Category Archives: Human Genetics

Human behaviour genetics is a subfield of the field of behaviour genetics that studies the role of genetic and environmental influences on human behaviour. Classically, human behavioural geneticists have studied the inheritance of behavioural traits. The field was originally focused on testing whether genetic influences were important in human behavior (e.g., do genes influence human behavior). It has evolved to address more complex questions such as: how important are genetic and/or environmental influences on various human behavioral traits; to what extent do the same genetic and/or environmental influences impact the overlap between human behavioral traits; how do genetic and/or environmental influences on behavior change across development; and what environmental factors moderate the importance of genetic effects on human behavior (gene-environment interaction).[1] The field is interdisciplinary, and draws from genetics, psychology, and statistics. Most recently, the field has moved into the area of statistical genetics, with many behavioral geneticists also involved in efforts to identify the specific genes involved in human behavior, and to understand how the effects associated with these genes changes across time, and in conjunction with the environment.[2]

In 1869, Francis Galton published the first empirical work in human behavioural genetics, Hereditary Genius. Here, Galton intended to demonstrate that "a man's natural abilities are derived by inheritance, under exactly the same limitations as are the form and physical features of the whole organic world." Like most seminal work, he overstated his conclusions. His was a family study on the inheritance of giftedness and talent. Galton was aware that resemblance among familial relatives can be a function of both shared inheritance and shared environments. Contemporary human behavioural quantitative genetics studies special populations such as twins and adoptees.

The initial impetus behind this research was to demonstrate that there were indeed genetic influences on human behaviour. In psychology, this phase lasted for the first half of the 20th century largely because of the overwhelming influence of behaviourism in the field. Later behavioural genetic research focused on quantitative methods.

Behavioral geneticists study both psychiatric and mental disorders, such as schizophrenia, bipolar disorder, and alcoholism, as well as behavioral and social characteristics, such as personality and social attitudes.

Recent trends in behaviour genetics have indicated an additional focus toward researching the inheritance of human characteristics typically studied in developmental psychology. For instance, a major focus in developmental psychology has been to characterize the influence of parenting styles on children. However, in most studies, genes are a confounding variable. Because children share half of their alleles with each parent, any observed effects of parenting styles could be effects of having many of the same alleles as a parent (e.g. harsh aggressive parenting styles have been found to correlate with similar aggressive child characteristics: is it the parenting or the genes?). Thus, behaviour genetics research is currently undertaking to distinguish the effects of the family environment from the effects of genes. This branch of behaviour genetics research is becoming more closely associated with mainstream developmental psychology and the sub-field of developmental psychopathology as it shifts its focus to the heritability of such factors as emotional self-control, attachment, social functioning, aggressiveness, etc.

Several academic bodies exist to support behaviour genetic research, including the International Behavioural and Neural Genetics Society, Behavior Genetics Association, the International Society of Psychiatric Genetics, and the International Society for Twin Studies. Behaviour genetic work features prominently in several more general societies, for instance the International Behavioral Neuroscience Society.

Human behavioural geneticists use several designs to answer questions about the nature and mechanisms of genetic influences on behaviour. All of these designs are unified by being based around human relationships which disentangle genetic and environmental relatedness.

So, for instance, some researchers study adopted twins: the adoption study. In this case the adoption disentangles the genetic relatedness of the twins (either 50% or 100%) from their family environments. Likewise the classic twin study contrasts the differences between identical twins and fraternal twins within a family compared to differences observed between families. This core design can be extended: the so-called "extended twin study" which adds additional family members, increasing power and allowing new genetic and environmental relationships to be studied. Excellent examples of this model are the Virginia 20,000 and the QIMR twin studies.

Also possible are the "children of twins" design (holding maternal genetic contributions equal across children with paternal genetics and family environments) and the "virtual twins" design - unrelated children adopted into a family who are very close or identical in age to biological children or other adopted children in the family. While the classical twin study has been criticized they continue to be of high utility. There are several dozen major studies ongoing, in countries as diverse as the USA, UK, Germany, France, the Netherlands, and Australia, and the method is used widely in fields as diverse as dental caries, BMI, ageing, substance abuse, sexuality, cognitive abilities, personality, values, and a wide range of psychiatric disorders. This is broad utility is reflected in several thousands of peer-review papers, and several dedicated societies and journals (See Twin study).

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Human behaviour genetics - Wikipedia

The Department of Human Genetics is dedicated to basic scientific research in human genetics and genetic disease, as well as the training of the next generation of scientists and health care providers.

Our faculty explore three broad areas of human genetics: molecular genetics, genetic disease, and statistical/population genetics. Within molecular genetics, research groups study DNA repair and recombination, genome instability, gene function and regulation, epigenetics, RNA modification and control, and genomic systems. Research in human genetic disease emphasizes the genetics of development, neurogenetics, stem cell biology, medical genetics, reproductive sciences, and the genetics of cancer. Evolutionary and population genetics research includes statistical tools for genetics, genetic epidemiology, and genetic mapping of complex traits and diseases.

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Carlson J, Li JZ, Zllner S. Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets. BMC Genomics. 2018 Nov 28;19(1):845. doi: 10.1186/s12864-018-5264-y. PubMed PMID: 30486787.

Ritter KE, Martin DM. Neural crest contributions to the ear: Implications for congenital hearing disorders. Hear Res. 2018 Nov 14. pii: S0378-5955(18)30414-3. doi: 10.1016/j.heares.2018.11.005. [Epub ahead of print] Review. PubMed PMID: 30455064.

Carethers JM, Quezada SM, Day LW. Diversity Within U.S. Gastroenterology Physician Practices: The Pipeline, Cultural Competencies, and GI Societies Approaches. Gastroenterology. 2018 Nov 16. pii: S0016-5085(18)35270-3. doi:10.1053/j.gastro.2018.10.056. [Epub ahead of print] PubMed PMID: 30452917.

Helbig I, Riggs ER, Barry CA, Klein KM, Dyment D, Thaxton C, Sadikovic B, Sands TT, Wagnon JL, Liaquat K, Cilio MR, Mirzaa G, Park K, Axeen E, Butler E, Bardakjian TM, Striano P, Poduri A, Siegert RK, Grant AR, Helbig KL, Mefford HC. The ClinGen Epilepsy Gene Curation Expert Panel-Bridging the divide between clinical domain knowledge and formal gene curation criteria. Hum Mutat. 2018 Nov;39(11):1476-1484. doi: 10.1002/humu.23632. PubMed PMID: 30311377.

Trenkwalder T, Nelson CP, Musameh MD, Mordi IR, Kessler T, Pellegrini C, Debiec R, Rheude T, Lazovic V, Zeng L, Martinsson A, Gustav Smith J, Gdin JR, Franco-Cereceda A, Eriksson P, Nielsen JB, Graham SE, Willer CJ, Hveem K, Kastrati A, Braund PS, Palmer CNA, Aracil A, Husser O, Koenig W, Schunkert H, Lang CC, Hengstenberg C, Samani NJ. Effects of the coronary artery disease associated LPA and 9p21 loci on risk of aortic valve stenosis. Int J Cardiol. 2018 Nov 17. pii: S0167-5273(18)31830-8. doi: 10.1016/j.ijcard.2018.11.094. [Epub ahead of print] PubMed PMID: 30482443.

Lessel D, Ozel AB, Campbell SE, Saadi A, Arlt MF, McSweeney KM, Plaiasu V, Szakszon K, Szlls A, Rusu C, Rojas AJ, Lopez-Valdez J, Thiele H, Nrnberg P, Nickerson DA, Bamshad MJ, Li JZ, Kubisch C, Glover TW, Gordon LB. Analyses of LMNA-negative juvenile progeroid cases confirms biallelic POLR3A mutations in Wiedemann-Rautenstrauch-like syndrome and expands the phenotypic spectrum of PYCR1 mutations. Hum Genet. 2018 Dec;137(11-12):921-939. doi: 10.1007/s00439-018-1957-1. Epub 2018 Nov 19. PubMed PMID: 30450527.

Paik YK, Lane L, Kawamura T, Chen YJ, Cho JY, LaBaer J, Yoo JS, Domont G, Corrales F, Omenn GS, Archakov A, Encarnacin-Guevara S, Lui S, Salekdeh GH, Cho JY, Kim CY, Overall CM. Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function. J Proteome Res. 2018 Nov 29. doi: 10.1021/acs.jproteome.8b00383. [Epub ahead of print] PubMed PMID: 30269496.

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Human Genetics | Michigan Medicine | University of Michigan

The intellectual exchange between human genetics and biomedical science has produced some of the most important and fruitful scientific discoveries of the past 50 years. The advances in human genetics and genomics made possible by the Human Genome Project and its aftermath have revolutionized the way scientific investigation is carried out in the areas of human disease and its animal models. In addition, the elucidation of the genetic contribution to literally thousands of human diseases has provided innumerable fundamental insights into normal biological function.

Many laboratories at UCSF use genetic approaches to untangle problems as diverse as infertility, cancer, adverse drug reactions, asthma, autism, birth defects, neurological diseases, obesity, diabetes and many others. Our faculty are leaders in the development of cutting-edge genome technologies including microarrays, comparative genome hybridization (CGH), whole-genome sequencing, population genetics, genetic epidemiology and computational genomics. They also use model organisms to discover and explore fundamental pathways that can lead to human disease.

All entering BMS Students take a core Genetics course (BMS 255) and can opt to take a seminar course BMS270 entitled Disease Discovery through the Lens of Genetics offered every other spring. Through the UCSF Institute for Human Genetics, students can interact with numerous genetic-oriented faculty and attend monthly genetic and genomic technology seminars.

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We study hypermutation, drug sensitization and oncogene network alterations in patients in order to improve precision medicine therapies for hormone-related and genitourinary cancers.

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Human Genetics | Biomedical Sciences Graduate Program

In human genetics, the Mitochondrial Eve (also mt-Eve, mt-MRCA) is the matrilineal most recent common ancestor (MRCA) of all currently living humans, i.e., the most recent woman from whom all living humans descend in an unbroken line purely through their mothers, and through the mothers of those mothers, back until all lines converge on one woman.

In terms of mitochondrial haplogroups, the mt-MRCA is situated at the divergence ofmacro-haplogroup L into L0and L16.As of 2013, estimates on the age of this split ranged at around 150,000 years ago,[2] consistent with a date later than the speciation of Homo sapiens but earlier than the recent Out-of-Africa dispersal.[3]

The male analog to the "Mitochondrial Eve" is the "Y-chromosomal Adam" (or Y-MRCA), the individual from whom all living humans are patrilineally descended. As the identity of both matrilineal and patrilineal MRCAs is dependent on genealogical history (pedigree collapse), they need not have lived at the same time.As of 2013, estimates for the age Y-MRCA are subject to substantial uncertainty, with a wide range of times from 180,000 to 580,000 years ago[4][5][6] (with an estimated age of between 120,000 and 156,000 years ago, roughly consistent with the estimate for mt-MRCA.).[7][8]

The name "Mitochondrial Eve" alludes to biblical Eve. This led to repeated misrepresentations or misconceptions in journalistic accounts on the topic. Popular science presentations of the topic usually point out such possible misconceptions by emphasizing the fact that the position of mt-MRCA is neither fixed in time (as the position of mt-MRCA moves forward in time as mtDNA lineages become extinct), nor does it refer to a "first woman", nor the only living female of her time, nor the first member of a "new species".[9]

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Early research using molecular clock methods was done during the late 1970s to early 1980s.Allan Wilson, Mark Stoneking, Rebecca L. Cann and Wesley M. Brown found that mutation in human mtDNA was unexpectedly fast, at 0.02 substitution per base (1%) in a million years, which is 510 times faster than in nuclear DNA.[10]Related work allowed for an analysis of the evolutionary relationships among gorillas, chimpanzees (common chimpanzee and bonobo) and humans.[11]With data from 21 human individuals, Brown published the first estimate on the age of the mt-MRCA at 180,000 years ago in 1980.[12]A statistical analysis published in 1982 was taken as evidence for recent African origin (a hypothesis which at the time was competing with Asian origin of H. sapiens).[13]

By 1985, data from the mtDNA of 145 women of different populations, and of two cell lines, HeLa and GM 3043, derived from a Black American and a !Kung respectively, was available. After more than 40 revisions of the draft, the manuscript was submitted to Nature in late 1985 or early 1986[14] and published on 1 January 1987. The published conclusion was that all current human mtDNA originated from a single population from Africa, at the time dated to between 140,000 and 200,000 years ago.[15]

The dating for "Eve" was a blow to the multiregional hypothesis, which was being controversially discussed at the time, and a boost to the theory of the recent origin model.[16]

Cann, Stoneking and Wilson did not use the term "Mitochondrial Eve" or even the name "Eve" in their original paper; it appears to originate with a 1987 article in Science by Roger Lewin, headlined "The Unmasking of Mitochondrial Eve."[17]The biblical connotation was very clear from the start. The accompanying research news in Nature had the title "Out of the garden of Eden."[18] Wilson himself preferred the term "Lucky Mother" [19] and thought the use of the name Eve "regrettable."[17][20]But the concept of Eve caught on with the public and was repeated in a Newsweek cover story (11 January 1988 issue featured a depiction of Adam and Eve on the cover, with the title "The Search for Adam and Eve"),[21] and a cover story in Time on 26 January 1987.[22]

Shortly after the 1987 publication, criticism of its methodology and secondary conclusions was published.[23]Both the dating of mt-Eve and the relevance of the age of the purely matrilineal descent for population replacement was controversially discussed during the 1990s;[24][25][26][27]Alan Templeton (1997) asserted that the study did "not support the hypothesis of a recent African origin for all of humanity following a split between Africans and non-Africans 100,000 years ago" and also did "not support the hypothesis of a recent global replacement of humans coming out of Africa."[28]

Cann, Stoneking & Wilson (1987)'s placement of a relatively small population of humans in sub-Saharan Africa was consistent with the hypothesis of Cann (1982) and lent considerable support for the "recent out-of-Africa" scenario.

In 1999 Krings et al. eliminated problems in molecular clocking postulated by Nei (1992)[citation needed] when it was found that the mtDNA sequence for the same region was substantially different from the MRCA relative to any human sequence.

Although the original research did have analytical limitations, the estimate on the age of the mt-MRCA has proven robust.[29][30] More recent age estimates have remained consistent with the 140200 kya estimate published in 1987: A 2013 estimate dated Mitochondrial Eve to about 160 kya (within the reserved estimate of the original research) and Out of Africa II to about 95 kya.[31]Another 2013 study (based on genome sequencing of 69 people from 9 different populations) reported the age of Mitochondrial Eve between 99 to 148 kya and that of the Y-MRCA between 120 and 156 kya.[7]

Without a DNA sample, it is not possible to reconstruct the complete genetic makeup (genome) of any individual who died very long ago. By analysing descendants' DNA, however, parts of ancestral genomes are estimated by scientists. Mitochondrial DNA (mtDNA) and Y-chromosome DNA are commonly used to trace ancestry in this manner. mtDNA is generally passed un-mixed from mothers to children of both sexes, along the maternal line, or matrilineally.[32][33] Matrilineal descent goes back to our mothers, to their mothers, until all female lineages converge.

Branches are identified by one or more unique markers which give a mitochondrial "DNA signature" or "haplotype" (e.g. the CRS is a haplotype). Each marker is a DNA base-pair that has resulted from an SNP mutation. Scientists sort mitochondrial DNA results into more or less related groups, with more or less recent common ancestors. This leads to the construction of a DNA family tree where the branches are in biological terms clades, and the common ancestors such as Mitochondrial Eve sit at branching points in this tree. Major branches are said to define a haplogroup (e.g. CRS belongs to haplogroup H), and large branches containing several haplogroups are called "macro-haplogroups".

The mitochondrial clade which Mitochondrial Eve defines is the species Homo sapiens sapiens itself, or at least the current population or "chronospecies" as it exists today. In principle, earlier Eves can also be defined going beyond the species, for example one who is ancestral to both modern humanity and Neanderthals, or, further back, an "Eve" ancestral to all members of genus Homo and chimpanzees in genus Pan. According to current nomenclature,Mitochondrial Eve's haplogroup was within mitochondrial haplogroup L because this macro-haplogroup contains all surviving human mitochondrial lineages today, and she must predate the emergence of L0.

The variation of mitochondrial DNA between different people can be used to estimate the time back to a common ancestor, such as Mitochondrial Eve. This works because, along any particular line of descent, mitochondrial DNA accumulates mutations at the rate of approximately one every 3,500 years per nucleotide.[34][35][36] A certain number of these new variants will survive into modern times and be identifiable as distinct lineages. At the same time some branches, including even very old ones, come to an end, when the last family in a distinct branch has no daughters.

Mitochondrial Eve is the most recent common matrilineal ancestor for all modern humans. Whenever one of the two most ancient branch lines dies out, the MRCA will move to a more recent female ancestor, always the most recent mother to have more than one daughter with living maternal line descendants alive today. The number of mutations that can be found distinguishing modern people is determined by two criteria: firstly and most obviously, the time back to her, but secondly and less obviously by the varying rates at which new branches have come into existence and old branches have become extinct. By looking at the number of mutations which have been accumulated in different branches of this family tree, and looking at which geographical regions have the widest range of least related branches, the region where Eve lived can be proposed.

Newsweek reported on Mitochondrial Eve based on the Cann et al. study in January 1988, under a heading of "Scientists Explore a Controversial Theory About Man's Origins". The edition sold a record number of copies.[37]

The popular name "mitochondrial Eve", of 1980s coinage,[17] has contributed to a number of popular misconceptions. At first, the announcement of a "mitochondrial Eve" was even greeted with endorsement from young earth creationists, who viewed the theory as a validation of the biblical creation story.[38]

Due to such misunderstandings, authors of popular science publications since the 1990s have been emphatic in pointing out that the name is merely a popular convention, and that the mt-MRCA was not in any way the "first woman".[39] Her position is purely the result of genealogical history of human populations later, and as matrilineal lineages die out, the position of mt-MRCA keeps moving forward to younger individuals over time.

In River Out of Eden (1995), Richard Dawkins discussed human ancestry in the context of a "river of genes", including an explanation of the concept of Mitochondrial Eve.[40]The Seven Daughters of Eve (2002) presented the topic of human mitochondrial genetics to a general audience.[41]The Real Eve: Modern Man's Journey Out of Africa" by Stephen Oppenheimer (2003)[42] was adapted into a Discovery Channel documentary.[43]

One common misconception surrounding mitochondrial Eve is that since all women alive today descended in a direct unbroken female line from her, she must have been the only woman alive at the time.[39][44] However, nuclear DNA studies indicate that the size of the ancient human population never dropped below tens of thousands. Other women living during Eve's time may have descendants alive today but not in a direct female line.[citation needed]

The definition of mitochondrial Eve is fixed, but the woman in prehistory who fits this definition can change. That is, not only can our knowledge of when and where Mitochondrial Eve lived change due to new discoveries, but the actual mitochondrial Eve can change. The mitochondrial Eve can change, when a mother-daughter line comes to an end by chance. It follows from the definition of Mitochondrial Eve that she had at least two daughters who both have unbroken female lineages that have survived to the present day. In every generation mitochondrial lineages end when a woman with unique mtDNA dies with no daughters. When the mitochondrial lineages of daughters of mitochondrial Eve die out, then the title of "Mitochondrial Eve" shifts forward from the remaining daughter through her matrilineal descendants, until the first descendant is reached who had two or more daughters who together have all living humans as their matrilineal descendants. Once a lineage has died out it is irretrievably lost and this mechanism can thus only shift the title of "Mitochondrial Eve" forward in time.

Because mtDNA mapping of humans is very incomplete, the discovery of living mtDNA lines which predate our current concept of "Mitochondrial Eve" could result in the title moving to an earlier woman. This happened to her male counterpart, "Y-chromosomal Adam," when older Y lines from Africa were discovered.

Sometimes mitochondrial Eve is assumed to have lived at the same time as Y-chromosomal Adam (from whom all living people are descended patrilineally), and perhaps even met and mated with him. Even if this were true, which is currently regarded as highly unlikely, this would only be a coincidence. Like mitochondrial "Eve", Y-chromosomal "Adam" probably lived in Africa. A recent study (March 2013) concluded however that "Eve" lived much later than "Adam" some 140,000 years later.[5] (Earlier studies considered, conversely, that "Eve" lived earlier than "Adam".)[45] More recent studies indicate that mitochondrial Eve and Y-chromosomal Adam may indeed have lived around the same time.[46]

Mitochondrial Eve is the most recent common matrilineal ancestor, not the most recent common ancestor. Since the mtDNA is inherited maternally and recombination is either rare or absent, it is relatively easy to track the ancestry of the lineages back to a MRCA; however, this MRCA is valid only when discussing mitochondrial DNA. An approximate sequence from newest to oldest can list various important points in the ancestry of modern human populations:

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Mitochondrial Eve - Wikipedia

Introduction

C H A P T E R 1

What Is in a Human Genome?

C H A P T E R 2

Cells

C H A P T E R 3

Meiosis, Development, and Aging

P A R T 2

Transmission Genetics

C H A P T E R 4

Single-Gene Inheritance

C H A P T E R 5

Beyond Mendels Laws

C H A P T E R 6

Matters of Sex

C H A P T E R 7

Multifactorial Traits

C H A P T E R 8

Genetics of Behavior

P A R T 3

DNA and Chromosomes

C H A P T E R 9

DNA Structure and Replication

C H A P T E R 10

Gene Action: From DNA to Protein

C H A P T E R 11

Gene Expression and Epigenetics

C H A P T E R 12

Gene Mutation

C H A P T E R 13

Chromosomes

P A R T 4

Population Genetics

C H A P T E R 14

Constant Allele Frequencies and DNA Forensics

C H A P T E R 15

Changing Allele Frequencies

C H A P T E R 16

Human Ancestry and Evolution

P A R T 5

Immunity and Cancer

C H A P T E R 17

Genetics of Immunity

C H A P T E R 18

Cancer Genetics and Genomics

P A R T 6

Genetic Technology

C H A P T E R 19

DNA Technologies

C H A P T E R 20

Genetic Testing and Treatment

C H A P T E R 21

Reproductive Technologies

C H A P T E R 22

Genomics

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Human Genetics - McGraw-Hill Education

Human genetic clustering is the degree to which human genetic variation can be partitioned into a small number of groups or clusters. A leading method of analysis uses mathematical cluster analysis of the degree of similarity of genetic data between individuals and groups in order to infer population structures and assign individuals to hypothesized ancestral groups. A similar analysis can be done using principal components analysis,[1] and several recent studies deploy both methods.[2][3]

Analysis of genetic clustering examines the degree to which regional groups differ genetically, the categorization of individuals into clusters, and what can be learned about human ancestry from this data. There is broad scientific agreement that a relatively small fraction of human genetic variation occurs between populations, continents, or clusters. Researchers of genetic clustering differ, however, on whether genetic variation is principally clinal or whether clusters inferred mathematically are important and scientifically useful.

One of the underlying questions regarding the distribution of human genetic diversity is related to the degree to which genes are shared between the observed clusters. It has been observed repeatedly that the majority of variation observed in the global human population is found within populations. This variation is usually calculated using Sewall Wright's fixation index (FST), which is an estimate of between to within group variation. The degree of human genetic variation is a little different depending upon the gene type studied, but in general it is common to claim that ~85% of genetic variation is found within groups, ~610% between groups within the same continent and ~610% is found between continental groups. Ryan Brown and George Armelagos described this as "a host of studies [that have] concluded that racial classification schemes can account for only a negligible proportion of human genetic diversity," including the studies listed in the table below.

(rather than among populations)

diversity[4]

Cavalli-Sforza

microsatellite loci

These average numbers, however, do not mean that every population harbors an equal amount of diversity. In fact, some human populations contain far more genetic diversity than others, which is consistent with the likely African origin of modern humans.[7][8] Therefore, populations outside of Africa may have undergone serial founder effects that limited their genetic diversity.[7][8]

The FST statistic has come under criticism by A. W. F. Edwards[9] and Jeffrey Long and Rick Kittles.[10] British statistician and evolutionary biologist A. W. F. Edwards faulted Lewontin's methodology for basing his conclusions on simple comparison of genes and rather on a more complex structure of gene frequencies. Long and Kittles' objection is also methodological: according to them the FST is based on a faulty underlying assumptions that all populations contain equally genetic diverse members and that continental groups diverged at the same time. Sarich and Miele have also argued that estimates of genetic difference between individuals of different populations understate differences between groups because they fail to take into account human diploidy.[11]

Keith Hunley, Graciela Cabana, and Jeffrey Long created a revised statistical model to account for unequally divergent population lineages and local populations with differing degrees of diversity. Their 2015 paper applies this model to the Human Genome Diversity Project sample of 1,037 individuals in 52 populations.[8] They found that least diverse population examined, the Surui, "harbors nearly 60% of the total species diversity." Long and Kittles had noted earlier that the Sokoto people of Africa contains virtually all of human genetic diversity.[12] Their analysis also found that non-African populations are a taxonomic subgroup of African populations, that "some African populations are equally related to other African populations and to non-African populations," and that "outside of Africa, regional groupings of populations are nested inside one another, and many of them are not monophyletic."[8]

Multiple studies since 1972 have backed up the claim that, "The average proportion of genetic differences between individuals from different human populations only slightly exceeds that between unrelated individuals from a single population."[13][4][14][5][15][16][17]

Edwards (2003) claims, "It is not true, as Nature claimed, that 'two random individuals from any one group are almost as different as any two random individuals from the entire world'" and Risch et al. (2002) state "Two Caucasians are more similar to each other genetically than a Caucasian and an Asian." However Bamshad et al. (2004) used the data from Rosenberg et al. (2002) to investigate the extent of genetic differences between individuals within continental groups relative to genetic differences between individuals between continental groups. They found that though these individuals could be classified very accurately to continental clusters, there was a significant degree of genetic overlap on the individual level, to the extent that, using 377 loci, individual Europeans were about 38% of the time more genetically similar to East Asians than to other Europeans.

Witherspoon et al. (2007) have argued that even when individuals can be reliably assigned to specific population groups, it may still be possible for two randomly chosen individuals from different populations/clusters to be more similar to each other than to a randomly chosen member of their own cluster, when sampling a small number of SNPs (as in the case with scientists James Watson, Craig Venter and Seong-Jin Kim). They state that using around one-thousand SNPs, individuals from different populations/clusters are never more similar, which they state some may find surprising. Witherspoon et al. conclude that "caution should be used when using geographic or genetic ancestry to make inferences about individual phenotypes".

A 1994 study by Cavalli-Sforza and colleagues evaluated genetic distances among 42 native populations based on 120 blood polymorphisms. The populations were grouped into nine clusters: African (sub-Saharan), Caucasoid (European), Caucasoid (extra-European), northern Mongoloid (excluding Arctic populations), northeast Asian Arctic, southern Mongoloid (mainland and insular Southeast Asia), Pacific islander, New Guinean and Australian, and American (Amerindian). Although the clusters demonstrate varying degrees of homogeneity, the nine-cluster model represents a majority (80 out of 120) of single-trait trees and is useful in demonstrating the phenetic relationship among these populations.[19]

The greatest genetic distance between two continents is between Africa and Oceania, at 0.2470. This measure of genetic distance reflects the isolation of Australia and New Guinea since the end of the Last Glacial Maximum, when Oceania was isolated from mainland Asia due to rising sea levels. The next-largest genetic distance is between Africa and the Americas, at 0.2260. This is expected, since the longest geographic distance by land is between Africa and South America. The shortest genetic distance, 0.0155, is between European and extra-European Caucasoids. Africa is the most genetically divergent continent, with all other groups more related to each other than to sub-Saharan Africans. This is expected, according to the single-origin hypothesis. Europe has a general genetic variation about three times less than that of other continents; the genetic contribution of Asia and Africa to Europe is thought to be two-thirds and one-third, respectively.[19][20]

Genetic structure studies are carried out using statistical computer programs designed to find clusters of genetically similar individuals within a sample of individuals. Studies such as those by Risch and Rosenberg use a computer program called STRUCTURE to find human populations (gene clusters). It is a statistical program that works by placing individuals into one of an arbitrary number of clusters based on their overall genetic similarity, many possible pairs of clusters are tested per individual to generate multiple clusters.[21] The basis for these computations are data describing a large number of single nucleotide polymorphisms (SNPs), genetic insertions and deletions (indels), microsatellite markers (or short tandem repeats, STRs) as they appear in each sampled individual. Cluster analysis divides a dataset into any prespecified number of clusters.

These clusters are based on multiple genetic markers that are often shared between different human populations even over large geographic ranges. The notion of a genetic cluster is that people within the cluster share on average similar allele frequencies to each other than to those in other clusters. (A. W. F. Edwards, 2003 but see also infobox "Multi Locus Allele Clusters") In a test of idealised populations, the computer programme STRUCTURE was found to consistently underestimate the numbers of populations in the data set when high migration rates between populations and slow mutation rates (such as single-nucleotide polymorphisms) were considered.[22] In 2004, Lynn Jorde and Steven Wooding 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."[23]

A number of genetic cluster studies have been conducted since 2002, including the following:

In a 2005 paper, Rosenberg and his team acknowledged that findings of a study on human population structure are highly influenced by the way the study is designed.[28][29] They reported that the number of loci, the sample size, the geographic dispersion of the samples and assumptions about allele-frequency correlation all have an effect on the outcome of the study.

In a review of studies of human genome diversity, Guido Barbujani and colleagues note that various cluster studies have identified different numbers of clusters with different boundaries. They write that discordant patterns of genetic variation and high within-population genetic diversity "make[] it difficult, or impossible, to define, once and for good, the main genetic clusters of humankind."[7]

Genetic clustering was also criticized by Penn State anthropologists Kenneth Weiss and Brian Lambert. They asserted that understanding human population structure in terms of discrete genetic clusters misrepresents the path that produced diverse human populations that diverged from shared ancestors in Africa. Ironically, by ignoring the way population history actually works as one process from a common origin rather than as a string of creation events, structure analysis that seems to present variation in Darwinian evolutionary terms is fundamentally non-Darwinian."[30]

A major finding of Rosenberg and colleagues (2002) was that when five clusters were generated by the program (specified as K=5), "clusters corresponded largely to major geographic regions." Specifically, the five clusters corresponded to Africa, Europe plus the Middle East plus Central and South Asia, East Asia, Oceania, and the Americas. The study also confirmed prior analyses by showing that, "Within-population differences among individuals account for 93 to 95% of genetic variation; differences among major groups constitute only 3 to 5%."

Rosenberg and colleagues (2005) have argued, based on cluster analysis, that populations do not always vary continuously and a population's genetic structure is consistent if enough genetic markers (and subjects) are included. "Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions." They also wrote, regarding a model with five clusters corresponding to Africa, Eurasia (Europe, Middle East, and Central/South Asia), East Asia, Oceania, and the Americas: "For population pairs from the same cluster, as geographic distance increases, genetic distance increases in a linear manner, consistent with a clinal population structure. However, for pairs from different clusters, genetic distance is generally larger than that between intracluster pairs that have the same geographic distance. For example, genetic distances for population pairs with one population in Eurasia and the other in East Asia are greater than those for pairs at equivalent geographic distance within Eurasia or within East Asia. Loosely speaking, it is these small discontinuous jumps in genetic distanceacross oceans, the Himalayas, and the Saharathat provide the basis for the ability of STRUCTURE to identify clusters that correspond to geographic regions".[31]

Rosenberg stated that their findings "should not be taken as evidence of our support of any particular concept of biological race (...). Genetic differences among human populations derive mainly from gradations in allele frequencies rather than from distinctive 'diagnostic' genotypes."[24] The study's overall results confirmed that genetic difference within populations is between 93 and 95%. Only 5% of genetic variation is found between groups.[28]

The Rosenberg study has been criticised on several grounds.

The existence of allelic clines and the observation that the bulk of human variation is continuously distributed, has led some scientists to conclude that any categorization schema attempting to partition that variation meaningfully will necessarily create artificial truncations. (Kittles & Weiss 2003). It is for this reason, Reanne Frank argues, that attempts to allocate individuals into ancestry groupings based on genetic information have yielded varying results that are highly dependent on methodological design.[32] Serre and Pbo (2004) make a similar claim:

The absence of strong continental clustering in the human gene pool is of practical importance. It has recently been claimed that "the greatest genetic structure that exists in the human population occurs at the racial level" (Risch et al. 2002). Our results show that this is not the case, and we see no reason to assume that "races" represent any units of relevance for understanding human genetic history.

In a response to Serre and Pbo (2004), Rosenberg et al. (2005) maintain that their clustering analysis is robust. Additionally, they agree with Serre and Pbo that membership of multiple clusters can be interpreted as evidence for clinality (isolation by distance), though they also comment that this may also be due to admixture between neighbouring groups (small island model). Thirdly they comment that evidence of clusterdness is not evidence for any concepts of "biological race".[26]

Clustering does not particularly correspond to continental divisions. Depending on the parameters given to their analytical program, Rosenberg and Pritchard were able to construct between divisions of between 4 and 20 clusters of the genomes studied, although they excluded analysis with more than 6 clusters from their published article. Probability values for various cluster configurations varied widely, with the single most likely configuration coming with 16 clusters although other 16-cluster configurations had low probabilities. Overall, "there is no clear evidence that K=6 was the best estimate" according to geneticist Deborah Bolnick (2008:76-77).[33] The number of genetic clusters used in the study was arbitrarily chosen. Although the original research used different number of clusters, the published study emphasized six genetic clusters. The number of genetic clusters is determined by the user of the computer software conducting the study. Rosenberg later revealed that his team used pre-conceived numbers of genetic clusters from six to twenty "but did not publish those results because Structure [the computer program used] identified multiple ways to divide the sampled individuals". Dorothy Roberts, a law professor, asserts that "there is nothing in the team's findings that suggests that six clusters represent human population structure better than ten, or fifteen, or twenty."[34] When instructed to find two clusters, the program identified two populations anchored around by Africa and by the Americas. In the case of six clusters, the entirety of Kalesh people, an ethnic group living in Northern Pakistan, was added to the previous five.[28][35]

Commenting on Rosenberg's study, law professor Dorothy Roberts wrote that "the study actually showed that there are many ways to slice the expansive range of human genetic variation.

Sarah A. Tishkoff and colleagues analyzed a global sample consisting of 952 individuals from the HGDP-CEPH survey, 2432 Africans from 113 ethnic groups, 98 African Americans, 21 Yemenites, 432 individuals of Indian descent, and 10 Native Australians. A global STRUCTURE analysis of these individuals examined 1327 polymorphic markers, including of 848 STRs, 476 indels, and 3 SNPs. The authors reported cluster results for K=2 to K=14. Within Africa, six ancestral clusters were inferred through Bayesian analysis, which were closely linked with ethnolinguistic heritage. Bantu populations grouped with other Niger-Congo-speaking populations from West Africa. African Americans largely belonged to this Niger-Congo cluster, but also had significant European ancestry. Nilo-Saharan populations formed their own cluster. Chadic populations clustered with the Nilo-Saharan groups, suggesting that most present-day Chadic speakers originally spoke languages from the Nilo-Saharan family and later adopted Afro-Asiatic languages. Nilotic populations from the African Great Lakes largely belonged to this Nilo-Saharan cluster too, but also had some Afro-Asiatic influence due to assimilation of Cushitic groups over the last 3,000 years. Khoisan populations formed their own cluster, which grouped closest with the Pygmy cluster. The Cape Coloured showed assignments from the Khoisan, European and other clusters due to the population's mixed heritage. The Hadza and Sandawe populations formed their own cluster. An Afro-Asiatic cluster was also discerned, with the Afro-Asiatic speakers from North Africa and the Horn of Africa forming a contiguous group. Afro-Asiatic speakers in the Great Lakes region largely belonged to this Afro-Asiatic cluster as well, but also had some Bantu and Nilotic influence due to assimilation of adjacent groups over the last 3,000 years. The remaining inferred ancestral clusters were associated with European, Middle Eastern, Oceanian, Indian, Native American and East Asian populations.[36]

Jinchuan Xing and colleagues used an alternate dataset of human genotypes including HapMap samples and their own samples (296 new individuals from 13 populations), for a total of 40 populations distributed roughly evenly across the Earth's land surface. They found that the alternate sampling reduced the FST estimate of inter-population differences from 0.18 to 0.11, suggesting that the higher number may be an artifact of uneven sampling. They conducted a cluster analysis using the ADMIXTURE program and found that "genetic diversity is distributed in a more clinal pattern when more geographically intermediate populations are sampled."[3]

A study by the HUGO Pan-Asian SNP Consortium in 2009 using the similar principal components analysis found that East Asian and South-East Asian populations clustered together, and suggested a common origin for these populations. At the same time they observed a broad discontinuity between this cluster and South Asia, commenting "most of the Indian populations showed evidence of shared ancestry with European populations". It was noted that "genetic ancestry is strongly correlated with linguistic affiliations as well as geography".[37]

Studies of clustering reopened a debate on the scientific reality of race, or lack thereof. In the late 1990s Harvard evolutionary geneticist Richard Lewontin stated that "no justification can be offered for continuing the biological concept of race. (...) Genetic data shows that no matter how racial groups are defined, two people from the same racial group are about as different from each other as two people from any two different racial groups.[38] This view has been affirmed by numerous authors[14][5][16] and the American Association of Physical Anthropologists since.[10] A.W.F. Edwards as well as Rick Kittles and Jeffrey Long have criticized Lewontin's methodology, with Long noting that there are more similarities between humans and chimpanzees than differences, and more genetic variation within chimps and humans than between them.[10] Edwards also charged that Lewontin made an "unjustified assault on human classification, which he deplored for social reasons".[39] In their 2015 article, Keith Hunley, Graciela Cabana, and Jeffrey Long recalculate the apportionment of human diversity using a more complex model than Lewontin and his successors. They conclude: "In sum, we concur with Lewontins conclusion that Western-based racial classifications have no taxonomic significance, and we hope that this research, which takes into account our current understanding of the structure of human diversity, places his seminal finding on firmer evolutionary footing."[8]

Genetic clustering studies, and particularly the five-cluster result published by Rosenberg's team in 2002, have been interpreted by journalist Nicholas Wade, evolutionary biologist Armand Marie Leroi, and others as demonstrating the biological reality of race.[40][41][42] For Leroi, "Race is merely a shorthand that enables us to speak sensibly, though with no great precision, about genetic rather than cultural or political differences." He states that, "One could sort the world's population into 10, 100, perhaps 1,000 groups", and describes Europeans, Basques, Andaman Islanders, Ibos, and Castillians each as a "race".[42] In response to Leroi's claims, the Social Science Research Council convened a panel of experts to discuss race and genomics online.[43] In their 2002 and 2005 papers, Rosenberg and colleagues disagree that their data implies the biological reality of race.[24][26]

In 2006, Lewontin wrote that any genetic study requires some priori concept of race or ethnicity in order to package human genetic diversity into a defined, limited number of biological groupings. Informed by genetics, zoologists have long discarded the concept of race for dividing groups of non-human animal populations within a species. Defined on varying criteria, in the same species a widely varying number of races could be distinguished. Lewontin notes that genetic testing revealed that "because so many of these races turned out to be based on only one or two genes, two animals born in the same litter could belong to different 'races'".[44]

Studies that seek to find genetic clusters are only as informative as the populations they sample. For example, Risch and Burchard relied on two or three local populations from five continents, which together were supposed to represent the entire human race.[28] Another genetic clustering study used three sub-Saharan population groups to represent Africa; Chinese, Japanese, and Cambodian samples for East Asia; Northern European and Northern Italian samples to represent "Caucasians". Entire regions, subcontinents, and landmasses are left out of many studies. Furthermore, social geographical categories such "East Asia" and "Caucasians" were not defined. "A handful of ethnic groups to symbolize an entire continent mimic a basic tenet of racial thinking: that because races are composed of uniform individuals, anyone can represent the whole group" notes Roberts.[28][45][46]

The model of Big Few fails when including overlooked geographical regions such as India. The 2003 study which examined fifty-eight genetic markers found that Indian populations owe their ancestral lineages to Africa, Central Asia, Europe, and southern China.[47][48] Reardon, from Princeton University, asserts that flawed sampling methods are built into many genetic research projects. The Human Genome Diversity Project (HGDP) relied on samples which were assumed to be geographically separate and isolated.[49] The relatively small sample sizes of indigenous populations for the HGDP do not represent the human species' genetic diversity, nor do they portray migrations and mixing population groups which has been happening since prehistoric times. Geographic areas such as the Balkans, the Middle East, North and East Africa, and Spain are seldom included in genetic studies.[28][50] East and North African indigenous populations, for example, are never selected to represent Africa because they do not fit the profile of "black" Africa. The sampled indigenous populations of the HGDP are assumed to be "pure"; the law professor Roberts claims that "their unusual purity is all the more reason they cannot stand in for all the other populations of the world that marked by intermixture from migration, commerce, and conquest."[28]

King and Motulsky, in a 2002 Science article, state that "While the computer-generated findings from all of these studies offer greater insight into the genetic unity and diversity of the human species, as well as its ancient migratory history, none support dividing the species into discrete, genetically determined racial categories".[51] Cavalli-Sforza asserts that classifying clusters as races would be a "futile exercise" because "every level of clustering would determine a different population and there is no biological reason to prefer a particular one". Bamshad, in 2004 paper published in Nature, asserts that a more accurate study of human genetic variation would use an objective sampling method, which would choose populations randomly and systematically across the world, including those populations which are characterized by historical intermingling, instead of cherry-picking population samples which fit a priori concepts of racial classification. Roberts states that "if research collected DNA samples continuously from region to region throughout the world, they would find it impossible to infer neat boundaries between large geographical groups."[28][52][53][54]

Anthropologists such as C. Loring Brace,[55] philosophers Jonathan Kaplan and Rasmus Winther,[56][56][57][58] and geneticist Joseph Graves,[59] have argued that while it is certainly possible to find biological and genetic variation that corresponds roughly to the groupings normally defined as "continental races", this is true for almost all geographically distinct populations. The cluster structure of the genetic data is therefore dependent on the initial hypotheses of the researcher and the populations sampled. When one samples continental groups the clusters become continental; if one had chosen other sampling patterns the clustering would be different. Weiss and Fullerton have noted that if one sampled only Icelanders, Mayans and Maoris, three distinct clusters would form and all other populations could be described as being clinally composed of admixtures of Maori, Icelandic and Mayan genetic materials.[60] Kaplan and Winther therefore argue that seen in this way both Lewontin and Edwards are right in their arguments. They conclude that while racial groups are characterized by different allele frequencies, this does not mean that racial classification is a natural taxonomy of the human species, because multiple other genetic patterns can be found in human populations that cross-cut racial distinctions. Moreover, the genomic data under-determines whether one wishes to see subdivisions (i.e., splitters) or a continuum (i.e., lumpers). Under Kaplan and Winther's view, racial groupings are objective social constructions (see Mills 1998 [61]) that have conventional biological reality only insofar as the categories are chosen and constructed for pragmatic scientific reasons.

Commercial ancestry testing companies, who use genetic clustering data, have been also heavily criticized. Limitations of genetic clustering are intensified when inferred population structure is applied to individual ancestry. The type of statistical analysis conducted by scientists translates poorly into individual ancestry because they are looking at difference in frequencies, not absolute differences between groups. Commercial genetic genealogy companies are guilty of what Pillar Ossorio calls the "tendency to transform statistical claims into categorical ones".[62] Not just individuals of the same local ethnic group, but two siblings may end up beings as members of different continental groups or "races" depending on the alleles they inherit.[28]

Many commercial companies use data from the International HapMap Project (HapMap)'s initial phrase, where population samples were collected from four ethnic groups in the world: Han Chinese, Japanese, Yoruba Nigerian, and Utah residents of Northern European ancestry. If a person has ancestry from a region where the computer program does not have samples, it will compensate with the closest sample that may have nothing to do with the customer's actual ancestry: "Consider a genetic ancestry testing performed on an individual we will call Joe, whose eight great-grandparents were from southern Europe. The HapMap populations are used as references for testing Joe's genetic ancestry. The HapMap's European samples consist of "northern" Europeans. In regions of Joe's genome that vary between northern and southern Europe (such regions might include the lactase gene), the genetic ancestry test is using the HapMap reference population is likely to incorrectly assign the ancestry of that portion of the genome to a non-European population because that genomic region will appear to be more similar to the HapMap's Yoruba or Han Chinese samples than to Northern European samples.[63] Likewise, a person having Western European and Western African ancestries may have ancestors from Western Europe and West Africa, or instead be assigned to East Africa where various ancestries can be found.[64] "Telling customers that they are a composite of several anthropological groupings reinforces three central myths about race: that there are pure races, that each race contains people who are fundamentally the same and fundamentally different from people in other races, and that races can be biologically demarcated." Many companies base their findings on inadequate and unscientific sampling methods. Researchers have never sampled the world's populations in a systematic and random fashion.[28]

Roberts argues against the use of broad geographical or continental groupings: "molecular geneticists routinely refer to African ancestry as if everyone on the continent is more similar to each other than they are to people of other continents, who may be closer both geographically and genetically.[28] Ethiopians have closer genetic affinity with Armenians than with Bantu populations.[65] Similarly, Somalis are genetically more similar to Gulf Arab populations than to other populations in Africa.[66] Braun and Hammonds (2008) asserts that the misperception of continents as natural population groupings is rooted in the assumption that populations are natural, isolated, and static. Populations came to be seen as "bounded units amenable to scientific sampling, analysis, and classification".[67] Human beings are not naturally organized into definable, genetically cohesive populations.

Software which support genetic clustering calculation.

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A hub of deep expertise, the Department of Human Genetics helps partners across UCLA interpret data and leverage genomic technology to improve study design and solve medical problems.

We demystify genetic complexities to provide vital insights for a range of clinical and research applications. We strive to improve the care of as many patients as possible by pushing our capabilities, developing novel ways to address unanswered questions.

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Julian Martinez-Agosto, MD, PhDGenetic sequencing unravels rare disease mysteries; among the first medical centers to use exome sequencing.Learn More

Jingyi "Jessica" Li, PhDStatistics professor honored as a leading woman in STEM at the intersection of statistics and biology.Learn More

Aldons J. Lusis, PhDScientists identify 2 hormones that burn fat faster, prevent and reverse diabetes in mice.Learn More

Daniel Geschwind, MD, PhDAutism, schizophrenia, bipolar disorder share molecular traits, study finds.Learn More

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The Department of Human Genetics is the home within the Division of Biological Sciences for the study of basic principles of genetics and genomics as applied to human disease. We provide broad training in experimental genetics and genomics, statistical and population genetics, bioinformatics, and clinical genetics. A common theme throughout our research is the application of basic genetic principles and strategies to the study of disease mechanism, disease susceptibility, and the genetic architecture of complex traits. Our faculty bridge between basic and clinical research and train students for careers in academia, industry, and medicine.

The Department of Human Genetics has an unwavering commitment to diversity, inclusion, free expression, and open discourse.These values are at the core of our roles as scientists, as teachers, and as citizens of a free society.

Science, including genetics, plays a central role in many crucial issues of our time. We are committed to generating rigorous scientific knowledge, training future scientists, and preparing our students to be well-informed citizens in a democratic society.

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This site is a resource dedicated to providing current information to our students, faculty, alumni, applicants, and anyone interested in genetic research at McGill.

The Department of Human Genetics is both a basic science and a clinical department in the Faculty of Medicine at McGill. It has the dual challenge of promoting excellence in research and teaching in the basic science of human genetics and also a similar challenge for excellence in professional training and patient care. As part of its mission, the department is responsible for the training of basic scientists in the area of human genetics and also the training of genetic counsellors, medical students, and medical specialists in the various clinical areas of medical genetics. The concepts of genomics, epigenomics, proteomics, andmetabolomics are at the frontier of modern biology and medicine. How to translate advances in basic sciences to public policy remains to be determined. Our department is charged with the mission to translate this scientific advancement to the training of health care professionals and to patient care. Out of our administrative office in the Strathacona Anatomy & Dentistry Building, we aim to serve our faculty which is housed in the Research Institutes of the McGill teaching hospitals (MUHC, JGH, and Douglas), the Montreal Neurological Institute, the Life Sciences Complex, and the Innovation Centre.

The Genetics Community in Montreal is greatly enriched by a multitude of genetically oriented research programs within the classical disciplines of biomedical science not only at McGill, but also at the three other universities in the city, most notably theUniversit de Montral and its affiliated hospitals. The Department of Human Genetics has a central administrative core surrounded by clinical genetics units and research laboratories in diverse locations of the main university campus, and in the research institutes of the several teaching hospitals. The department is accredited for service and training (clinical, biochemical, cytogenetic, and molecular) by the Canadian College of Medical Geneticists (CCMG), and medical genetics training by the Royal College of Physicians and Surgeons in Canada and theCollge desMdecins du Qubec. The department coordinates Genetic Health-Care Services through the McGill University Health Centre, and participates fully in the teaching of human/medical genetics to baccalaureate, medical and postgraduate students. The department offers an M.Sc. in Genetic Counselling Training Program, and M.Sc. and Ph.D. Programs in Human Genetics.

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By: IANS | London | Updated: September 3, 2017 7:01 pm The researchers expect this mechanism to help and play a role in a much larger proportion for patients with intellectual disabilities. (Source: Thinkstock Images)

Researchers have discovered 15 genes that play a role in the development of intellectual disabilities. Intellectual disabilities are often caused by a mutation that damages a gene responsible for protein production in cells thus preventing the associated protein from functioning properly.

In a number of disease-related genes, it is shown that a de novo mutation does not eliminate the gene, but probably alters its function. To find out how often this mechanism is involved, researchers combined the gene mutations in Dutch patients with a large international database comprising de novo mutations in patients.

A de novo mutation is a genetic alteration that is present for the first time in one family member as a result of a variant (or mutation) in a germ cell (egg or sperm) of one of the parents, or a variant that arises in the fertilised egg itself during early embryogenesis.

The results of the study, published in the American Journal of Human Genetics, showed 15 genes in which mutations cluster closely together, 12 of which were associated with developmental disorders. With our method, we were able to detect genes in which mutations not so much eliminate as affect the gene in another way, said Christian Gilissen, geneticist from Radboud University in Netherlands.

We also found three new genes that are likely to play a role in the development of intellectual disabilities as well, Gilissen added. The de novo mutations that were found only change a very small part of a protein. The function of the protein remains largely, but not entirely the same.

The mutations are more likely to affect superficial parts of the proteins. These disturb interactions with other proteins and cause problems, Gilissen said. The three newly-discovered genes playing a role in the development of intellectual disabilities provide new diagnostic possibilities for patients. It is important that we have discovered a mechanism that has not yet been a focus of study. We expect this mechanism to play a role in a much larger proportion of patients with intellectual disabilities.

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Genes causing intellectual disabilities identified - The Indian Express