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Are there any chromosomal/molecular difference in following human reproductive- traits?

Are there any chromosomal/molecular difference in following human reproductive- traits?


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Are there any chromosomal/molecular difference in 5 following human traits?Klinefelter-syndrome, Transgender, Bisexual, intersex, hermaphrodite. Are all 5 terms synonymous?


Well… Strictly speaking… They are not the same. Each of them has actually a different meaning and some of them such as hermaphrodite is used for lower animals while bisexual for plants animals etc. Klinefelter's syndrome is totally different and has got nothing to do with the normal bisexual conditions.

Yeah… sometimes we can use these words interchangeably.


Molecular Development

This page is a link to many different resources related to molecular development. I am 2.85 billion nucleotides of DNA, but so is a chimpanzee, and all this DNA encodes only about 20,000-25,000 protein-coding genes. In development, I am not that different from a mouse or a fly and many of the signals that regulate development are used time and time again.


We have come a long way from just observing development to now wanting to understand how the complex program of development is controlled. Using new research tools and some excellent animal models researchers have discovered common themes and mechanisms that tie all embryonic development together.


What is remarkable, given our biological diversity, is the strong evolutionary conservation of developmental mechanisms. This has been a boon in allowing the use of many (easier) model systems such as the genetist's tool the fruitfly, and the worm, frog, chicken, zebrafish and mouse (see Animal Development page).

Gene Regulatory Network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. In the embryo, these are the molecular mechanisms/pathways used in the development and differentiation of specific cell types and tissues.

A continuing theme also seems to be the reuse of signals at different times and places within the embryo, for diiferent jobs. This has given rise to the concept of "switches" which by themselves may contain no "information" but to activate other genes or switches. Finally, you can imagine that of our 20,000-25,000 protein-coding genes, a large number of these may only be expressed during development or if reused, have a completely different role in the mature animal. The new field of epigenetics has also begun to florish with some recent important findings.


Molecular mechanisms of development is an exciting area and requires a variety of different skills. This page introduces only a few examples and should give you a feel for the topic. Note that each section of system notes has a page covering molecular development in that system. I have included information about the basic building blocks of proteins (amino acids).

Molecular Links: molecular | genetics | epigenetics | mitosis | meiosis | X Inactivation | Signaling | Factors | Mouse Knockout | microRNA | Mechanisms | Developmental Enhancers | Protein | Genetic Abnormal | Category:Molecular
Historic Embryology  
By Morgan, T. H. (1925). Evolution and genetics. Princeton: Princeton University Press.
Evolution and Genetics: 1 Different Kinds of Evolution | 2 Four Great Historical Speculations | 3 Evidence for Organic Evolution | 4 Materials of Evolution | 5 Mendel's Two Laws of Heredity | 6 Chromosomes and Mendel’s Two Laws | 7 Linkage Groups and the Chromosomes | 8 Sex-Linked Inheritance | 9 Crossing-over | 10 Natural Selection and Evolution | 11 Origin of Species by Natural Selection | 12 Non-Inheritance of Acquired Characters | 13 Human Inheritance | Figures


What are Autosomes

Non-sex chromosomes which determine the trait of an organism is identified as autosomes. They are also known as somatic chromosomes since they determine the somatic characters of an individual. A genome mainly consists of autosomes. For example, human body contains 46 chromosomes within its genome and 44 chromosomes of them are autosomes. Autosomes exist as homologous pairs and 22 autosome pairs can be identified in the human genome.

Both autosomal chromosomes contain the same genes, which are arranged in the same order. But an autosomal chromosome pair differs from other autosomal chromosome pairs within the same genome. These pairs are labeled from 1 to 22, according to the base pair sizes contained in each chromosome.

Autosomes also participate in sex determination. SOX9 gene is an autosomal gene on chromosome 17. It activates the function of TDF factor which is encoded by Y chromosome. TDF factor is critical in male sex determination. Hence, a mutation of SOX9 causes the development of Y chromosome, resulting in a female.

Autosomal genetic disorders occur due to either the non-disjunction in parent chromosomes (Aneuploidy) during gametogenesis or the Mendelian inheritance of deleterious alleles. An example for aneuploidy is Dawn’s Syndrome, which possesses three copies of chromosome 21 per cell. Disorders with Mendelian inheritance can either be dominant or recessive (Ex: Sickle cell anemia).

Figure 1: Human male karyotype


Molecular Genetics of Sex Determination

In this era of accelerated discovery and prolific output, Molecular Genetics of Sex Determination keeps readers abreast of this fields fast-moving biology. Its chapters were completed by experts in eacharea only months before publication. The text is organized into two parts. First, it reviews the basic biology of sex determination and summarizes ground-breaking work in mouse, marsupial, and Drosophila systems. Second, it covers current human genetics, clinical studies, and the syndromes of abnormal sex differentiation.

With chapters by preeminent reproductive biologists, this is a capital work. Ohno's law is described by Ohno the Lyon hypothesis, by Lyon Sinclair tells how he cloned the testis-determining gene and so on. Molecular Genetics of Sex Determination is authoritative, comprehensive, and current. It is prime reading for geneticists, developmental biologists, graduate students in these and related fields, clinical researchers, physicians, and medical students.

In this era of accelerated discovery and prolific output, Molecular Genetics of Sex Determination keeps readers abreast of this fields fast-moving biology. Its chapters were completed by experts in eacharea only months before publication. The text is organized into two parts. First, it reviews the basic biology of sex determination and summarizes ground-breaking work in mouse, marsupial, and Drosophila systems. Second, it covers current human genetics, clinical studies, and the syndromes of abnormal sex differentiation.

With chapters by preeminent reproductive biologists, this is a capital work. Ohno's law is described by Ohno the Lyon hypothesis, by Lyon Sinclair tells how he cloned the testis-determining gene and so on. Molecular Genetics of Sex Determination is authoritative, comprehensive, and current. It is prime reading for geneticists, developmental biologists, graduate students in these and related fields, clinical researchers, physicians, and medical students.


2 MATERIALS AND METHODS

2.1 Sampling and DNA extraction

Peripheral whole blood (male international studbook #20612) and liver tissue (female international studbook #42652) were collected from the captive herd at the Smithsonian Conservation Biology Institute in Front Royal, Virginia, USA. The male oryx represents approximately 15% of founders to the global population documented in the international studbook. Whole blood was collected into EDTA blood tubes (BD Vacutainer Blood Tube, Becton, Dickinson and Company) and stored frozen until analysis. Total genomic DNA was isolated and used to generate the de novo reference genome assembly (see below for details). Additional blood and tissue samples were obtained for whole genome resequencing from six individuals representing three of the main captive populations: the EEP (n = 2, international studbook numbers #35552 and #34412), the SSP (n = 2, international studbook numbers #33556 and #36948) and the EAD (n = 2, for further details see Table S1). EEP blood samples were collected by qualified veterinarians during routine health procedures and protocols were approved by Marwell Wildlife Ethics Committee. Total genomic DNA was extracted between one and five times using either the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Cat. No. 69504) or the QuickGene DNA Whole Blood or Tissue Kit (Kurabo Industries). Elutions were pooled and concentrated in an Eppendorf Concentrator Plus at 45°C and 250 x g until roughly 50 µl remained.

2.2 10X Genomics sequencing and assembly

Two technologies were employed to sequence and assemble the SHO reference genome: 10X Genomics linked-read sequencing and chromosome conformation capture (Hi-C). For the 10X assembly, high molecular weight genomic DNA was isolated from

2 ml of whole blood from individual #20612 using Nanobind magnetic discs (Circulomics, Inc.). Genomic DNA concentration and purity were assessed with a Qubit 2.0 Fluorometer (ThermoFisher Scientific) and NanoDrop 2000 spectrophotometer (ThermoFisher Scientific). Capillary electrophoresis was carried out using a Fragment Analyzer (Agilent Technologies, CA, USA) to ensure that the isolated DNA had a minimum molecule length of 40 kb. Genomic DNA was diluted to

1.2 ng/µl and libraries were prepared using Chromium Genome Reagents Kits Version 2 and the 10X Genomics Chromium Controller instrument fitted with a microfluidic Genome Chip (10X Genomics). DNA molecules were captured in Gel Bead-In-Emulsions (GEMs) and nick-translated using bead-specific unique molecular identifiers (UMIs Chromium Genome Reagents Kit Version 2 User Guide). Size and concentration were determined using an Agilent 2100 Bioanalyzer DNA 1000 chip (Agilent Technologies). Libraries were then sequenced on an Illumina NovaSeq 6000 System following the manufacturer's protocols (Illumina, CA, USA) to produce >60X read depth using paired-end 150 bp reads. The reads were assembled into phased pseudohaplotypes using Supernova Version 2.0 (10X Genomics). This assembly will hereafter be referred to as the 10X assembly.

2.3 Hi-C sequencing and scaffolding

Using liver tissue from individual #42652, an in situ Hi-C library was prepared as previously described (Rao et al., 2014 ). The Hi-C library was sequenced on a HiSeq X Platform (Illumina) to a coverage of 60X. The Hi-C data were aligned to the 10X Genomics linked-read assembly using Juicer (Durand et al., 2016 ). Hi-C genome assembly was then performed using the 3D-DNA pipeline (Dudchenko et al., 2017 ) and the output was reviewed using juicebox assembly tools (Dudchenko et al., 2018 ). No bases were changed, added or removed during Hi-C scaffolding and therefore the genome assembly represents a single individual: #201612. Where alternative haplotypes were present due to allelic variation in the 10X assembly, hereafter referred to as under-collapsed heterozygosity, one variant was chosen at random and incorporated into the 29 chromosome-length scaffolds. Alternative haplotypes are reported as unanchored sequences so that the primary scaffolds were free from duplication. This assembly will hereafter be referred to as the 10X + HiC assembly.

2.4 Genome annotation and completeness

To identify and annotate interspersed repeat regions we used repeatmasker v4.0.7 to screen the 10X assembly against both the Dfam_consensus (release 20170127: Wheeler et al., 2013 ) and RepBase Update (release 20170127: Bao, Kojima, & Kohany, 2015 ) repeat databases. Sequence comparisons were performed using rmblastn v2.6.0 + with the -species option set to mammal. We next predicted protein-coding genes with augustus version 3.3.2 (Stanke et al., 2006 ) using the gene model trained in humans. Prediction of untranslated regions of the genes was disabled. Functional annotation of the predicted genes was then performed using eggnog-mapper v2.0 (Huerta-Cepas et al., 2017 ) against the eggNOG 5.0 orthology database (Huerta-Cepas et al., 2019 ). The alignment algorithm diamond was specified as the search tool (Buchfink, Xie, & Huson, 2015 ). A final set of protein-coding genes was obtained by filtering the genes predicted by augustus for those with gene names assigned by eggnog-mapper . We then transferred the repeat and gene annotations from the 10X assembly to the 10X + HiC assembly using custom scripts. Genome completeness of both the 10X and 10X + Hi-C assemblies was assessed using busco v2 with 4,104 genes from the Mammalia odb9 database (Simão, Waterhouse, Ioannidis, Kriventseva, & Zdobnov, 2015 ) and the gVolante web interface (Nishimura, Hara, & Kuraku, 2017 ).

2.5 Genome synteny

We aligned the SHO chromosomes from the 10X + HiC assembly to the cattle genome (Bos taurus assembly version 3.1.1, GenBank accession number GCA_000003055.5: Zimin et al., 2009 ) using last v746 (Kiełbasa, Wan, Sato, Horton, & Frith, 2011 ). The cattle assembly was first prepared for alignment using the command lastdb. Next, lastal and last-split commands in combination with parallel-fastq were used to align the SHO chromosomes to the cattle assembly. Coordinates for all alignments were extracted from the resulting multiple alignment format file and visualised using the R package rcircos v1.2.0 (Zhang, Meltzer, & Davis, 2013 ) and the JavaScript library D3.js.

2.6 Whole-genome resequencing and alignment

Library construction was carried out for whole genome resequencing of the six focal individuals using the Illumina TruSeq Nano High Throughout library preparation kit (Illumina). Paired-end sequencing was performed on an Illumina HiSeq X Ten platform at a depth of coverage of 15X. Sequencing reads were mapped to the SHO 10X + HiC chromosomes using bwa mem v0.7.17 (Li, 2013 ) with the default parameters. Any unmapped reads were removed from the alignment files using samtools v1.9 (Li, 2011 ). We then used picard tools to sort each bam file, add read groups and mark and remove duplicate reads. This resulted in a set of six filtered alignments, one for each of the resequenced individuals.

2.7 SNP calling and filtering

HaplotypeCaller in gatk v3.8 (Van der Auwera et al., 2013 ) was first used to call variants separately for each filtered bam file. GenomicVCF files for each individual were then used as input to GenotypeGVCFs for joint genotyping. The resulting SNP data set was filtered to include only biallelic SNPs using bcftools v1.9 (Li, 2011 ). We then applied a set of filters to obtain a high-quality data set of variants using vcftools v0.1.13 (Danecek et al., 2011 ). First, loci with Phred-scaled quality scores of <50 and genotypes with a depth of coverage <5 or >38 (twice the mean sequence read depth) were removed. Second, loci with any missing data were discarded. Finally, we removed loci with a minor allele frequency of less than 0.16 to ensure the minor allele was observed at least twice.

2.8 Mitochondrial genome assembly

Sequencing reads for the six resequenced individuals were mapped using bwa mem v0.7.17 (Li, 2013 ) to a published mitochondrial reference genome of an SHO originating from the Paris Zoological Park (NCBI accession number: JN632677, Hassanin et al., 2012 ). Alignment files were filtered to contain only reads that mapped with their proper pair. Variants were called using samtools mpileup and bcftools (Li, 2011 ) call commands and filtered to include only those with Phred quality scores over 200 using vcftools (Danecek et al., 2011 ). The resulting VCF file was manually checked and sites where the called allele was supported by fewer reads than the alternative allele were corrected. Consensus sequences for each individual were extracted using the bcftools consensus command. We next used geneious prime v2019.2.1 (https://www.geneious.com) to annotate the mitochondrial consensus sequences and extract the cytochrome b, 16S and control region from each individual. Sequence similarity and haplotype frequencies were calculated using the R package pegas (Paradis, 2010 ). To place the mitochondrial data into a broader geographic context, the six control region sequences were aligned to 43 previously described haplotypes (NCBI accession numbers DQ159406–DQ159445 and MN689133–MN689138, Iyengar et al., 2007 Ogden et al., 2020 ) using Geneious Prime . A median-joining haplotype network was generated using popart v1.7 (Leigh & Bryant, 2015 ).

2.9 Genetic diversity

We assessed genetic diversity of SHO using two genome-wide measures. First, we used vcftools to estimate nucleotide diversity (π) across all six resequenced individuals based on high-quality variants called by gatk . Second, we estimated individual genome-wide heterozygosity as the proportion of polymorphic sites over the total number of sites using the site-frequency spectrum of each individual sample. For this, filtered bam files were used as input to estimate the observed folded site-frequency spectrum (SFS) using the -doSaf and -realSFS functions in the program angsd (Korneliussen, Albrechtsen, & Nielsen, 2014 ). We excluded the X chromosome and skipped any bases and reads with quality scores below 20. Genome-wide heterozygosity was then calculated as the second value of the SFS (number of heterozygous genotypes) over the total number of sites, for each chromosome separately. To compare the level of diversity in SHO with other species, we visualised genome-wide heterozygosity values for a number of mammalian species collected from the literature (Table S2) against census population size and International Union for Conservation of Nature (IUCN) status. Finally, assuming a per site/per generation mutation rate (μ) of 1.1 × 10 –08 , we used our estimate of nucleotide diversity (π) as a proxy for θ to infer long-term Ne, given that θ = 4Ne μ.

2.10 Demographic history

To reconstruct the historical demography of the SHO, we used PSMC. (Li & Durbin, 2011 ). This method uses the presence of heterozygous sites across a diploid genome to infer the time to the most recent common ancestor between two alleles. The inverse distribution of coalescence events is referred to as the instantaneous inverse coalescence rate (IICR) and for an unstructured and panmictic population, can be interpreted as the trajectory of Ne over time (Chikhi et al., 2018 ). To estimate the PSMC trajectory, we first generated consensus sequences for all autosomes in each of the filtered bam files from the six resequenced individuals using samtools mpileup, bcftools call and vcfutils.pl vcf2fq. Sites with a root-mean-squared mapping quality less than 30, and a depth of coverage below four or above 40, were masked as missing data. PSMC inference was then carried out using the default input parameters to generate a distribution of IICR through time for each individual. To generate a measure of uncertainty around our PSMC estimates, we ran 100 bootstrap replicates per individual. For this, consensus sequences were first split into 47 nonoverlapping segments using the splitfa function in PSMC. We then randomly sampled from these, 100 times with replacement, and re-ran PSMC on each of the bootstrapped data sets.

To determine the extent to which the PSMC trajectory could vary, we scaled the coalescence rates and time intervals to population size and years based on three categories of neutral mutation rate and generation time. Our middle scaling values corresponded to a mutation rate of 1.1 × 10 –08 and a generation time of 6.2 years. These were based on the per site/per generation mutation rate recently estimated for gemsbok (Oryx gazella, Chen et al., 2019 ) and the generation time reported in the International Studbook for the SHO (Gilbert, 2019 ) and are therefore considered the most appropriate estimates. Low scaling values corresponded to a mutation rate of 0.8 × 10 –08 and a generation time of three, and high scaling values corresponded to a mutation rate of 1.3 × 10 –08 and a generation time of 10. Finally, to test the reliability of our IICR trajectories, we simulated sequence data under the inferred PSMC models and compared estimates of genome-wide heterozygosity with empirical values (Beichman, Phung, & Lohmueller, 2017 ). To do this, we used the program macs (Chen, Marjoram, & Wall, 2009 ) to simulate 1,000 × 25 Mb sequence blocks under the full demographic model of each individual, assuming a recombination rate of 1.0 × 10 –08 base pair per generation and a mutation rate of 1.1 × 10 –08 . Simulated heterozygosity was then calculated as the number of segregating sites over the total number of sites for each 25 Mb sequence. Empirical heterozygosity was calculated for each individual as the number of variable sites over the total number of sites in 25 Mb nonoverlapping sliding windows along the genome. This was carried out using the filtered SNP data set and the R package windowscanr .


Culex species

C. pipiens is common in temperate regions and is subdivided into two subspecies, Culex pipiens pipiens (Europe and North and South Africa) and Culex pipiens pallens (East Asia) (Dumas et al., 2016 ). In addition, two recognized forms, 'pipiens' and 'molestus', also appeared in C. pipiens pipiens in the Northern Hemisphere (Dumas et al., 2016 ). A second species, C. quinquefasciatus, is found across the tropics and the lower latitudes of temperate regions (Dumas et al., 2016 ). Rasgon et al. reported that a cryptic species is present within the C. pipiens complex in South Africa (Rasgon and Scott, 2003 Rasgon et al. 2006 Dumas et al., 2016 ). The authors observed the presence of Wolbachia-uninfected C. pipiens specimens in several breeding sites in Europe and North Africa. Using a multilocus typing scheme, they further confirmed that these uninfected specimens unambiguously belonged to the C. pipiens complex. On the basis of ace-2 DNA sequences, they were included within the C. pipiens pipiens clade (Dumas et al., 2016 ). Remarkably, novel mitochondrial(mt)DNA haplotypes were found in samples from Europe and North Africa that were related, but different to the mtDNA haplotypes found in Wolbachia-infected C. pipiens complex members (Dumas et al., 2016 ). This genetic pattern demonstrated that uninfected specimens are not a result of imperfect maternal transmission from Wolbachia-infected specimens, but rather belong to a specific lineage (Dumas et al., 2016 ). Compelling evidence suggested that specimens of the cryptic species do not readily hybridize with Wolbachia-infected C. pipiens and C. quinquefasciatus specimens. However, Dumas et al. suggest that a Wolbachia-uninfected population of C. pipiens was present in South Africa and was recently proposed as a cryptic species (Dumas et al., 2016 ).

Key vectors of Japanese encephalitis are Cx. tritaeniorhynchu, Cx. vishnui, Cx. pseudovishnui, Cx. gelidus, Cx. fuscocephala, Cx. quinquefasciatus, Culex pipiens pallens (Coquillett), Culex bitaeniorhynchus Giles and Culex annulirostris Skuse. Cx. tritaeniorhynchus and Cx. vishnui were considered under the Cx. vishnui subgroup. Karthikaa et al. using DNA barcoding analysis of Culex fuscocephala, Culex gelidus, Culex tritaeniorhynchus, Culex pseudovishnui and Culex vishnui, showed that C. tritaeniorhynchus exhibited the highest variation in all the ranges. C. tritaeniorhynchus exhibited high numbers of polymorphic sites and mutations, suggesting high nucleotide diversity. By contrast, the sister species C. vishnui and C. pseudovishnui showed a moderate rise. The results suggested that one or more new cryptic subspecies may exist in Culex mosquitoes (Karthika et al., 2018 ).


18.2 | Mendel’s Principles of Inheritance

By the end of this section, you will be able to:

  • Describe the three principles of inheritance.
  • Explain the relationship between phenotype and genotype.
  • Develop a Punnett square to calculate the expected proportions of genotypes and phenotypes in a monohybrid cross.
  • Explain the purpose and methods of a test cross.
  • Draw and interpret a pedigree.

Mendel generalized the results of his pea-plant experiments into three principles that describe the basis of inheritance in diploid organisms. They are: the principle of segregation, the principle of dominance, and the principle of independent assortment. Together, these principles summarize the basics of classical, or Mendelian, genetics.


References

Mitchell-Olds T, Schmitt J: Genetic mechanisms and evolutionary significance of natural variation in Arabidopsis. Nature. 2006, 441: 947-952. 10.1038/nature04878.

Alonso-Blanco C, Koornneef M: Naturally occurring variation in Arabidopsis: An underexploited resource for plant genetics. Trends Plant Sci. 2000, 5: 22-29. 10.1016/S1360-1385(99)01510-1.

Risch N, Merikangas K: The future of genetic studies of complex human diseases. Science. 1996, 273: 1516-1517. 10.1126/science.273.5281.1516.

Steinmetz LM, Mindrinos M, Oefner PJ: Combining genome sequences and new technologies for dissecting the genetics of complex phenotypes. Trends Plant Sci. 2000, 5: 397-401. 10.1016/S1360-1385(00)01724-6.

Cavalli-Sforza LL, Feldman MW: The application of molecular genetic approaches to the study of human evolution. Nat Genet. 2003, 33 (Suppl): 266-275.

Garte S: Locus-specific genetic diversity between human populations: An analysis of the literature. Am J Hum Biol. 2003, 15: 814-823. 10.1002/ajhb.10215.

Rosenberg NA, Pritchard JK, Weber JL, Cann HM, et al: Genetic structure of human populations. Science. 2002, 298: 2381-2385. 10.1126/science.1078311.

Bamshad M, Wooding S, Salisbury BA, Stephens JC: Deconstructing the relationship between genetics and race. Nat Rev Genet. 2004, 5: 598-609.

Wright S: The genetical structure of populations. Ann Eugen. 1951, 15: 323-354.

Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95: 14863-14868. 10.1073/pnas.95.25.14863.

Paabo S: The mosaic that is our genome. Nature. 2003, 421: 409-412. 10.1038/nature01400.

Charlesworth D, Charlesworth B, Morgan MT: The pattern of neutral molecular variation under the background selection model. Genetics. 1995, 141: 1619-1632.

Caron H, van Schaik B, van der Mee M, Baas F, et al: The human transcriptome map: Clustering of highly expressed genes in chromosomal domains. Science. 2001, 291: 1289-1292. 10.1126/science.1056794.

Crawley JJ, Furge KA: Identification of frequent cytogenetic aberrations in hepatocellular carcinoma using gene-expression microarray data. Genome Biol. 2002, 3: RESEARCH0075-

Husing J, Zeschnigk M, Boes T, Jockel KH: Combining DNA expression with positional information to detect functional silencing of chromosomal regions. Bioinformatics. 2003, 19: 2335-2342. 10.1093/bioinformatics/btg314.

Kano M, Nishimura K, Ishikawa S, Tsutsumi S, et al: Expression imbalance map: A new visualization method for detection of mRNA expression imbalance regions. Physiol Genomics. 2003, 13: 31-46.

Pollack JR, Sorlie T, Perou CM, Rees CA, et al: Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA. 2002, 99: 12963-12968. 10.1073/pnas.162471999.

Levin AM, Ghosh D, Cho KR, Kardia SL: A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors. Bioinformatics. 2005, 21: 2867-2874. 10.1093/bioinformatics/bti417.

Smith JM, Haigh J: The hitch-hiking effect of a favourable gene. Genet Res. 1974, 23: 23-35. 10.1017/S0016672300014634.

Grimwood J, Gordon LA, Olsen A, Terry A, et al: The DNA sequence and biology of human chromosome 19. Nature. 2004, 428: 529-535. 10.1038/nature02399.

Plagnol V, Wall JD: Possible ancestral structure in human populations. PLoS Genet. 2006, 2: e105-10.1371/journal.pgen.0020105.

Hey J, Nielsen R: Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics. 2004, 167: 747-760. 10.1534/genetics.103.024182.

HapMap: A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007, 449: 851-861. 10.1038/nature06258.

Hoehe MR, Timmermann B, Lehrach H: Human inter-individual DNA sequence variation in candidate genes, drug targets, the importance of haplotypes and pharmacogenomics. Curr Pharm Biotechnol. 2003, 4: 351-378. 10.2174/1389201033377300.

HapMap: A haplotype map of the human genome. Nature. 2005, 437: 1299-1320. 10.1038/nature04226.

Patterson N, Price AL, Reich D: Population structure and eigenanalysis. PLoS Genet. 2006, 2: e190-10.1371/journal.pgen.0020190.

Rohlf FJ: NTSYS-pc: Numerical taxonomy and multivariate analysis system, ver.2.11f, Exeter Software, New York, NY. 2002

Mantel N: The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27: 209-220.

Rechner A: Methods of Multivariate Analysis. 2002, John Wiley Sons New York NY, 270-321.

Johnson RA, Wichern DW: Applied Multivariate statistical Analysis (4th edn). 1998, Prentice Hall Upper Saddle River NJ

SAS: SAS Version 9.1. SAS Institute, Cary, NC. 2004

Hair JF, Anderson RE, Tatham RL, Black WC: Multivariate Data Analysis (5th edn). 1998, Macmillan Publishing Company New York NY

Ganter B, Giroux CN: Emerging applications of network and pathway analysis in drug discovery and development. Curr Opin Drug Discov Devel. 2008, 11: 86-94.

Keinan A, Mullikin JC, Patterson N, Reich D: Accelerated genetic drift on chromosome X during the human dispersal out of Africa. Nat Genet. 2009, 41: 66-70. 10.1038/ng.303.

Baer CF: Among-locus variation in Fst: Fish, allozymes and the Lewontin-Krakauer test revisited. Genetics. 1999, 152: 653-659.

Hammer MF, Mendez FL, Cox MP, Woerner AE, et al: Sex-biased evolutionary forces shape genomic patterns of human diversity. PLoS Genet. 2008, 4: e1000202-10.1371/journal.pgen.1000202.

Greenwood TA, Rana BK, Schork NJ: Human haplotype block sizes are negatively correlated with recombination rates. Genome Res. 2004, 14: 1358-1361. 10.1101/gr.1540404.

Wright S: Evolution and the Genetics of Populations, Vol. 4: Variability Within and Among Natural Populations. 1978, University of Chicago Press Chicago IL, 1-573.

Weir BS, Hill WG: Estimating F-statistics. Annu Rev Genet. 2002, 36: 721-750. 10.1146/annurev.genet.36.050802.093940.

Nei M: Molecular Population Genetics. 1987, Columbia University Press New York NY

Cavalli-Sforza L, Menozzi P, Piazza A: The History and Geography of Human Genes. 1994, Princeton University Press Princeton NJ

Deka R, Shriver MD, Yu LM, Ferrell RE, et al: Intra-and inter-population diversity at short tandem repeat loci in diverse populations of the world. Electrophoresis. 1995, 16: 1659-1664. 10.1002/elps.11501601275.

Hey J, Won YJ, Sivasundar A, Nielsen R, et al: Using nuclear haplotypes with microsatellites to study gene flow between recently separated cichlid species. Mol Ecol. 2004, 13: 909-919. 10.1046/j.1365-294X.2003.02031.x.

Barbujani G: Human races: Classifying people vs understanding diversity. Curr Genomics. 2005, 6: 215-226. 10.2174/1389202054395973.

Witherspoon DJ, Wooding S, Rogers AR, Marchani EE, et al: Genetic similarities within and between human populations. Genetics. 2007, 176: 351-359. 10.1534/genetics.106.067355.

Kiambi D, Newbury HJ, Ford-Lloyd BV, Dawson I: Contrasting genetic diversity among Oryza longistaminata (A. Chev et Roehr) populations from different geographic origins using AFLP. Afr J Biotechnol. 2005, 4: 308-317.

Stevens VM, Pavoine S, Baguette M: Variation within and between closely related species uncovers high intra-specific variability in dispersal. PLoS One. 2010, 5: e11123-10.1371/journal.pone.0011123.

Lewontin RC: The apportionment of human diversity. Evol Biol. 1972, 6: 381-398.

Baye TM: Genetic Diversity Analyses in Populations of Vernonia galamensis. 2004, Cuvillier Verlag Göttingen Germany, 170-

Jorde LB, Wooding SP: Genetic variation, classification and 'race'. Nat Genet. 2004, 36: S28-S33. 10.1038/ng1435.

Kachigan S: Multivariate Statistical Analysis. 1991, Radius Press New York NY

Zar J: Biostatistical Analysis. 1999, Prentice Hall Upper Saddle River NJ, 4

Qin H, Morris N, Kang SJ, Li M, et al: Interrogating local population structure for fine mapping in genome-wide association studies. Bioinformatics. 2010, 26: 2961-2968. 10.1093/bioinformatics/btq560.

Thomas JH: Thinking about genetic redundancy. Trends Genet. 1993, 9: 395-399. 10.1016/0168-9525(93)90140-D.

Becquet C, Patterson N, Stone AC, Przeworski M, et al: Genetic structure of chimpanzee populations. PLoS Genet. 2007, 3: e66-10.1371/journal.pgen.0030066.

Casa AM, Pressoir G, Brown PJ, Mitchell SE, et al: Community resources and strategies for association mapping in sorghum. Crop Sci. 2008, 48: 30-40. 10.2135/cropsci2007.02.0080.

Gardner M, Williamson S, Casals F, Bosch E, et al: Extreme individual marker F(ST)values do not imply population-specific selection in humans: The NRG1 example. Hum Genet. 2007, 121: 759-762. 10.1007/s00439-007-0364-9.

Monreal AW, Zonana J, Ferguson B: Identification of a new splice form of the EDA1 gene permits detection of nearly all X-linked hypohidrotic ectodermal dysplasia mutations. Am J Hum Genet. 1998, 63: 380-389. 10.1086/301984.

Sabeti PC, Varilly P, Fry B, Lohmueller J, et al: Genome-wide detection and characterization of positive selection in human populations. Nature. 2007, 449: 913-918. 10.1038/nature06250.

Yan M, Wang LC, Hymowitz SG, Schilbach S, et al: Two-amino acid molecular switch in an epithelial morphogen that regulates binding to two distinct receptors. Science. 2000, 290: 523-527. 10.1126/science.290.5491.523.

Nan H, Kraft P, Hunter DJ, Han J: Genetic variants in pigmentation genes, pigmentary phenotypes, and risk of skin cancer in Caucasians. Int J Cancer. 2009, 125: 909-917. 10.1002/ijc.24327.

Clark AG, Hubisz MJ, Bustamante CD, Williamson SH, et al: Ascertainment bias in studies of human genome-wide polymorphism. Genome Res. 2005, 15: 1496-1502. 10.1101/gr.4107905.

Reich DE, Lander ES: On the allelic spectrum of human disease. Trends Genet. 2001, 17: 502-510. 10.1016/S0168-9525(01)02410-6.

Lowry DB: Landscape evolutionary genomics. Biol Lett. 2010, 6: 502-504. 10.1098/rsbl.2009.0969.

Wall JD, Cox MP, Mendez FL, Woerner A, et al: A novel DNA sequence database for analyzing human demographic history. Genome Res. 2008, 18: 1354-1361. 10.1101/gr.075630.107.

Miller RD, Phillips MS, Jo I, Donaldson MA, et al: High-density single-nucleotide polymorphism maps of the human genome. Genomics. 2005, 86: 117-126. 10.1016/j.ygeno.2005.04.012.

Myles S, Hradetzky E, Engelken J, Lao O, et al: Identification of a candidate genetic variant for the high prevalence of type II diabetes in Polynesians. Eur J Hum Genet. 2007, 15: 584-589. 10.1038/sj.ejhg.5201793.

Ayodo G, Price AL, Keinan A, Ajwang A, et al: Combining evidence of natural selection with association analysis increases power to detect malaria-resistance variants. Am J Hum Genet. 2007, 81: 234-242. 10.1086/519221.

Myles S, Davison D, Barrett J, Stoneking M, et al: Worldwide population differentiation at disease-associated SNPs. BMC Med Genomics. 2008, 1: 22-10.1186/1755-8794-1-22.

Redden DT, Divers J, Vaughan LK, Tiwari HK, et al: Regional admixture mapping and structured association testing: Conceptual unification and an extensible general linear model. PLoS Genet. 2006, 2: e137-10.1371/journal.pgen.0020137.

Campbell CD, Ogburn EL, Lunetta KL, Lyon HN, et al: Demonstrating stratification in a European American population. Nat Genet. 2005, 37: 868-872. 10.1038/ng1607.

Linhart Y, Grant M: Evolutionary significance of local genetic differentiation in plants. Annu Rev Ecol Syst. 1996, 27: 237-277. 10.1146/annurev.ecolsys.27.1.237.

Erksson L, Johansson E, Kettaneh-Wold N: Multi- and Megavariate Analysis. 2001, Umetrics Umea Sweden

McKeigue PM: Mapping genes that underlie ethnic differences in disease risk: Methods for detecting linkage in admixed populations, by conditioning on parental admixture. Am J Hum Genet. 1998, 63: 241-251. 10.1086/301908.

Raponi M, Belly RT, Karp JE, Lancet JE, et al: Microarray analysis reveals genetic pathways modulated by tipifarnib in acute myeloid leukemia. BMC Cancer. 2004, 4: 56-10.1186/1471-2407-4-56.

Shriner D, Baye TM, Padilla MA, Zhang S, et al: Commonality of functional annotation: A method for prioritization of candidate genes from genome-wide linkage studies. Nucleic Acids Res. 2008, 36: e26-


Change history

Centers for Disease Control and Prevention. CDC health disparities and inequalities report — United States, 2013. MMWR Suppl. 62, 1–189 (2013).

Clayton, J. A. Applying the new SABV (sex as a biological variable) policy to research and clinical care. Physiol. Behav. 187, 2–5 (2017). This seminal paper outlines the US National Institutes of Health policies for considering SABV in biomedical research, presents general guidelines on how to adhere to the policy and provides examples of how SABV impacts clinical care.

Gao, F. et al. XWAS: a software toolset for genetic data analysis and association studies of the X chromosome. J. Hered. 106, 666–671 (2015).

Wise, A. L., Gyi, L. & Manolio, T. A. eXclusion: toward integrating the X chromosome in genome-wide association analyses. Am. J. Hum. Genet. 92, 643–647 (2013). This commentary explores the reasons underlying the relative lack of reported genetic associations on human ChrX, highlighting technical challenges, many of which are still relevant today.

Heidari, S., Babor, T. F., Castro, P. D., Tort, S. & Curno, M. Sex and gender equity in research: rationale for the SAGER guidelines and recommended use. Epidemiol. Serv. Saude 26, 665–675 (2017). Developed by a panel of experts representing nine countries, this paper outlines comprehensive guidelines for reporting of sex and gender information in study design, data analyses, results and interpretation of findings.

[No authors listed.] Accounting for sex in the genome. Nat. Med 23, 1243 (2017).

König, I. R., Loley, C., Erdmann, J. & Ziegler, A. How to include chromosome x in your genome-wide association study. Genet. Epidemiol. 38, 97–103 (2014).

Wang, J., Talluri, R. & Shete, S. Selection of X-chromosome Inactivation Model. Cancer Inform. 16, 1176935117747272 (2017).

Webster, T. H. et al. Identifying, understanding, and correcting technical biases on the sex chromosomes in next-generation sequencing data Preprint at bioRxiv. https://doi.org/10.1101/346940 (2018).

Arnold, A. P., Chen, X. & Itoh, Y. What a difference an X or Y makes: sex chromosomes, gene dose, and epigenetics in sexual differentiation. Handb. Exp. Pharmacol. 214, 67–88 (2012).

Arnold, A. P. Y chromosome’s roles in sex differences in disease. Proc. Natl Acad. Sci. USA 114, 3787–3789 (2017).

Zore, T., Palafox, M. & Reue, K. Sex differences in obesity, lipid metabolism, and inflammation — A role for the sex chromosomes? Mol. Metab. 15, 35–44 (2018).

Carter, C. O. & Evans, K. A. Inheritance of congenital pyloric stenosis. J. Med. Genet. 6, 233–254 (1969). This classic paper defines the sex-dependent liability threshold model based on evidence from patients with pyloric stenosis.

Carter, C. O. The inheritance of congenital pyloric stenosis. Br. Med. Bull 17, 251–254 (1961).

Robinson, E. B., Lichtenstein, P., Anckarsäter, H., Happé, F. & Ronald, A. Examining and interpreting the female protective effect against autistic behavior. Proc. Natl Acad. Sci. USA 110, 5258–5262 (2013).

Rhee, S. H. & Waldman, I. D. Etiology of sex differences in the prevalence of ADHD: An examination of inattention and hyperactivity — impulsivity. Am. J. Med. Genet. B Neuropsychiatr. Genet. 127, 60–64 (2004).

Taylor, M. J. et al. Is there a female protective effect against attention-deficit/hyperactivity disorder? evidence from two representative twin samples. J. Am. Acad. Child Adolesc. Psychiatry 55, 504–512 (2016).

Kruse, L. M., Buchan, J. G., Gurnett, C. A. & Dobbs, M. B. Polygenic threshold model with sex dimorphism in adolescent idiopathic scoliosis: the Carter effect. J. Bone Joint Surg. Am. 94, 1485–1491 (2012).

Kantarci, O. H. et al. Men transmit MS more often to their children vs women: the Carter effect. Neurology 67, 305–310 (2006).

Ge, T., Chen, C.-Y., Neale, B. M., Sabuncu, M. R. & Smoller, J. W. Phenome-wide heritability analysis of the UK Biobank. PLOS Genet. 13, e1006711 (2017). This is one of the first studies analysing a large population-based cohort to estimate heritabilities of over 550 phenotypes and to identify traits for which heritabilities are moderated by age, sex and socio-economic status.

Wang, K., Gaitsch, H., Poon, H., Cox, N. J. & Rzhetsky, A. Classification of common human diseases derived from shared genetic and environmental determinants. Nat. Genet. 49, 1319–1325 (2017).

Stringer, S., Polderman, T. & Posthuma, D. Majority of human traits do not show evidence for sex-specific genetic and environmental effects. Sci. Rep. 7, 8688 (2017). This meta-analysis of 2,335,920 twin pairs and over 2,600 phenotypes reports that only a small portion of human traits exhibit significant sex differences in heritability.

Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

Traglia, M. et al. Genetic mechanisms leading to sex differences across common diseases and anthropometric traits. Genetics 205, 979–992 (2017). This is one of the first studies to comprehensively evaluate multiple genetic models for evidence of their contribution to sex differences in several diseases and anthropometric traits.

Rawlik, K., Canela-Xandri, O. & Tenesa, A. Evidence for sex-specific genetic architectures across a spectrum of human complex traits. Genome Biol. 17, 166 (2016).

Vink, J. M. et al. Sex differences in genetic architecture of complex phenotypes? PLOS ONE 7, e47371 (2012).

Duncan, L. E. et al. Largest GWAS of PTSD (N = 20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666–673 (2017).

Sartor, C. E. et al. Common genetic and environmental contributions to post-traumatic stress disorder and alcohol dependence in young women. Psychol. Med. 41, 1497–1505 (2011).

Kalgotra, P., Sharda, R. & Croff, J. M. Examining health disparities by gender: a multimorbidity network analysis of electronic medical record. Int. J. Med. Inform. 108, 22–28 (2017).

Martin, J. et al. A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder. Biol. Psychiatry 83, 1044–1053 (2017).

Gilks, W. P., Abbott, J. K. & Morrow, E. H. Sex differences in disease genetics: evidence, evolution, and detection. Trends Genet. 30, 453–463 (2014).

Davies, W. Genomic imprinting on the X chromosome: implications for brain and behavioral phenotypes. Ann. N.Y. Acad. Sci. 1204, E14–E19 (2010).

Tukiainen, T. et al. Landscape of X chromosome inactivation across human tissues. Nature 550, 244–248 (2017). This important study demonstrates that both escape from XCI and incomplete XCI, which affect a portion of ChrX genes, result in sex-biased gene expression across human tissues.

Carrel, L. & Willard, H. F. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 434, 400–404 (2005).

Raznahan, A. et al. Sex-chromosome dosage effects on gene expression in humans. Proc. Natl Acad. Sci. USA 115, 7398–7403 (2018).

Alvarez-Nava, F. et al. Effect of the parental origin of the X-chromosome on the clinical features, associated complications, the two-year-response to growth hormone (rhGH) and the biochemical profile in patients with turner syndrome. Int. J. Pediatr. Endocrinol. 2013, 10 (2013).

Sawalha, A. H., Harley, J. B. & Scofield, R. H. Autoimmunity and Klinefelter’s syndrome: when men have two X chromosomes. J. Autoimmun. 33, 31–34 (2009).

Burgoyne, P. S. & Arnold, A. P. A primer on the use of mouse models for identifying direct sex chromosome effects that cause sex differences in non-gonadal tissues. Biol. Sex. Differ. 7, 68 (2016).

Burdett, T. et al. GWAS catalog: the NHGRI-EBI catalog of published genome-wide association studies. EBI www.ebi.ac.uk/gwas (2016).

MacArthur, J. et al. The new NHGRI-EBI catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

Chang, D. et al. Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases. PLOS ONE 9, e113684 (2014). This article reports a novel software package for XWAS and applies it to 16 autoimmune and related phenotypes.

Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

Tukiainen, T. et al. Chromosome X-wide association study identifies Loci for fasting insulin and height and evidence for incomplete dosage compensation. PLOS Genet. 10, e1004127 (2014).

Charchar, F. J. et al. Inheritance of coronary artery disease in men: an analysis of the role of the Y chromosome. Lancet 379, 915–922 (2012). Using ChrY phylogenetic tree analysis, this study reports a role of the Y haplogroup I in coronary artery disease in men.

Sezgin, E. et al. Association of Y chromosome haplogroup I with HIV progression, and HAART outcome. Hum. Genet. 125, 281–294 (2009).

Krementsov, D. N. et al. Genetic variation in chromosome Y regulates susceptibility to influenza A virus infection. Proc. Natl Acad. Sci. USA 114, 3491–3496 (2017).

Case, L. K. et al. Chromosome y regulates survival following murine coxsackievirus b3 infection. G3 (Bethesda) 2, 115–121 (2012).

Case, L. K. et al. The Y chromosome as a regulatory element shaping immune cell transcriptomes and susceptibility to autoimmune disease. Genome Res. 23, 1474–1485 (2013).

Eng, A. et al. Gender differences in occupational exposure patterns. Occup. Environ. Med. 68, 888–894 (2011).

Allen, A. M., Scheuermann, T. S., Nollen, N., Hatsukami, D. & Ahluwalia, J. S. Gender differences in smoking behavior and dependence motives among daily and nondaily smokers. Nicotine Tob. Res. 18, 1408–1413 (2016).

Campos-Serna, J., Ronda-Pérez, E., Artazcoz, L., Moen, B. E. & Benavides, F. G. Gender inequalities in occupational health related to the unequal distribution of working and employment conditions: a systematic review. Int. J. Equity Health 12, 57 (2013).

Moorman, J. E. et al. Vital and health statistics, series 3, number 35: national surveillance of asthma: United States, 2001-2010. CDC https://www.cdc.gov/nchs/data/series/sr_03/sr03_035.pdf (2012).

Zein, J. G. & Erzurum, S. C. Asthma is different in women. Curr. Allergy Asthma Rep. 15, 28 (2015).

Haast, R. A. M., Gustafson, D. R. & Kiliaan, A. J. Sex differences in stroke. J. Cereb. Blood Flow Metab. 32, 2100–2107 (2012).

Murphy, V. E. & Gibson, P. G. Premenstrual asthma: prevalence, cycle-to-cycle variability and relationship to oral contraceptive use and menstrual symptoms. J. Asthma 45, 696–704 (2008).

Murphy, V. E., Clifton, V. L. & Gibson, P. G. Asthma exacerbations during pregnancy: incidence and association with adverse pregnancy outcomes. Thorax 61, 169–176 (2006).

Forray, A., Focseneanu, M., Pittman, B., McDougle, C. J. & Epperson, C. N. Onset and exacerbation of obsessive-compulsive disorder in pregnancy and the postpartum period. J. Clin. Psychiatry 71, 1061–1068 (2010).

Guglielmi, V. et al. Obsessive-compulsive disorder and female reproductive cycle events: results from the OCD and reproduction collaborative study. Depress. Anxiety 31, 979–987 (2014).

Soares, C. N. & Zitek, B. Reproductive hormone sensitivity and risk for depression across the female life cycle: a continuum of vulnerability? J. Psychiatry Neurosci. 33, 331–343 (2008).

Schiller, C. E., Meltzer-Brody, S. & Rubinow, D. R. The role of reproductive hormones in postpartum depression. CNS Spectr. 20, 48–59 (2015).

Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626–638 (2016).

Cephus, J.-Y. et al. Testosterone attenuates group 2 innate lymphoid cell-mediated airway inflammation. Cell Rep. 21, 2487–2499 (2017). This study reports a mechanism by which testosterone regulates immune cells involved in the development of asthma and thus acts as a protective mechanism in males.

Patsopoulos, N. A., Tatsioni, A. & Ioannidis, J. P. A. Claims of sex differences: an empirical assessment in genetic associations. JAMA 298, 880–893 (2007).

Krohn, J. et al. Genetic interactions with sex make a relatively small contribution to the heritability of complex traits in mice. PLOS ONE 9, e96450 (2014).

Schaafsma, S. M. et al. Sex-specific gene–environment interactions underlying ASD-like behaviors. Proc. Natl Acad. Sci. USA 114, 1383–1388 (2017).

Havill, L. M., Mahaney, M. C. & Rogers, J. Genotype-by-sex and environment-by-sex interactions influence variation in serum levels of bone-specific alkaline phosphatase in adult baboons (Papio hamadryas). Bone 35, 198–203 (2004).

Bearoff, F. et al. Identification of genetic determinants of the sexual dimorphism in CNS autoimmunity. PLOS ONE 10, e0117993 (2015).

Parks, B. W. et al. Genetic architecture of insulin resistance in the mouse. Cell Metab. 21, 334–346 (2015).

Nuzhdin, S. V., Pasyukova, E. G., Dilda, C. L., Zeng, Z. B. & Mackay, T. F. Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 94, 9734–9739 (1997).

Boraska, V. et al. Genome-wide meta-analysis of common variant differences between men and women. Hum. Mol. Genet. 21, 4805–4815 (2012).

Desachy, G. et al. Increased female autosomal burden of rare copy number variants in human populations and in autism families. Mol. Psychiatry 20, 170–175 (2015).

Han, J. et al. Gender differences in CNV burden do not confound schizophrenia CNV associations. Sci. Rep. 6, 25986 (2016).

Barson, N. J. et al. Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon. Nature 528, 405–408 (2015).

Hawkes, M. F. et al. Intralocus sexual conflict and insecticide resistance. Proc. Biol. Sci. 283, 20161429 (2016).

Foerster, K. et al. Sexually antagonistic genetic variation for fitness in red deer. Nature 447, 1107–1110 (2007).

Johnston, S. E. et al. Life history trade-offs at a single locus maintain sexually selected genetic variation. Nature 502, 93–95 (2013).

Mank, J. E. Population genetics of sexual conflict in the genomic era. Nat. Rev. Genet. 18, 721–730 (2017).

Mitra, I. et al. Pleiotropic mechanisms indicated for sex differences in autism. PLOS Genet. 12, e1006425 (2016). This is one of the first studies to comprehensively test multiple genetic models that might contribute to sex differences in autism spectrum disorder.

Taylor, K. C. et al. Investigation of gene-by-sex interactions for lipid traits in diverse populations from the population architecture using genomics and epidemiology study. BMC Genet. 14, 33 (2013).

Randall, J. C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLOS Genet. 9, e1003500 (2013).

Myers, R. A. et al. Genome-wide interaction studies reveal sex-specific asthma risk alleles. Hum. Mol. Genet. 23, 5251–5259 (2014).

Winkler, T. W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLOS Genet. 11, e1005378 (2015). As a follow-up to reference 80, this work is one of the first to provide evidence for sexually differentiated genetic architecture of anthropometric traits, specifically reporting cases of opposite effects at individual loci.

Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry. Hum. Mol. Genet. https://doi.org/10.1093/hmg/ddy327 (2018).

Liu, L. Y., Schaub, M. A., Sirota, M. & Butte, A. J. Sex differences in disease risk from reported genome-wide association study findings. Hum. Genet. 131, 353–364 (2012).

Hartiala, J. A. et al. Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat. Commun. 7, 10558 (2016).

Orozco, G., Ioannidis, J. P. A., Morris, A., Zeggini, E. & The DIAGRAM consortium. Sex-specific differences in effect size estimates at established complex trait loci. Int. J. Epidemiol. 41, 1376–1382 (2012).

Zhuang, J. J. & Morris, A. P. Assessment of sex-specific effects in a genome-wide association study of rheumatoid arthritis. BMC Proc. 3 (Suppl. 7), S90 (2009).

Singh, S. K. et al. A childhood acute lymphoblastic leukemia genome-wide association study identifies novel sex-specific risk variants. Medicine 95, e5300 (2016).

Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

The Brainstorm Consortium. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

Heid, I. M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).

Do, R. et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45, 1345–1352 (2013).

Andreassen, O. A. et al. Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci. Mol. Psychiatry 20, 207–214 (2015).

Rahmioglu, N. et al. Genome-wide enrichment analysis between endometriosis and obesity-related traits reveals novel susceptibility loci. Hum. Mol. Genet. 24, 1185–1199 (2015).

Khramtsova, E. A. et al. Sex Differences in the Genetic Architecture of Obsessive-Compulsive Disorder. Am. J. Med. Genet. B Neuropsychiatr. Genet. https://doi.org/10.1002/ajmg.b.32687 (2018).

Ayroles, J. F. et al. Systems genetics of complex traits in Drosophila melanogaster. Nat. Genet. 41, 299–307 (2009).

Cheng, C. & Kirkpatrick, M. Environmental plasticity in the intersexual correlation and sex bias of gene expression. J. Hered. 108, 754–758 (2017).

Seo, M. et al. Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq. BMC Genomics 17, 81 (2016).

Mayne, B. T. et al. Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans. Front. Genet. 7, 183 (2016). This is a large-scale meta-analysis of human sex-biased gene expression from 22 publicly available data sets including over 2,500 samples from 15 different tissues and 9 different organs.

Melé, M. et al. Human genomics. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

Chen, C. Y., Lopes-Ramos, C. M., Kuijjer, M. L. & Paulson, J. N. Sexual dimorphism in gene expression and regulatory networks across human tissues. Preprint at bioRxiv https://doi.org/10.1101/082289 (2016).

Gershoni, M. & Pietrokovski, S. The landscape of sex-differential transcriptome and its consequent selection in human adults. BMC Biol. 15, 7 (2017).

Zhang, Y. et al. Transcriptional profiling of human liver identifies sex-biased genes associated with polygenic dyslipidemia and coronary artery disease. PLOS ONE 6, e23506 (2011).

Welle, S., Tawil, R. & Thornton, C. A. Sex-related differences in gene expression in human skeletal muscle. PLOS ONE 3, e1385 (2008).

Trabzuni, D. et al. Widespread sex differences in gene expression and splicing in the adult human brain. Nat. Commun. 4, 2771 (2013). This study reports widespread sex-biased gene expression in 12 regions of the human brain.

Jansen, R. et al. Sex differences in the human peripheral blood transcriptome. BMC Genomics 15, 33 (2014). This study describes sex differences in the whole-blood transcriptome of over 5,200 study participants, revealing differential expression of genes from both autosomes and sex chromosomes.

Ecker, S. et al. Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types. Genome Biol. 18, 18 (2017).

Zhang, W., Bleibel, W. K., Roe, C. A., Cox, N. J. & Eileen Dolan, M. Gender-specific differences in expression in human lymphoblastoid cell lines. Pharmacogenet. Genomics 17, 447–450 (2007).

McRae, A. F. et al. Replicated effects of sex and genotype on gene expression in human lymphoblastoid cell lines. Hum. Mol. Genet. 16, 364–373 (2007).

Johnston, C. M. et al. Large-scale population study of human cell lines indicates that dosage compensation is virtually complete. PLOS Genet. 4, e9 (2008).

Shi, L., Zhang, Z. & Su, B. Sex biased gene expression profiling of human brains at major developmental stages. Sci. Rep. 6, 21181 (2016).

Ma, J., Malladi, S. & Beck, A. H. Systematic analysis of sex-linked molecular alterations and therapies in cancer. Sci. Rep. 6, 19119 (2016).

Labonté, B. et al. Sex-specific transcriptional signatures in human depression. Nat. Med. 23, 1102–1111 (2017). This study reports the remodelling of human brain transcriptional profiles in major depression, with little overlap in the alterations occurring in males and females.

Qin, W., Liu, C., Sodhi, M. & Lu, H. Meta-analysis of sex differences in gene expression in schizophrenia. BMC Syst. Biol. 10 (Suppl. 1), 9 (2016).

Mennecozzi, M., Landesmann, B., Palosaari, T., Harris, G. & Whelan, M. Sex differences in liver toxicity—do female and male human primary hepatocytes react differently to toxicants in vitro? PLOS ONE 10, e0122786 (2015).

Furman, D. et al. Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination. Proc. Natl Acad. Sci. USA 111, 869–874 (2014).

Ngo, S. T., Steyn, F. J. & McCombe, P. A. Gender differences in autoimmune disease. Front. Neuroendocrinol. 35, 347–369 (2014).

Yang, X. et al. Tissue-specific expression and regulation of sexually dimorphic genes in mice. Genome Res. 16, 995–1004 (2006).

Mank, J. E., Hultin-Rosenberg, L., Webster, M. T. & Ellegren, H. The unique genomic properties of sex-biased genes: insights from avian microarray data. BMC Genomics 9, 148 (2008).

Bouman, A., Heineman, M. J. & Faas, M. M. Sex hormones and the immune response in humans. Hum. Reprod. Update 11, 411–423 (2005).

Manning, K. S. & Cooper, T. A. The roles of RNA processing in translating genotype to phenotype. Nat. Rev. Mol. Cell. Biol. 18, 102–114 (2017).

Gamazon, E. R. & Stranger, B. E. Genomics of alternative splicing: evolution, development and pathophysiology. Hum. Genet. 133, 679–687 (2014).

Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016).

Lindholm, M. E. et al. The human skeletal muscle transcriptome: sex differences, alternative splicing, and tissue homogeneity assessed with RNA sequencing. FASEB J. 28, 4571–4581 (2014).

Blekhman, R., Marioni, J. C., Zumbo, P., Stephens, M. & Gilad, Y. Sex-specific and lineage-specific alternative splicing in primates. Genome Res. 20, 180–189 (2010).

The Johns Hopkins University School of Medicine. Online Mendelian Inheritance in Man®: an online catalog of human genes and genetic disorders. OMIM https://omim.org/ (2018).

Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLOS Genet. 6, e1000888 (2010).

Nica, A. C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLOS Genet. 6, e1000895 (2010).

GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

Kasela, S. et al. Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+versus CD8+T cells. PLOS Genet. 13, e1006643 (2017).

Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

Takata, A., Matsumoto, N. & Kato, T. Genome-wide identification of splicing QTLs in the human brain and their enrichment among schizophrenia-associated loci. Nat. Commun. 8, 14519 (2017).

Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016).

Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

Kim-Hellmuth, S. et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nat. Commun. 8, 266 (2017).

Ye, C. J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).

Stranger, B. E. & Raj, T. Genetics of human gene expression. Curr. Opin. Genet. Dev. 23, 627–634 (2013).

Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

Stranger, B. E. et al. Population genomics of human gene expression. Nat. Genet. 39, 1217–1224 (2007).

Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

Nica, A. C. et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLOS Genet. 7, e1002003 (2011).

Pala, M. et al. Population- and individual-specific regulatory variation in Sardinia. Nat. Genet. 49, 700–707 (2017).

Kwan, T. et al. Tissue effect on genetic control of transcript isoform variation. PLOS Genet. 5, e1000608 (2009).

Gutierrez-Arcelus, M. et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLOS Genet. 11, e1004958 (2015).

Yao, C. et al. Sex- and age-interacting eQTLs in human complex diseases. Hum. Mol. Genet. 23, 1947–1956 (2014).

Kukurba, K. R. et al. Impact of the X Chromosome and sex on regulatory variation. Genome Res. 26, 768–777 (2016). This study characterizes human whole blood cis-eQTLs and SNP-by-sex interaction eQTLs on ChrX and autosomes and the relationship to sex-biased chromatin accessibility.

Lindén, M. et al. Sex influences eQTL effects of SLE and Sjögren’s syndrome-associated genetic polymorphisms. Biol. Sex. Differ. 8, 34 (2017).

Dimas, A. S. et al. Sex-biased genetic effects on gene regulation in humans. Genome Res. 22, 2368–2375 (2012).

Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

Awadalla, P. et al. Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics. Int. J. Epidemiol. 42, 1285–1299 (2013).

Hussin, J. G. et al. Recombination affects accumulation of damaging and disease-associated mutations in human populations. Nat. Genet. 47, 400–404 (2015).

Westra, H.-J. et al. Cell specific eQTL analysis without sorting cells. PLOS Genet. 11, e1005223 (2015).

Naranbhai, V. et al. Genomic modulators of gene expression in human neutrophils. Nat. Commun. 6, 7545 (2015).

Chen, Y. et al. Difference in leukocyte composition between women before and after menopausal age, and distinct sexual dimorphism. PLOS ONE 11, e0162953 (2016).

Kassam, I. et al. Autosomal genetic control of human gene expression does not differ across the sexes. Genome Biol. 17, 248 (2016).

Xu, X. et al. Modular genetic control of sexually dimorphic behaviors. Cell 148, 596–607 (2012). In addition to demonstrating sex-biased gene expression in mouse brain, this study demonstrates that targeted disruption of sex-biased genes impacts sexually differentiated behaviours.

Quinn, M. A. & Cidlowski, J. A. Endogenous hepatic glucocorticoid receptor signaling coordinates sex-biased inflammatory gene expression. FASEB J. 30, 971–982 (2016).

Gomez-Santos, C. et al. Profile of adipose tissue gene expression in premenopausal and postmenopausal women: site-specific differences. Menopause 18, 675–684 (2011).

Kósa, J. P. et al. Effect of menopause on gene expression pattern in bone tissue of nonosteoporotic women. Menopause 16, 367–377 (2009).

Zhu, M.-L. et al. Sex bias in CNS autoimmune disease mediated by androgen control of autoimmune regulator. Nat. Commun. 7, 11350 (2016).

Boks, M. P. et al. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLOS ONE 4, e6767 (2009).

Tapp, H. S. et al. Nutritional factors and gender influence age-related DNA methylation in the human rectal mucosa. Aging Cell 12, 148–155 (2013).

Liu, J., Morgan, M., Hutchison, K. & Calhoun, V. D. A study of the influence of sex on genome wide methylation. PLOS ONE 5, e10028 (2010).

Hall, E. et al. Sex differences in the genome-wide DNA methylation pattern and impact on gene expression, microRNA levels and insulin secretion in human pancreatic islets. Genome Biol. 15, 522 (2014).

McCormick, H. et al. Isogenic mice exhibit sexually-dimorphic DNA methylation patterns across multiple tissues. BMC Genomics 18, 966 (2017).

Singmann, P. et al. Characterization of whole-genome autosomal differences of DNA methylation between men and women. Epigenetics Chromatin 8, 43 (2015). Analysing whole blood, this important study reports thousands of sexually differentiated DNA methylation sites, which are enriched among imprinted genes.

van Dongen, J. et al. Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat. Commun. 7, 11115 (2016).

VanderKraats, N. D., Hiken, J. F., Decker, K. F. & Edwards, J. R. Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes. Nucleic Acids Res. 41, 6816–6827 (2013).

Ling, G., Sugathan, A., Mazor, T., Fraenkel, E. & Waxman, D. J. Unbiased, genome-wide in vivo mapping of transcriptional regulatory elements reveals sex differences in chromatin structure associated with sex-specific liver gene expression. Mol. Cell. Biol. 30, 5531–5544 (2010). This study characterizes sex-biased DNase occupancy in mouse liver associated with sex-biased gene expression and shows how chromatin accessibility can be altered by sex hormones.

Sugathan, A. & Waxman, D. J. Genome-wide analysis of chromatin states reveals distinct mechanisms of sex-dependent gene regulation in male and female mouse liver. Mol. Cell. Biol. 33, 3594–3610 (2013).

Thakur, M. K., Asaithambi, A. & Mukherjee, S. Sex-specific alterations in chromatin conformation of the brain of aging mouse. Mol. Biol. Rep. 26, 239–247 (1999).

Arnold, A. P. & Lusis, A. J. Understanding the sexome: measuring and reporting sex differences in gene systems. Endocrinology 153, 2551–2555 (2012).

de Vries, G. J. & Forger, N. G. Sex differences in the brain: a whole body perspective. Biol. Sex. Differ. 6, 15 (2015).

Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

Furtado, M. & Katzman, M. A. Neuroinflammatory pathways in anxiety, posttraumatic stress, and obsessive compulsive disorders. Psychiatry Res. 229, 37–48 (2015).

Furtado, M. & Katzman, M. A. Examining the role of neuroinflammation in major depression. Psychiatry Res. 229, 27–36 (2015).

Marsh, S. E. et al. The adaptive immune system restrains Alzheimer’s disease pathogenesis by modulating microglial function. Proc. Natl Acad. Sci. USA 113, E1316–E1325 (2016).

Heneka, M. T., Golenbock, D. T. & Latz, E. Innate immunity in Alzheimer’s disease. Nat. Immunol. 16, 229–236 (2015).

Sorge, R. E. et al. Different immune cells mediate mechanical pain hypersensitivity in male and female mice. Nat. Neurosci. 18, 1081–1083 (2015).

Grassmann, F. et al. A candidate gene association study identifies DAPL1 as a female-specific susceptibility locus for age-related macular degeneration (AMD). Neuromolecular Med. 17, 111–120 (2015).

Kim, S.-G. Gender differences in the genetic risk for alcohol dependence — the results of a pharmacogenetic study in Korean alcoholics. Nihon Arukoru Yakubutsu Igakkai Zasshi 44, 680–685 (2009).

Yu, Y. et al. Systematic analysis of adverse event reports for sex differences in adverse drug events. Sci. Rep. 6, 24955 (2016).

Rademaker, M. Do women have more adverse drug reactions? Am. J. Clin. Dermatol. 2, 349–351 (2001).

Tharpe, N. Adverse drug reactions in women’s health care. J. Midwifery Womens Health 56, 205–213 (2011).

Heinrich, J., Gahart, M. T., Rowe, E. J. & Bradley, L. Drug safety: most drugs withdrawn in recent years had greater health risks for women. GAO https://www.gao.gov/assets/100/90642.pdf (2001).

Anderson, G. D. Sex and racial differences in pharmacological response: where is the evidence? Pharmacogenetics, pharmacokinetics, and pharmacodynamics. J. Womens Health 14, 19–29 (2005).

Kim, S.-G. et al. A micro opioid receptor gene polymorphism (A118G) and naltrexone treatment response in adherent Korean alcohol-dependent patients. Psychopharmacology 201, 611–618 (2009).

Zhou, Q. et al. CYP2C9*3(1075 A>C), ABCB1 and SLCO1B1 genetic polymorphisms and gender are determinants of inter-subject variability in pitavastatin pharmacokinetics. Pharmazie 68, 187–194 (2013).

Hubacek, J. A. et al. Possible gene-gender interaction between the SLCO1B1 polymorphism and statin treatment efficacy. Neuro Endocrinol. Lett. 33 (Suppl. 2), 22–25 (2012).

McCullough, L. D., Zeng, Z., Blizzard, K. K., Debchoudhury, I. & Hurn, P. D. Ischemic nitric oxide and poly (ADP-ribose) polymerase-1 in cerebral ischemia: male toxicity, female protection. J. Cereb. Blood Flow Metab. 25, 502–512 (2005).

U.S. Food and Drug Administration. Questions and answers: risk of next-morning impairment after use of insomnia drugs FDA requires lower recommended doses for certain drugs containing zolpidem (Ambien, Ambien CR, Edluar, and Zolpimist). FDA https://www.fda.gov/drugs/drugsafety/ucm334041.htm (2018).

Bogetto, F., Venturello, S., Albert, U., Maina, G. & Ravizza, L. Gender-related clinical differences in obsessive-compulsive disorder. Eur. Psychiatry 14, 434–441 (1999).

Mancebo, M. C., Garcia, A. M. & Pinto, A. Juvenile-onset OCD: clinical features in children, adolescents and adults. Acta Psychiatr. Scand. 118, 149–159 (2008).

Tükel, R. et al. Influence of age of onset on clinical features in obsessive–compulsive disorder. Depress. Anxiety 21, 112–117 (2005).

Santangelo, S. L. et al. Tourette’s syndrome: what are the influences of gender and comorbid obsessive-compulsive disorder? J. Am. Acad. Child Adolesc. Psychiatry 33, 795–804 (1994).

Mandy, W. et al. Sex differences in autism spectrum disorder: evidence from a large sample of children and adolescents. J. Autism Dev. Disord. 42, 1304–1313 (2012).

Towbin, J. A. et al. X-linked dilated cardiomyopathy. Molecular genetic evidence of linkage to the Duchenne muscular dystrophy (dystrophin) gene at the Xp21 locus. Circulation 87, 1854–1865 (1993).

The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

Glas, R., Marshall Graves, J. A., Toder, R., Ferguson-Smith, M. & O’Brien, P. C. Cross-species chromosome painting between human and marsupial directly demonstrates the ancient region of the mammalian X. Mamm. Genome 10, 1115–1116 (1999).

Ritchie, M. E., Liu, R., Carvalho, B. S. & Irizarry, R. A. & Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene). Comparing genotyping algorithms for Illumina’s Infinium whole-genome SNP BeadChips. BMC Bioinformatics 12, 68 (2011).

Loley, C., Ziegler, A. & König, I. R. Association tests for X-chromosomal markers—a comparison of different test statistics. Hum. Hered. 71, 23–36 (2011).

Clayton, D. Testing for association on the X chromosome. Biostatistics 9, 593–600 (2008).

Clayton, D. snpStats: SnpMatrix and XSnpMatrix classes and methods. bioconductor https://bioconductor.org/packages/release/bioc/html/snpStats.html (2018).

Sultan, M. et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008).

Castagné, R. et al. The choice of the filtering method in microarrays affects the inference regarding dosage compensation of the active X-chromosome. PLOS ONE 6, e23956 (2011).

Polderman, T. J. C. et al. The biological contributions to gender identity and gender diversity: bringing data to the table. Behav. Genet. 48, 95–108 (2018).

Werling, D. M. & Geschwind, D. H. Sex differences in autism spectrum disorders. Curr. Opin. Neurol. 26, 146–153 (2013).

Reich, R., Cloninger, C. R. & Guze, S. B. The multifactorial model of disease transmission: I. Description of the model and its use in psychiatry. Br. J. Psychiatry 127, 1–10 (1975).


Definition

Male Gametes: A male gamete is the male reproductive cell, which unites with the female gamete to produce the zygote.

Female Gametes: A female gamete is the female reproductive cell, which unites with the male gamete to produce the zygote.

Formed by

Male Gametes: Male gametes are produced by spermatogenesis.

Female Gametes: Female gamates are produced by oogenesis.

In Seed-bearing Plants

Male Gametes: Male gametes can be found inside the pollen grains of the seed-bearing plants.

Female Gametes: Female gametes can be found inside the ovary of seed-bearing plants.

In Animals

Male Gametes: Male gametes are produced in the testes.

Female Gametes: Female gametes are produced in the ovaries.

Male Gametes: Male gametes are smaller than female gametes.

Female Gametes: Female gametes of humans are 100 000 times larger than male gametes of humans.

Shape

Male Gametes: Male gametes are corn-shaped cells.

Female Gametes: Female gametes are spherical-shaped cells.

Size of Cytoplasm

Male Gametes: Male gametes contains a small cytoplasm. Therefore, male gametes have a lower weight, enabling the swimming.

Female Gametes: Female gametes contains a larger cytoplasm to nourish the embryo.

Mobility

Male Gametes: Male gametes are mobile.

Female Gametes: female gametes are immobile.

Tail/Flagella

Male Gametes: Male gametes contain a tail or flagella, which helps in swimming.

Female Gametes: Female gametes do not contain tails or flagella.

Amount

Male Gametes: Male gametes are produced in large numbers to ensure a successful fertilization.

Female Gametes: Only one female gamete is released per month in humans.

Zona Pellucida

Male Gametes: male gametes lack a zona pellucida.

Female Gametes: Female gametes comprise a jelly coat called zona pellucida to which the male gametes bind to.

Acrosomes

Male Gametes: Male gametes comprise an acrosome, which contains the enzymes to degrade the layers surrounding the female gamete.

Female Gametes: Female gametes lack acrosomes.

Mitochondrial Level

Male Gametes: Male gametes comprise a lot of mitochondria to produce energy to swim.

Female Gametes: Female gametes comprise a few mitochondria since they are immobile.

Conclusion

Male and female gametes are the two types of haploid reproductive cells produced by plants and animals. Male gametes are called sperms. Female gametes are called egg cells. Both male and female gametes are formed by the meiosis. Therefore, both types of gametes comprise a single set of chromosomes of the species. One male gamete unites with one female gamete to form the zygote, which develops into a new organism.

Reference:

1. “Sperm.” Encyclopædia Britannica. Encyclopædia Britannica, inc., n.d. Web. Available here. 14 Aug. 2017.
2. Battista, Jeremy. “Male Gamete in Plants: Definition & Concept.” Study.com. N.p., n.d. Web. Available here. 14 Aug. 2017.
3. Alberts, Bruce. “Eggs.” Molecular Biology of the Cell. 4th edition.U.S. National Library of Medicine, 01 Jan. 1970. Web. Available here. 14 Aug. 2017.

Image Courtesy:

1. “Image 1” (Public Domain) via Pixino
2. “Sperms (urine) – Spermler (idrar) – 02” By Doruk Salancı – Own work (CC BY-SA 3.0) via Commons Wikimedia
3. “Rose hip 02 ies” By Frank Vincentz – Own work (CC BY-SA 3.0) via Commons Wikimedia
4. “Gray3” Von Henry Vandyke Carter – Henry Gray (1918) Anatomy of the Human Body (See “Buch” section below)Bartleby.com: Gray’s Anatomy, Tafel 3 (Gemeinfrei) via Commons Wikimedia

About the Author: Lakna

Lakna, a graduate in Molecular Biology & Biochemistry, is a Molecular Biologist and has a broad and keen interest in the discovery of nature related things



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