Julian Savulescu

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https://en.wikipedia.org/wiki/Julian_Savulescu

 

Julian Savulescu
Born	22 December 1963 (age 61) Melbourne, Victoria, Australia
Education
Alma mater	Monash University
Doctoral advisor	Peter Singer
Philosophical work
Era	Contemporary philosophy
Region	Western philosophy
School	Analytic philosophy
Main interests	Ethics · Bioethics
Notable ideas	Procreative beneficence

Julian Savulescu (born 1963) is an Australian philosopher and bioethicist. He is Chen Su Lan Centennial Professor in Medical Ethics and Director of the Centre for Biomedical Ethics at the Yong Loo Lin School of Medicine, National University of Singapore. He is also the Uehiro Chair in Practical Ethics at the University of Oxford, and was previously the Fellow of St Cross College, Oxford, Director of the Oxford Uehiro Centre for Practical Ethics, and co-director of the Wellcome Centre for Ethics and Humanities. He is a visiting professorial fellow in Biomedical Ethics at the Murdoch Children's Research Institute in Australia, and distinguished visiting professor in Law at Melbourne University since 2017. He directs the Biomedical Ethics Research Group and is a member of the Centre for Ethics of Pediatric Genomics in Australia. He is a former editor and current board member of the Journal of Medical Ethics (2001–2004 and 2011–2018).

Career

Savulescu completed a Bachelor of Medical Sciences and a PhD at Monash University, under the supervision of philosopher Peter Singer. His doctoral thesis was on good reasons to die and euthanasia. After graduating, he took a Menzies Foundation postdoctoral scholarship, supervised by Derek Parfit before returning to Australia. He established a group on the ethics of genetics at the Murdoch Children's Research Institute, Australia. In 2002, he took up the Uehiro Chair in Practical Ethics in Oxford. In 2003, he established the Oxford Uehiro Centre for Practical Ethics as Director. He edits the Oxford University Press book series, the Uehiro Series in Practical Ethics.

Views

Procreative beneficence

Savulescu coined the term procreative beneficence. He describes it as the moral obligation (rather than mere permission) of parents who can select among potential children to choose those expected to have the best life prospects. For instance through preimplantation genetic diagnosis (PGD) and subsequent embryo selection or selective termination. A similar position was defended by John Harris. One argument is that some traits such as memory are "all-purpose means", in the sense of being instrumental in realizing whatever life plans the child may come to have.

Philosopher Walter Veit has argued that if one accepts both procreative beneficence and consequentialism, then a parental obligation for genetic enhancement logically follows, as there is no intrinsic moral difference between selecting and enhancing embryos for welfare-maximizing traits.

Reception

See also: Nonidentity problem

The principle of procreative beneficience is controversial. Bioethicist Rebecca Bennett argued against Savulescu's position, contending that not selecting the best offspring harms no one since those potential individuals would otherwise never have existed. She further wrote that the intuitions supporting such a selection merely reflect non-moral preferences rather than genuine moral obligations. Peter Herissone-Kelly argued against this criticism.

Moral Enhancement

In 2009, Professor Savulescu presented a paper at the "Festival of Dangerous Ideas", held at the Sydney Opera House in October 2009, entitled "Unfit for Life: Genetically Enhance Humanity or Face Extinction", which can be seen on Vimeo. Savulescu argues that unless humans are willing to undergo "moral enhancement", they may be on the brink of disappearing in a metaphorical "Bermuda Triangle", which he describes as a dangerous convergence of three factors: widespread access to destructive technologies, inherent limitations of human moral nature (such as parochialism and self-interest), and inadequacies of liberal democracy to address global challenges.

Norbert Paulo criticised Savulescu's argument for moral enhancement, arguing that if democratic governments had to morally enhance their populations because the majoritarian population are morally deficient, they could not be legitimate as they manipulated the population's will. Thus in Paulo's view, those advocating large-scale, state-driven and partially mandatory moral enhancement are advocating a non-democratic order.

Embryonic stem cells

Savulescu also justifies the destruction of embryos and foetuses as a source of organs and tissue for transplantation to adults. In his abstract he argues, "The most publicly justifiable application of human cloning, if there is one at all, is to provide self-compatible cells or tissues for medical use, especially transplantation. Some have argued that this raises no new ethical issues above those raised by any form of embryo experimentation. I argue that this research is less morally problematic than other embryo research. Indeed, it is not merely morally permissible but morally required that we employ cloning to produce embryos or fetuses for the sake of providing cells, tissues or even organs for therapy, followed by abortion of the embryo or fetus." He argues that if it is permissible to destroy foetuses, for social reasons, or no reasons at all, it must be justifiable to destroy them to save lives.

He argues that stem cell research is important enough as to be justifiable even if one conceptualizes the embryo as a person.

Abortion debate

Further, as editor of the Journal of Medical Ethics, he published, in 2012, an article by two Italian academics which stated that a new-born baby is effectively no different from a foetus, is not a "person" and, morally, could be killed at the decision of the parents etc. This article was published as part of a special double issue, "Abortion, Infanticide, and Allowing Babies to Die". The double issue included articles by Peter Singer, Michael Tooley, Jeff McMahan, C. A. J. Coady, Leslie Francis, John Finnis, and others. In an editorial, Savulescu wrote: "The Journal aims in this issue to promote further and more extensive rational debate concerning this controversial and important topic by providing a range of arguments from a variety of perspectives. We have tried to be as inclusive as possible and provided a double issue to include as many as possible of the submissions we received. Infanticide is an important issue and one worthy of scholarly attention because it touches on an area of concern that few societies have had the courage to tackle honestly and openly: euthanasia. We hope that the papers in this issue will stimulate ethical reflection on practices of euthanasia that are occurring and its proper justification and limits." He also stated, "I am strongly opposed to the legalisation of infanticide along the lines discussed by Giubilini and Minerva."

Other positions

Along with neuroethicist Guy Kahane, Savulescu's article "Brain Damage and the Moral Significance of Consciousness" appears to be the first mainstream publication to argue that increased evidence of consciousness in patients diagnosed with being in persistent vegetative state actually supports withdrawing or withholding care.

Other information

He has co-authored two books: Medical Ethics and Law: The Core Curriculum with Tony Hope and Judith Hendrick and Unfit for the Future: The Need for Moral Enhancement (published by Oxford University Press) with Ingmar Persson.

Savulescu is a member of the board of directors executive committee of the International Neuroethics Society.

He has also edited the books Der neue Mensch? Enhancement und Genetik (together with Nikolaus Knoepffler), Human Enhancement (together with Nick Bostrom), Enhancing Human Capacities, The Ethics of Human Enhancement. He was also a co-author of Love Is the Drug: The Chemical Future of Our Relationships addressing the future potential widespread use of aphrodisiacs. In it, he argued, that certain forms of medications can be ethically consumed as a "helpful complement" in relationships. Both to fall in love, and, to fall out of it.

Awards

In 2009, Savulescu was awarded a Distinguished Alumni Award by Monash University. In the same year, he was also announced as the winner in the Thinking category of The Australian newspaper's Emerging Leaders Awards.

Savulescu has a Honorary degree from the University of Bucharest (2014). He was awarded the 'Thinker' Award in the top 100 Australian Future Leaders (2009), and is a Monash University Distinguished Alumni (2009). He was ASMR Gold Medalist (2005).

In 2018, Savulescu and a team of co-authors were awarded the Daniel M. Wegner Theoretical Innovation Prize. This prize recognises the author of an article or book chapter judged to provide the most innovative theoretical contribution to social/personality psychology within a given year. He was also shortlisted for the AHRC Medal for Leadership in Medical Humanities in 2018. He was elected a Corresponding Fellow of the Australian Academy of the Humanities in 2023.

Human genetic variation

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Human_genetic_variation

A graphical representation of the typical human karyotype

The human mitochondrial DNA

Human genetic variation is the genetic differences in and among populations. There may be multiple variants of any given gene in the human population (alleles), a situation called polymorphism.

No two humans are genetically identical. Even monozygotic twins (who develop from one zygote) have infrequent genetic differences due to mutations occurring during development and gene copy-number variation. Differences between individuals, even closely related individuals, are the key to techniques such as genetic fingerprinting.

The human genome has a total length of approximately 3.2 billion base pairs (bp) in 46 chromosomes of DNA as well as slightly under 17,000 bp DNA in cellular mitochondria. In 2015, the typical difference between an individual's genome and the reference genome was estimated at 20 million base pairs (or 0.6% of the total). As of 2017, there were a total of 324 million known variants from sequenced human genomes.

Comparatively speaking, humans are a genetically homogeneous species. Although a small number of genetic variants are found more frequently in certain geographic regions or in people with ancestry from those regions, this variation accounts for a small portion (~15%) of human genome variability. The majority of variation exists within the members of each human population. For comparison, rhesus macaques exhibit 2.5-fold greater DNA sequence diversity compared to humans. These rates differ depending on what macromolecules are being analyzed. Chimpanzees have more genetic variance than humans when examining nuclear DNA, but humans have more genetic variance when examining at the level of proteins.

The lack of discontinuities in genetic distances between human populations, absence of discrete branches in the human species, and striking homogeneity of human beings globally, imply that there is no scientific basis for inferring races or subspecies in humans, and for most traits, there is much more variation within populations than between them. Despite this, modern genetic studies have found substantial average genetic differences across human populations in traits such as skin colour, bodily dimensions, lactose and starch digestion, high altitude adaptions, drug response, taste receptors, and predisposition to developing particular diseases. The greatest diversity is found within and among populations in Africa, and gradually declines with increasing distance from the African continent, consistent with the Out of Africa theory of human origins.

The study of human genetic variation has evolutionary significance and medical applications. It can help scientists reconstruct and understand patterns of past human migration. In medicine, study of human genetic variation may be important because some disease-causing alleles occur more often in certain population groups. For instance, the mutation for sickle-cell anemia is more often found in people with ancestry from certain sub-Saharan African, south European, Arabian, and Indian populations, due to the evolutionary pressure from mosquitos carrying malaria in these regions.

New findings show that each human has on average 60 new mutations compared to their parents.

Causes of variation

Further information: Recent human evolution

Causes of differences between individuals include independent assortment, the exchange of genes (crossing over and recombination) during reproduction (through meiosis) and various mutational events.

There are at least three reasons why genetic variation exists between populations. Natural selection may confer an adaptive advantage to individuals in a specific environment if an allele provides a competitive advantage. Alleles under selection are likely to occur only in those geographic regions where they confer an advantage. A second important process is genetic drift, which is the effect of random changes in the gene pool, under conditions where most mutations are neutral (that is, they do not appear to have any positive or negative selective effect on the organism). Finally, small migrant populations have statistical differences – called the founder effect – from the overall populations where they originated; when these migrants settle new areas, their descendant population typically differs from their population of origin: different genes predominate and it is less genetically diverse.

In humans, the main cause is genetic drift. Serial founder effects and past small population size (increasing the likelihood of genetic drift) may have had an important influence in neutral differences between populations. The second main cause of genetic variation is due to the high degree of neutrality of most mutations. A small, but significant number of genes appear to have undergone recent natural selection, and these selective pressures are sometimes specific to one region.

Measures of variation

Genetic variation among humans occurs on many scales, from gross alterations in the human karyotype to single nucleotide changes. Chromosome abnormalities are detected in 1 of 160 live human births. Apart from sex chromosome disorders, most cases of aneuploidy result in death of the developing fetus (miscarriage); the most common extra autosomal chromosomes among live births are 21, 18 and 13.

Nucleotide diversity is the average proportion of nucleotides that differ between two individuals. As of 2004, the human nucleotide diversity was estimated to be 0.1% to 0.4% of base pairs. In 2015, the 1000 Genomes Project, which sequenced one thousand individuals from 26 human populations, found that "a typical [individual] genome differs from the reference human genome at 4.1 million to 5.0 million sites … affecting 20 million bases of sequence"; the latter figure corresponds to 0.6% of total number of base pairs. Nearly all (>99.9%) of these sites are small differences, either single nucleotide polymorphisms or brief insertions or deletions (indels) in the genetic sequence, but structural variations account for a greater number of base-pairs than the SNPs and indels.

As of 2017, the Single Nucleotide Polymorphism Database (dbSNP), which lists SNP and other variants, listed 324 million variants found in sequenced human genomes.

Single nucleotide polymorphisms

DNA molecule 1 differs from DNA molecule 2 at a single base-pair location (a C/T polymorphism).

Main article: Single nucleotide polymorphism

A single nucleotide polymorphism (SNP) is a difference in a single nucleotide between members of one species that occurs in at least 1% of the population. The 2,504 individuals characterized by the 1000 Genomes Project had 84.7 million SNPs among them. SNPs are the most common type of sequence variation, estimated in 1998 to account for 90% of all sequence variants. Other sequence variations are single base exchanges, deletions and insertions. SNPs occur on average about every 100 to 300 bases and so are the major source of heterogeneity.

A functional, or non-synonymous, SNP is one that affects some factor such as gene splicing or messenger RNA, and so causes a phenotypic difference between members of the species. About 3% to 5% of human SNPs are functional (see International HapMap Project). Neutral, or synonymous SNPs are still useful as genetic markers in genome-wide association studies, because of their sheer number and the stable inheritance over generations.

A coding SNP is one that occurs inside a gene. There are 105 Human Reference SNPs that result in premature stop codons in 103 genes. This corresponds to 0.5% of coding SNPs. They occur due to segmental duplication in the genome. These SNPs result in loss of protein, yet all these SNP alleles are common and are not purified in negative selection.

Structural variation

Main article: Structural variation

Structural variation is the variation in structure of an organism's chromosome. Structural variations, such as copy-number variation and deletions, inversions, insertions and duplications, account for much more human genetic variation than single nucleotide diversity. This was concluded in 2007 from analysis of the diploid full sequences of the genomes of two humans: Craig Venter and James D. Watson. This added to the two haploid sequences which were amalgamations of sequences from many individuals, published by the Human Genome Project and Celera Genomics respectively.

According to the 1000 Genomes Project, a typical human has 2,100 to 2,500 structural variations, which include approximately 1,000 large deletions, 160 copy-number variants, 915 Alu insertions, 128 L1 insertions, 51 SVA insertions, 4 NUMTs, and 10 inversions.

Copy number variation

Main article: Copy number variation

A copy-number variation (CNV) is a difference in the genome due to deleting or duplicating large regions of DNA on some chromosome. It is estimated that 0.4% of the genomes of unrelated humans differ with respect to copy number. When copy number variation is included, human-to-human genetic variation is estimated to be at least 0.5% (99.5% similarity). Copy number variations are inherited but can also arise during development.

A visual map with the regions with high genomic variation of the modern-human reference assembly relatively to a Neanderthal of 50k has been built by Pratas et al.

Epigenetics

Epigenetic variation is variation in the chemical tags that attach to DNA and affect how genes get read. The tags, "called epigenetic markings, act as switches that control how genes can be read."^[41] At some alleles, the epigenetic state of the DNA, and associated phenotype, can be inherited across generations of individuals.

Genetic variability

Main article: Genetic variability

Genetic variability is a measure of the tendency of individual genotypes in a population to vary (become different) from one another. Variability is different from genetic diversity, which is the amount of variation seen in a particular population. The variability of a trait is how much that trait tends to vary in response to environmental and genetic influences.

Clines

Main article: Cline (biology)

In biology, a cline is a continuum of species, populations, varieties, or forms of organisms that exhibit gradual phenotypic and/or genetic differences over a geographical area, typically as a result of environmental heterogeneity. In the scientific study of human genetic variation, a gene cline can be rigorously defined and subjected to quantitative metrics.

Haplogroups

Main article: Haplogroup

In the study of molecular evolution, a haplogroup is a group of similar haplotypes that share a common ancestor with a single nucleotide polymorphism (SNP) mutation. The study of haplogroups provides information about ancestral origins dating back thousands of years.

The most commonly studied human haplogroups are Y-chromosome (Y-DNA) haplogroups and mitochondrial DNA (mtDNA) haplogroups, both of which can be used to define genetic populations. Y-DNA is passed solely along the patrilineal line, from father to son, while mtDNA is passed down the matrilineal line, from mother to both daughter or son. The Y-DNA and mtDNA may change by chance mutation at each generation.

Variable number tandem repeats

Main article: Variable number tandem repeat

A variable number tandem repeat (VNTR) is the variation of length of a tandem repeat. A tandem repeat is the adjacent repetition of a short nucleotide sequence. Tandem repeats exist on many chromosomes, and their length varies between individuals. Each variant acts as an inherited allele, so they are used for personal or parental identification. Their analysis is useful in genetics and biology research, forensics, and DNA fingerprinting.

Short tandem repeats (about 5 base pairs) are called microsatellites, while longer ones are called minisatellites.

History and geographic distribution

Map of the migration of modern humans out of Africa, based on mitochondrial DNA. Colored rings indicate thousand years before present.

Genetic distance map by Magalhães et al. (2012)

See also: Human evolutionary genetics § Modern humans, and Recent human evolution

Recent African origin of modern humans

The recent African origin of modern humans paradigm assumes the dispersal of non-African populations of anatomically modern humans after 70,000 years ago. Dispersal within Africa occurred significantly earlier, at least 130,000 years ago. The "out of Africa" theory originates in the 19th century, as a tentative suggestion in Charles Darwin's Descent of Man, but remained speculative until the 1980s when it was supported by the study of present-day mitochondrial DNA, combined with evidence from physical anthropology of archaic specimens.

According to a 2000 study of Y-chromosome sequence variation, human Y-chromosomes trace ancestry to Africa, and the descendants of the derived lineage left Africa and eventually were replaced by archaic human Y-chromosomes in Eurasia. The study also shows that a minority of contemporary populations in East Africa and the Khoisan are the descendants of the most ancestral patrilineages of anatomically modern humans that left Africa 35,000 to 89,000 years ago. Other evidence supporting the theory is that variations in skull measurements decrease with distance from Africa at the same rate as the decrease in genetic diversity. Human genetic diversity decreases in native populations with migratory distance from Africa, and this is thought to be due to bottlenecks during human migration, which are events that temporarily reduce population size.

A 2009 genetic clustering study, which genotyped 1327 polymorphic markers in various African populations, identified six ancestral clusters. The clustering corresponded closely with ethnicity, culture and language. A 2018 whole genome sequencing study of the world's populations observed similar clusters among the populations in Africa. At K=9, distinct ancestral components defined the Afroasiatic-speaking populations inhabiting North Africa and Northeast Africa; the Nilo-Saharan-speaking populations in Northeast Africa and East Africa; the Ari populations in Northeast Africa; the Niger-Congo-speaking populations in West-Central Africa, West Africa, East Africa and Southern Africa; the Pygmy populations in Central Africa; and the Khoisan populations in Southern Africa.

In May 2023, scientists reported, based on genetic studies, a more complicated pathway of human evolution than previously understood. According to the studies, humans evolved from different places and times in Africa, instead of from a single location and period of time.

Population genetics

See also: Population genetics

Because of the common ancestry of all humans, only a small number of variants have large differences in frequency between populations. However, some rare variants in the world's human population are much more frequent in at least one population (more than 5%).

Genetic variation

Genetic variation of Eurasian populations showing different frequency of West- and East-Eurasian components

It is commonly assumed that early humans left Africa, and thus must have passed through a population bottleneck before their African-Eurasian divergence around 100,000 years ago (ca. 3,000 generations). The rapid expansion of a previously small population has two important effects on the distribution of genetic variation. First, the so-called founder effect occurs when founder populations bring only a subset of the genetic variation from their ancestral population. Second, as founders become more geographically separated, the probability that two individuals from different founder populations will mate becomes smaller. The effect of this assortative mating is to reduce gene flow between geographical groups and to increase the genetic distance between groups.

The expansion of humans from Africa affected the distribution of genetic variation in two other ways. First, smaller (founder) populations experience greater genetic drift because of increased fluctuations in neutral polymorphisms. Second, new polymorphisms that arose in one group were less likely to be transmitted to other groups as gene flow was restricted.

Populations in Africa tend to have lower amounts of linkage disequilibrium than do populations outside Africa, partly because of the larger size of human populations in Africa over the course of human history and partly because the number of modern humans who left Africa to colonize the rest of the world appears to have been relatively low. In contrast, populations that have undergone dramatic size reductions or rapid expansions in the past and populations formed by the mixture of previously separate ancestral groups can have unusually high levels of linkage disequilibrium

Distribution of variation

Human genetic variation calculated from genetic data representing 346 microsatellite loci taken from 1484 individuals in 78 human populations. The upper graph illustrates that as populations are further from East Africa, they have declining genetic diversity as measured in average number of microsatellite repeats at each of the loci. The bottom chart illustrates isolation by distance. Populations with a greater distance between them are more dissimilar (as measured by the Fst statistic) than those which are geographically close to one another. The horizontal axis of both charts is geographic distance as measured along likely routes of human migration. (Chart from Kanitz et al. 2018)

The distribution of genetic variants within and among human populations are impossible to describe succinctly because of the difficulty of defining a "population," the clinal nature of variation, and heterogeneity across the genome (Long and Kittles 2003). In general, however, an average of 85% of genetic variation exists within local populations, ~7% is between local populations within the same continent, and ~8% of variation occurs between large groups living on different continents. The recent African origin theory for humans would predict that in Africa there exists a great deal more diversity than elsewhere and that diversity should decrease the further from Africa a population is sampled.

Phenotypic variation

Further information: Phenotype § Phenotypic variation

Sub-Saharan Africa has the most human genetic diversity and the same has been shown to hold true for phenotypic variation in skull form. Phenotype is connected to genotype through gene expression. Genetic diversity decreases smoothly with migratory distance from that region, which many scientists believe to be the origin of modern humans, and that decrease is mirrored by a decrease in phenotypic variation. Skull measurements are an example of a physical attribute whose within-population variation decreases with distance from Africa.

The distribution of many physical traits resembles the distribution of genetic variation within and between human populations (American Association of Physical Anthropologists 1996; Keita and Kittles 1997). For example, ~90% of the variation in human head shapes occurs within continental groups, and ~10% separates groups, with a greater variability of head shape among individuals with recent African ancestors (Relethford 2002).

A prominent exception to the common distribution of physical characteristics within and among groups is skin color. Approximately 10% of the variance in skin color occurs within groups, and ~90% occurs between groups (Relethford 2002). This distribution of skin color and its geographic patterning – with people whose ancestors lived predominantly near the equator having darker skin than those with ancestors who lived predominantly in higher latitudes – indicate that this attribute has been under strong selective pressure. Darker skin appears to be strongly selected for in equatorial regions to prevent sunburn, skin cancer, the photolysis of folate, and damage to sweat glands.

Understanding how genetic diversity in the human population impacts various levels of gene expression is an active area of research. While earlier studies focused on the relationship between DNA variation and RNA expression, more recent efforts are characterizing the genetic control of various aspects of gene expression including chromatin states, translation, and protein levels. A study published in 2007 found that 25% of genes showed different levels of gene expression between populations of European and Asian descent. The primary cause of this difference in gene expression was thought to be SNPs in gene regulatory regions of DNA. Another study published in 2007 found that approximately 83% of genes were expressed at different levels among individuals and about 17% between populations of European and African descent.

Wright's fixation index as measure of variation

The population geneticist Sewall Wright developed the fixation index (often abbreviated to F_ST) as a way of measuring genetic differences between populations. This statistic is often used in taxonomy to compare differences between any two given populations by measuring the genetic differences among and between populations for individual genes, or for many genes simultaneously. It is often stated that the fixation index for humans is about 0.15. This translates to an estimated 85% of the variation measured in the overall human population is found within individuals of the same population, and about 15% of the variation occurs between populations. These estimates imply that any two individuals from different populations may be more similar to each other than either is to a member of their own group. "The shared evolutionary history of living humans has resulted in a high relatedness among all living people, as indicated for example by the very low fixation index (F_ST) among living human populations." Richard Lewontin, who affirmed these ratios, thus concluded neither "race" nor "subspecies" were appropriate or useful ways to describe human populations.

Wright himself believed that values >0.25 represent very great genetic variation and that an F_ST of 0.15–0.25 represented great variation. However, about 5% of human variation occurs between populations within continents, therefore F_ST values between continental groups of humans (or races) of as low as 0.1 (or possibly lower) have been found in some studies, suggesting more moderate levels of genetic variation. Graves (1996) has countered that F_ST should not be used as a marker of subspecies status, as the statistic is used to measure the degree of differentiation between populations, although see also Wright (1978).

Jeffrey Long and Rick Kittles give a long critique of the application of F_ST to human populations in their 2003 paper "Human Genetic Diversity and the Nonexistence of Biological Races". They find that the figure of 85% is misleading because it implies that all human populations contain on average 85% of all genetic diversity. They argue the underlying statistical model incorrectly assumes equal and independent histories of variation for each large human population. A more realistic approach is to understand that some human groups are parental to other groups and that these groups represent paraphyletic groups to their descent groups. For example, under the recent African origin theory the human population in Africa is paraphyletic to all other human groups because it represents the ancestral group from which all non-African populations derive, but more than that, non-African groups only derive from a small non-representative sample of this African population. This means that all non-African groups are more closely related to each other and to some African groups (probably east Africans) than they are to others, and further that the migration out of Africa represented a genetic bottleneck, with much of the diversity that existed in Africa not being carried out of Africa by the emigrating groups. Under this scenario, human populations do not have equal amounts of local variability, but rather diminished amounts of diversity the further from Africa any population lives. Long and Kittles find that rather than 85% of human genetic diversity existing in all human populations, about 100% of human diversity exists in a single African population, whereas only about 70% of human genetic diversity exists in a population derived from New Guinea. Long and Kittles argued that this still produces a global human population that is genetically homogeneous compared to other mammalian populations.

Archaic admixture

Main article: Archaic human admixture with modern humans

Anatomically modern humans interbred with Neanderthals during the Middle Paleolithic. In May 2010, the Neanderthal Genome Project presented genetic evidence that interbreeding took place and that a small but significant portion, around 2–4%, of Neanderthal admixture is present in the DNA of modern Eurasians and Oceanians, and nearly absent in sub-Saharan African populations.

Between 4% and 6% of the genome of Melanesians (represented by the Papua New Guinean and Bougainville Islander) appears to derive from Denisovans – a previously unknown hominin which is more closely related to Neanderthals than to Sapiens. It was possibly introduced during the early migration of the ancestors of Melanesians into Southeast Asia. This history of interaction suggests that Denisovans once ranged widely over eastern Asia.

Thus, Melanesians emerge as one of the most archaic-admixed populations, having Denisovan/Neanderthal-related admixture of ~8%.

In a study published in 2013, Jeffrey Wall from University of California studied whole sequence-genome data and found higher rates of introgression in Asians compared to Europeans. Hammer et al. tested the hypothesis that contemporary African genomes have signatures of gene flow with archaic human ancestors and found evidence of archaic admixture in the genomes of some African groups, suggesting that modest amounts of gene flow were widespread throughout time and space during the evolution of anatomically modern humans.

A study published in 2020 found that the Yoruba and Mende populations of West Africa derive between 2% and 19% of their genome from an as-yet unidentified archaic hominin population that likely diverged before the split of modern humans and the ancestors of Neanderthals and Denisovans, potentially making these groups the most archaic-admixed human populations identified yet.

Categorization of the world population

Chart showing human genetic clustering

Individuals mostly have genetic variants which are found in multiple regions of the world. Based on data from "A unified genealogy of modern and ancient genomes".

See also: Race (human classification) and Race and genetics

New data on human genetic variation has reignited the debate about a possible biological basis for categorization of humans into races. Most of the controversy surrounds the question of how to interpret the genetic data and whether conclusions based on it are sound. Some researchers argue that self-identified race can be used as an indicator of geographic ancestry for certain health risks and medications.

Although the genetic differences among human groups are relatively small, these differences in certain genes such as duffy, ABCC11, SLC24A5, called ancestry-informative markers (AIMs) nevertheless can be used to reliably situate many individuals within broad, geographically based groupings. For example, computer analyses of hundreds of polymorphic loci sampled in globally distributed populations have revealed the existence of genetic clustering that roughly is associated with groups that historically have occupied large continental and subcontinental regions (Rosenberg et al. 2002; Bamshad et al. 2003).

Some commentators have argued that these patterns of variation provide a biological justification for the use of traditional racial categories. They argue that the continental clusterings correspond roughly with the division of human beings into sub-Saharan Africans; Europeans, Western Asians, Central Asians, Southern Asians and Northern Africans; Eastern Asians, Southeast Asians, Polynesians and Native Americans; and other inhabitants of Oceania (Melanesians, Micronesians & Australian Aborigines) (Risch et al. 2002). Other observers disagree, saying that the same data undercut traditional notions of racial groups (King and Motulsky 2002; Calafell 2003; Tishkoff and Kidd 2004). They point out, for example, that major populations considered races or subgroups within races do not necessarily form their own clusters.

Racial categories are also undermined by findings that genetic variants which are limited to one region tend to be rare within that region, variants that are common within a region tend to be shared across the globe, and most differences between individuals, whether they come from the same region or different regions, are due to global variants. No genetic variants have been found which are fixed within a continent or major region and found nowhere else.

Furthermore, because human genetic variation is clinal, many individuals affiliate with two or more continental groups. Thus, the genetically based "biogeographical ancestry" assigned to any given person generally will be broadly distributed and will be accompanied by sizable uncertainties (Pfaff et al. 2004).

In many parts of the world, groups have mixed in such a way that many individuals have relatively recent ancestors from widely separated regions. Although genetic analyses of large numbers of loci can produce estimates of the percentage of a person's ancestors coming from various continental populations (Shriver et al. 2003; Bamshad et al. 2004), these estimates may assume a false distinctiveness of the parental populations, since human groups have exchanged mates from local to continental scales throughout history (Cavalli-Sforza et al. 1994; Hoerder 2002). Even with large numbers of markers, information for estimating admixture proportions of individuals or groups is limited, and estimates typically will have wide confidence intervals (Pfaff et al. 2004).

Genetic clustering

Main article: Human genetic clustering

Genetic data can be used to infer population structure and assign individuals to groups that often correspond with their self-identified geographical ancestry. Jorde and Wooding (2004) argued that "Analysis of many loci now yields reasonably accurate estimates of genetic similarity among individuals, rather than populations. Clustering of individuals is correlated with geographic origin or ancestry." However, identification by geographic origin may quickly break down when considering historical ancestry shared between individuals back in time.

An analysis of autosomal SNP data from the International HapMap Project (Phase II) and CEPH Human Genome Diversity Panel samples was published in 2009. The study of 53 populations taken from the HapMap and CEPH data (1138 unrelated individuals) suggested that natural selection may shape the human genome much more slowly than previously thought, with factors such as migration within and among continents more heavily influencing the distribution of genetic variations. A similar study published in 2010 found strong genome-wide evidence for selection due to changes in ecoregion, diet, and subsistence particularly in connection with polar ecoregions, with foraging, and with a diet rich in roots and tubers. In a 2016 study, principal component analysis of genome-wide data was capable of recovering previously-known targets for positive selection (without prior definition of populations) as well as a number of new candidate genes.

Forensic anthropology

Forensic anthropologists can assess the ancestry of skeletal remains by analyzing skeletal morphology as well as using genetic and chemical markers, when possible. While these assessments are never certain, the accuracy of skeletal morphology analyses in determining true ancestry has been estimated at 90%.

Ternary plot showing average admixture of five North American ethnic groups. Individuals that self-identify with each group can be found at many locations on the map, but on average groups tend to cluster differently.

Gene flow and admixture

Main article: Gene flow

Gene flow between two populations reduces the average genetic distance between the populations, only totally isolated human populations experience no gene flow and most populations have continuous gene flow with other neighboring populations which create the clinal distribution observed for most genetic variation. When gene flow takes place between well-differentiated genetic populations the result is referred to as "genetic admixture".

Admixture mapping is a technique used to study how genetic variants cause differences in disease rates between population. Recent admixture populations that trace their ancestry to multiple continents are well suited for identifying genes for traits and diseases that differ in prevalence between parental populations. African-American populations have been the focus of numerous population genetic and admixture mapping studies, including studies of complex genetic traits such as white cell count, body-mass index, prostate cancer and renal disease.

An analysis of phenotypic and genetic variation including skin color and socio-economic status was carried out in the population of Cape Verde which has a well documented history of contact between Europeans and Africans. The studies showed that pattern of admixture in this population has been sex-biased (involving mostly matings between European men and African women) and there is a significant interaction between socioeconomic status and skin color, independent of ancestry. Another study shows an increased risk of graft-versus-host disease complications after transplantation due to genetic variants in human leukocyte antigen (HLA) and non-HLA proteins.

Impact on gene function and health

See also: Race and health

Given that each individual has millions of genetic variants (compared to the reference genome), it is an important question what impact these variants have on human health or gene function. Most genetic variants have only small to moderate effects, if any. Frequently cited examples include hypertension (Douglas et al. 1996), diabetes, obesity (Fernandez et al. 2003), and prostate cancer (Platz et al. 2000). However, the role of genetic factors in generating these differences remains uncertain.

Effect on protein function

The human genome encodes about 20,000 protein-coding genes with about 550 amino acids each. Hence, human proteins span about 11 million amino acids (22 million per diploid genome). The median number of missense mutations in individual human genomes is about 8600, that is, two individuals differ by 1 in about 2600 amino acids or in about 20% of their proteins. The average individual has about 137 (predicted) loss of function mutations, including 71 frameshift and 148 in-frame deletions or insertions. Mutations at 32.2% and 9.5% of all possible genomic positions, respectively, can lead to missense and stop-gained variants (i.e., truncated proteins). In a sample of almost 1 million people, almost 5000 genes were identified that had loss-of-function variants in both alleles of the same individual. That is, if these 5000 genes can tolerate homozygous loss of function mutations, they are unlikely to be essential.

Monogenetic diseases

Differences in allele frequencies contribute to group differences in the incidence of some monogenic diseases, and they may contribute to differences in the incidence of some common diseases. For the monogenic diseases, the frequency of causative alleles usually correlates best with ancestry, whether familial (for example, Ellis–Van Creveld syndrome among the Pennsylvania Amish), ethnic (Tay–Sachs disease among Ashkenazi Jewish populations), or geographical (hemoglobinopathies among people with ancestors who lived in malarial regions). To the extent that ancestry corresponds with racial or ethnic groups or subgroups, the incidence of monogenic diseases can differ between groups categorized by race or ethnicity, and health-care professionals typically take these patterns into account in making diagnoses.

Beneficial variants

Some other variations on the other hand are beneficial to human, as they prevent certain diseases and increase the chance to adapt to the environment. For example, mutation in CCR5 gene that protects against AIDS. CCR5 gene is absent on the surface of cell due to mutation. Without CCR5 gene on the surface, there is nothing for HIV viruses to grab on and bind into. Therefore, the mutation on CCR5 gene decreases the chance of an individual's risk with AIDS. The mutation in CCR5 is also quite common in certain areas, with more than 14% of the population carry the mutation in Europe and about 6–10% in Asia and North Africa.

HIV attachment

Many genetic variants may have aided humans in ancient times but plague us today. For example, genes that allow humans to more efficiently process food also make people susceptible to obesity and diabetes today.

Genome projects and organizations

Further information: Category:Human genome projects

Human genome projects are scientific endeavors that determine or study the structure of the human genome. The Human Genome Project was a landmark genome project.

There are numerous related projects that deal with genetic variation (or variation in the encoded proteins), e.g. organized by the following organizations:

• HUman Genome Organisation (HUGO) -- organizes activities around human genome sequencing, including variants
• Human Genome Variation Society (HGVS) -- develops nomenclatural standards for human genetic variants
• HGVS Variant Nomenclature Committee (HVNC) -- maps and organizes variant nomenclature

Genetic distance

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Genetic_distance

 

Figure 1: Genetic distance map by Cavalli-Sforza et al. (1994) 

Genetic distance is a measure of the genetic divergence between species or between populations within a species, whether the distance measures time from common ancestor or degree of differentiation. Populations with many similar alleles have small genetic distances. This indicates that they are closely related and have a recent common ancestor.

Genetic distance is useful for reconstructing the history of populations, such as the multiple human expansions out of Africa. It is also used for understanding the origin of biodiversity. For example, the genetic distances between different breeds of domesticated animals are often investigated in order to determine which breeds should be protected to maintain genetic diversity.

Biological foundation

Life on earth began from very simple unicellular organisms evolving into most complex multicellular organisms through the course of over three billion years. Creating a comprehensive tree of life that represents all the organisms that have ever lived on earth is important for understanding the evolution of life in the face of all challenges faced by living organisms to deal with similar challenges in future. Evolutionary biologists have attempted to create evolutionary or phylogenetic trees encompassing as many organisms as possible based on the available resources. Fossil dating and molecular clock are the two means of generating evolutionary history of living organisms. Fossil record is random, incomplete and does not provide a continuous chain of events like a movie with missing frames cannot tell the whole plot of the movie.

Molecular clocks on the other hand are specific sequences of DNA, RNA or proteins (amino acids) that are used to determine at molecular level the similarities and differences among species, to find out the timeline of divergence, and to trace back the common ancestor of species based on the mutation rates and sequence changes being accumulated in those specific sequences. The primary driver of evolution is the mutation or changes in genes and accounting for those changes over time determines the approximate genetic distance between species. These specific molecular clocks are fairly conserved across a range of species and have a constant rate of mutation like a clock and are calibrated based on evolutionary events (fossil records). For example, gene for alpha-globin (constituent of hemoglobin) mutates at a rate of 0.56 per base pair per billion years. The molecular clock can fill those gaps created by missing fossil records.

In the genome of an organism, each gene is located at a specific place called the locus for that gene. Allelic variations at these loci cause phenotypic variation within species (e.g. hair colour, eye colour). However, most alleles do not have an observable impact on the phenotype. Within a population new alleles generated by mutation either die out or spread throughout the population. When a population is split into different isolated populations (by either geographical or ecological factors), mutations that occur after the split will be present only in the isolated population. Random fluctuation of allele frequencies also produces genetic differentiation between populations. This process is known as genetic drift. By examining the differences between allele frequencies between the populations and computing genetic distance, we can estimate how long ago the two populations were separated.

Let’s suppose a sequence of DNA or a hypothetical gene that has mutation rate of one base per 10 million years. Using this sequence of DNA, the divergence of two different species or genetic distance between two different species can be determined by counting the number of base pair differences among them. For example, in Figure 2 a difference of 4 bases in the hypothetical sequence among those two species would indicate that they diverged 40 million years ago, and their common ancestor would have lived at least 20 million years ago before their divergence. Based on molecular clock, the equation below can be used to calculate the time since divergence.

Number of mutation ÷ Mutation per year (rate of mutation) = time since divergence

Figure 2: Divergence timeline between two hypothetical species.

Process of determining genetic distance

Recent advancement in sequencing technology and the availability of comprehensive genomic databases and bioinformatics tools that are capable of storing and processing colossal amount of data generated by the advanced sequencing technology has tremendously improved evolutionary studies and the understanding of evolutionary relationships among species.

Markers for genetic distance

Different biomolecular markers such DNA, RNA and amino acid sequences (protein) can be used for determining the genetic distance.

The selection criteria of appropriate biomarker for genetic distance entails the following three steps:

1. choice of variability
2. choice of specific region of DNA or RNA
3. the use of technique

The choice of variability depends on the intended outcome. For example, very high level of variability is recommended for demographic studies and parentage analyses, medium to high variability for comparing distinct populations, and moderate to very low variability is recommended for phylogenetic studies. The genomic localization and ploidy of the marker is also an important factor. For example, the gene copy number is inversely proportional to the robustness with haploid genome (mitochondrial DNA) more prone to genetic drift than diploid genome (nuclear DNA).

The choice and examples of molecular markers for evolutionary biology studies.

	Biological issues/biodiversity level	Level of variability	Nature of information required	Examples of most used markers
Intra-population	Population structure, reproduction system	Medium to high	(N) codominant loci = (Multilocus) genotype	Microsatellites, allozymes
	Fingerprinting. parentage analysis	Very high	Codominant loci or numerous dominant loci	Microsatellites (RAPD, AFLP)
	Demography	Medium to high	Allele frequency in samples taken at different times	Allozymes, Microsatellites
	Demographic history	Medium to high	Allele frequency + evolutionary relationships	Mt-DNA sequences
Inter-population	Phylogeography, definition of evolutionary significant units (population structure)	Medium to high	Allele frequency in each population	Allozymes, microsatellites (risk of size homoplasy)
	Bio-conservation	Medium	Allele evolutionary relationships	Mt-DNA (if variable enough)
Inter-specific	Close species	ca. 1%/my	No variability within species if possible	Sequences of Mt-DNA, ITS rDNA

Application of genetic distance

• Phylogenetics: Exploring the genetic distance among species can help in establishing evolutionary relationships among them, the time of divergence between them and creating a comprehensive phylogenetic tree that connect them to their common ancestors.
•  Accuracy of genomic prediction: Genetic distance can be used to predict unobserved phenotypes which has implication in medical diagnostics, and breeding of plants and animals.
• Population Genetics: Genetic distance can help in studying population genetics, understanding intra and inter-population genetic diversity.
• Taxonomy and Species Delimitation: Determining genetic distance through DNA barcoding is an effective tool for delimiting species especially identifying cryptic species. An optimized percentage threshold genetic distance is recommended based on the data and species being studied to improve and enhance the reliability and applicability of delimitation that can delineate species boundaries and identify cryptic species that look similar but are genetically distinct.

Evolutionary forces affecting genetic distance

Evolutionary forces such as mutation, genetic drift, natural selection, and gene flow drive the process of evolution and genetic diversity. All these forces play significant role in genetic distance within and among species.

Measures

Figure 3: Image depicts speciation stemmed from geographic isolation where a starting population is separated. Over vast amounts of time, isolated groups of a particular taxa may diverge into distinct species.

Different statistical measures exist that aim to quantify genetic deviation between populations or species. By utilizing assumptions gained from experimental analysis of evolutionary forces, a model that more accurately suits a given experiment can be selected to study a genetic group. Additionally, comparing how well different metrics model certain population features such as isolation can identify metrics that are more suited for understanding newly studied groups The most commonly used genetic distance metrics are Nei's genetic distance, Cavalli-Sforza and Edwards measure, and Reynolds, Weir and Cockerham's genetic distance.

Jukes-Cantor Distance

One of the most basic and straight forward distance measures is Jukes-Cantor distance. This measure is constructed based on the assumption that no insertions or deletions occurred, all substitutions are independent, and that each nucleotide change is equally likely. With these presumptions, we can obtain the following equation:

${\displaystyle d_{AB}=-\frac{3}{4}\ln (1-\frac{4}{3}{f}_{AB})}$

where ${\displaystyle d_{AB}}$ is the Jukes-Cantor distance between two sequences A, and B, and ${\displaystyle f_{AB}}$ being the dissimilarity between the two sequences.

Nei's standard genetic distance

In 1972, Masatoshi Nei published what came to be known as Nei's standard genetic distance. This distance has the nice property that if the rate of genetic change (amino acid substitution) is constant per year or generation then Nei's standard genetic distance (D) increases in proportion to divergence time. This measure assumes that genetic differences are caused by mutation and genetic drift.

${\displaystyle D=-\ln \frac{\sum _{\ell }\sum _u{X}_u{Y}_u}{\sqrt{\left(\sum _u{X}_u^2\right)\left(\sum _u{Y}_u^2\right)}}}$

This distance can also be expressed in terms of the arithmetic mean of gene identity. Let ${\displaystyle j_X}$ be the probability for the two members of population ${\displaystyle X}$ having the same allele at a particular locus and ${\displaystyle j_Y}$ be the corresponding probability in population ${\displaystyle Y}$. Also, let ${\displaystyle j_{XY}}$ be the probability for a member of ${\displaystyle X}$ and a member of ${\displaystyle Y}$ having the same allele. Now let ${\displaystyle J_X}$, ${\displaystyle J_Y}$ and ${\displaystyle J_{XY}}$ represent the arithmetic mean of ${\displaystyle j_X}$, ${\displaystyle j_Y}$ and ${\displaystyle j_{XY}}$ over all loci, respectively. In other words,

${\displaystyle J_X=\sum _u\frac{{X_u}^2}{L}}$

${\displaystyle J_Y=\sum _u\frac{{Y_u}^2}{L}}$

${\displaystyle J_{XY}=\sum _{\ell }\sum _u\frac{X_u{Y}_u}{L}}$

where ${\displaystyle L}$ is the total number of loci examined.

Nei's standard distance can then be written as

${\displaystyle D=-\ln \frac{J_{XY}}{\sqrt{J_X{J}_Y}}}$

Cavalli-Sforza chord distance

In 1967 Luigi Luca Cavalli-Sforza and A. W. F. Edwards published this measure. It assumes that genetic differences arise due to genetic drift only. One major advantage of this measure is that the populations are represented in a hypersphere, the scale of which is one unit per gene substitution. The chord distance in the hyperdimensional sphere is given by

${\displaystyle D_{\text{CH}}=\frac{2}{\pi }\sqrt{2\left(1-\sum _{\ell }\sum _u\sqrt{X_u{Y}_u}\right)}}$

Some authors drop the factor ${\displaystyle \frac{2}{\pi }}$ to simplify the formula at the cost of losing the property that the scale is one unit per gene substitution.

Reynolds, Weir, and Cockerham's genetic distance

In 1983, this measure was published by John Reynolds, Bruce Weir and C. Clark Cockerham. This measure assumes that genetic differentiation occurs only by genetic drift without mutations. It estimates the coancestry coefficient ${\displaystyle \mathrm{\Theta }}$ which provides a measure of the genetic divergence by:

${\displaystyle {\mathrm{\Theta }}_w=\sqrt{\frac{\sum _{\ell }\sum _u(X_u-Y_u{)}^2}{2\sum _{\ell }\left(1-\sum _u{X}_u{Y}_u\right)}}}$

Kimura 2 Parameter distance

Figure 4: A diagram showing the relationship between DNA base-pairs and the type of mutation needed to convert each base to another based on the Kimura 2 parameter substitution model.

The Kimura two parameter model (K2P) was developed in 1980 by Japanese biologist Motoo Kimura. It is compatible with the neutral theory of evolution, which was also developed by the same author. As depicted in Figure 4, this measure of genetic distance accounts for the type of mutation occurring, namely whether it is a transition (i.e. purine to purine or pyrimidine to pyrimidine) or a transversion (i.e. purine to pyrimidine or vice versa). With this information, the following formula can be derived:

${\displaystyle K=-\frac{1}{2}{\log }_e[(1-2P-Q)\sqrt{1-2Q}]}$

where P is ${\displaystyle \frac{n_1}{n}}$ and Q is ${\displaystyle \frac{n_2}{n}}$, with ${\displaystyle n_1}$ being the number of transition type conversions, ${\displaystyle n_2}$ being the number of transversion type conversions, and ${\displaystyle n}$ being the number of nucleotides sites compared.

It is worth noting when transition and transversion type substitutions have an equal chance of occurring, and ${\displaystyle P}$ is assumed to equal ${\displaystyle \frac{Q}{2}}$, then the above formula can be reduced down to the Jukes Cantor model. In practice however, ${\displaystyle P}$ is typically larger than ${\displaystyle Q}$.

It has been shown that while K2P works well in classifying distantly-related species, it is not always the best choice for comparing closely-related species. In these cases, it may be better to use p-distance instead.

Kimura 3 Parameter distance

Figure 5: A diagram showing the relationship between DNA base-pairs and the type of mutation needed to convert each base to another based on the Kimura 3 parameter substitution model.

The Kimura three parameter (K3P) model was first published in 1981. This measure assumes three rates of substitution when nucleotides mutate, which can be seen in Figure 5. There is one rate for transition type mutations, one rate for transversion type mutations to corresponding bases (e.g. G to C; transversion type 1 in the figure), and one rate for transversion type mutations to non-corresponding bases (e.g. G to T; transversion type 2 in the figure).

With these rates of substitution, the following formula can be derived:

${\displaystyle K=-\frac{1}{4}{\log }_e[(1-2P-2Q)(1-2P-2R)(1-2Q-2R)]}$

where ${\displaystyle P}$ is the probability of a transition type mutation, ${\displaystyle Q}$ is the probability of a transversion type mutation to a corresponding base, and ${\displaystyle R}$ is the probability of a transversion type mutation to a non-corresponding base. When ${\displaystyle Q}$ and ${\displaystyle R}$ are assumed to be equal, this reduces down to the Kimura 2 parameter distance.

Other measures

Many other measures of genetic distance have been proposed with varying success.

Nei's D_A distance 1983

Nei's D_A distance was created by Masatoshi Nei, a Japanese-American biologist in 1983. This distance assumes that genetic differences arise due to mutation and genetic drift, but this distance measure is known to give more reliable population trees than other distances particularly for microsatellite DNA data. This method is not ideal in cases where natural selection plays a significant role in a populations genetics.

${\displaystyle D_A=1-\sum _{\ell }\sum _u\sqrt{X_u{Y}_u}/L}$

${\displaystyle D_A}$: Nei's DA distance, the genetic distance between populations X and Y

${\displaystyle \ell }$: A locus or gene studied with ${\displaystyle \sum _{\ell }}$ being the sum of loci or genes

${\displaystyle X_u}$ and ${\displaystyle Y_u}$: The frequencies of allele u in populations X and Y, respectively

L: The total number of loci examined

Euclidean distance

Figure 6: Euclidean genetic distance between 51 worldwide human populations, calculated using 289,160 SNPs. Dark red is the most similar pair and dark blue is the most distant pair.

Euclidean distance is a formula brought about from Euclid's Elements, a 13 book set detailing the foundation of all euclidean mathematics. The foundational principles outlined in these works is used not only in euclidean spaces but expanded upon by Issac Newton and Gottfried Leibniz in isolated pursuits to create calculus. The euclidean distance formula is used to convey, as simply as possible, the genetic dissimilarity between populations, with a larger distance indicating greater dissimilarity. As seen in figure 6, this method can be visualized in a graphical manner, this is due to the work of René Descartes who created the fundamental principle of analytic geometry, or the cartesian coordinate system. In an interesting example of historical repetitions, René Descartes was not the only one who discovered the fundamental principle of analytical geometry, this principle was as discovered in an isolated pursuit by Pierre de Fermat who left his work unpublished.

Main article: Euclidean distance

${\displaystyle D_{EU}=\sqrt{\sum _u(X_u-Y_u{)}^2}}$

${\displaystyle D_{EU}}$: Euclidean genetic distance between populations X and Y

${\displaystyle X_u}$ and ${\displaystyle Y_u}$: Allele frequencies at locus u in populations X and Y, respectively

Goldstein distance 1995

It was specifically developed for microsatellite markers and is based on the stepwise-mutation model (SMM). The Goldstein distance formula is modeled in such a way that expected value will increase linearly with time, this property is maintained even when the assumptions of single-step mutations and symmetrical mutation rate are violated. Goldstein distance is derived from the average square distance model, of which Goldstein was also a contributor.

${\displaystyle (\delta \mu {)}^2=\sum _{\ell }\frac{({\mu }_X-{\mu }_Y{)}^2}{L}}$

${\displaystyle \delta \mu }$: Goldstein genetic distance between populations X and Y

${\displaystyle {\mu }_x}$ and ${\displaystyle {\mu }_y}$: Mean allele sizes in populations X and Y

L: Total number of microsatallite loci examined

Nei's minimum genetic distance 1973

This calculation represents the minimum amount of codon differences for each locus. The measurement is based on the assumption that genetic differences arise due to mutation and genetic drift.

${\displaystyle D_m=\frac{J_X+J_Y}{2}-J_{XY}}$

$D_m$: Minimum amount of codon difference per locus

${\displaystyle J_X}$ and ${\displaystyle J_Y}$: Average probability of two members of the X population having the same allele

${\displaystyle J_{XY}}$: Average probability of members of the X and Y populations having the same allele

Czekanowski (Manhattan) Distance

Figure 7: Representation of path between points that is calculated for the Czekanwski (Manhattan) distance formula.

Similar to Euclidean distance, Czekanowski distance involves calculated the distance between points of allele frequency that are graphed on an axis created by . However, Czekanowski assumes a direct path is not available and sums the sides of the triangle formed by the data points instead of finding the hypotenuse. This formula is nicknamed the Manhattan distance because its methodology is similar to the nature of the New York City burrow. Manhattan is mainly built on a grid system requiring resentence to only make 90 degree turns during travel, which parallels the thinking of the formula.

${\displaystyle D_{Cz}=\frac{1}{2}|X_u-Y_u|}$

${\displaystyle |X_u-Y_u|=|P_{Xx}-P_{Yx}|+|P_{Xy}-P_{Yy}|}$

${\displaystyle X_u}$ and ${\displaystyle Y_u}$: Allele frequencies at locus u in populations X and Y, respectively

${\displaystyle P_{Xx}}$ and ${\displaystyle P_{Yx}}$: X-axis value of the frequency of an allele for X and Y populations

${\displaystyle P_{Xy}}$ and ${\displaystyle P_{Yy}}$: Y-axis value of the frequency of an allele for X and Y populations

Roger's Distance 1972

Figure 8: Representation of path between points that is calculated for the Roger's distance formula.

Similar to Czekanowski distance, Roger's distance involves calculating the distance between points of allele frequency. However, this method takes the direct distance between the points.

${\displaystyle D_R=\frac{1}{L}\sqrt{\frac{\sum _u(X_u-Y_u{)}^2}{2}}}$

${\displaystyle X_u}$ and ${\displaystyle Y_u}$: Allele frequencies at locus u in populations X and Y, respectively

${\displaystyle L}$: Total number of microsatallite loci examined

Limitations of Simple Distance Formulas

While these formulas are easy and quick calculations to make, the information that is provided gives limited information. The results of these formulas do not account for the potential effects of the number of codon changes between populations, or separation time between populations.

Fixation Index

Main article: Fixation index

A commonly used measure of genetic distance is the fixation index (F_ST) which varies between 0 and 1. A value of 0 indicates that two populations are genetically identical (minimal or no genetic diversity between the two populations) whereas a value of 1 indicates that two populations are genetically different (maximum genetic diversity between the two populations). No mutation is assumed. Large populations between which there is much migration, for example, tend to be little differentiated whereas small populations between which there is little migration tend to be greatly differentiated. F_ST is a convenient measure of this differentiation, and as a result F_ST and related statistics are among the most widely used descriptive statistics in population and evolutionary genetics. But F_ST is more than a descriptive statistic and measure of genetic differentiation. F_ST is directly related to the Variance in allele frequency among populations and conversely to the degree of resemblance among individuals within populations. If F_ST is small, it means that allele frequencies within each population are very similar; if it is large, it means that allele frequencies are very different.

Software

• PHYLIP uses GENDIST
  • Nei's standard genetic distance 1972
  • Cavalli-Sforza and Edwards 1967
  • Reynolds, Weir, and Cockerham's 1983
• TFPGA
  • Nei's standard genetic distance (original and unbiased)
  • Nei's minimum genetic distance (original and unbiased)
  • Wright's (1978) modification of Roger's (1972) distance
  • Reynolds, Weir, and Cockerham's 1983
• GDA
• POPGENE
• POPTREE2 Takezaki, Nei, and Tamura (2010, 2014)
  • Commonly used genetic distances and gene diversity analysis
• DISPAN Archived 2017-04-27 at the Wayback Machine
  • Nei's standard genetic distance 1972
  • Nei's D_A distance between populations 1983