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Genetic drift is the change in allele frequencies of a population due to random chance events, such as natural disasters.
- Distinguish between selection and genetic drift
- Genetic drift is the change in the frequency of an allele in a population due to random sampling and the random events that influence the survival and reproduction of those individuals.
- The bottleneck effect occurs when a natural disaster or similar event randomly kills a large portion (i.e. random sample) of the population, leaving survivors that have allele frequencies that were very different from the previous population.
- The founder effect occurs when a portion of the population (i.e. “founders”) separates from the old population to start a new population with different allele frequencies.
- Small populations are more susceptible genetic drift than large populations, whose larger numbers can buffer the population against chance events.
- genetic drift: an overall shift of allele distribution in an isolated population, due to random sampling
- founder effect: a decrease in genetic variation that occurs when an entire population descends from a small number of founders
- random sampling: a subset of individuals (a sample) chosen from a larger set (a population) by chance
Genetic Drift vs. Natural Selection
Genetic drift is the converse of natural selection. The theory of natural selection maintains that some individuals in a population have traits that enable to survive and produce more offspring, while other individuals have traits that are detrimental and may cause them to die before reproducing. Over successive generation, these selection pressures can change the gene pool and the traits within the population. For example, a big, powerful male gorilla will mate with more females than a small, weak male and therefore more of his genes will be passed on to the next generation. His offspring may continue to dominate the troop and pass on their genes as well. Over time, the selection pressure will cause the allele frequencies in the gorilla population to shift toward large, strong males.
Unlike natural selection, genetic drift describes the effect of chance on populations in the absence of positive or negative selection pressure. Through random sampling, or the survival or and reproduction of a random sample of individuals within a population, allele frequencies within a population may change. Rather than a male gorilla producing more offspring because he is stronger, he may be the only male available when a female is ready to mate. His genes are passed on to future generation because of chance, not because he was the biggest or the strongest. Genetic drift is the shift of alleles within a population due to chance events that cause random samples of the population to reproduce or not.
Small populations are more susceptible to the forces of genetic drift. Large populations, on the other hand, are buffered against the effects of chance. If one individual of a population of 10 individuals happens to die at a young age before leaving any offspring to the next generation, all of its genes (1/10 of the population’s gene pool) will be suddenly lost. In a population of 100, that individual represents only 1 percent of the overall gene pool; therefore, genetic drift has much less impact on the larger population’s genetic structure.
The Bottleneck Effect
Genetic drift can also be magnified by natural events, such as a natural disaster that kills a large portion of the population at random. The bottleneck effect occurs when only a few individuals survive and reduces variation in the gene pool of a population. The genetic structure of the survivors becomes the genetic structure of the entire population, which may be very different from the pre-disaster population.
The Founder Effect
Another scenario in which populations might experience a strong influence of genetic drift is if some portion of the population leaves to start a new population in a new location or if a population gets divided by a physical barrier of some kind. In this situation, it is improbable that those individuals are representative of the entire population, which results in the founder effect. The founder effect occurs when the genetic structure changes to match that of the new population’s founding fathers and mothers.
The founder effect is believed to have been a key factor in the genetic history of the Afrikaner population of Dutch settlers in South Africa, as evidenced by mutations that are common in Afrikaners, but rare in most other populations. This was probably due to the fact that a higher-than-normal proportion of the founding colonists carried these mutations. As a result, the population expresses unusually high incidences of Huntington’s disease (HD) and Fanconi anemia (FA), a genetic disorder known to cause blood marrow and congenital abnormalities, even cancer.
Drift and fixation
The Hardy–Weinberg principle states that within sufficiently large populations, the allele frequencies remain constant from one generation to the next unless the equilibrium is disturbed by migration, genetic mutation, or selection.
Because the random sampling can remove, but not replace, an allele, and because random declines or increases in allele frequency influence expected allele distributions for the next generation, genetic drift drives a population towards genetic uniformity over time. When an allele reaches a frequency of 1 (100%) it is said to be “fixed” in the population and when an allele reaches a frequency of 0 (0%) it is lost. Once an allele becomes fixed, genetic drift for that allele comes to a halt, and the allele frequency cannot change unless a new allele is introduced in the population via mutation or gene flow. Thus even while genetic drift is a random, directionless process, it acts to eliminate genetic variation over time.
Plane Crash Analogy
4 people in a plane crash
In a small aeroplane, there are 2 people that wear a blue shirt and 2 people that wear a green shirt. The plane crashes, half of the people died. The 2 survivors are those wearing the green shirt… well, nothing so surprising!
400 people in a plane crash
In a very big aeroplane, there are 200 people that wear a blue shirt and 200 people that wear a green shirt. The plane crashes, half of the people died. The 200 survivors are those wearing green shirt… This is quite surprising!
The same logic applies to genetic drift. Genetic drift is caused by events that modify the reproductive success of individuals in a random way (independently of their genotype). We usually referred to this as random sampling. At each generation, individuals are randomly chosen to reproduce and some genotypes might just happen to be chosen more often than others at a given draw (=at a given generation). Genetic drift pushes the frequency of allele slightly away from what would be predicted. According to the Wright-Fisher model, the frequency of alleles (of a bi-allelic gene) in a haploid population (to make it easier) in the next time step is given by:
where $p$ is there frequency of an allele at time = $t$ and $p'$ is the frequency at time = $t+1$ . $WA$ is the fitness of the genotype which frequency is $p$ and $Wa$ is the fitness of the genotype which frequency is $1-p$ . If the population is infinite, the predictions of this equation are exactly correct.
Now if we say that meteorites fall and half of the individual get killed. The probability of getting killed by a meteorite obviously does not depend on genetic predisposition, it is a question of chance! If you look at a population of 1 million individuals, half of them having the genotype $A$ , the other half having the genotype $a$ . It is very unlikely that more than 60% of all individuals that get killed are of the same genotype. Therefore, the meteorites won't change much the frequency of the genotypes. If you look at a population of 4 individuals 2 are $a$ and 2 are $A$ , 2 of them get killed by a meteorite. Well, you have a probability of one half that the two survivors are of the same genotype and that the genotypes frequency would have changed drastically.
Genetic drift refers to these changes in allele frequency which are due to random events (such as meteorites) and the strength of genetic drift indeed depends on the population size for probabilistic reasons. The greatest the population size, the lowest is the strength (or the relative importance) of genetic drift.
How to model genetic drift
There are three famous models of genetic drift that all lead to very similar expectations. I shall just name them here but I will not develop the underlying mathematics.
ORIGINAL RESEARCH article
- 1 National Engineering Research Center for Marine Aquaculture, Zhejiang Ocean University, Zhoushan, China
- 2 National Engineering Laboratory of Marine Germplasm Resources Exploration and Utilization, Zhejiang Ocean University, Zhoushan, China
- 3 Department of Aquatic Science, Faculty of Science, Burapha University, Chon Buri, Thailand
- 4 Scientific Observing and Experimental Station of Fishery Resources for Key Fishing Grounds, MOA, Key Laboratory of Sustainable Utilization of Technology Research, Marine Fisheries Research Institute of Zhejiang, Zhoushan, China
The hard clam Meretrix meretrix is ecologically and economically important in the coastal regions of China. We evaluated the genetic diversity and population structure among eight M. meretrix samples from the Yellow Sea (YS) and South China Sea (SCS) using nine microsatellite DNA loci. Both conventional and model-based population genetic analyses suggested significant genetic divergence between YS and SCS regions (pairwise FST values ranging from 0.014 to 0.056). Samples within each region were not genetically different, except for Zhanjiang which clearly differed from other the four SCS samples. Membership coefficients, estimated by STRUCTURE, suggested some genetic admixture of the two genetic clusters in ZJ. Population genetic structure was detected in SCS region. We detected moderate levels of genetic variation in all eight samples (mean A = 16.111.111, mean Ar = 14.512.029, mean Ho = 0.736𠄰.843, mean He = 0.823𠄰.868) and two genetic clusters (mean A = 27.167.833, mean Ae = 8.834𠄹.471, mean Ar = 26.032.005, mean Ho = 0.824𠄰.839, and mean He = 0.821𠄰.850). Low levels of Ne estimates were detected in M. meretrix populations. None of the genetic populations had signs of recent genetic bottlenecks. Knowledge on genetic variation and population structure of M. meretrix populations along the Chinese coasts will support the aquaculture management and conservation of M. meretrix, and will provide insights for stock selection in selective breeding programs for these species and delineating management units.
The theory of natural selection stems from the observation that some individuals in a population are more likely to survive longer and have more offspring than others thus, they will pass on more of their genes to the next generation. A big, powerful male gorilla, for example, is much more likely than a smaller, weaker one to become the population’s silverback, the pack’s leader who mates far more than the other males of the group. The pack leader will father more offspring, who share half of his genes, and are likely to also grow bigger and stronger like their father. Over time, the genes for bigger size will increase in frequency in the population, and the population will, as a result, grow larger on average. That is, this would occur if this particular selection pressure, or driving selective force, were the only one acting on the population. In other examples, better camouflage or a stronger resistance to drought might pose a selection pressure.
Another way a population’s allele and genotype frequencies can change is genetic drift (Figure 2), which is simply the effect of chance. By chance, some individuals will have more offspring than others—not due to an advantage conferred by some genetically-encoded trait, but just because one male happened to be in the right place at the right time (when the receptive female walked by) or because the other one happened to be in the wrong place at the wrong time (when a fox was hunting).
Figure 2. Click for a larger image. Genetic drift in a population can lead to the elimination of an allele from a population by chance. In this example, rabbits with the brown coat color allele (B) are dominant over rabbits with the white coat color allele (b). In the first generation, the two alleles occur with equal frequency in the population, resulting in p and q values of .5. Only half of the individuals reproduce, resulting in a second generation with p and q values of .7 and .3, respectively. Only two individuals in the second generation reproduce, and by chance these individuals are homozygous dominant for brown coat color. As a result, in the third generation the recessive b allele is lost.
Do you think genetic drift would happen more quickly on an island or on the mainland?
Small populations are more susceptible to the forces of genetic drift. Large populations, on the other hand, are buffered against the effects of chance. If one individual of a population of 10 individuals happens to die at a young age before it leaves any offspring to the next generation, all of its genes—1/10 of the population’s gene pool—will be suddenly lost. In a population of 100, that’s only 1 percent of the overall gene pool therefore, it is much less impactful on the population’s genetic structure.
Watch this animation of random sampling and genetic drift in action:
Figure 3. A chance event or catastrophe can reduce the genetic variability within a population.
Genetic drift can also be magnified by natural events, such as a natural disaster that kills—at random—a large portion of the population. Known as the bottleneck effect, it results in a large portion of the genome suddenly being wiped out (Figure 3). In one fell swoop, the genetic structure of the survivors becomes the genetic structure of the entire population, which may be very different from the pre-disaster population.
Another scenario in which populations might experience a strong influence of genetic drift is if some portion of the population leaves to start a new population in a new location or if a population gets divided by a physical barrier of some kind. In this situation, those individuals are unlikely to be representative of the entire population, which results in the founder effect. The founder effect occurs when the genetic structure changes to match that of the new population’s founding fathers and mothers. The founder effect is believed to have been a key factor in the genetic history of the Afrikaner population of Dutch settlers in South Africa, as evidenced by mutations that are common in Afrikaners but rare in most other populations. This is likely due to the fact that a higher-than-normal proportion of the founding colonists carried these mutations. As a result, the population expresses unusually high incidences of Huntington’s disease (HD) and Fanconi anemia (FA), a genetic disorder known to cause blood marrow and congenital abnormalities—even cancer.
Watch this short video to learn more about the founder and bottleneck effects. Note that the video has no audio.
4.4.3B: Genetic Drift - Biology
Recommendations made by the USPSTF are independent of the U.S. government. They should not be construed as an official position of the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services.
Figure 1 is a flow diagram of risk assessment, genetic counseling, and genetic testing. Women without cancer and with unknown BRCA mutation status, which are clinically significant mutations of the BRCA1 and BRCA2 genes, are assessed for their familial cancer risk. These women may experience adverse effects as they are determined to have either no increased risk or increased risk for BRCA mutations. Women with an increased risk for potentially harmful mutations are referred for genetic counseling, during which they may experience adverse effects. Women with known BRCA mutations in their family may be referred directly to genetic counseling. Following genetic counseling, women are determined to either have no increased risk or increased risk for BRCA mutations. Women with an increased risk for BRCA mutations are referred for genetic testing, during which they may experience adverse effects. Testing may be done on the unaffected woman, her relative with cancer, or her relative with highest risk, as appropriate. Women who have genetic testing may have benign or likely benign results, which means that the genetic test showed no indication of a harmful mutation or they may have a result that is pathogenic, likely pathogenic, or uncertain significance. Women with a result that is not benign or likely benign may be referred for interventions, which may include intensive screening (earlier and more frequent mammography, breast MRI), risk-reducing medications (aromatase inhibitors, tamoxifen, raloxifene), and risk-reducing surgery (mastectomy, salpingo-oophorectomy). Women who undergo interventions may experience adverse effects. Women who undergo interventions may also have reduced incidence of BRCA-related cancer and reduced cause-specific and all-cause mortality.
- In women without breast or ovarian cancer whose BRCA mutation carrier status is unknown, does risk assessment, genetic counseling, and genetic testing reduce the incidence of BRCA-related cancer and disease-specific and all-cause mortality?
- a. What is the accuracy of familial risk assessment for BRCA-related cancer when performed by a nonspecialist in genetics in a clinical setting? What are the optimal ages and intervals for risk assessment?
b. What are the benefits of genetic counseling in determining eligibility for genetic testing for BRCA-related cancer? (Potential benefits include improved accuracy of risk assessment and pretest probability for testing and improved patient knowledge, understanding of potential benefits and harms of interventions to reduce risk, risk perception, satisfaction, and health and psychological outcomes.)
c. What are the optimal testing approaches to determine the presence of potentially harmful BRCA mutations in women at increased risk for BRCA-related cancer? (Approaches include testing other high-risk family members, including men, before testing the index patient and using specific types or panels of tests.)
- What are the potential adverse effects of a) risk assessment, b) genetic counseling, and c) genetic testing for BRCA-related cancer? (Adverse effects include, but are not limited to, inaccurate risk assessment inappropriate testing false-positive and false-negative results adverse effects on the patient&rsquos family relationships overdiagnosis and overtreatment false reassurance incomplete testing misinterpretation of test results anxiety cancer worry and ethical, legal, and social implications.)
- Do interventions reduce the incidence of BRCA-related cancer and mortality in women at increased risk? (Interventions include intensive screening [earlier and more frequent mammography or breast magnetic resonance imaging], risk-reducing medications [aromatase inhibitors, tamoxifen, or raloxifene], and risk-reducing surgery [mastectomy or salpingo-oophorectomy].)
- What are the potential adverse effects of interventions to reduce risk for BRCA-related cancer? (Adverse effects include, but are not limited to, immediate and long-term harms associated with breast imaging, risk-reducing medications, and risk-reducing surgery and ethical, legal, and social implications.)
The Proposed Research Approach identifies the study characteristics and criteria that the Evidence-based Practice Center will use to search for publications and to determine whether identified studies should be included or excluded from the Evidence Review. Criteria are overarching as well as specific to each of the key questions (KQs).
KQs 2a, 3a: Risk assessment by a nonspecialist in genetics
KQs 2b, 3b: Genetic counseling
KQs 2c, 3c: Genetic testing
KQs 2a, 3a: Risk assessment by a nonspecialist in genetics vs. usual care or risk assessment by another approach
KQs 2b, 3b: Genetic counseling vs. usual care
KQs 2c, 3c: Genetic testing vs. usual care or an alternate approach to genetic testing (to compare effectiveness)
KQ 2a: Measures of test performance (sensitivity, specificity, positive and negative likelihood ratios, c-statistic)
KQ 2b: Patient outcomes of genetic counseling (improved accuracy of risk assessment and pretest probability for testing and improved patient knowledge, understanding of potential benefits and harms of interventions to reduce risk, risk perception, satisfaction, and health and psychological outcomes)
KQ 2c: Patient health and psychological outcomes of testing
KQ 3a: Inaccurate risk assessment, false-positive and false-negative results adverse effects on the patient's family relationships false reassurance anxiety cancer worry and ethical, legal, and social implications
KQ 3b: Inaccurate risk assessment inappropriate testing false-positive and false-negative results adverse effects on the patient's family relationships overdiagnosis and overtreatment false reassurance, anxiety cancer worry and ethical, legal, and social implications
KQ 3c: Inappropriate testing false-positive and false-negative results adverse effects on the patient's family relationships overdiagnosis and overtreatment false reassurance incomplete testing misinterpretation of test results anxiety cancer worry and ethical, legal, and social implications
Emergence of Drift Variants That May Affect COVID-19 Vaccine Development and Antibody Treatment
New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat COVID-19. Several approaches are being used by scientists for investigation, including (1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease and (2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries, such as the Netherlands, Switzerland, and France, but seldom observed in China.
Keywords: COVID-19 SARS-CoV-2 antibody convalescent plasma genomic drift immune escape spike protein vaccine variant.
Brushing the dust off ancient DNA genetic relics reveal hidden details of prehistoric life.
The oldest reported DNA comes from some bugs that stepped in the wrong place about 30 million years ago.
This dramatic evidence of DNA's durability emerged last month in two papers announcing the successful extraction of DNA from fossil insects. Descriptions of DNA extracted from a fossil bee by California researchers appeared in the September MEDICAL SCIENTIFIC RESEARCH. A similar report by researchers at the American Museum of Natural History in New York City -- focusing on an extinct termite -- followed in the Sept. 25 SCIENCE.
In each case, scientists managed to amplify small fragments of DNA with a molecular copying process known as polymerase chain reaction (SN: 4/23/88, p.262). And both teams examined insects preserved in pieces of amber from the Dominican Republic, one of the world's most significant sources of this gem.
Amber-encased fossils, long valued for their excellent three-dimensional detail, are particularly suited for molecular studies. Some "specimens are so well preserved that you can identify cellular structures" under the microscope, says Raul J. Cano, a microbiologist at California Polytechnic State University in San Luis Obispo, who took part in the bee study.
People have long recognized that amber, a form of fossilized tree resin, preserves organic tissue extremely well. The ancient Egyptians used crushed amber to preserve mummies, notes Ward Wheeler, who coauthored the termite report.
Normally, organic tissue -- and the DNA it contains -- degrades rapidly after an animal dies. When sealed in amber, however, tissues remain isolated from the decay-promoting effects of external air and water. Amber not only acts as a natural antibiotic that prevents the growth of microbes, but it also dries out the creatures it entombs to form natural mummies. An animal trapped in a glob of sap "is there for good," Wheeler says.
Such prisoners include "pretty much anything you can imagine that would be on the side of a tree -- small frogs, small lizards, bird feathers, land snails, and a tremendous variety of insects," he says. The animals thus trapped are typically small, since the largest pieces of amber reach only about 6 inches across.
Wheeler's group studied an extinct termite called Mastotermes electrodominicus. Considered by some "a missing link between cockroaches and termintes," this particular bug had the potential to solve "an interesting evolutionary question," explains entomologist David Grimaldi, who participated in the investigation. In the past, researchers had debated whether termites evolved from cockroaches or in parallel with them. Using sequenced fragments of DNA from M. electrodominicus, the New York scientists determined that their ancient bug was more termite than roach, suggesting separate origins for the two groups.
The California researchers extracted DNA from an extinct species of stingless bee known as Proplebeia dominicana. They hope their sample will reveal details about the evolution of this bee's modern relatives and provide a reference point for measuring evolutionary changes over time, says Cano.
Such investigations can provide detailed information that may not be available from studies of modern groups or from the anatomical details of fossils. In the future, "amber is going to be looked at for a wealth of information" about ancient lineages, predicts study coauthor George O. Poinar Jr., a paleontologist at the University of California, Berkeley.
Amber fossils represent only a small fraction of the many potential sources of prehistoric DNA.
Other researchers have examined a wide range of preserved bones for information about animals much larger than those trapped in amber. As with the amber-entombed insects, however, only tissues protected from weathering and microbial decay yield remnants of their original DNA.
These remains must be naturally mummified -- as in the case of 3,300-year-old bird bones discovered in a dry cave (SN: 9/19/92, p.183) -- or preserved in rare deposits such as the tar pits of Rancho La Brea in Los Angeles.
Although not nearly as ancient as some of the amber reserves, the La Brea tar pits represent one of the world's richest fossil deposits. They have yielded approximately 2 million specimens representing more than 460 animal species, some of which date back almost 40,000 years.
The first report of sequenced DNA from bones preserved in these tar pits appeared in the Oct. 15 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES. The study focused on the 14,000-year-old bones of an extinct saber-toothed cat, known as Smilodon fatalis, in the collection of the George C. Page Museum of La Brea Discoveries in Los Angeles.
These animals, which brandished long, knife-like canine teeth, have been placed in several different groups of carnivores over the years. The DNA sequencing results now indicate that they belonged to the same family as modern cats and were closely related to the great cats, such as lions, leopards, and tigers, according to a research group led by Stephen J. O'Brien of the National Cancer Institute's Laboratory of Viral Carcinogenesis in Frederick, Md.
Additional studies of fossil DNA may clarify the evolutionary histories of other extinct animals from the La Brea deposits, among them mammoths, mastodons, giant ground sloths, and dire wolves. But such investigations may also have "implications beyond paleontology," asserts O'Brien.
DNA studies "offer the prospect of relating evolutionary adaptations to gene sequences and open the door for the search for ancient pathogens that may have contributed to species extinction," he says. For example, if a virus drove these animals to extinction, then traces of viral DNA might appear in their remains. O'Brien hopes such pathogenic studies will also allow researchers to test hypotheses about the relationship between modern epidemics and modern species.
Regardless of the exact information DNA sequencing may provide in the future, it seems certain that the current chronological record holders will not reign for long. Specimens in amber date back approximately 100 million years, providing the potential for DNA studies of animals that lived during the time of the dinosaurs.
Moreover, researchers have achieved "positive results" in their preliminary attempts to obtain DNA from the scales of fossil fish preserved in lake sediments approximately 200 million years old, says Amy R. McCune, a paleontologist at Cornell University. McCune hopes that DNA from these ancient species will not only show their connections to modern species, but also clarify their relationships to one another.
On the other hand, the new excitement over ancient DNA won't put conventional paleontologists out of work, predicts Wheeler. DNA extraction "is a different tool, but not necessarily one that will supersede morphologic or anatomical analyses," he says. Not only are the majority of fossils preserved in conditions that do not protect DNA, but anatomical comparisons will continue to provide overall details that an isolated fragment of DNA may not reveal.
Scientists might not go through the elaborate effort needed to sequence ancient DNA when their questions can be answered through more traditional comparisons. But in some cases the new tool may provide answers to formerly unanswerable questions.
Although "there are a lot of problems and difficulties associated with working with fossil material," says O'Brien, "the prospect of being able to look at the DNA sequences of species that are no longer alive makes this kind of exercise worth it."
Make your genes fit genetic manipulation through nutrition and exercise may help achieve weight loss goals. (Research).
In the twenty-first century, the buzz is about genes--how they mold us and whether or not we can shake the bad ones. Scientists possess a rough map of the human genetic code, unearthing clues to complex conditions that have baffled them for years. Not only do genes determine height, hair and eye color, but when it comes to the contours of your tummy and thighs, genes are also hard at work. Although you cannot do much about the genes for brown hair and blue eyes, you can coax your gene interactions into boosting your weight loss goals.
Nutrition researcher, Neal Barnard, M.D., author of Turn Off the Fat Genes, says, "Human chromosomes may be extraordinarily complex, but there are just a few key gene interactions we need to understand to stay slim and healthy." Simple diet and exercise strategies can alter these genetic blueprints and provide a foundation for weight control.
Arresting the Fat-Building Gene
So, why all the trouble with fat these days? Nutritionists explain that what we once thought of as "diet foods" (e.g., skinless chicken breasts, fish, egg white omelets, dairy products and lean meats) still leave plenty of fat for LPL to store in your body. All humans have a gene that produces a fat-building enzyme, coded on chromosome 8, called lipoprotein lipase or "LPL." LPL lies dormant along blood capillaries and sends fat in the bloodstream to be stored or used for energy. Sixty-one percent of Americans are overweight, many of whom have repeatedly tried these diets and given up.
"The way to drive LPL out of business is to give it little fat to work with," explains Dr. Barnard. However, this doesn't mean eating tiny portions or counting every calorie. "The key," says nutritionist Amy Lanou, Ph.D., "is trading animal products for plant foods." She suggests meal substitutions and compares the fat content, "Have oatmeal with fresh berries instead of eggs and buttered toast and you'll reduce your fat intake by 22 grams. Try a vegetable burrito with salsa instead of a beef taco and save another 17 grams. These savings add up over the course of a day and much more over weeks and months. Soon, these substitutions will become new favorites and unwanted pounds will vanish effortlessly."
Exercise also has an effect on gene interactions and counteracts the fat-storage effect of LPL. LPL extracts fat from the bloodstream and passes it into fat tissue for storage--which means more body fat or into muscle cells where it is burned for energy. Exercise slows down LPL in fat tissues (making it harder to store fat) and increases LPL's activity in muscles (pushing fat into muscle cells to be burned). Whether your passions lie in outdoor hiking, biking, aerobics or brisk evening walks, choose an activity you enjoy so it doesn't feel like a Think of these sessions as a luxury you deserve.
Leptin--The Natural Appetite Suppressant
What if you already eat healthy foods, but cannot control you appetite? There is an appetite-taming gene on chromosome 7 that produces leptin--an important component in the delicate feedback system that lets you know when you are full. Fat cells produce leptin and send it to the brain appetite, but the system can be easily disrupted. "In rare mutation shuts off the production of leptin, resulting in low-calorie diets can hinder its functioning," says Dr. Barnard.
To keep leptin working efficiently, eat at least 10 calories per day per pound of your ideal weight. For instance, for a goal of 120 pounds, eat 1,200 calories or more per day, especially if you exercise regularly. Eating less will slow the production of leptin and cause your appetite to soar.
Many of Dr. Barnard's research studies at the Physicians Committee for Responsible Medicine in Washington, D.C., compared groups of participants who followed either a vegetarian diet (free of meat, eggs and dairy products) or a more typical "low-fat" diet. The majority of participants on the vegetarian diet reported weight loss, developed new taste preferences and ate until they felt satisfied. "I lost 20 pounds in 14 weeks, my cholesterol level is out of the danger zone and I feel better than I have in years," says a grandmother who participated in Dr. Barnard's study.
The Love of Broccoli is in the Genes . Sort of
If you cringe at the thought of drinking grapefruit juice, black coffee or green tea, you may have a parent to thank (or blame). As it turns out, taste is partly genetic. However, taste preferences also develop through habit-something we can change for the better.
Researchers can distinguish your taste sensitivity by putting a substance called 6-n-propylthiouracil on your tongue. About one in four people cannot taste it (genetic "nontasters"), half can detect it and the remaining one-fourth find it unbearable (genetic "supertasters"). Here's what this means at the dinner table.
Individuals in the supertaster category can detect every bit of sweetness in candy and every hint of bitterness in alcohol--which curbs their appetite for these less than optimal choices. However, supertasters may also snub vegetables, such as broccoli and cauliflower, because they can detect a slight bitterness in their flavor as well. Dr. Lanou suggests eating sweet potatoes, green beans, corn, carrots and other vegetables that are agreeable to almost everyone. "The natural fiber in vegetables is important for weight loss because it provides a sense of fullness and satisfaction," adds Dr. Lanou.
On the other hand, for genetic nontasters, pungent cheeses, biting martinis and heavy salad dressings go down with no trouble at all. However, problems come later in the form of clogged arteries and excess pounds. "Once again, the basis of each meal should be fiber-rich whole grains, vegetables and legumes," explains Dr. Lanou.
Jumpstart Your Thin Genes
Participants in Dr. Barnard's weight loss studies have the benefit of nutrition lectures, cooking demonstrations and the support of other participants who embarked on a new eating style. Nevertheless, you can apply the same effective techniques at home. Dr. Lanou recommends buying vegan or dairy-free vegetarian cookbooks, "Then you'll have the freedom to eat realistic portions and still lose weight." You can also find an array of frozen and ready-to-eat vegetarian meals in health food stores.
"Make a total commitment for three weeks. Set aside meat, dairy and eggs, keep oils to a minimum and [exercise daily]. Most people feel so good they never go back. Permanent weight loss is just one of many advantages we see," says Dr. Barnard.
RELATED ARTICLE: Helpful Tips
1. To slow LPL, the fat-building gene, base each meal on whole grains (pasta, brown rice, risotto, polenta) then add vegetables, beans, peas or lentils.
2. To boost leptin, the appetite-taming hormone, eat a daily minimum of 10 calories per pound of ideal body weight.
3. Learn to flavor meals with fresh herbs and spices, instead of oil and butter.
4. Invite the whole family to join-a vegetarian diet is healthy at any age.
5. Go ethnic-local Indian, Japanese, Thai and Italian restaurants also prepare delicious, vegetarian meals.
6. For a sweet treat, try fresh papaya, Ugli fruit, persimmons, star fruit or any fruit that is new to you.
Identification and Molecular Mapping of a Gene Conferring Resistance to Pyrenophora tritici-repentis Race 3 in Tetraploid Wheat
ABSTRACT Race 3 of the fungus Pyrenophora tritici-repentis, causal agent of tan spot, induces differential symptoms in tetraploid and hexaploid wheat, causing necrosis and chlorosis, respectively. This study was conducted to examine the genetic control of resistance to necrosis induced by P. tritici-repentis race 3 and to map resistance genes identified in tetraploid wheat (Triticum turgidum). A mapping population of recombinant inbred lines (RILs) was developed from a cross between the resistant genotype T. tur-gidum no. 283 (PI 352519) and the susceptible durum cv. Coulter. Based on the reactions of the Langdon-T. dicoccoides (LDN[DIC]) disomic substitution lines, chromosomal location of the resistance genes was determined and further molecular mapping of the resistance genes for race 3 was conducted in 80 RILs of the cross T. turgidum no. 283/Coulter. Plants were inoculated at the two-leaf stage and disease reaction was assessed 8 days after inoculation based on lesion type. Disease reaction of the LDN(DIC) lines and molecular mapping on the T. turgidum no. 283/Coulter population indicated that the gene, designated tsn2, conditioning resistance to race 3 is located on the long arm of chromosome 3B. Genetic analysis of the F(2) generation and of the F(4:5) and F(6:7) families indicated that a single recessive gene controlled resistance to necrosis induced by race 3 in the cross studied.
5. Philosophical Issues in Population Genetics
Population genetics raises a number of interesting philosophical issues. One such issue concerns the concept of the gene itself. As we have seen, population genetics came into being in the 1920s and 1930s, long before the molecular structure of genes had been discovered. In these pre-molecular days, the gene was a theoretical entity, postulated in order to explain observed patterns of inheritance in breeding experiments what genes were made of, how they caused phenotypic changes, and how they were transmitted from parent to offspring were not known. Today we do know the answers to these questions, thanks to the spectacular success of the molecular genetics ushered in by Watson and Crick's discovery of the structure of DNA in 1953. The gene has gone from being a theoretical entity to being something that can actually be manipulated in the laboratory.
The relationship between the gene of classical (pre-molecular) genetics, and the gene of modern molecular genetics is a subtle and much discussed topic (Beurton, Falk and Rheinberger (eds.) 2000, Griffiths and Stotz 2006, Moss 2003). In molecular genetics, &lsquogene&rsquo refers, more or less, to a stretch of DNA that codes for a particular protein&mdashso a gene is a unit of function. But in classical population genetics, &lsquogene&rsquo refers, more or less, to a portion of hereditary material that is inherited intact across generations&mdashso a gene is a unit of transmission, not a unit of function. In many cases, the two concepts of gene will pick out roughly the same entities&mdashwhich has led some philosophers to argue that classical genetics can be &lsquoreduced&rsquo to molecular genetics (Sarkar 1998). But it is clear that the two concepts do not have precisely the same extension not every molecular gene is a classical gene, nor vice-versa. Some theorists go further than this, arguing that what molecular biology really shows is that there are no such things as classical genes.
Whatever one's view of this debate, it is striking that virtually all of the central concepts of population genetics were devised in the pre-molecular era, when so little was known about what genes were the basic structure of population-genetic theory has changed little since the days of Fisher, Haldane and Wright. This reflects the fact that the empirical presuppositions of population-genetic models are really quite slim the basic presupposition is simply the existence of hereditary particles which obey the Mendelian rules of transmission, and which somehow affect the phenotype. Therefore, even without knowing what these hereditary particles are made of, or how they exert their phenotypic effects, the early population geneticists were able to devise an impressive body of theory. That the theory continues to be useful today illustrates the power of abstract models in science.
This leads us to another facet of population genetics that has attracted philosophers' attention: the way in which abstract models, that involve simplifying assumptions known to be false, can illuminate actual empirical phenomena. Idealized models of this sort play a central role in many sciences, including physics, economics and biology, and raise interesting methodological issues. In particular, there is often a trade-off between realism and tractability the more realistic a model the more complicated it becomes, which typically limits its usefulness and its range of applicability. This general problem and others like it have been extensively discussed in the philosophical literature on modelling (e.g. Godfrey-Smith 2006, Weisberg 2006, Frigg and Hartmann 2006), and are related to population genetics by Plutynski (2006).
It is clear that population genetics models rely on assumptions known to be false, and are subject to the realism / tractability trade-off. The simplest population-genetic models assume random mating, non-overlapping generations, infinite population size, perfect Mendelian segregation, frequency-independent genotype fitnesses, and the absence of stochastic effects it is very unlikely (and in the case of the infinite population assumption, impossible) that any of these assumptions hold true of any actual biological population. More realistic models, that relax one of more of the above assumptions, have been constructed, but they are invariably much harder to analyze. It is an interesting historical question whether these &lsquostandard&rsquo population-genetic assumptions were originally made because they simplified the mathematics, or because they were believed to be a reasonable approximation to reality, or both. This question is taken up by Morrison (2004) in relation to Fisher's early population-genetic work.
Another philosophical issue raised by population genetics is reductionism. It is often argued that the population-genetic view of evolution is inherently reductionistic, by both its critics and its defenders. This is apparent from how population geneticists define evolution: change in gene frequency. Implicit in this definition is the idea that evolutionary phenomena such as speciation, adaptive radiation, diversification, as well as phenotypic evolution, can ultimately be reduced to gene frequency change. But do we really know this to be true? Many biologists, particularly &lsquowhole organism&rsquo biologists, are not convinced, and thus reject both the population-genetic definition of evolution and the primacy traditionally accorded to population genetics within evolutionary biology (Pigliucci 2008).
This is a large question, and is related to the issues discussed in section 4. The question can be usefully divided into two: (i) can microevolutionary processes explain all of evolution? (ii) can all of micro-evolution be reduced to population genetics? &lsquoMicroevolution&rsquo refers to evolutionary changes that take place within a given population, over relatively short periods of time (e.g. three hundred generations). These changes typically involve the substitution of a gene for its alleles, of exactly the sort modelled by population genetics. So over microevolutionary time-scales, we do not typically expect to see extinction, speciation or major morphological change &mdash phenomena which are called &lsquomacroevolutionary&rsquo. Many biologists believe that macroevolution is simply &lsquomicroevolution writ large&rsquo, but this view is not universal. Authors such as Gould (2002) and Eldredge (1989), for example, have argued persuasively that macro-evolutionary phenomena are governed by autonomous dynamics, irreducible to a microevolutionary basis. Philosophical discussions of this issue include Sterelny (1996), Grantham (1995) and Okasha (2006).
Setting aside the reducibility of macro to micro-evolution, there is still the issue of whether an exclusively population-genetic approach to the latter is satisfactory. Some reasons for doubting this have been discussed already they include the complexity of the genotype-phenotype relation, the fact that population genetics treats development as a black-box, and the idealizing assumptions that its models rest on. Another point, not discussed above, is the fact that population genetics models are (deliberately) silent about the causes of the fitness differences between genotypes whose consequences they model (Sober 1984, Glymour 2006). For example, in the simple one-locus model of section 3.1, nothing is said about why the three genotypes leave different numbers of successful gametes. To fully understand evolution, the ecological factors that lead to these fitness differences must also be understood. While this is a valid point, the most it shows is that an exclusively population-genetic approach cannot yield a complete understanding of the evolutionary process. This does not really threaten the traditional view that population genetics is fundamental to evolutionary theory.
A final suite of philosophical issues surrounding population genetics concerns causation. Evolutionary biology is standardly thought of as a science that yields causal explanations: it tells us the causes of particular evolutionary phenomena (Okasha 2009). This causal dimension to evolutionary explanations is echoed in population genetics, where selection, mutation, migration and random drift are often described as causes, or &lsquoforces&rsquo, which lead to gene frequency change (Sober 1984). The basis for this way of speaking is obvious enough. If the frequency of gene A in a population increases from one generation to another, and if the population obeys the rules of Mendelian inheritance, then as a matter of logic one of three things must have happened: organisms bearing gene A must have outreproduced organisms without (I) organisms bearing gene A must have migrated into the population (II) or there must have been mutation to gene A from one of its alleles (III). It is straightforward to verify that if none of (I)-(III) had happened, then the frequency of gene A would have been unchanged. Note that case (I) covers both selection and random drift, depending on whether the A and non-A organisms reproduced differentially because of their genotypic difference, or by chance.
Despite this argument, a number of philosophers have objected to the idea that evolutionary change can usefully be thought of as caused by different factors, including natural selection (e.g. Matthen and Ariew 2009, Walsh 2007). A variety of objections to this apparently innocent way of speaking have been levelled some of these seem to be objections to the metaphor of &lsquoevolutionary forces&rsquo in particular, while others turn on more general considerations to do with causality and chance. The status of these objections is a controversial matter see Reisman and Forber (2005), Brandon and Ramsey (2007) and Sarkar (2011) for critical discussion. The &lsquonon-causal&rsquo (or &lsquostatistical&rsquo as it is sometimes called) view of evolution is certainly a radical one, since the idea that natural selection, in particular, is a potential cause of evolutionary change is virtually axiomatic in evolutionary biology, and routinely taught to students of the subject. As Millstein (2002) points out, if one abandons this view it becomes hard to make sense of important episodes in the history of evolutionary biology, such as the selectionist / neutralist controversy.
A full resolution of this issue cannot be attempted here however, it is worth making one observation about the idea that mutation, selection, migration and drift should be regarded as &lsquocauses&rsquo of gene frequency change. There is an important difference between drift on the other hand and the other three factors on the other. This is because mutation, selection and migration are directional they typically lead to a non-zero expected change in gene frequencies (Rice 2004 p. 132). Random drift on the other hand is non-directional the expected change due to drift is by definition zero. As Rice (2004) points out, this means that mutation, selection and migration can each be represented by a vector field on the space of gene frequencies their combined effects on the overall evolutionary change is then represented by ordinary vector addition. But drift cannot be treated this way, for it has a magnitude but not a direction. In so far as proponents of the &lsquonon-causal&rsquo view are motivated by the oddity of regarding drift, or chance, as a causal force, they have a point. However this line of argument is specific to random drift it does not generalize to all the factors that affect gene frequency change.
A related consideration is this. Suppose that instead of selection and drift we use the expression &lsquodifferential reproduction&rsquo to cover both. This gives us three &lsquofactors&rsquo that can lead to gene-frequency change in Mendelian populations: differential reproduction, mutation, and migration. It is straightforward to verify that at least one of these three factors must have operated, if gene frequencies in a population change. It seems unproblematic to regard these three factors as causes of evolution. However, the idea that differential reproduction can be decomposed into two &lsquosub-causes&rsquo, namely natural selection and random drift, is much more dubious. When we speak of differential reproduction as being &lsquodue&lsquo to random drift, or chance, this is not happily construed as a causal attribution. Rather, what we mean is that the differential reproduction was not the result of systematic differences in how well the genotypes were adapted to the environment.
To conclude, it is unsurprising to find so much philosophical discussion of population genetics given its centrality to evolutionary biology, a science which has long attracted the attention of philosophers. The preceding discussion has focused on the most prominent debates surrounding population genetics in the recent philosophical literature but in fact population genetics is relevant, at least indirectly, to virtually all of the topics traditionally discussed by philosophers of evolutionary biology.