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Posts Tagged ‘Evolution’


Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

Curator and Reporter: Stephen J. Williams, Ph.D.

Genomic instability is considered a hallmark and necessary for generating the mutations which drive tumorigenesis. Multiple studies had suggested that there may be multiple driver mutations and a plethora of passenger mutations driving a single tumor.  This diversity of mutational spectrum is even noticed in cultured tumor cells (refer to earlier post Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell).  Certainly, intratumor heterogeneity has been a concern to clinicians in determining the proper personalized therapy for a given cancer patient, and has been debated if multiple biopsies of a tumor is required to acquire a more complete picture of a tumor’s mutations.  In the New England Journal of Medicine, lead author Dr. Marco Gerlinger in the laboratory of Dr. Charles Swanton of the Cancer Research UK London Research Institute, and colleagues reported the results of a study to determine if intratumoral differences exist in the mutational spectrum of primary and metastatic renal carcinomas, pre- and post-treatment with the mTOR (mammalian target of rapamycin) inhibitor, everolimus (Afinitor®)[1].

The authors compared exome sequencing of multiregion biopsies from four patients with metastatic renal-cell carcinoma who had been enrolled in the Personalized RNA Interference to Enhance the Delivery of Individualized Cytotoxic and Targeted Therapeutics clinical trial of everolimus (E-PREDICT) before and after cytoreductive surgery.

Biopsies taken:

  • Multiregion spatial biopsy of primary tumor (representing 9 regions of the tumor)
  • Chest-wall metastases
  • Perinephric metastases
  • Germline DNA as control

Multiple platforms were used to determine aberrations as follows:

  1. Illumina Genome Analyzer IIx and Hiseq: for sequencing and mutational analysis
  2. Illumina Omni 2.5: for SNP (single nucleotide polymorphism)-array-based allelic imbalance detection for chromosomal imbalance and ploidy analysis
  3. Affymetrix Gene 1.0 Array: for mRNA analysis

A phylogenetic reconstruction of all somatic mutations occurring in primary disease and associated metastases was  performed to determine the clonal evolution of the metastatic disease given the underlying heterogeneity of the tumor.  Basically the authors wanted to know if the mutational spectra of one metastasis could be found in biopsies taken from the underlying primary tumor or if the mutational landscape of metastases had drastically changed.

Results

Multiregion exon-capture sequencing of DNA from pretreatment biopsy samples of the primary tumor, chest wall metastases, and perinephrous metastasis revealed 128 mutations classified as follows:

  • 40 ubiquitous mutations
  • 59 mutations shared by several but not all regions
  • 29 mutations unique to specific regions
  • 31 mutations shared by most primary tumor regions
  • 28 mutations shared by most metastatic regions

The authors mapped these mutations out with respect to their location, in order to determine how the metastatic lesions evolved from the primary tumor, given the massive heterogeneity in the primary tumor.  Construction of this “phylogenetic tree” (see Merlo et. al[2]) showed that the disease evolves in a branched not linear pattern, with one branch of clones evolving into a metastatic disease while another branch of clones and mutations evolve into the primary disease.

One of the major themes of the study is shown by results that an average of 70 somatic mutations were found in a single biopsy (a little more than just half of all tumor mutations) yet only 34% of the mutations in multiregion biopsies were detected in all tumor regions.

This indicated to the authors that “a single biopsy was not representative of the mutational landscape of the entire bulk tumor”. In addition, microarray studies concluded that gene-expression signatures from a single biopsy would not be able to predict outcome.

Everolimus therapy did not change the mutational landscape.  Interestingly, allelic composition and ploidy analyses revealed an extensive intratumor heterogeneity, with ploidy heterogeneity in two of four tumors and 26 of 30 tumor samples containing divergent allelic-imbalances.  This strengthens the notion that multiple clones with diverse genomic instability exist in various regions of the tumor.

 The intratumor heterogeneity reveals a convergent tumor evolution with associated heterogeneity in target function

Genes commonly mutated in clear cell carcinoma[3, 4] (and therefore considered the prevalent driver mutations for renal cancer) include:

Only VHL mutations were found in all regions of a given tumor, however there were three distinct SETD2 mutations (frameshift, splice site, missense) which were located in different regions of the tumor.

SETD2 trimethylates histones at various lysine residues, such as lysine residue 36 (H3K36).  The trimethylation of H3K36 is found on many actively transcribed genes.  Immunohistochemistry showed trimethylated H3K36 was reduced in cancer cells but positive in most stromal cells and in SETD2 wild-type clear-cell carcinomas.

Interestingly most regions of the primary tumor, except one, contained a kinase-domain activating mutation in mTOR.  Immunohistochemistry analysis of downstream target genes of mTOR revealed that mTOR activity was enhanced in regions containing this mutation.  Therefore the intratumoral heterogeneity corresponded to therapeutic activity, leading to the impression that a single biopsy may result in inappropriate targeted therapy.   Additional downstream biomarkers of activity confirmed both the intratumoral heterogeneity of mutational spectrum as well as an intratumoral heterogeneity of therapeutic-target function.

The authors conclude that “intratumor heterogeneity can lead to underestimation of the tumor genomics landscape from single tumor biopsies and may present major challenges to personalized-medicine and biomarker development”.

In an informal interview with Dr. Swanton, he had stressed the importance of performing these multi-region biopsies and the complications that intratumoral heterogeneity would present for personalized medicine, biomarker development, and chemotherapy resistance.

Q: Your data clearly demonstrates that multiple biopsies must be done to get a more complete picture of the tumor’s mutational landscape.  In your study, what percentage of the tumor would be represented by the biopsies you had performed?

Dr. Swanton: Realistically this is a very difficult question to answer, the more biopsies we sequence, the more we find, in the near term it may be very difficult to ever formally address this in large metastatic tumours

Q:  You have very nice data which suggest that genetic intratumor heterogeneity complicates the tumor biomarker field? do you feel then that quests for prognostic biomarkers may be impossible to attain?

Dr. Swanton: Not necessarily although heterogeneity is likely to complicate matters

Identifying clonally dominant lesions may provide better drug targets

Predicting resistance events may be difficult given the potential impact of tumour sampling bias and the concern that in some tumours a single biopsy may miss a relevant subclonal mutation that may result in resistance

Q:  Were you able to establish the degree of genomic instability among the various biopsies?

Dr. Swanton:  Yes, we did this by allelic imbalance analysis and found that the metastases were more genomically unstable than the primary region from which the metastasis derived

Q: I was actually amazed that there was a heterogeneity of mTOR mutations and SETD2 after everolimus therapy?   Is it possible these clones obtained a growth advantage?

Dr. Swanton: We think so yes, otherwise we wouldn’t identify recurrent mutations in these “driver genes”

Dr. Swanton will present his results at the 2013 AACR meeting in Washington D.C. (http://www.aacr.org/home/scientists/meetings–workshops/aacr-annual-meeting-2013.aspx)

The overall points of the article are as follows:

  • Multiple biopsies of primary tumor and metastases are required to determine the full mutational landscape of a patients tumor
  • The intratumor heterogeneity will have an impact on the personalized therapy strategy for the clinician

 

  • Metastases arising from primary tumor clones will have a greater genomic instability and mutational spectrum than the tumor from which it originates

 

  • Tumors and their metastases do NOT evolve in a linear path but have a branched evolution and would complicate biomarker development and the prognostic and resistance outlook for the patient

A great video of Dr. Swanton discussing his research can be viewed here

VIEW VIDEO

Everolimus: an inhibitor of mTOR

The following information was taken from the New Medicine Oncology Database (http://www.nmok.net)

Developer

Designation

Description

Approved/Filed Indications

Novartis PharmaCurrent as of: August 30, 2012 Generic Name: Everolimus
Brand Name: Afinitor
Other Designation: RAD001, RAD001C
RAD001, an ester of the macrocytic immunosuppressive agent sirolimus (rapamycin), is an inhibitor of mammalian target of rapamycin (mTOR) kinase.Administration Route: intravenous (IV) • PO • solid organ transplant
• renal cell carcinoma (RCC), metastatic after failure of treatment with sunitinib, sorafenib, or sunitinib plus sorafenib
• renal cell carcinoma, advanced, refractory to treatment with vascular endothelial growth factor (VEGF)-targeted therapy
• treatment of progressive neuroendocrine tumors (NET) of pancreatic origin (PNET) in patients with inoperable, locally advanced or metastatic disease

Marker Designation
Alias
Gene Location

Marker Description

Indications

5’-AMP-activated Protein Kinase (AMPK)AMPK beta 1 (beta1 non-catalytic subunit) • HAMPKb (beta1 non-catalytic subunit) • MGC17785 (beta1 non-catalytic subunit) • AMPK2 (alpha1 catalytic subunit) • PRKAA (alpha1 catalytic subunit) • AMPK alpha 1 (alpha1 catalytic subunit) • AMPKa1 ( AMPK is a member of a metabolite-sensing protein kinase family found in all eukaryotes. It functions as a cellular fuel sensor and its activation strongly suppresses cell proliferation in non-malignant cells and cancer cells. AMPK regulates the cell cycle by upregulating the p53-p21 axis and modulating the TSC2-mTOR (mammalian target of rapamycin) pathway. The AMPK signaling network contains a number of tumor suppressor genes including LKB1, p53, TSC1 and TSC2, and modulates growth factor signaling involving proto-oncogenes including PI3K, Akt and ERK. AMPK activation is therefore therapeutic target for cancer (Motoshima H, etal, J Physiol, 1 Jul 2006; 574(Pt 1): 63–71).AMPK is a protein serine/threonine kinase consisting of a heterotrimeric complex of a catalytic alpha subunit and regulatory ß and gamma subunits. AMPK is activated by increased AMP/ATP ratio, under conditions such as glucose deprivation, hypoxia, ischemia and heat shock. It is also activated by several hormones and cytokines. AMPK inhibits ATP-consuming cellular events, protein synthesis, de novo fatty acid synthesis, and generation of mevalonate and the downstream products in the cholesterol synthesis pathway (Motoshima H, etal, J Physiol, 1 Jul 2006; 574(Pt 1): 63–71). – ovarian cancer
– brain cancer
– liver cancer
– leukemia
– colon cancer
CREB regulated transcription coactivator 2 (CRTC2)TOR complex 2 (TORC2, mTORC2) • RP11-422P24.6 • transducer of regulated cAMP response element-binding protein (CREB)2 • transducer of CREB protein 2 • TOR1Location: 1q21.3 The mammalian target of rapamycin (mTOR) exists in two complexes, TORC1 and TORC2, which are differentially sensitive to rapamycin. cAMP response element-binding protein (CREB) regulated transcription coactivator 2 (CRTC2) or TORC2 is a multimeric kinase composed of mTOR, mLST8, mSin1, and rictor. The complex is insensitive to acute rapamycin exposure and functions in controlling cell growth and actin cytoskeletal assembly.TORC2 controls gene silencing, telomere length maintenance, and survival under DNA-damaging conditions. It is primaily located in the cytoplasm but also shuttles into the nucleus (Schonbrun M, etal, Mol Cell Biol, Aug 2009;29(16):4584-94). – brain cancer
Hypoxia inducible factor 1 alpha (HIF1A)HIF1-alpha (HIF-1 alpha) • HIF-1A • PASD8 • MOP1 • bHLHe78Location: 14q21-q24 The alpha subunit of the hypoxia inducible factor 1 (HIF-1alpha) is a 826 amino acid antigen consisting of a basic helix-loop-helix (bHLH)-PAS domain at its N-terminus. HIF-1alpha is rapidly degraded by the proteasome under normal conditions, but is stabilized by hypoxia resulting in the transactivation of several proangiogenic genes. HIF-1alpha is responsible for inducing production of new blood vessels as needed when tumors outgrow existing blood supplies. HIF-1alpha serves as a transcriptional factor that regulates gene expression involved in response to hypoxia and promotes angiogenesis.HIF-1alpha is a proangiogenic transcription factor induced primarily by tumor hypoxia that is critically involved in tumor progression, metastasis and overall tumor survival. HIF-1alpha functions as a survival factor that is required for tumorigenesis in many types of malignancies, and is expressed in a majority of metastases and late-stage tumors. HIF-1alpha is overexpressed in brain, breast, colon, endometrial, head and neck, lung, ovarian, and pancreatic cancer, and is associated with increased microvessel density and/or VEGF expression – prostate cancer
– bladder cancer
– nasopharyngeal cancer
– head and neck cancer
– kidney cancer
– pancreatic cancer
– endometrial cancer
– breast cancer
Mammalian target of rapamycin (mTOR)FK506 binding protein 12-rapamycin associated protein 1 • RAFT1 • FK506 binding protein 12-rapamycin associated protein 2 • FRAP • FRAP1 • FRAP2 • RAPT1 • FKBP-rapamycin associated protein • FKBP12-rapamycin complex-associated protein 1 • rapamycin target protein • TOR • FLJ44809 • MTORC1 • MTORC2 • RPTOR • RAPTOR • KIAA1303 • mammalian target of rapamycin complex 1Location: 1p36.22 The mammalian target of rapamycin (mTOR) is a large serine/threonine protein (Mr 300,000) having heat repeats, and protein-protein interaction domains at its amino terminus, and a protein kinase domain at its carboxy terminus. mTOR is a member of the phosphoinositide 3-kinase (PI3K)-related kinase (PIKK) family and a central modulator of cell growth. It regulates cell growth, proliferation and survival by impacting on protein synthesis and transcription. mTOR is present in two multi-protein complexes, a rapamycin-sensitive complex, TOR complex 1 (TORC1), defined by the presence of Raptor and a rapamycin insensitive complex, TOR complex 2 (TORC2), with Rictor, Protor and Sin1. Rapamycin selectively inhibits mTORC1 by binding indirectly to the mTOR/Raptor complex via FKBP12, resulting in inhibition of p70S6kinase but not the mTORC2 substrate AKTSer473. Selective inhibition of p70S6K attenuates negative feedback loops to IRS1 and TORC2 resulting in an increase in pAKT which may limit the activity of rapamycin.In a hypoxic environment the increase in mass of solid tumors is dependent on the recruitment of mitogens and nutrients. As a function of nutrient levels, particularly essential amino acids, mTOR acts as a checkpoint for ribosome biogenesis and cell growth. Ribosome biogenesis has long been recognized in the clinics as a predictor of cancer progression; increase in size and number of nucleoli is known to be associated with the most aggressive tumors and a poor prognosis. In bacteria, ribosome biogenesis is independently regulated by amino acids and energy charge. The mTOR pathway is controlled by intracellular ATP levels, independent of amino acids, and mTOR itself is an ATP sensor (Kozma SC, etal, AACR02, Abs. 5628). – breast cancer
– pancreatic cancer
– multiple myeloma
– liver cancer
– brain cancer
– prostate cancer
– kidney cancer
– lymphoma
Signal transducer and activator of transcription 3 (STAT3)Stat-3 • acute-phase response factor (APRF) • FLJ20882 • HIESLocation: 17q21 Signal transducer and activator of transcription 3 (STAT3) is a member of the STAT protein family. STAT3, plays a critical role in hematopoiesis. STAT3 is located in the cytoplasm and translocated to the nucleus after tyrosine phosphorylation. In response to cytokines and growth and other activation factors, STAT family members are phosphorylated by the receptor associated kinases and then form homo- or heterodimers, which translocate to the cell nucleus where they act as transcription activators. – multiple myeloma
– hematologic malignancy
– lymphoma
Sonic hedgehog homolog (SHH)Shh • HHG1 • HHG-1 • holoprosencephaly 3 (HPE3) • HLP3 • SMMCILocation: 7q36 Sonic hedgehog, a secreted hedgehog ligand, is a human homolog of the Drosophila segment polarity gene hedgehog, cloned by investigators at Harvard University (Marigo V, etal, Genomics, 1 Jul 1995;28 (1):44-51).The mammalian sonic hedgehog (Shh) pathway controls proliferation of granule cell precursors in the cerebellum and is essential for normal embryonic development. Shh signaling is disrupted in a variety of malignancies. – pancreatic cancer
– CNS cancer

References:

1.         Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P et al: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England journal of medicine 2012, 366(10):883-892.

2.         Merlo LM, Pepper JW, Reid BJ, Maley CC: Cancer as an evolutionary and ecological process. Nature reviews Cancer 2006, 6(12):924-935.

3.         Varela I, Tarpey P, Raine K, Huang D, Ong CK, Stephens P, Davies H, Jones D, Lin ML, Teague J et al: Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011, 469(7331):539-542.

4.         Dalgliesh GL, Furge K, Greenman C, Chen L, Bignell G, Butler A, Davies H, Edkins S, Hardy C, Latimer C et al: Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 2010, 463(7279):360-363.

Other Articles related to this topic appeared on this Open Access Online Scientific Journal, including the following:

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

Genomics of bronchial epithelial dysplasia

Genomics in Medicine- Tomorrow’s Promise

Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease

CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics and Computational Genomics – Part IIB

Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell

Directions for Genomics in Personalized Medicine

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

In Focus: Targeting of Cancer Stem Cells

Modulating Stem Cells with Unread Genome: microRNAs

What can we expect of tumor therapeutic response?

 

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Reporter: Aviva Lev-Ari, PhD, RN

UPDATED 9/16/2013

VIDEO CLIPS
Enzymes That Are Not Proteins: The Discovery of Ribozymes
Listen to past HHMI President Dr. Thomas Cech discussing his Nobel Prize-winning discovery of RNA’s catalytic properties.

http://www.hhmi.org/biointeractive/enzymes-are-not-proteins-discovery-ribozymes

Stanford Report, March 15, 2013

Long-term evolution is ‘surprisingly predictable,’ Stanford experiment shows

A protein-folding simulation shows that the debated theory of long-term evolution is not only possible, but that the outcomes are predictable. The Stanford experiment provides a framework for testing evolutionary outcomes in living organisms.

BY BJORN CAREY

L.A. CiceroVisiting scholar Mike Palmer left, and Professor Marcus FeldmanDr. Michael Palmer, left, and Professor Marcus Feldman, with co-author Arnav Moudgil (not pictured), found that the long-term evolutionary dynamics were surprisingly predictable in a model of protein folding and binding.

Two birds are vying for food. One bird’s beak is shaped, by virtue of a random mutation, such that it’s slightly more adept at cracking seeds. This sets the bird on the road toward acquiring more food, a better chance of scoring a mate and, most important, passing on its genetic endowment.

This individual’s success is an example of short-term evolution, the widely accepted Darwinian process of natural selection by which individual organisms that have better adapted to their surroundings prevail.

In recent years, however, some scientists have argued that natural selection occurs not just at the individual organism level, but also between lineages over the course of many generations. In a new study, Stanford biologists have demonstrated that not only is this long-term evolution possible, but that long-term evolutionary outcomes can be surprisingly predictable.

The group set up a computer simulation in which 128 lineages of proteins continuously folded into new shapes, competing to bind with other molecules, called ligands, in each new configuration. The better each protein could attach itself to the ligands, the more ligands it would scoop up, and the higher its fitness – that is, its average number of “offspring” – would be. The simulation was run for 10,000 generations.

Although the chaos of 128 lineages – a total of more than 16,000 individual proteins – mutating over thousands of generations might seem unpredictable, and that it would be nearly impossible for the same thing to happen twice, it’s actually the opposite.

“Even though things look complicated, the possible evolutionary trajectories are quite constrained,” said lead author Michael Palmer, a computational biologist at Stanford. “There are only a few viable mutations at any point, which makes the dynamics predictable and repeatable, even over the long term.”

The study, co-authored by Marcus Feldman, a biology professor at Stanford, and Stanford research biologist Arnav Moudgil, was recently published in the Journal of the Royal Society Interface.

In some experiments, the lineages that consistently came out on top in the long term were not initially the best adapted at binding to ligands. “The immediate fitness is not the only important thing,” Palmer said. “Yes, a lineage does have to survive in the short term. But just as important is how it is able to adapt to new and potentially variable environments over the longer term.”

A good example of this scenario is Darwin’s famous finches. It’s thought that individuals – perhaps just a single pair of birds – from a South American species ended up on the Galápagos Islands about 1 million years ago. Today their descendants have diversified into about 15 modern species. Some eat seeds, some eat insects, or flowers. Some eat ticks, or even drink the blood of other birds.

“If there was some catastrophe that removed one of those food sources, it might wipe out one or more of the 15 species, but the rest of the lineage – the descendants of that initial pair of birds – would persist,” Palmer said. “Now say there was a competing lineage that was great at cracking seeds, but unable to evolve to other diets due to some prior genetic constraint. The same catastrophe could wipe it out.”

The finding, and others like it, could represent a significant shift in viewpoint for biologists. For one thing, it means that in certain situations, scientists should look beyond the details at the level of the individual organism, as the evolutionary dynamics can be accurately understood as lineage selection.

It also has implications on a species’ genomic architecture, or how a genome is organized on the lineage level. While a lineage’s genome might primarily select for a particular set of traits in order for individuals to survive in the short term, in order to out-compete other lineages, it must also be able to adapt to new conditions over the long term.

“An individual can have a lucky mutation that produces an immediate adaptation,” said Palmer. “Or a lineage can have a lucky mutation that happens to position it to adapt to the range of environments it will experience over the next thousand generations. A single mutation can have a distinct short-term and long-term fitness.”

The authors believe that the work can be replicated in microorganisms, and are now hoping that microbiologists will apply the new metrics of selection in vitro.

“There is already some evidence in vitro that there is a lot of constraint on evolutionary trajectories,” Palmer said, “and we think we’ve come up with a good framework to quantify evolutionary predictability and long-term fitness.”

Media Contact

Michael Palmer, Biology: (415) 867-3653, mepalmer@charles.stanford.edu

Bjorn Carey, Stanford News Service: (650) 725-1944, bccarey@stanford.edu

SOURCE:

http://news.stanford.edu/news/2013/march/long-term-evolution-031513.html

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Author: Marcus W. Feldman, PhD

Genomics and Evolution

Word Cloud by Daniel Menzin

https://pharmaceuticalintelligence.com/contributors-biographies/marcus-feldman-phd-member-of-the-board/

Insofar as the genetic evolution of modern humans is concerned, large scale SNP studies of worldwide populations have provided a consistent picture of a migration out of Africa that gave rise to the human populations of the other continents. This migration probably began 60–80 kya, was probably not continuous, and could have resulted in a division during the passage through the Levant en route from east Africa. One division may have moved in a more southerly direction towards south and east Asia, possibly to Australia, and eventually, 15–30 kya into the Americas. The other division may have “turned left” and moved towards Europe.

In this process, which we call the “serial founder” model of human expansion (refs. 1, 2), migration and demography probably had effects that constrained the subsequent action of natural selection on human genes.

  • Variation in skin pigmentation genes today provides some of the strongest signals of natural selection during this human expansion. However, it is also likely that the
  • Immune response genes, e.g., MHC genes, achieved their high levels of polymorphism in response to new pathogens encountered in the great expansion.

Many of the strongest signals of natural selection indicate the importance of the innovations of farming and pastoralism. The gene sequences involved in lactose tolerance and starch metabolism, for example, are strikingly different in groups that adopted dairying or farming, respectively, from hunter-gatherers, who did not.

From the analysis of SNPs, I take home two messages.

  • The first is that although some parts of the genome show clear signals of selection, most of our DNA perceived via SNPs does not.
  • The second is that population growth and migration have been major forces in determining the patterns of variation. Indeed,
  • recent analyses of exome sequences confirm that the spectrum of rare allele frequencies is compatible only with recent and rapid population growth (ref. 3). Indeed,
  • recent analyses of the 1000 genomes data, that is, data from whole genome sequencing of one-thousand human genomes representing Africa (Yoruba), Europe (from Utah), and East Asia (China and Japan), identified only 35 non-synonymous SNPs from 33 genes as having been subject to recent adaptive selection (ref. 4).

The next phase of genomic analysis of humans, complete exome sequencing of large cohorts, or whole genome sequencing of samples from many representative populations, will focus more on two themes.

  • The first will be the role of rare alleles in human phenotypes, especially diseases. The previous phase, GWAS (genome-wide association studies), has been disappointing in revealing genetic “causes” of complex traits. However, my view is that
  • the second theme, the molecular genetics of gene regulation, and interaction of this regulation with the environment, is likely to have bigger payoffs, not only for determination of phenotypes, but also in showing where in the genome the strongest signals of selection lie. As more methylation profiles, small RNA patterns of interference, and other gene-regulatory analyses of whole genomes are completed, both the medical and evolutionary significance of DNA variation will become clearer.

Pemberton, T. J., D. Absher, M. W. Feldman, R. M. Myers, N. A. Rosenberg, and J. Z. Li. 2012. Genomic patterns of homozygosity in worldwide human populations. Am. J. Hum. Genet. 91: 275–292.

Genome-wide patterns of homozygosity runs and their variation across individuals provide a valuable and often untapped resource for studying human genetic diversity and evolutionary history. Using genotype data at 577,489 autosomal SNPs, we employed a likelihood-based approach to identify runs of homozygosity (ROH) in 1,839 individuals representing 64 worldwide populations, classifying them by length into three classes—short, intermediate, and long—with a model-based clustering algorithm. For each class, the number and total length of ROH per individual show considerable variation across individuals and populations. The total lengths of short and intermediate ROH per individual increase with the distance of a population from East Africa, in agreement with similar patterns previously observed for locus-wise homozygosity and linkage disequilibrium. By contrast, total lengths of long ROH show large inter-individual variations that probably reflect recent inbreeding patterns, with higher values occurring more often in populations with known high frequencies of consanguineous unions. Across the genome, distributions of ROH are not uniform, and they have distinctive continental patterns. ROH frequencies across the genome are correlated with local genomic variables such as recombination rate, as well as with signals of recent positive selection. In addition, long ROH are more frequent in genomic regions harboring genes associated with autosomal- dominant diseases than in regions not implicated in Mendelian diseases. These results provide insight into the way in which homozygosity patterns are produced, and they generate baseline homozygosity patterns that can be used to aid homozygosity mapping of genes associated with recessive diseases.

Pepperell, C. S., J. M. Granka, D. C. Alexander, M. A. Behr, L. Chui, J. Gordon, J. L. Guthrie, F. B. Jamieson, D. Langlois-Klassen, R. Long, D. Nguyen, W. Wobeser, and M. W. Feldman. 2011. Dispersal of Mycobacterium tuberculosis via the Canadian fur trade. Proc. Natl. Acad. Sci. USA 108: 6526–6531.

Patterns of gene flow can have marked effects on the evolution of populations. To better understand the migration dynamics of Mycobacterium tuberculosis, we studied genetic data from European M. tuberculosis lineages currently circulating in Aboriginal and French Canadian communities. A single M. tuberculosis lineage, characterized by the DS6Quebec genomic deletion, is at highest frequency among Aboriginal populations in Ontario, Saskatchewan, and Alberta; this bacterial lineage is also dominant among tuberculosis (TB) cases in French Canadians resident in Quebec. Substantial contact between these human populations is limited to a specific historical era (1710–1870), during which individuals from these populations met to barter furs. Statistical analyses of extant M. tuberculosis minisatellite data are consistent with Quebec as a source population for M. tuberculosis gene flow into Aboriginal populations during the fur trade era. Historical and genetic analyses suggest that tiny M. tuberculosis populations persisted for ∼100 y among indigenous populations and subsequently expanded in the late 19th century after environmental changes favoring the pathogen. Our study suggests that spread of TB can occur by two asynchronous processes: (i) dispersal of M. tuberculosis by minimal numbers of human migrants, during which small pathogen populations are sustained by ongoing migration and slow disease dynamics, and (ii) expansion of the M. tuberculosis population facilitated by shifts in host ecology. If generalizable, these migration dynamics can help explain the low DNA sequence diversity observed among isolates of M. tuberculosis and the difficulties in global elimination of tuberculosis, as small, widely dispersed pathogen populations are difficult both to detect and to eradicate.

Henn, B. M., C. R. Gignoux, M. Jobin, J. M. Granka, J. M. Macpherson, J. M. Kidd, L. Rodríguez-Botigué, S. Ramachandran, L. Hon, A. Brisbin, A. A. Lin, P. A. Underhill, D. Comas, K. K. Kidd, P. J. Norman, P. Parham, C. D. Bustamante, J. L. Mountain, and M. W. Feldman. 2011. Hunter-gatherer genomic diversity suggests a southern African origin for modern humans. Proc. Natl. Acad. Sci. USA 108: 5154–5162.

Africa is inferred to be the continent of origin for all modern human populations, but the details of human prehistory and evolution in Africa remain largely obscure owing to the complex histories of hundreds of distinct populations. We present data for more than 580,000 SNPs for several hunter-gatherer populations: the Hadza and Sandawe of Tanzania, and the !Khomani Bushmen of South Africa, including speakers of the nearly extinct N|u language. We find that African hunter-gatherer populations today remain highly differentiated, encompassing major components of variation that are not found in other African populations. Hunter-gatherer populations also tend to have the lowest levels of genome-wide linkage disequilibrium among 27 African populations. We analyzed geographic patterns of linkage disequilibrium and population differentiation, as measured by FST, in Africa. The observed patterns are consistent with an origin of modern humans in southern Africa rather than eastern Africa, as is generally assumed. Additionally, genetic variation in African hunter-gatherer populations has been significantly affected by interaction with farmers and herders over the past 5,000 y, through both severe population bottlenecks and sex-biased migration. However, African hunter-gatherer populations continue to maintain the highest levels of genetic diversity in the world.

Casto, A. M., and M. W. Feldman. 2011. Genome-wide association study SNPs in the human genome diversity project populations: does selection affect unlinked SNPs with shared trait associations? PLoS Genet. 7(1): e1001266.

Genome-wide association studies (GWAS) have identified more than 2,000 trait-SNP associations, and the number continues to increase. GWAS have focused on traits with potential consequences for human fitness, including many immunological, metabolic, cardiovascular, and behavioral phenotypes. Given the polygenic nature of complex traits, selection may exert its influence on them by altering allele frequencies at many associated loci, a possibility which has yet to be explored empirically. Here we use 38 different measures of allele frequency variation and 8 iHS scores to characterize over 1,300 GWAS SNPs in 53 globally distributed human populations. We apply these same techniques to evaluate SNPs grouped by trait association. We find that groups of SNPs associated with pigmentation, blood pressure, infectious disease, and autoimmune disease traits exhibit unusual allele frequency patterns and elevated iHS scores in certain geographical locations. We also find that GWAS SNPs have generally elevated scores for measures of allele frequency variation and for iHS in Eurasia and East Asia. Overall, we believe that our results provide evidence for selection on several complex traits that has caused changes in allele frequencies and/or elevated iHS scores at a number of associated loci. Since GWAS SNPs collectively exhibit elevated allele frequency measures and iHS scores, selection on complex traits may be quite widespread. Our findings are most consistent with this selection being either positive or negative, although the relative contributions of the two are difficult to discern. Our results also suggest that trait-SNP associations identified in Eurasian samples may not be present in Africa, Oceania, and the Americas, possibly due to differences in linkage disequilibrium patterns. This observation suggests that non-Eurasian and non-East Asian sample populations should be included in future GWAS.

Casto, A. M., J. Z. Li, D. Absher, R. Myers, S. Ramachandran, and M. W. Feldman. 2010. Characterization of X-linked SNP genotypic variation in globally distributed human populations. Genome Biol. 11:R10.

Background: The transmission pattern of the human X chromosome reduces its population size relative to the autosomes, subjects it to disproportionate influence by female demography, and leaves X-linked mutations exposed to selection in males. As a result, the analysis of X-linked genomic variation can provide insights into the influence of demography and selection on the human genome. Here we characterize the genomic variation represented by 16,297 X-linked SNPs genotyped in the CEPH human genome diversity project samples.
Results: We found that X chromosomes tend to be more differentiated between human populations than autosomes, with several notable exceptions. Comparisons between genetically distant populations also showed an excess of Xlinked SNPs with large allele frequency differences. Combining information about these SNPs with results from tests designed to detect selective sweeps, we identified two regions that were clear outliers from the rest of the X chromosome for haplotype structure and allele frequency distribution. We were also able to more precisely define the geographical extent of some previously described X-linked selective sweeps.
Conclusions: The relationship between male and female demographic histories is likely to be complex as evidence supporting different conclusions can be found in the same dataset. Although demography may have contributed to the excess of SNPs with large allele frequency differences observed on the X chromosome, we believe that selection is at least partially responsible. Finally, our results reveal the geographical complexities of selective sweeps on the X chromosome and argue for the use of diverse populations in studies of selection.

REFERENCES

1.  Cavalli-Sforza, L.L., and M.W. Feldman. 2003. The application of molecular genetic approaches to the study of human evolution. Nat. Genet. Supp. 33: 266–275.

2.  Henn, B. M., L. L. Cavalli-Sforza, and M. W. Feldman. 2012. The great human expansion. Proc. Natl. Acad. Sci. USA 109: 17758–17764.

3.  Keinan, A., and A. G. Clark. 2012. Recent explosive human population growth has resulted in an excess of rate genetic variants. Science 336: 740–743.

4.  Grossman, S. R., K. G. Andersen, I. Shlyakhter, S. Tabrizi, S. Winnicki, A. Yen, D. J. Park, D. Griesemer, E. K. Karlsson, S. H. Wong, M. Cabili, R. A. Adegbola, R. N. K. Bamezai, A. V. S. Hill, F. O. Vannberg, J. L. Rinn, 1000 Genomes Project, E. S. Lander, S. F. Schaffner, and P. C. Sabeti. 2013. Identifying recent adaptations in large-scale genomic data. Cell 152: 703–713.

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