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4.1: Genetics Protocol - Biology

4.1: Genetics Protocol - Biology


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We all know that children tend to resemble their parents. Parents and their children tend to have similar appearance because children inherit genes from their parents and these genes influence characteristics such as skin and hair color.

How do genes influence our characteristics?

1. A gene is a segment of a ________ molecule that gives the instructions for making a protein. Different versions of the same gene are called alleles, and different alleles give the instructions for making different versions of a __________________. The different versions of a protein can result in different observable characteristics (i.e. different phenotypes).

Each cell in your body has two copies of each gene (one inherited from your mother and one inherited from your father).

  • If both copies of a gene have the same allele, the person is homozygous for that gene.
  • If the two copies of a gene have different alleles, the person is heterozygous for that gene.

This chart shows an example of how genes influence our characteristics.

GenotypeProteinPhenotype (characteristics)
AA or Aa

Enough normal enzyme to make melanin, the molecule that gives color to our skin and hair.

Normal skin and hair color

aa

Defective enzymes that do not make melanin

Very pale skin and hair color (albino)

2. Circle the genotypes in the chart that are homozygous. Explain how these two different homozygous genotypes result in different phenotypes. What is the molecular mechanism?

3a. In a heterozygous person, often a dominant allele determines the phenotype and the other recessive allele does not affect the phenotype. This means that a heterozygous person has the same phenotype as a person who is homozygous for the ___________________(dominant/recessive) allele.

For example, a person who is heterozygous Aa has the same phenotype as a person who is homozygous AA because skin cells that have at least one 'A' allele produce enough melanin to result in normal skin color.

3b. For this gene, which allele is dominant? ___A ___a

  • Which allele is recessive? ___A ___a
  • What evidence supports your conclusion about which allele is dominant and which is recessive?

How does a Baby Inherit Genes from His or Her Mother and Father?

Each gene is a part of a DNA molecule which is contained in a chromosome. During meiosis, the gene-carrying chromosomes move from the parent’s cells to the gametes, and during fertilization, the gene-carrying chromosomes move from the gametes to a zygote which develops into a baby. Thus, we can understand how a baby inherits genes from his or her mother and father by understanding how the gene-carrying chromosomes move during meiosis and fertilization.

Inheritance of Albinism:

To learn more about how genes are inherited, we will start with a specific question:

If both parents are heterozygous (Aa), what different combinations of 'A' and/or 'a' alleles could be observed in the children of these parents?

To answer this question, your group will use model chromosomes to show how meiosis and fertilization result in inheritance. Each parent will have a pair of homologous chromosomes, one with an 'A' allele and the other with an 'a' allele.

  • One of you should use your model chromosomes to demonstrate how meiosis produces different types of eggs, and another group member should demonstrate how meiosis produces different types of sperm.

4. In this chart, record the allele in each type of egg produced by meiosis. Record the allele in each type of sperm.

  • Next, use chalk to outline the rectangles shown in this chart on your lab table and put a model chromosome for each type of sperm and egg in the appropriate positions. Model fertilization for each type of sperm and egg.

5. Record the genetic makeup (the alleles) for each type of zygote produced by fertilization.

Biologists use a similar chart to analyze inheritance However, biologists omit much of the detail shown above and use a simplified version called a Punnett Square.

6. For this Punnett square:

  • Write "gametes" and draw arrows to each symbol that represents the genetic makeup of a gamete.
  • Write "zygotes" and draw arrows to each symbol that represents the genetic makeup of a zygote.

7. The genetic makeup of each zygote in the Punnett square represents a possible genotype of a child of this couple. Explain why the genotype of each child is the same as the genetic makeup of the zygote that he or she developed from.

8. For an 'Aa' mother, what fraction of her eggs have an 'a' allele? _____

  • What fraction of an Aa father's sperm have an 'a' allele? _____
  • What fraction of this couple's children would you expect to have the 'aa' genotype? _____
  • Explain your reasoning.

9a. Complete this Punnett square for two parents who are homozygous AA.

9b. Complete this Punnett square for two parents who are homozygous aa.

9c. Complete this Punnett square for a mother who is heterozygous Aa and a father who is homozygous aa.

10. For each of the four Punnett squares above, circle the genotype of anyone who would have normal skin and hair color.

  • In these four Punnett squares, there is only one example of a child who would have a different phenotype that was not observed in either parent. Use an asterisk (*) to indicate this example.

Notice that all of the children with normal skin and hair color have at least one parent who also has normal skin and hair color. Also, almost all of the albino children have at least one albino parent. These findings fit with our general observation that children tend to resemble their parents.

11. Explain why two albino parents will not have any children with normal skin and hair color, but two parents with normal skin and hair color could have an albino child.

12. Albino children are rare in the general population. Based on this observation, what is the most common genotype for parents? Explain your reasoning.

Coin Toss Genetics

The way genes behave during meiosis and fertilization can be modeled by using two-sided coins, where heads represent the dominant allele (A) and tails represent the recessive allele (a). This table explains how the coin toss model of inheritance represents the biological processes of meiosis and fertilization for heterozygous (Aa) parents.

Biological ProcessHow This Will Be Modeled in Coin Toss Genetics

Meiosis in an Aa parent produces gametes. Each gamete has an equal probability of having an 'A' allele or an 'a' allele.

You toss your coin and check for heads up vs. tails up. This represents the 50-50 chance of getting an 'A' allele or an 'a' allele.

Fertilization of an egg by a sperm produces a zygote. Each gamete contributes one allele to the genotype of the child that develops from the zygote.

Two students each toss a coin and the result of this pair of coin tosses indicates the genotype of the child that develops from the zygote.

  • Find someone to “mate” with.
  • Each of you will toss your coin; record the results as the genotype of the first child in the first family of four children in the table below. Make three more pairs of coin tosses and record the genotypes for the second, third and fourth children in this family.
Genotypes of Coin Toss "Children" Produced by Two Heterozygous (Aa) Parents
Result for Each Coin TossNumber with Each Genotype
1st2nd3rd4thAAAaaa
First family of 4 children
Next family of 4 children
Next family of 4 children
Next family of 4 children
Totals

Predictions based on Punnett square

1/4 = 25%2/4 = 50%1/4 = 25%
Class Data (Percents)
  • Repeat this procedure three times to determine the genotypes for three more families of four children each, and record your results in the table.
  • Complete the last three columns for these four families of coin toss children, and add your results. Give your teacher the total numbers for the AA, Aa, and aa genotypes.
  • Use a checkmark to indicate any coin toss family of 4 children that has exactly the numbers of AA, Aa, and aa genotypes predicted by the Punnett square.

To understand why some of the coin toss families do not have exactly the predicted number of children with each genotype, answer these questions.

1. Does the genotype produced by the first pair of coin tosses have any effect on the genotype produced by the second pair of coin tosses? ___ yes ___ no

2. If a coin toss family has one aa child, could the second child in this family also have the aa genotype? ___ yes ___ no

Explain your reasoning.

In real families, the genotype of each child depends on which specific sperm fertilized which specific egg, and this is not influenced by what happened during the fertilizations that resulted in previous children. Therefore, the genotype of each child is independent of the genotype of any previous children.

3. Suppose that a mother and father who are both heterozygous Aa have two children who also are heterozygous Aa. If this couple has a third child, what is the probability that this third child will also be heterozygous Aa?

  • Explain your reasoning.

As a result of random variation in which particular sperm fertilizes which particular egg to form a zygote, the proportions of each genotype and phenotype vary in different families, and the observed proportions of each genotype and phenotype often do not match the predictions of the Punnett square.

4. Suppose that you had data for 20 families of four children each where both parents were heterozygous Aa. Would each of these families have exactly one albino child, as predicted by the Punnett square? Explain why or why not.

5. Your teacher will give you the class data to enter in the last line of the table on page 5. Are the percents of each genotype in the class data similar to the predictions of the Punnett Square?

The random variation observed in small samples usually averages out in large samples. Therefore, the predictions of the Punnett Square are usually more accurate for larger samples of children.

Genetics of Sickle Cell Anemia

Red blood cells are full of hemoglobin, the protein that carries oxygen. One hemoglobin allele codes for normal hemoglobin, and another allele codes for sickle cell hemoglobin. In a person is homozygous for the sickle cell allele, sickle cell hemoglobin tends to clump into long rods that cause the red blood cells to assume a sickle shape or other abnormal shapes, instead of the normal disk shape. This causes a disease called sickle cell anemia.

GenotypeHemoglobin in Red Blood CellsShape of Red Blood Cells
Homozygous for alleles for normal hemoglobin

Normal hemoglobin dissolves in the cytosol.

Homozygous for alleles for sickle cell hemoglobin

Sickle cell hemoglobin tends to clump in long rods.

1. Normal disk-shaped red blood cells can barely squeeze through the capillaries (the tiniest blood vessels). What problems might be caused by red blood cells that are sickle-shaped or have other abnormal shapes?

2. Most children with sickle cell anemia have parents who do not have sickle cell anemia. Explain how a person can inherit sickle cell alleles from parents who do not have sickle cell anemia. Is the sickle cell allele dominant (S) or recessive (s)? Explain your reasoning. Include a Punnett Square in your answer.

The sickle-cell allele illustrates some common complexities of genetics that we have ignored thus far. Read the information in this box, and then answer questions 3 and 4.

Sickle Cell Anemia

People who are homozygous for the sickle cell allele have sickle cell anemia, including pain and organ damage, because of blocked circulation and anemia (low red blood cell levels) due to more rapid breakdown of red blood cells. People who are heterozygous for the sickle cell allele almost never experience these symptoms. Therefore, the allele for sickle cell hemoglobin is generally considered to be recessive and the allele for normal hemoglobin is generally considered to be dominant.

However, a heterozygous person does not have exactly the same phenotype as a person who is homozygous for the allele for normal hemoglobin. Specifically, people who are heterozygous for the allele for sickle cell hemoglobin are less likely to develop severe malaria than people who are homozygous for the allele for normal hemoglobin.

Malaria is caused by a parasite that infects red blood cells. The red blood cells of heterozygous individuals have both sickle cell and normal hemoglobin. Malaria parasites are less able to reproduce in red blood cells that have some sickle cell hemoglobin. This explains why people who are heterozygous for the allele for sickle cell hemoglobin have less severe malaria infections than people who are homozygous for the allele for normal hemoglobin.

3. Explain how the hemoglobin gene illustrates the following generalization:

A single gene often has multiple phenotypic effects.

4. Often, when geneticists investigate a pair of alleles, neither allele is completely dominant or completely recessive. In other words, the phenotype of a person who is heterozygous for these two alleles is different from the phenotypes of people who are homozygous for either allele. Explain how this general principle is illustrated by the sickle cell and normal alleles for the hemoglobin gene.


4.1: Genetics Protocol - Biology

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Genetic engineering is the modification of the genetic code, the DNA of an organism.

The nucleotide sequence of a gene may be changed, using genome editing techniques such as CRISPR/Cas9. A gene could be removed, or knocked out, altogether, or, a new one could be inserted.

For instance, adding a gene from another species to create a transgenic organism.

As a result, researchers and clinicians can alter the proteins produced by an organism. For example, in gene therapy, a gene can be introduced into a patient to produce a protein they are lacking, potentially curing their disease.

15.1: What is Genetic Engineering?

Overview

Genetic engineering is the process of modifying an organism&rsquos DNA to introduce new, desirable traits. Many organisms, from bacteria to plants and animals, have been genetically modified for academic, medical, agricultural, and industrial purposes. While genetic engineering has definite benefits, ethical concerns surround modifying humans and our food supply.

Scientists can Deliberately Modify an Organism&rsquos Genome

Genetic engineering is possible because the genetic code&mdashthe way information is encoded by DNA&mdashand the structure of DNA are universal among all life forms. As a result, an organism&rsquos genetic code may be modified in several ways.

The nucleotide sequence may be selectively edited by using techniques such as the CRISPR/Cas9 system. Known as the "molecular scissors," the CRISPR/Cas9 system is an innate, prokaryotic immune response that has been co-opted for editing genetic information.

A gene may also be removed from an organism to create a &ldquoknockout,&rdquo or introduced to create a &ldquoknockin,&rdquo through a process called gene targeting. This method relies on homologous recombination&mdashgenetic exchange between DNA molecules that share an extended region with similar sequences&mdashto modify an endogenous gene.

Scientists can also insert a gene from one organism into the genome of another, resulting in a transgenic organism. Generally, DNA combined from different sources is called recombinant DNA. The organism that receives that DNA is considered a genetically modified organism or GMO.

Genetic Engineering Impacts Human Health and Well-Being

Genome editing has significantly impacted scientific research, agriculture, industry, and medicine. Molecular biology research often inserts transgenes&mdashforeign genes&mdashinto bacteria and viruses to study gene function and expression. Bacteria were the first organisms to be genetically engineered. Scientists introduced the human insulin gene to produce synthetic insulin that is used by people with diabetes.

A technique called gene therapy allows a new gene to be inserted into a person so that the protein it encodes can be expressed within their cells. Gene therapy provides a cure or treatment for some serious and otherwise untreatable genetic diseases. Scientists modified viruses to deliver new genes to host cells. These customized viruses can infect diseased cells and insert a correct copy of a defective gene, treating human disorders such as Severe Combined Immunodeficiency (SCID).

Although many gene therapy treatments use modified viruses, the CRISPR/Cas9 system has become an increasingly popular technique. The CRISPR/Cas9 system cuts DNA by using a guide&mdashRNA sequences known as CRISPR&mdashto direct the &ldquomolecular scissors&rdquo&mdashan enzyme called Cas9&mdashto specific sites in the genome. Scientists use this molecular tool to add, remove, or alter genetic material. CRISPR/Cas9 has been used in mouse models to correct errors in genes that are responsible for Duchenne muscular dystrophy, Hepatitis B, cataracts, and cardiovascular disease.

While genetic engineering can yield new treatments for diseases, it can also be used for other practical purposes. Transgenic goats have been developed that produce spider silk in their milk for industrial use. In agriculture, some plants have been genetically modified to improve characteristics such as nutritional content and pest resistance. Recent and future advances in genetic engineering will likely continue to impact both human health and well-being.

Ethical Concerns Regarding Genetic Engineering

Genetic engineering has great potential, but where do we draw the line? Scientists and society must answer this question. Human genome editing, especially in germline cells, is a major ethical concern. Most gene therapies modify somatic cells, so genetic changes only affect the individual. Changes to a person&rsquos germline, however, are also inherited by their offspring.

In 2018, a scientist shocked the world when he allegedly created the first babies genetically modified with CRISPR. He attempted to make the twin baby girls resistant to HIV by introducing an unstudied germline mutation. His actions sparked outrage and concern as scientists and the public grappled with what this meant for humankind. It remains unclear how this will affect the girls&rsquo health, their future offspring, or the population.

Another concern is the use of foreign genetic material to improve the food supply. Plants are the most common genetically modified food source, with 28 countries growing nearly 450 million acres of GM crops globally. While there is enormous potential to secure food supply for a growing world population, scientifically sound, long-term studies are needed to address the concerns of GMO critics.

Georges, Fawzy, and Heather Ray. &ldquoGenome Editing of Crops: A Renewed Opportunity for Food Security.&rdquo GM Crops & Food 8, no. 1 (January 2, 2017): 1&ndash12. [Source]

Thurtle‐Schmidt, Deborah M., and Te‐Wen Lo. 2018. &ldquoMolecular Biology at the Cutting Edge: A Review on CRISPR/CAS9 Gene Editing for Undergraduates.&rdquo Biochemistry and Molecular Biology Education 46 (2): 195&ndash205. [Source]


The biological sciences major with no concentration is designed for students who are committed to studying and training in multiple disciplines in biology and for students who want to get more exposure to the life sciences before deciding whether they want to declare a concentration as an undergraduate. Therefore, students in this major take core courses from several concentrations. Students in this major have more flexibility to customize their program of study so they can focus on their own area of interest that may not have a concentration, such as plant biology or genomics.

The biological science major provides an excellent foundation for graduate study as well as preparation for professional schooling in:

Graduates may enter positions in:


Contents

Historically, microscopy was the primary method of investigating nuclear organization, [6] which can be dated back to 1590. [7]

  • In 1879, Walther Flemming coined the term chromatin. [8]
  • In 1883, August Weismann connected chromatin with heredity.
  • In 1884, Albrecht Kossel discovered histones.
  • In 1888, Sutton and Boveri proposed the theory of continuity of chromatin during the cell cycle [9]
  • In 1889, Wilhelm von Waldemeyer created the term "chromosome". [10]
  • In 1928, Emil Heitz coined the term Heterochromatin and Euchromatin. [11]
  • In 1942, Conrad Waddington postulated the epigenetic landscapes. [12]
  • In 1948, Rollin Hotchkiss discovered DNA methylation. [13]
  • In 1953, Watson and Crick discovered the double helix structure of DNA. [14]
  • In 1961, Mary Lyon postulated the principle of X-inactivation.
  • In 1973/1974, chromatin fiber was discovered. [12]
  • In 1975, Pierre Chambon coined the term nucleosomes. [12]
  • In 1982, Chromosome territories were discovered. [15]
  • In 1984, John T. Lis innovated the Chromatin immunoprecipitation technique.
  • In 1993, the Nuclear Ligation Assay was published, a method that could determine circularization frequencies of DNA in solution. This assay was used to show that estrogen induces an interaction between the prolactin gene promoter and a nearby enhancer. [16]
  • In 2002, Job Dekker introduced the new idea that dense matrices of interaction frequencies between loci could be used to infer the spatial organization of genomes. This idea was the basis for his development of the chromosome conformation capture (3C) assay, published in 2002 by Job Dekker and colleagues in the Kleckner lab at Harvard University. [17][18]
  • In 2003, the Human Genome Project was finished.
  • In 2006, Marieke Simonis invented 4C, [19] Dostie, in the Dekker lab, invented 5C. [20]
  • In 2007, B. Franklin Pugh innovated ChIP-seq technique. [21]
  • In 2009, Lieberman-Aiden and Job Dekker invented Hi-C, [22] Melissa J. Fullwood and Yijun Ruan invented ChIA-PET. [23]
  • In 2012, The Ren group, and the groups led by Edith Heard and Job Dekker discovered Topologically Associating Domains (TADs) in mammals. [24][25]
  • In 2013, Takashi Nagano and Peter Fraser introduced in-nuclei ligation for Hi-C and single-cell Hi-C. [26]

All 3C methods start with a similar set of steps, performed on a sample of cells.

First, the cell genomes are cross-linked with formaldehyde, [27] which introduces bonds that "freeze" interactions between genomic loci. Treatment of cells with 1-3% formaldehyde, for 10-30min at room temperature is most common, however, standardization for preventing high protein-DNA cross linking is necessary, as this may negatively affect the efficiency of restriction digestion in the subsequent step. [28] The genome is then cut into fragments with a restriction endonuclease. The size of restriction fragments determines the resolution of interaction mapping. Restriction enzymes (REs) that make cuts on 6bp recognition sequences, such as EcoR1 or HindIII, are used for this purpose, as they cut the genome once every 4000bp, giving

1 million fragments in the human genome. [28] [29] For more precise interaction mapping, a 4bp recognizing RE may also be used. The next step is, proximity based ligation. This takes place at low DNA concentrations or within intact, permeabilized nuclei [26] in the presence of T4 DNA ligase, [30] such that ligation between cross-linked interacting fragments is favored over ligation between fragments that are not cross-linked. Subsequently, interacting loci are quantified by amplifying ligated junctions by PCR methods. [28] [30]

Original methods Edit

3C (one-vs-one) Edit

The chromosome conformation capture (3C) experiment quantifies interactions between a single pair of genomic loci. For example, 3C can be used to test a candidate promoter-enhancer interaction. Ligated fragments are detected using PCR with known primers. [2] [17] That is why this technique requires the prior knowledge of the interacting regions.

4C (one-vs-all) Edit

Chromosome conformation capture-on-chip (4C) captures interactions between one locus and all other genomic loci. It involves a second ligation step, to create self-circularized DNA fragments, which are used to perform inverse PCR. Inverse PCR allows the known sequence to be used to amplify the unknown sequence ligated to it. [31] [2] [19] In contrast to 3C and 5C, the 4C technique does not require the prior knowledge of both interacting chromosomal regions. Results obtained using 4C are highly reproducible with most of the interactions that are detected between regions proximal to one another. On a single microarray, approximately a million interactions can be analyzed. [ citation needed ]

5C (many-vs-many) Edit

Chromosome conformation capture carbon copy (5C) detects interactions between all restriction fragments within a given region, with this region's size typically no greater than a megabase. [2] [20] This is done by ligating universal primers to all fragments. However, 5C has relatively low coverage. The 5C technique overcomes the junctional problems at the intramolecular ligation step and is useful for constructing complex interactions of specific loci of interest. This approach is unsuitable for conducting genome-wide complex interactions since that will require millions of 5C primers to be used. [ citation needed ]

Hi-C (all-vs-all) Edit

Hi-C uses high-throughput sequencing to find the nucleotide sequence of fragments [2] [22] and uses paired end sequencing, which retrieves a short sequence from each end of each ligated fragment. As such, for a given ligated fragment, the two sequences obtained should represent two different restriction fragments that were ligated together in the proximity based ligation step. The pair of sequences are individually aligned to the genome, thus determining the fragments involved in that ligation event. Hence, all possible pairwise interactions between fragments are tested.

Sequence capture-based methods Edit

A number of methods use oligonucleotide capture to enrich 3C and Hi-C libraries for specific loci of interest. [32] [33] These methods include Capture-C, [34] NG Capture-C, [35] Capture-3C, [34] HiCap, [32] [36] Capture Hi-C. [37] and Micro Capture-C. [38] These methods are able to produce higher resolution and sensitivity than 4C based methods, [39] Micro Capture-C provides the highest resolution of the available 3C techniques and it is possible to generate base pair resolution data. [38]

Single-cell methods Edit

Single-cell adaptations of these methods, such as ChIP-seq and Hi-C can be used to investigate the interactions occurring in individual cells. [40] [41]

Immunoprecipitation-based methods Edit

ChIP-loop Edit

ChIP-loop combines 3C with ChIP-seq to detect interactions between two loci of interest mediated by a protein of interest. [2] [42] The ChIP-loop may be useful in identifying long-range cis-interactions and trans interaction mediated through proteins since frequent DNA collisions will not occur. [ citation needed ]

Genome wide methods Edit

ChIA-PET combines Hi-C with ChIP-seq to detect all interactions mediated by a protein of interest. [2] [23] HiChIP was designed to allow similar analysis as ChIA-PET with less input material. [43]

3C methods have led to a number of biological insights, including the discovery of new structural features of chromosomes, the cataloguing of chromatin loops, and increased understanding of transcriptional regulation mechanisms (the disruption of which can lead to disease). [6]

3C methods have demonstrated the importance of spatial proximity of regulatory elements to the genes that they regulate. For example, in tissues that express globin genes, the β-globin locus control region forms a loop with these genes. This loop is not found in tissues where the gene is not expressed. [44] This technology has further aided the genetic and epigenetic study of chromosomes both in model organisms and in humans. [ not verified in body ]

These methods have revealed large-scale organization of the genome into topologically associating domains (TADs), which correlate with epigenetic markers. Some TADs are transcriptionally active, while others are repressed. [45] Many TADs have been found in D. melanogaster, mouse and human. [46] Moreover, CTCF and cohesin play important roles in determining TADs and enhancer-promoter interactions. The result shows that the orientation of CTCF binding motifs in an enhancer-promoter loop should be facing to each other in order for the enhancer to find its correct target. [47]

Human disease Edit

There are several diseases caused by defects in promoter-enhancer interactions, which are reviewed in this paper. [48]

Beta thalassemia is a certain type of blood disorders caused by a deletion of LCR enhancer element. [49] [50]

Holoprosencephaly is cephalic disorder caused by a mutation in the SBE2 enhancer element, which in turn weakened the production of SHH gene. [51]

PPD2 (polydactyly of a triphalangeal thumb) is caused by a mutation of ZRS enhancer, which in turn strengthened the production of SHH gene. [52] [53]

Adenocarcinoma of the lung can be caused by a duplication of enhancer element for MYC gene. [54]

T-cell acute lymphoblastic leukemia is caused by an introduction of a new enhancer. [55]

The different 3C-style experiments produce data with very different structures and statistical properties. As such, specific analysis packages exist for each experiment type. [33]

Hi-C data is often used to analyze genome-wide chromatin organization, such as topologically associating domains (TADs), linearly contiguous regions of the genome that are associated in 3-D space. [45] Several algorithms have been developed to identify TADs from Hi-C data. [4] [60]

Hi-C and its subsequent analyses are evolving. Fit-Hi-C [3] is a method based on a discrete binning approach with modifications of adding distance of interaction (initial spline fitting, aka spline-1) and refining the null model (spline-2). The result of Fit-Hi-C is a list of pairwise intra-chromosomal interactions with their p-values and q-values. [59]

The 3-D organization of the genome can also be analyzed via eigendecomposition of the contact matrix. Each eigenvector corresponds to a set of loci, which are not necessarily linearly contiguous, that share structural features. [61]

A significant confounding factor in 3C technologies is the frequent non-specific interactions between genomic loci that occur due to random polymer behavior. An interaction between two loci must be confirmed as specific through statistical significance testing. [3]

Normalization of Hi-C contact map Edit

There are two major ways of normalizing raw Hi-C contact heat maps. The first way is to assume equal visibility, meaning there is an equal chance for each chromosomal position to have an interaction. Therefore, the true signal of a Hi-C contact map should be a balanced matrix (Balanced matrix has constant row sums and column sums). An example of algorithms that assumes equal visibility is Sinkhorn-Knopp algorithm, which scales the raw Hi-C contact map into a balanced matrix.

The other way is to assume there is a bias associated with each chromosomal position. The contact map value at each coordinate will be the true signal at that position times bias associated with the two contact positions. An example of algorithms that aim to solve this model of bias is iterative correction, which iteratively regressed out row and column bias from the raw Hi-C contact map. There are a number of software tools available for analysis of Hi-C data. [62]

DNA motif analysis Edit

DNA motifs are specific short DNA sequences, often 8-20 nucleotides in length [63] which are statistically overrepresented in a set of sequences with a common biological function. Currently, regulatory motifs on the long-range chromatin interactions have not been studied extensively. Several studies have focused on elucidating the impact of DNA motifs in promoter-enhancer interactions.

Bailey et al. has identified that ZNF143 motif in the promoter regions provides sequence specificity for promoter-enhancer interactions. [64] Mutation of ZNF143 motif decreased the frequency of promoter-enhancer interactions suggesting that ZNF143 is a novel chromatin-looping factor.

For genome-scale motif analysis, in 2016, Wong et al. reported a list of 19,491 DNA motif pairs for K562 cell line on the promoter-enhancer interactions. [65] As a result, they proposed that motif pairing multiplicity (number of motifs that are paired with a given motif) is linked to interaction distance and regulatory region type. In the next year, Wong published another article reporting 18,879 motif pairs in 6 human cell lines. [66] A novel contribution of this work is MotifHyades, a motif discovery tool that can be directly applied to paired sequences.

Cancer genome analysis Edit

The 3C-based techniques can provide insights into the chromosomal rearrangements in the cancer genomes. [67] Moreover, they can show changes of spatial proximity for regulatory elements and their target genes, which bring deeper understanding of the structural and functional basis of the genome. [68]


DNA Analysis Methods (SWGDAM)

The Scientific Working Group on DNA Analysis Methods, known as SWGDAM, serves as a forum to discuss, share, and evaluate forensic biology methods, protocols, training, and research to enhance forensic biology services as well as provide recommendations to the FBI Director on quality assurance standards for forensic DNA analysis.

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Upcoming Meetings
SWGDAM Regular Meeting July 13-15, 2021 (Virtual) Agenda
SWGDAM Regular Meeting January 11-13, 2022
About SWGDAM

SWGDAM is currently comprised of dedicated forensic scientists, from international, federal, state and local forensic DNA laboratories as well as guests representing academia and other Federal agencies. These forensic scientists serve as the DNA technical leaders or Combined DNA Index System (CODIS) Administrators for their laboratories, and are able to offer the perspectives of practitioners in the areas of nuclear and mitochondrial DNA technologies. SWGDAM is also fortunate to have invited guests attend each meeting representing academia, other Federal laboratories and international agencies to provide their specific expertise in areas such as mitochondrial DNA, population genetics, statistics, and YSTRs.

The responsibilities of SWGDAM are: (1) to recommend revisions, as necessary, to the Quality Assurance Standards for Forensic DNA Testing Laboratories and the Quality Assurance Standards for DNA Databasing Laboratories (2) to serve as a forum to discuss, share, and evaluate forensic biology methods, protocols, training, and research to enhance forensic biology services and (3) to recommend and conduct research to develop and/or validate forensic biology methods. We hold semi-annual meetings in January and July and our Committees meet more frequently as issues arise.

Updates

New Report. SWGDAM has published its Report on Y-Screening of Sexual Assault Evidence Kits (SAEKs), please click here for more information.

Revised Training Guidelines. SWGDAM has revised its Training Guidelines, please click here for more information.

New Letter to the Editor. SWGDAM has collaborated with ENFSI and the Rapid DNA Crime Scene Technology Advancement Task Group on the use of Rapid DNA at crime scenes, please click here for more information.

Investigative Genetic Genealogy!! SWGDAM has issued an Overview of Investigative Genetic Genealogy, please click here for more information.

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Method

Subjects

All the subjects enrolled in the WGS and the PheWAS were the BioBank Japan Project participants. BioBank Japan Project is a multi-institutional hospital-based study that collected peripheral blood DNA, serum, and clinical information from the participants affected with any of the 47 target diseases 30 . The participants provided written informed consent as approved by the ethical committees of the Institute of Medical Science, the University of Tokyo. The details of the characteristics of individuals were described in elsewhere 30,39,40 . This study was approved by the ethical committee of Osaka University Graduate School of Medicine.

Mitochondrial DNA variant calling from the WGS data

WGS was conducted by using HiSeq 2500 with 160 bp and 125 bp pair end (n = 1269) or HiSeq X with 150 bp pair end (n = 659), which achieved high-depth on autosomal chromosomes (20–35×) as described elsewhere 18 . In this study, we realigned the sequenced reads on the human reference genome sequences of all the chromosomes and mitochondria. We extracted the uniquely mapped reads on the mitochondrial region to minimize the contamination of nuMTs 19 . In brief, the sequence reads were trimmed to remove Illumina adapter by Trimmomatic (version 0.36). The trimmed reads were mapped by BWA-MEM (version 0.7.15) on human reference genome sequence GRCh37 including rCRS (NC_012920.1) as mitochondrial reference genome sequence. After sorting by Samtools (version 1.4.1), mitochondrial regions (MT index) were extracted. Duplicated reads were removed using picard (version 2.9.2). Base recalibration was done by using GATK (version 3.7.0). Individual variant calling was performed using GATK HaplotypeCaller in the setting of ploidy 1 to assess homoplasmy. Multi-sample joint-calling of the variants were also conducted by GATK GenotypeGVCFs. Finally, manual filtering was done according to GATK best practice pipeline because of an insufficient number of variants to perform variant quality score recalibration 41 . The Ti/Tv ratio was estimated using bcftools (version 1.4.1).

Assignment protocol of the haplogroups based on the mtDNA variants

Using the detected mtDNA variants as an input, each individual was classified into a mitochondrial haplogroup by using HaploGrep (v2.1.14), an unsupervised clustering method based on Phylotree17 24 . The first letter of the haplogroup was defined as a marohaplogroup (e.g., “M”). From the second to the ninth letters of the haplogroups were decided as sub-haplogroups 1 to 8 (e.g., “M9”, “M9a”, “M9a1”,…, “M9a1a1c1a”). For comparison, we parallelly defined haplogroups of the 2504 individuals from the 1KG phase 3 in the same way.

Unsupervised classification of the samples using the ML methods

We adopted the three unsupervised mtDNA variant classification methods as follows: (1) phylogenetic analysis, (2) PCA as a linear dimensionality reduction technique, and (3) UMAP as a nonlinear dimensionality reduction technique. In the phylogenetic analysis, the variant list of the individual was converted to the FASTA format by using bcftools (version 1.4.1), realigned by using MUSCLE (version 3.8.31), and switched to the Phylip format. Phylogenetic tree was inferred using the maximum-parsimony method of PHYLIP (version 3.697) and figured using FigTree (version 1.4.3). We conducted PCA and UMAP using Scikit-learn of python (version 3.7) with the haploid genomes as inputs. PCA was calculated to obtain the top 20 components. The setting in applying UMAP was n_neighbors = 100 and min_dist = 0.99.

MtDNA variant characterization by LD structure assessment

To evaluate LD structure in mtDNA, we calculated pairwise haplotype correlations, equivalent to the original definition of the LD metric of r 2 , of the common mtDNA variants (MAF ≥ 5%). As a reference, we calculated pairwise LD of the randomly selected identical numbers of the phased autosomal haplotypes in a genome-wide manner. We adjusted the variant distance differences by restricting the autosomal variant pairs within the distances corresponding to those in mtDNA (±8.3 kbp). As for the tag variant identification, r 2 -values of the common variants with all the neighboring autosomal variants (±5 Mbp) were calculated. Common haplotypes consisting of the mtDNA variants were defined by selecting sets of the mtDNA variant pairs with strong LD (r 2 ≥ 0.8).

Assessment of mtDNA–nDNA and mtCN–nDNA genotype associations

We evaluated genetic correlations between mtDNA and nDNA variants (or mtCN). Autosomal DNA variants with call rate ≥ 0.99, MAF ≥ 1%, and Hardy–Weinberg equilibrium P-value ≥ 1.0 × 10 −6 were chosen from the previous WGS study (n = 1928) 18 . MtDNA variants with MAF ≥ 5% in the same set of individuals were selected. The mtCN of each individual was quantified by the formula, as descried elsewhere 5 :

Both of autosomal and mitochondrial average depths were extracted by the VCFtools (version 0.1.14) from the VCF files. By averaging the mtCN for each autosomal chromosome, the mean mtCN across all the chromosomes was calculated 5,42 . Regarding mtDNA–nDNA genotype association, logistic regression by PLINK (version 1.90b4.4) was performed using nDNA variants as explanatory variables. Regarding mtCN–nDNA genotype association, linear regression by PLINK (version 1.90b4.4) was conducted using nDNA variants as explanatory variables. To handle potential confounding factors, we included the top 20 principal components (PCs) of the autosomal variants and the WGS run batches as covariates. Especially, to examine genetic correlation with the nDNA variants in the mitochondria-related genes, the variants within ±10 kbp of the 1105 autosomal genes registered in mitoCarta2.0 were selected 29 . We adopted a genome-wide significance threshold considering the multiple comparison of the mtDNA variants for mtDNA–nDNA genotype association (P < 5.0 × 10 −8 /86 = 5.8 × 10 −10 ) and the typical genome-wide significance threshold for mtCN–nDNA genotype association (P < 5.0 × 10 −8 ) 43 . Next, we extracted autosomal DNA genotype dosages with Rsq ≥ 0.7 and MAF ≥ 1% from previous imputed GWAS data, and mtDNA variants with MAF ≥ 5% of the same individuals were selected (n = 141,552). Regarding mtDNA–nDNA genotype association, a logistic regression by PLINK (version 2.00a2LM) was performed in the same way. We included age, sex, the top 20 PCs, genotype microarray platforms, and geographical regions of participants as covariates. We adopted a genome-wide significance threshold considering the multiple comparison of the mtDNA variants (P < 5.0 × 10 −8 /8 = 6.3 × 10 −9 ).

Mitochondrial PheWAS

We genotyped the BioBank Japan Project participants using the Illumina HumanOmniExpressExome BeadChip or a combination of the Illumina HumanOmniExpress and HumanExome BeadChips. Normalized probe intensities were extracted for all the individuals passing standard laboratory quality control thresholds and genotypes were called using optiCall (version 0.7.0). We specified the -MT option to call mitochondrial variants and used the default settings. Genotypes with an individual posterior probability lower than 0.7 were defined as unknown. We excluded the individuals with (i) closely related individuals identified by identity-by-descent analysis or (ii) non-East Asian outliers identified by PCA of autosomal variants. Then, we applied the quality control criteria for mtDNA variants, excluding the individuals with sample call rate < 0.9, the variants with call rate < 0.99, or the variants with the concordance rate < 0.99 between the SNP array and the WGS data (n = 1446).

We conducted mtDNA variant imputation of the BioBank Japan Project GWAS data. We constructed the WGS-based mtDNA imputation reference panel of the Japanese population (n = 1928). The imputation accuracy of the constructed mtDNA imputation reference panel was evaluated by a cross-validation method as described previously 38 . The minor allele concordances of the sequence and imputed variants were calculated for each of the MAF bins separately (0

50%, respectively). We selected the variants with MAF ≥ 0.5% in the GWAS genotype data, filled missing genotype by Eagle (v2.4.1), and imputed by IMPUTE2 (version 2.3.2) with the options of –m 0 and –chrX using the constructed mtDNA imputation reference panel. We applied post-imputation QC filtering of the variants (MAF ≥ 0.5% and imputation score info ≥ 0.7) for the PheWAS.

We curated the phenotype record of the disease status and clinical values for the BioBank Japan Project participants (n = 147,310). The diseases composed of the five major categories (immune related [n = 10], metabolic and cardiovascular [n = 10], cancers [n = 13], ophthalmologic [n = 2], and other diseases [n = 11]). The quantitative traits composed of the ten categories (anthropometric [n = 2], metabolic [n = 6], protein [n = 4], kidney related [n = 4], electrolyte [n = 5], liver related [n = 5], other biochemical [n = 6], hematological [n = 13], blood pressure [n = 4], and behavior [n = 4]). A group of individuals not affected by the disease under scope was used as a control group in the analysis. Then, we performed the logistic regression analyses for the 46 diseases and the 2 binary traits in behavior category, with the adjustment for age, sex, the top 20 PCs, geographic regions, and the genotype microarray platforms as covariates. For the 51 quantitative traits, we performed the linear regression analyses on the rank-normalized residuals after regressing out using age, sex, the top 20 PCs, and the trait-specific covariates as described elsewhere 31,32,44,45 . We additionally included geographic regions and the genotype microarray platforms as covariates in linear regression. Association studies were done with a glm() function implemented in R statistical software (version 3.4.0) using the imputed dosage data.

Statistics and reproducibility

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.


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Publications

Blackiston, D., Adams, D. S., Lemire, J. M., Lobikin, M., and Levin, M., (2011), Transmembrane potential of GlyCl-expressing instructor cells induces a neoplastic-like conversion of melanocytes via a serotonergic pathway, Disease Models and Mechanisms, 4(1): 67-85 [cover]
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Beane, W. S., Morokuma, J., Adams, D. S., and Levin, M., (2011), A chemical genetics approach reveals H,K-ATPase-mediated membrane voltage is required for planarian head regeneration. Chemistry & Biology,
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Lange, C., Prenninger, S., Knuckles, P., Taylor, V., Levin, M., and Calegari, F., (2011), The H(+) vacuolar ATPase maintains neural stem cells in the developing mouse cortex, Stem Cells and Development, 20(5): 843-850
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Carneiro, K., Donnet, C., Rejtar, T., Karger, B. L., Díaz, E., Kortagere, S., Lemire, J. M., and Levin, M. (2011), Histone deacetylase activity is necessary for left-right patterning during vertebrate development, BMC Developmental Biology, 11: 29
PubMed | PDF

Vandenberg, L. N., Pennarola, B. W., and Levin, M., (2011), Low frequency vibrations disrupt left-right patterning in the Xenopus embryo, PLoS One, 6(8): e23306
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Mondia, J. P., Adams, D. S., Orendorff, R. D., Levin, M., and Omenetto, F., (2011), Patterned femtosecond-laser ablation of Xenopus laevis melanocytes for studies of cell migration, wound repair, and developmental processes, Biomedical Optics Express, 2(8): 2383-2391
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Mondia, J. P., Levin, M., Omenetto, F. G., Orendorff, R. D., Branch, M. R., and Adams, D. S., (2011), Long-distance signals are required for morphogenesis of the regenerating Xenopus tadpole tail, PLoS One, 6(9): e24953
PDF

Tseng, A-S., Carneiro, K., Lemire, J. M., and Levin, M., (2011), HDAC activity is required during Xenopus tail regeneration, PLoS One, 6(10): e26382
PDF

Levin, M. (2011), Endogenous bioelectrical signals in development, regeneration, and neoplasm, in Topical Talks: The Biomedical & Life Sciences Collection, Henry Stewart Talks Ltd, London.
Presentation

Levin, M. (2011), Left-Right Asymmetry in Embryonic Development: How epigenetic, biophysical forces and gene activity interplay to determine a major embryonic axis, in Topical Talks: The Biomedical & Life Sciences Collection, Henry Stewart Talks Ltd, London.
Presentation

Levin, M., (2011), Endogenous Bioelectric Signals as Morphogenetic Controls of Development, Regeneration, and Neoplasm, in The Physiology of Bioelectricity in Development, Tissue Regeneration, and Cancer, C. Pullar (Ed.), CRC Press: Boca Raton, FL, p. 39-89
Available here

Levin, M., (2011), The wisdom of the body: future techniques and approaches to morphogenetic fields in regenerative medicine, developmental biology, and cancer. Regenerative Medicine, 6(6): 667-673
PDF

Blackiston, D., Shomrat, T., Nicolas, C. L., Granata, C., and Levin, M., (2010), A second-generation device for automated training and quantitative behavior analyses of molecularly-tractable model organisms, PLoS One, 5(12): e14370
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Oviedo, N. J., Morokuma, J., Walentek, P., Kema, I. P., Gu, M. B., Ahn, J. M., Hwang, J. S., Gojobori, T., and Levin, M., (2010), Long-range Neural and Gap Junction Protein-mediated Cues Control Polarity During Planarian Regeneration, Developmental Biology, 339: 188-199 [cover]
PDF

Vandenberg, L. N., and M. Levin, (2010), Consistent left-right asymmetry cannot be Established by late organizers in Xenopus unless the late organizer is a conjoined twin, Development, 137, 1095-1105 [cover]
PDF

Aw, S., Koster, J., Pearson, W., Nicols, C., Shi, N. Q., Carneiro, K., and Levin, M., (2010), The ATP-sensitive K+-channel (KATP) controls early left-right patterning in Xenopus and chick embryos. Developmental Biology, 346: 39-53
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Tseng, A-S., Beane, W. S., Lemire, J. M., Masi, A., and M. Levin, (2010),
Induction of vertebrate regeneration by a transient sodium current
, Journal of Neuroscience, 30(39): 13192-13200 [cover]
PDF

Hechavarria, D., Dewilde, A., Braunhut, S., Levin, M., and Kaplan, D. K., (2010), BioDome regenerative sleeve for biochemical and biophysical stimulation of tissue regeneration. Medical Engineering and Physics, 32: 1065-1073
PDF

Blackiston, D., Vandenberg, L. N., and Levin, M., (2010), High-throughput Xenopus laevis immunohistochemistry using agarose sections. Cold Spring Harbor Protocols, doi:10.1101/pdb.prot5532
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Vandenberg, L. N., and Levin, M., (2010), Far from solved: a perspective on what we know about early mechanisms of left-right asymmetry. Developmental Dynamics, 239: 3131-3146 [cover]
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Aw, S., and Levin, M., (2009), Is left-right asymmetry a form of planar cell polarity?, Development, 136: 355-366

Vandenberg, L., and Levin, M., (2009), Perspectives and open problems in the early phases of left-right patterning, Seminars in Cell and Developmental Biology, 20: 456-463 [cover]
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Zhang, Y., and M. Levin, (2009), Particle tracking model of electrophoretic morphogen movement reveals stochastic dynamics of embryonic gradient, Developmental Dynamics, 238(8): 1923-1935
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Zhang, Y., and M. Levin, (2009), Left-right asymmetry in the chick embryo requires core planar cell polarity protein Vangl2, Genesis, 47(11): 719-728
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Levin, M. (2009), Bioelectric mechanisms in regeneration: Unique aspects and future perspectives. Seminars in Cell and Developmental Biology, 20: 543-556
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Levin, M., (2009), Errors of Geometry: regeneration in a broader perspective. Seminars in Cell and Developmental Biology, 20(6): 643-645
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Levin, M., (2009), Regeneration: recent advances, major puzzles, and biomedical opportunities. Seminars in Cell and Developmental Biology, 20(5): 515-516
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Levin, M., Sundelacruz, S., Levin M., Kaplan, D. L., (2009), Role of membrane potential in the regulation of cell proliferation and differentiation, Stem Cell Reviews, 5(3): 231-46
PDF

Blackiston, D. J., K. McLaughlin, and Levin, M., (2009), Bioelectric controls of cell proliferation: ion channels, membrane voltage, and the cell cycle, Cell Cycle, 8(21): 3527-3536 [cover]
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Morokuma, J., Blackiston, D., and Levin, M., (2008), KCNQ1 and KCNE1 K+ channel components are involved in early left-right patterning in Xenopus embryos, Cellular Physiology and Biochemistry, 21: 357-372
PDF

Oviedo, N. J., B. J. Pearson, M. Levin, and A. S. Alvarado, (2008), Planarian PTEN homologs regulate stem cells and regeneration through TOR signaling, Disease Models and Mechanisms, 1: 131-143
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Morokuma, J., Blackiston, D., Adams, D. S., Seebohm, G., Trimmer, B., and Levin, M., (2008), Modulation of potassium channel confers a hyper-proliferative invasive phenotype on embryonic stem cells, Proceedings of the National Academy of Sciences of the United States, 105(43): 16608-16613
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Sundelacruz, S., M. Levin, and D. L. Kaplan, (2008), Membrane potential controls adipogenic and osteogenic differentiation of mesenchymal stem cells, PLoS One, 3(11): e3737, 1-15
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Oviedo, N. J., Nicolas, C. L., Adams, D. S., and Levin, M., (2008), Live imaging of planarian membrane potential using DiBAC4(3). Cold Spring Harbor Protocols, doi:10.1101/pdb.prot5055
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Oviedo, N. J., Nicolas, C. L., Adams, D. S., and Levin, M., (2008), Gene knockdown in planarians using RNA interference. Cold Spring Harbor Protocols, doi:10.1101/pdb.prot5054
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Oviedo, N. J., Nicolas, C. L., Adams, D. S., and Levin, M., (2008), Establishing and maintaining a colony of planarians. Cold Spring Harbor Protocols, doi:10.1101/pdb.prot5053
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Oviedo, N., and Levin, M., (2008), Planarian regeneration model as a context for the study of drug effects and mechanisms, in Planaria: A Model for Drug Action and Abuse, R. B. Raffa & S.M. Rawls (Eds.), RG Landes Co.: Austin, p. 95-104

Nicolas, C.L., Abramson, C.I., and Levin, M. (2008), Analysis of behavior in the planarian model, in Planaria: A Model for Drug Action and Abuse, R. B. Raffa & S.M. Rawls (Eds.), RG Landes Co.: Austin, p. 83-94

Aw, S., and Levin, M., (2008), What's Left in Asymmetry?, Developmental Dynamics, 237: 3453-3464
PDF

Oviedo, N., Nicolas, C. L., Adams, D. S., and Levin, M. (2008), Planarians: a versatile and powerful model system for molecular studies of regeneration, adult stem cell regulation, aging, and behavior, in Emerging Model Organisms: A Laboratory Manual, Volume 1, Cold Spring Harbor Press: Cold Spring Harbor, NY
PDF

Tseng, A-S., and Levin, M., (2008), Tail regeneration in Xenopus laevis as a model for understanding tissue repair, Journal of Dental Research, 87(9): 806-816
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Tseng, A-S., Adams, D. S., Qiu, D., Koustubhan, P., and Levin, M. (2007), Apoptosis is required during early stages of tail regeneration in Xenopus laevis. Developmental Biology, 301: 62-69
PDF

Adams, D. S., Masi, A., and Levin, M. (2007), H+ Pump-dependent changes in membrane voltage are an early mechanism necessary and sufficient to induce Xenopus tail regeneration. Development, 134: 1323-1335 [cover]
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Oviedo, N. J., Levin, M., (2007), smedxin-11 is a Planarian Stem Cell Gap Junction Gene Required for Regeneration and Homeostasis. Development, 134(17): 3121-3131
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Aw, S., Adams, D. S., Qiu, D., and Levin, M., (2007), H,K-ATPase protein Localization and Kir4.1 function reveal concordance of three axes during early determination of left-right asymmetry, Mechanisms of Development, 125: 353-372
PDF

Koustubhan, P., Sorocco, D., and Levin, M., (2007), Establishing and maintaining a Xenopus laevis colony for research laboratories, in M. Conn (Ed.), Source Book of Models for Biomedical Research, Humana Press, p. 139-160

Levin, M., Palmer, R., (2007), Left-right patterning from the inside out: widespread evidence for intracellular control, BioEssays, 29: 271-287
PDF

Levin, M., (2007), Gap junctional communication in morphogenesis. Progress in Biophysics and Molecular Biology, 94 (1-2): 186-206
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Levin, M., (2007), Large-Scale Biophysics: Ion Flows and Regeneration. Trends in Cell Biology, 17(6): 261-270 [cover]
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Ingber, D., and Levin, M. (2007), What lies at the interface
of regenerative medicine and developmental biology?.
Development, 134: 2541-2547 [cover]
PDF

Oviedo, N., and Levin, M. (2007), Gap junctions provide new links in Left-Right patterning, Cell, 129: 645-647
PDF

Hibino, T., Ishii, Y., Levin, M., and Nishino, A., (2006), Ion flow regulates left-right asymmetry in sea urchin development. Development, Genes and Evolution, 216(5): 265-76
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Shimeld, S. M., and Levin, M., (2006), Evidence for the regulation of left-right asymmetry in Ciona intestinalis by ion flux. Developmental Dynamics, 235(6): 1543-1553
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Adams, D. S., Robinson, K. R., Fukumoto, T., Yuan, S., Albertson, R. C., Yelick, P., Kuo, L., McSweeney, M., and Levin M., (2006), Early, H+-V-ATPase-dependent proton flux is necessary for consistent left-right patterning of non-mammalian vertebrates. Development, 133: 1657-1671
PDF

Hicks, C., Sorocco, D., and Levin, M., (2006), Automated Analysis of Behavior: A Computer-Controlled System for Drug Screening and the Investigation of Learning. Journal of Neurobiology, 66(9): 977-90
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Esser, A. T., Smith, K. C., Weaver, J. C., and Levin, M., (2006), Mathematical Model of Morphogen Electrophoresis through Gap Junctions. Developmental Dynamics, 235(8): 2144-2159
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Adams, D. S., and Levin, M., (2006), Inverse Drug Screens: a rapid and inexpensive method for implicating molecular targets. Genesis, 44: 530-540
PDF

Adams, D., and Levin, M., (2006), Strategies and techniques for investigation of biophysical signals in patterning, in Analysis of Growth Factor signaling in Embryos, M. Whitman and A. K. Sater eds., pp. 177-264, Methods in Signal Transduction Series, CRC Press

Levin, M., Lauder, J., and Buznikov, G., (2006), Of Minds and Embryos: Left-Right Asymmetry and the Serotonergic Controls of Pre-Neural Morphogenesis. Developmental Neuroscience, 28:171-185 [cover]
PDF

Levin, M., (2006), Is the Early Left-Right Axis like a Plant, a Kidney, or a Neuron? The Integration of Physiological Signals in Left-Right Asymmetry. Birth Defects Research (Part C), 78: 191-223
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Fukumoto, T., and Levin, M., (2005), Asymmetric expression of Syndecan-2 in early chick embryogenesis, Syndecan-2 , 5(4): 525-528
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Fukumoto, T., Kema, I. P., and Levin, M., (2005), Serotonin signaling is a very early step in patterning of the left-right axis in chick and frog embryos. Current Biology, 15(9): 794-803
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Nogi, T., Yuan, Y. E., Sorocco, D., Perez-Tomas, R., and Levin, M., (2005), Eye regeneration assay reveals an invariant functional left-right asymmetry in the early bilaterian, Dugesia japonica. Laterality, 10(3): 193-205
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Qiu, D., Cheng, S.M., Wozniak, L., McSweeney, M., Perrone, E., and Levin, M., (2005), Localization and loss-of-function implicates ciliary proteins in early, cytoplasmic roles in left-right asymmetry, Developmental Dynamics, 234(1): 176-189
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Shin, J-B., Adams, D., Paukert, M., Siba, M., Sidi, S., Levin, M., Gillespie, P. G., and Grunder, S., (2005), Xenopus TRPN1 (NOMPC) localizes to microtubule-based cilia in epithelial cells, including inner-ear hair cells. Proceedings of the National Academy of Sciences of the United States, 102(35): 12572-12577
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Gamer, L. W., Nove, J., Levin, M., and Rosen, V., (2005), BMP-3 is a novel inhibitor of both activin and BMP-4 signaling in Xenopus embryos, Developmental Biology, 285(1): 156-168
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Nogi, T., and Levin, M., (2005), Characterization of innexin gene expression and functional roles of gap-junctional communication in planarian regeneration. Developmental Biology, 287: 314-335
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Fukumoto, T., Blakely, R., and Levin, M., (2005), Serotonin transporter function is an early step in left-right patterning in chick and frog embryos. Developmental Neuroscience, 27(6): 349-363
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Levin, M., (2005), Left-right asymmetry in embryonic development: a comprehensive review. Mechanisms of Development, 122(1): 3-25 [cover]
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Levin, M., (2004), A novel immunohistochemical method for evaluation of antibody specificity and detection of labile targets in biological tissue, Journal of Biophysical and Biochemical Methods, 58(1): 85-96
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Levin, M., (2004), The embryonic origins of left-right asymmetry. Critical Reviews in Oral Biology and Medicine, 15(4): 197-206
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Adams, D. S., and Levin, M. (2004). Early Patterning of the Left/Right Axis. in C. D. Stern (Ed.), Gastrulation: from cells to embryo Cold Spring Harbor, New York, pp. 403-417
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Bunney, T. D., De Boer, A. H., and Levin, M., (2003), Fusicoccin signaling reveals 14-3-3 protein function as a novel step in left-right patterning during amphibian embryogenesis, Development, 130(20): 4847-4858
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Levin, Michael, (2003), Left-Right Asymmetry in Amphibian Embryogenesis, in Developmental Biology, Vol. 6 of Biology of the Amphibia, edited by Harold Heatwole and Brenda Brizuela

Levin, M., (2003), Bioelectromagnetics in morphogenesis. Bioelectromagnetics, 24(5): 295-315
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Levin, M., (2003), Motor protein control of ion flux is an early step in embryonic left-right asymmetry, BioEssays, 25(10): 1002-1010
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Levin, M., Thorlin, T., Robinson, K., Nogi, T., and Mercola, M., (2002), Asymmetries in H+/K+-ATPase and cell membrane potentials comprise a very early step in left-right patterning, Cell, 111(1): 77-89
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Rutenberg, J., Cheng, S. M., and Levin, M., (2002), Early embryonic expression of ion channels and pumps in chick and Xenopus development. Developmental Dynamics, 225(4): 469-484
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Cheng, S. M., Chen, I., and Levin, M., (2002), Katp channel activity is required for hatching in Xenopus. Developmental Dynamics, 225(4): 588-591
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Levin, Michael, (2002), Isolation and community: A review of the role of gap-junctional communication in embryonic patterning. Journal of Membrane Biology, 185(3): 177-192
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Mercola, M., and Levin, M., (2001), Left-Right asymmetry determination in vertebrates. Annual Review of Cell and Developmental Biology, 17: 779-805
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Levin, M., (2001), Asymmetry of Body and Brain: Embryological and Twin Studies, in N. Smelser and P. Baltes (Eds.), International Encyclopedia of the Social and Behavioral Sciences, Elsevier, Oxford, UK, pp. 853-859
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Levin, Michael, and Mercola, M., (2000), Expression of Connexin30 in Xenopus embryos and its involvement in hatching gland function, Developmental Dynamics, 219(1): 96-101
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Levin, M., (1999), Matrix-based GA representations in a model of evolving animal communication, in L. Chambers (Ed.), The Practical Handbook of Genetic Algorithms: Complex Coding Systems, Vol. 3, ch.5, pp. 103-117, CRC Press: Boca Raton, FL

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Acknowledgements

We thank members of the Li laboratory, Gerardo Ramos-Mandujano, Khaled Alsayegh, Samhan Alsolami, and Deng Luo for helpful discussions and Jinna Xu, Manal Andijani, Marie Krenz Y. Sicat, and Xingxing Zhang for administrative support. We thank Chenyang Geng at the BIOPIC core facility at Peking University for technical assistance in PacBio and Illumina sequencing.

Peer review information

Yixin Yao was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Review history

The review history is available as Additional file 2.


Results and discussion

The UK Biobank [12] has genotyped

500,000 participants for an array that contains

847,441 single nucleotide polymorphisms (SNPs). After employing stringent quality control (QC) criteria (see ‘Methods’), we extracted 13,068 self-reported and genetically inferred White-British (Additional file 1: Figure S1) male–female pairs that shared the same household address but were less related to each other than first cousins once removed, that is, with a coefficient of relationship (r) below 0.0625 (Additional file 1: Figure S2). Of these male–female pairs,

92 % reported that they lived with their spouses, which is consistent with our hypothesis that these pairs were couples. We kept relatives (i.e. individuals with r > 0.0625) in our dataset providing they lived in different households (Additional file 1: Figure S3). Rare variants (those with minor allele frequency < 0.05) were removed from the analysis because they are known to distort the estimates of relatedness [13]. After removing possible outliers (see ‘Methods’), we modelled two phenotypes for each individual: the person’s own measured height and their partner’s measured height. The couples’ phenotypic correlation was 0.26 (95 % confidence interval [CI] 0.24, 0.27) (Additional file 1: Figure S4). We then adjusted for social and genetic population structure, correcting for the first 20 principal components (PCs) derived from an LD-pruned genomic relationship matrix (see ‘Methods’), age, gender, and Townsend deprivation index. The phenotypic correlation between couples remained high, at 0.23 (95 % CI 0.22, 0.24).

To estimate the contribution of genetic and environmental factors to variation in choice of mate height, we estimated relationships (Additional file 1: Figure S3) between the 26,136 individuals available [14] using the 318,852 autosomal SNPs that passed our QC protocol. We used a mixed linear model to estimate variance components [15]. To account for population and social structure, the analyses included the first 20 PCs, gender, age at recruitment, and Townsend deprivation index as fixed effects, and a genetic and an environmental (residual) random effect.

First, we used a univariate analysis to estimate to what degree attraction to a mate of similar height was explained by a person’s genotype. To that purpose, we treated the height of the partner as the person’s own trait (i.e. the choice of mate height). We estimated that the heritability of choice of mate height was 0.041 (standard error 0.014), which indicates that there is a significant genetic component for choice of mate height in humans. This is consistent with a model where mate selection for height is driven by one’s own height (see ‘Methods’).

We then asked whether the genetic determinants of choice of mate height were shared with the genetic determinants of a person’s own height. To answer this question, we treated the height of the partner as a phenotype of an individual and used a bivariate analysis to estimate the genetic and environmental correlation between the two traits. A genetic correlation equal to zero would imply that one’s own height and the choice of mate by height are not affected by the same genetic variants or that there is no directional pleiotropy, whilst a genetic correlation of one would imply that the two traits share the same genetic determinants, working in the same direction. Similarly, a non-zero environmental correlation would imply that the factors that affect the environmental and non-additive genetic deviations are at least partly shared between the two traits. The bivariate analysis (Table 1) performed using all available autosomal SNPs revealed that additive genetic factors explained 60 % and 3.6 % of the phenotypic variation for height and choice of mate height, respectively. These estimates are consistent with the estimates obtained in the univariate analysis. By analysing both traits jointly, we also demonstrated that 89 % of the genetic variation that affects height and choice of mate height is shared. Overall, this indicates that there is an innate preference for partners of similar height. To investigate this further we removed all related individuals (r > 0.0625) and performed two genome-wide association studies, one for height and one for choice of mate height. The correlation among estimated SNP effects was 0.25 (Additional file 1: Figure S5), which supports the hypothesis that height and choice of mate height share a substantial number of contributing loci and that alleles that increase height also, on average, increase attraction for increased height.

To strengthen the evidence for this hypothesis, we estimated, using genetic marker information and a univariate mixed-linear model (see ‘Methods’), the additive genetic effect (also known as breeding value in the quantitative genetics literature) for the height of individuals whose partner had not been genotyped, but for whom we had information on height. We reasoned that if the genetic correlation between height and choice of mate height was high, then we would be able to predict the height of one of the partners from the additive genetic effect (i.e. breeding value) for the height of the other partner. The correlation between the additive genetic effect for one’s own height and one’s partner’s height phenotype (i.e. the accuracy of prediction) was 0.13 (P = 7.55 × 10 −59 ), that is, 64 % of the maximum expected correlation the expected maximum correlation between the additive genetic effect for choice of mate height and phenotype for choice of mate height being 0.2, the square root of the heritability of choice of mate height.

The genetic consequences of assortative mating depend on whether the primary cause of assortment among partners is phenotypic (e.g. tall people are attracted to tall people), genetic (e.g. matings are within differentiated ethnic groups) or environmental (e.g. matings are with socially homologous groups). Primary genetic or environmental correlations arise when mating occurs within groups that are either genetically or environmentally differentiated. We argue that for human height the primary source of partner similarity is phenotypic, rather than caused by genetic or environmental structure within the population. We believe that the observed correlation in height between partners is not an artefact of mating within groups or populations that are genetically differentiated, because our analyses were adjusted for the first 20 PCs and because, for mixed-origin couples (those for which a partner is classified as White-British and the other as non White-British), we observed similar heritabilities to those of White-British couples for both height and mate’s height (Additional file 1: Table S1). In addition, we performed an analysis following a permutation approach that, whilst maintaining a height-associated mating structure, removed any genetic (Fig. 1) and environmental (Fig. 2) within-pair structure due to assortment based on alternative factors like geography, age or socio-economic status (see ‘Methods’). Specifically, we swapped the male partners amongst pairs of couples with similar phenotypes for both individuals. The results of this analysis (Additional file 1: Table S2) were practically identical to the results obtained for the original data, indicating that the genetic or environmental structure of the population is not driving the correlation between mates (Additional file 1: Table S3 and Fig. 2).

Correlation between distance of birthplaces and relatedness. The regression coefficient of relatedness on distance (m) was −7.9 × 10 −10 (P = 0.026) and −4.9 × 10 −10 (P = 0.134), for the real husband and swapped husband, respectively


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