text{Cov}}_{GE}$

where V_p , V_G , V_E , and 2Cov _GE , are the phenotypic, genetic, and environmental variance and the genotype–environment covariance, respectively. The genotype–environment covariance is positive when genotypes with higher values are in better environments and poorer genotypes have poorer environments. This may occur in animals when one member of a litter is large because of its genes and gets more food from its parents or when an animal is socially dominant for genetic reasons and therefore has more resources in food and spaces. A plant genotype which grows faster may have a better environment because it is less likely to be shaded.

In controlled plant and animal breeding, efforts are made to randomize genotypes and environments, so that Cov _GE is minimized. In this situation, Cov _GE can be neglected, and

$V_{\text{p}}=V_G+V_E$

Conventionally,

$V_G/V_P+V_E/V_P=h^2+e^2$

where h ² and e ² are the proportion of phenotypic variation due to genetic and environmental factors, respectively. The term h ² is known as heritability in the broad sense,

$h_{\text{B}}^2=V_G/V_P$

In practice the genetic variance is composed of a range of different interactions between loci, which may be additive, dominant, or epistatic. Variability in the narrow sense is defined as

$h_{\text{N}}^2=V_A/V_P$

where V_A is the variance due to additive genetic factors. It is an important statistic in determining the rate and amount of response to directional selection in breeding programs.

Heritability is a population-specific measurement. It does not measure an invariant property of a particular trait but only the relative contributions of genetic and environmental differences to phenotypic variation in a specific situation. If either genetic or environmental variation changes, heritability estimates will also change; heritability measures the proportion of phenotypic variation in a particular population due to genetic variation.

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Schizophrenia

Aiden Corvin , in Genomics, Circuits, and Pathways in Clinical Neuropsychiatry, 2016

Genetic Architecture of Schizophrenia

GWAS arrays made hypothesis-free SNP association analysis feasible and allowed empirical testing of the hypothetical role of polygenic inheritance in SCZ risk. This was achieved using a "polygene score" method that summed variation across a large number of nominally associated loci into quantitative scores to ask whether these scores could predict disease state in independent samples ( Wray, Goddard, & Visscher, 2007). From such analysis, up to a third of total variation in genetic liability could be explained by common risk variants. A subsequent analysis using a different approach (Genome-wide Complex Trait Analysis) provided further evidence that polygenic inheritance contributes to SCZ (Lee et al., 2012). By examining GWAS data from 9087 affected individuals and 12,171 control subjects from the PGC1 data set, the authors quantified the lower limit of the genetic contribution to SCZ from common variants as being 23% based on the GWAS platforms of the time. They showed that the variance explained per chromosome was linearly related to the length of the chromosome, another expected feature of polygenic disorders. More recently, a larger study, including additional Swedish samples (16,245 cases and 31,829 controls) applied approximate Bayesian polygenic analysis and estimated that 8300 independent common loci explain up to 50% of the variance in genetic risk of SCZ (Ripke et al., 2013).

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Mechanisms and Molecular Pathways in Hypertension

Roger Brown , ... David J. Webb , in Molecular Basis of Cardiovascular Disease (Second Edition), 2004

Conclusions from Genome Scans

The previous analysis suggests that human primary HT has no widely penetrant genes of huge or even major effect dominating the genetic contribution to this disorder; instead, there are multiple polygenes of modest effect. Human genome scans appear to have amassed sufficient evidence to identify up to 12 QTLs for HT/BP/HT-associated phenotypes of modest effect. Independent confirmation of these loci in more than one human genome scan and their overlap with regions syntenic to BP QTLs in mice, rats, or both (see Table 31-6) have produced evidence highlighting certain loci (Table 31-7) for a role in BP and HT for humans, which is strongest overall for QTL1 (Ch 2p11-q12), QTL5 (17q11-23 especially 17q21), QTL8 (6q14-16 especially 6q14), QTL9 (1q23-24), and QTL11 (5p15-12 especially 5p13); evidence is moderately strong for QTLs 4, 6, 7, and 12 and supportive but weaker for QTL2 and 3 because, although both these loci were linked to human BP with significant LOD scores in their original reports, they have not been linked to it subsequently (although QTL2 overlaps a region syntenic to a BP QTL in rats). The final QTL (QTL10) has not been directly linked to human BP or HT originally or subsequently; the original linkage was to potentially related body fluid volume phenotypes. Thus, the status of this QTL in relation to human BP remains unsubstantiated, despite overlapping with a syntenic QTL for HT in rats. Likewise, a number of linkage hits falling short of significance individually have, by their repetitive occurrence in tightly colocalized clusters especially on Ch19p13-12, 3p26 and regions of 7q11-31, raised the possibility of linkage to BP at these sites. These hits are reinforced and suggestive of linkage to human BP but require a more conclusive study to establish this at a genome-wide significant level. Overlap of the 19p and 7q sites with those syntenic to rodent QTLs for HT seems to corroborate their candidacy further.

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CROP IMPROVEMENT | Plant Breeding, Principles

N.W. Simmonds , A.E. Arthur , in Encyclopedia of Applied Plant Sciences, 2003

Genetics and Plant Breeding

There are fairly numerous examples of characters affected by the action of "major" genes or "oligogenes," as they may be called for contrast with the following category (i.e., polygenes giving rise to quantitative variation). Major genes affect characters in a fairly dramatic, all-or-nothing, qualitative fashion and they were studied extensively by Gregor Mendel (tall or short stature, smooth or wrinkled seed coats, etc.). The plant breeder often sees segregation of major genes in his material but they are often of no economic importance. Stature, colors, spininess, etc., are all affected and a full list of such traits for crop plants would be a long one. Examples of major genes that are of importance include some conferring disease resistance and plant stature. Short plants have been a prominent feature of selection over the past decades, primarily because of their improved partitioning (stature affects vegetable matter rather than grain or other floral products). Monogenic disease resistance is often hazardous because of the evolution of new races of the pathogen in question. Where major genes are of economic importance, they are generally easy to handle because they present straightforward segregations and simple Mendelian ratios. However, if several distinct major gene loci affect a single plant character, without dominance, F ₂ segregations will approximate to 1:2:1, 1:3:3:1, 1:4:6:4:1, etc., and individual classes may be very hard to recognize. It should be noted that distributions quickly build up to a curve approximating the "normal," in which case the distinction between major genes and polygenes is blurred (see below).

In contrast to the above, there are "polygenes" and "polygenic characters," commonly called quantitative traits, that give rise to quantitative or continuous variation. Polygenes have small individual effects and, without critical evidence, are usually thought to be additive and open to a much simplified biometrical treatment. A great deal of crop variability is caused by polygenes and they are the major object of the plant breeder's activities. If the numerical argument of the preceding paragraph is extended to more numerous genes of smaller effects, then binomial distributions, defined by the marginal sums of a Pascal triangle, quickly emerge and approximate to normality. Normal statistics can be used to deal with these genes and this fact only became realized in about 1920, following Galton's pioneering work in the late nineteenth century. A normal distribution is characterized by its mean and variance, the latter being a measure of variability. Thus, denoting the mean as x bar ( $\overline{x}$ ) the variance is the sum of squared deviations from it: $V=\sum {\left(x-\overline{x}\right)}^2/n-1$ and the standard deviation is the root of the variance. A crude variance contains both genetic and environmental elements, the latter being estimated from variation between replicates but called "error." The two sources of variation can, with appropriate designs, be separated, but details of the process will not be treated here. Note that "errors" are not mistakes, but are biological departures and interesting in their own right.

In assessing traits, breeders usually have to work with phenotypic (P) variation, which is the result of the combined effects of the influences of the genotype (G) and the environment (E), and the interaction between the two (G×E, discussed in more detail below). This can easily and most usefully be expressed in the simple equation:

$\mathrm{P}=\mathrm{G}+\mathrm{E}+\left(\mathrm{G}\times \mathrm{E}\right)$

The role of genetic and biometrical analyses is to help determine the extent of the contributions of these parameters to the phenotype and, where possible and appropriate, attempt to partition the genetic effects further into additive and dominance contributions, along with more complex effects such as epistatic interactions.

However this is achieved, it is possible to derive equations of the general form:

$h^2=V_{\mathrm{G}}/\left(V_{\mathrm{G}}+V_{\mathrm{E}}\right)$

where h ² is the heritability, V _G and V _E are the genetic and environmental variance, respectively, and V _P indicates phenotypic variation, that is total variation due both to genotype and environment, which is what the plant breeder nearly always has to deal with. Herein lies the difficulty; if V _E were always zero, V _G would be the same as V _P or unity and plant breeding would be simple. But heritabilities rarely approach unity and what the plant breeder actually sees is but a shadow of V _G.

The reader should be warned that this treatment is much simplified, but for practical plant and animal breeding purposes, the simple additive model serves quite well; indeed it has to, because it is the basis for prediction of response to selection (see below).

To summarize, then, the plant breeder may have to deal with major genes, which are easy to handle but relatively uncommon. Polygenic or quantitative variation, however, is much more important and selection for quantitative trait characters makes up the bulk of the breeder's work.

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Quantitative Inheritance☆

J. Gai , in Reference Module in Life Sciences, 2017

Generalized Major Gene and Polygene Mixed Inheritance Based on QTL Linkage Mapping

The capacity of segregation analysis (biometrical analysis) is limited in detecting number of major genes. Up to now the analytical procedure worked out is limited only for up to four major genes plus polygenes mixed inheritance model. But fortunately, the QTL (quantitative trait locus) mapping using molecular markers has made the study on quantitative inheritance much easier and broader. Here a QTL is not necessary limited as a gene, but might include several genes in terms of DNA segments. Originally, QTL mapping was aimed at detecting additive major genes, then it expanded to include epistatic QTL pairs and QTL×Environment interactions. Along with the improvement of mapping procedure and experiment precision, the small-effect major QTLs could also be detected. Among the quantitative traits, some performed to have major QTLs only, some performed to have no major QTLs detected, but most of the traits performed to have a few or several large effect major QTLs and a relatively large number of small-effect major QTLs.

In mapping QTLs responsible for Aluminum tolerance and other traits in soybean, Gai and his group found that the sum of contributions from mapped additive QTLs and epistatic QTL pairs was much less than the total genetic variation estimated from genotypic variances in ANOVA. The remaining part of genetic variation was attributed to those QTLs not detected in the mapping procedure. In this way, the total phenotypic variance (100%) was partitioned into QTL, QTL×Environment and Environment variance components. In terms of the trait of Aluminum tolerance, the heritability from the joint analysis of multiple environment data was 77.80% and additive QTL effects contributed about 22.30%, while epistasis QTL pairs contributed about 14.86% to phenotypic variance. Thus 40.64%, a substantial part of the phenotypic variance was due to unmapped QTLs. The unmapped QTLs are mainly QTLs with very small effect because the saturation level of the used genetic linkage map should be able to detect most of the major QTLs, including small effect major QTLs. Here they used unmapped QTL collective (or unmapped minor QTL collective) to represent this part of genetic variation, which in fact accounted for a major portion of both the genetic and phenotypic variation in the trait. The same results have been observed in the QTL mapping studies for a number of traits, such as oil and fatty acid contents, protein and protein component contents in soybeans. Accordingly, the above QTL mapping results support further the hypothesis of generalized major gene plus minor gene mixed inheritance.

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Quantitative Genetics

J. Gai , J. Lu , in Brenner's Encyclopedia of Genetics (Second Edition), 2013

Molecular Quantitative Genetics Based on Quantitative Trait Locus Mapping

In fact, the capacity of segregation analysis (biometrical analysis) is limited in detecting the number of major genes. Up to now, the analytical procedure worked out is limited only for up to four major genes plus polygenes mixed inheritance model. Fortunately, the 'Quantitative trait locus' (QTL) mapping using molecular markers has made the study on quantitative inheritance much easier and broader. Here, a QTL is not necessarily limited as a single gene, but might include several genes in terms of DNA segments. Originally, QTL mapping was aimed at detecting additive major QTLs; subsequently, it moved to including epistatic QTL pairs and QTL × enviroment interactions. Along with the mapping procedure and the experiment precision improved, the small effect major QTLs could also be detected. Among the quantitative traits, some performed to have only major QTLs detected and some performed to have no major QTLs detected, but most of the traits performed to have a few large effect major QTLs and a relatively large number of small effect major QTLs. Here, the number and effects (large or small) of the detected QTLs are relative to the genetic variation of the mapping population determined by the genetic difference between the two parents or among the parental lines of the mapping population. For a thorough detection of QTLs of a quantitative trait, scientist should choose the parental lines genetically as different as possible. In addition to the QTLs mapped, there might be another part of variation due to collective unmapped minor QTLs that sometimes might account for a large part of the genetic variation. Figures 1–3 show the results of genetic dissection for aluminum tolerance in soybean by Junyi Gai and his group. The total phenotypic variance (100%) was partitioned into QTL, QTL × environment, and environment variance components where the heritability from the joint analysis of multiple environment data was 77.80% and the additive QTL effects contributed about 22.30%, while epistasis QTL pairs contributed about 14.86% to phenotypic variance. Thus, a substantial part (40.64%) of the phenotypic variance was due to collective unmapped minor QTLs. Accordingly, the above QTL mapping results further demonstrate the hypothesis of generalized major gene plus minor gene mixed inheritance and also provide an example of genetic dissection of a quantitative trait in molecular quantitative genetics based on QTL mapping.

Figure 1. Genetic locations of additive QTLs conferring aluminum tolerance in soybean detected with three growth-related traits. RRDW, relative root dry weight; RSDW, relative shoot dry weight; RTDW, relative total plant dry weight. Totally, the QTLs for the three traits were detected on six linkage groups. Here, only the linkage group B1 on which a QTL was detected jointly for the three traits is shown.

Adapted from Korir PC, Qi B, Wang Y, et al. (2011) A study on relative importance of additive, epistasis and unmapped QTL for aluminium tolerance at seedling stage in soybean. Plant Breeding 130: 551–562. doi:10.1111/j.1439-0523.2011.01862.x.

Figure 2. The additive and epistatic QTLs on linkage groups detected by QTLNetwork-2.0. Lines joining two QTLs represent epistatic interactions between them.

Adapted from Korir PC, Qi B, Wang Y, et al. (2011) A study on relative importance of additive, epistasis and unmapped QTL for aluminium tolerance at seedling stage in soybean. Plant Breeding 130: 551–562. doi:10.1111/j.1439-0523.2011.01862.x.

Figure 3. Dissection of phenotypic variance into QTL, QTL × year, and environment components in the soybean aluminum tolerance genetic system. a, additive QTL; e, epistatic QTL pair.

Adapted from Korir PC, Qi B, Wang Y, et al. (2011) A study on relative importance of additive, epistasis and unmapped QTL for aluminium tolerance at seedling stage in soybean. Plant Breeding 130: 551–562. doi:10.1111/j.1439-0523.2011.01862.x.

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