สรุปสั้นๆ การศึกษาจีโนมของถั่วเมนเดล ได้ทำให้รู้ลักษณะสุดท้ายสามอย่าง ว่า ควบคุมโดยยีนใด
ลักษณะสามอย่างนั้นคือ สีของฝักถั่ว การเรียงตัวของดอก และ ฝักนั้นจะกินได้หรือไม่
ในข่าวอธิบายสั้นๆว่า สีของฝักถั่ว ควบคุมโดยยีนที่สร้างคลอโรฟิลล์ ถ้าไม่มีหรือเป็นลักษณะด้อยจะทำให้ฝักเป็นสีเหลือง
ข่าวเต็ม และบทความวิชาการที่เกี่ยวข้อง อ่านได้ด้านล่าง
ที่มา
https://www.science.org/content/article/massive-pea-study-solves-last-genetic-riddles-famed-friar?utm_source=sfmc&utm_medium=email&utm_content=alert&utm_campaign=SCIeToc&et_rid=17103467&et_cid=5597924
https://www.nature.com/articles/s41586-025-08891-6
https://www.nature.com/articles/s41588-019-0480-1
https://www.nature.com/articles/s41588-024-01867-8
https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.19800
Massive pea study solves last genetic riddles of famed friar
DNA sequencing reveals basis of traits studied by Gregor Mendel—and gives breeders new ways to improve the crop
Generations of high school students have learned how the 19th century Austro-Hungarian friar Gregor Mendel discovered basic principles of genetics by studying peas, which he planted by the thousands in the garden of his abbey. After cross-pollinating varieties and noting the proportions in which traits such as flower color occurred in their offspring, he revealed the mathematical patterns of recessive and dominant inheritance—a fundamental breakthrough in genetics. But, working decades before genes were identified as the mechanism of heredity, Mendel knew nothing about the molecular basis of his seven traits, which remained “seven riddles,” says Shifeng Cheng, an evolutionary geneticist at the Agricultural Genomics Institute at Shenzhen (AGIS).
In recent decades, researchers have gradually mapped those traits onto DNA sequences, identifying the genes behind four of them. Now, in the largest genomic study of peas yet, published this week in Nature, Cheng and his colleagues reveal the genes associated with the remaining three, as well as many other genes that pea breeders could use to improve the plants. “This is another milestone in plant genomics,” says Aureliano Bombarely, a plant genomicist at the Institute of Molecular and Cellular Biology of Plants who wasn’t involved in the work.
The first of Mendel’s traits to be linked to a gene was seed shape. Some pea varieties have seeds that wrinkle if dried and taste sweet when served fresh. Mendel showed they have recessive “wrinkled” alleles. Peas with a dominant “round” allele stay smooth when dry and are less sweet, often going to soup or animal feed. In 1990, researchers at the John Innes Centre (JIC) identified the responsible gene, which codes for an enzyme that helps convert sugars to starch. Its dominant form packs the seeds with starch and keeps them smooth, whereas the recessive allele makes an inactive enzyme that leaves more sugar in the seeds. Scientists at JIC and elsewhere subsequently discovered the genes behind three other traits: plant height, and flower and seed color.
The large size of the pea genome and a general emphasis on higher profile crops such as wheat, maize, and rice slowed further progress. “Peas don’t get a whole lot of attention,” says Rebecca McGee, a plant scientist at Washington State University. But as sequencing costs fall, that’s changing.
The entire pea genome was sequenced in 2019. Researchers in China went on to sequence 237 kinds of peas and compile their genetic differences into a map, published last year. This diversity allowed them to identify 29 million genetic markers, called single nucleotide polymorphisms (SNPs), that pea breeders can use to guide and accelerate crop improvement.
Now, Cheng has partnered with colleagues at JIC to vastly expand the catalog of variations. JIC has a historic connection to Mendel: In the early 20th century its first director, pioneering geneticist William Bateson, helped disseminate Mendel’s findings and prioritized research on pea genetics. Since then it has collected several thousand varieties of pea, including from the Middle East, where the crop was domesticated, and from Ethiopia and the Himalayas, two other hot spots of diversity, amassing a large and varied collection.
Together, AGIS and JIC sequenced nearly 700 pea varieties, spanning the diversity of the collection. This yielded 155 million SNPs that they correlated with physical traits of the plants, allowing them to narrow down the location of important genes. “It is a great accomplishment for the pea,” says Tom Warkentin, a plant breeder at the University of Saskatchewan.
Among those genes are ones for the three remaining Mendel traits: the color of the pea pod, the arrangement of flowers, and whether the pods are edible. “We have finally provided an answer to this 160-year-old riddle,” Cheng says. The new details show, for example, that yellow pods occur in plants with DNA missing next to a gene involved in making chlorophyll. Cheng’s group believes the defective RNA transcribed from that DNA region interferes with chlorophyll synthesis, leading to pallid pods.
That particular insight might not lead directly to improved peas, but others likely will. Take the genetic basis for tendrils. By intertwining, these modified leaflets help pea plants stay upright and make harvesting much easier. In the 1980s breeders produced varieties with plentiful tendrils, a trait controlled by a gene called afila. But the same afila alleles that cause pea plants to grow more tendrils and fewer leaves can also lower yield by somehow deleting adjacent genes that influence the number and weight of seeds. By revealing exactly where on the genome the deletions start and stop, Cheng and colleagues hope to help breeders select afila alleles that don’t delete the flanking genes.
Many other traits in peas are determined by multiple genes, and there, too, genomic maps with plentiful markers will help breeders build on the heritage of Mendel, Warkentin says. “All these developments add to the toolbox of plant breeders.”
The p-value histogram can reveal a LOT about your data. Let's break it down using real examples.👇
1/ First, a quick fact: P-values follow a uniform distribution under the null hypothesis.
What does that mean? 🤔
If there’s truly no difference between groups, the p-value behaves like rolling a fair die:
• P(p < 0.01) = 0.01
• P(p < 0.02) = 0.02
2/ Here's the catch: With 100 t-tests, if the null hypothesis is true, we expect ~1 test with p < 0.01, ~2 tests with p < 0.02, and so on.
This randomness underlies why p-values are so tricky—and why multiple testing correction is critical
3/ Why should bioinformaticians care? 🧬
When you analyze RNA-seq, ChIP-seq, or other large datasets, you’re running thousands of tests.
Plotting the p-value histogram can tell you:
✔️ If the null hypothesis holds
✔️ If your experiment reveals meaningful signals
4/ you should see a peak around p=0 if you have real differences. Then you apply Bonferroni correction or Benjamini & Hochberg (BH method) to control the FDR. In R, uses p.adjust(pvals). dive deeper in my blog post here https://lnkd.in/ex3S3V5g
5/ Example: A perfect null distribution (no effect) looks flat like this:
📊 [0.01] [0.02] [0.03] ... evenly distributed across the range 0 to 1.
If you see a lot of p-values near zero, it suggests true effects in your data—signals worth exploring!
6/ 🚩 Red flag: A "U-shaped" histogram, with many p-values near 0 and 1.
This often indicates technical artifacts in your data (batch effects, low-quality samples).
Always check your p-value histogram before jumping to conclusions. reference: https://lnkd.in/eVctWN_j
7/ Want to explore this further? Try it with your RNA-seq results!
• Run your differential expression analysis.
• Plot the histogram of your p-values.
• Interpret: Do you see flat (null), enriched (signals), or U-shaped (artifacts)?
8/ I saw mine in one of my RNAseq analysis. what's going on for the p-values? there is a spike around p=0.8
9/ After removing the genes with low counts, I got the normal good looking histogram. read more in details in my blog post https://lnkd.in/ezsMVE3s
10/ TL;DR:
✔️ Under the null, p-values are uniform (flat distribution).
✔️ A "spike near 0" suggests true effects.
✔️ A "U-shape" signals artifacts—time to troubleshoot.
Plot your p-values. They’ll tell you more than you think!
Have questions? Let’s chat 👇
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